Latent Class Analysis of Behavioral and Metabolic Risk Factors Among Patients with Acute Coronary Syndrome
Latent Class Analysis of ACS risk factors
-
Maryam Shakiba
,
Arsalan Salari
,
Sima Masudi
,
Salman Nikfarjam
,
marjan mahdavi Roshan
,
elnaz abhari
,
yasaman borgheie
Abstract
Introduction: The aim of this study was to explore latent classes of risk factors among patients with acute coronary
syndrome.
Methods: A cross-sectional study was performed on patients with symptoms of chest pain, unstable angina, or myocardial
infarction who had at least one coronary vascular involvement confirmed by angiography. A latent class analysis (LCA) using
five categorical risk factors, including metabolic syndrome, physical activity, tobacco use, alcohol, and opium consumption,
was conducted on 380 eligible patients. A logistic regression model was used to explore the associations of demographic
and clinical variables with latent classes.
Results: The mean age of the patients was 59.05 years (SD= 9.82). A two-class model showed the best fit; Class I (45.1%)
was characterized by a high probability of smoking, alcohol, and opium consumption, and Class II was characterized by a
high probability of metabolic syndrome (54.9%). There was a significant difference between the two classes in terms of
age, sex, job, and educational status. The multiple logistic regression model revealed that age and sex were independent
predictors of latent class membership.
Conclusion: This study revealed two distinct latent risk factor patterns among ACS patients emphasizing the need for
personalized prevention approaches. Behavioral interventions should be prioritized in younger patients. While, sex-specific
metabolic syndrome management strategies should be underscored in older patients.
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5. Mahmood SS, Levy D, Vasan RS, Wang TJ. The Framingham Heart Study and the epidemiology
of cardiovascular disease: a historical perspective. Lancet (London, England). 2014;383(9921):999-
1008.
6. Ho FK, Gray SR, Welsh P, Gill JMR, Sattar N, Pell JP, et al. Ethnic differences in cardiovascular
risk: examining differential exposure and susceptibility to risk factors. BMC Med. 2022;20(1):149.
7. Toms R, Bonney A, Mayne DJ, Feng X, Walsan R. Geographic and area-level socioeconomic
variation in cardiometabolic risk factor distribution: a systematic review of the literature. Int J Health
Geogr. 2019;18(1):1.
8. Muilwijk M, Ho F, Waddell H, Sillars A, Welsh P, Iliodromiti S, et al. Contribution of type
2 diabetes to all-cause mortality, cardiovascular disease incidence and cancer incidence in white
Europeans and South Asians: findings from the UK Biobank population-based cohort study. BMJ
Open Diabetes Res Care. 2019;7(1):e000765.
9. Weller BE, Bowen NK, Faubert SJ. Latent Class Analysis: A Guide to Best Practice. Journal
of Black Psychology. 2020;46(4):287-311.
10. Kim S, Cho S, Nah EH. The patterns of lifestyle, metabolic status, and obesity among
hypertensive Korean patients: a latent class analysis. Epidemiology and health. 2020;42:e2020061.
11. Liberali R, Del Castanhel F, Kupek E, Assis MAA. Latent Class Analysis of Lifestyle Risk
Factors and Association with Overweight and/or Obesity in Children and Adolescents: Systematic
Review. Childhood obesity (Print). 2021;17(1):2-15.
12. Ahanchi NS, Hadaegh F, Alipour A, Ghanbarian A, Azizi F, Khalili D. Application of Latent
Class Analysis to Identify Metabolic Syndrome Components Patterns in adults: Tehran Lipid and
Glucose study. Scientific reports. 2019;9(1):1572-80
13. Jahangiry L, Abbasalizad Farhangi M, Najafi M, Sarbakhsh P. Clusters of the Risk Markers
and the Pattern of Premature Coronary Heart Disease: An Application of the Latent Class Analysis.
Front Cardiovasc Med. 2021;8:707070.
14. Ju E, Choi J. [Identifying Latent Classes of Risk Factors for Coronary Artery Disease].
2017;47(6):817-27. Article in Korea
15. Nylund-Gibson K, Garber AC, Carter DB, Chan M, Arch DAN, Simon O, et al. Ten frequently
asked questions about latent transition analysis. Psychol Methods. 2023;28(2):284-300.
16. Armstrong T, Bull F. Development of the World Health Organization Global Physical Activity
Questionnaire (GPAQ). Journal of Public Health. 2006;14(2):66-70.
17. Bull FC, Maslin TS, Armstrong T. Global physical activity questionnaire (GPAQ): nine country
reliability and validity study. Journal of physical activity & health. 2009;6(6):790-804.
18. Djalalinia S, Modirian M, Sheidaei A, Yoosefi M, Zokaiee H, Damirchilu B, et al. Protocol
Design for Large-Scale Cross-Sectional Studies of Surveillance of Risk Factors of Non-Communicable
Diseases in Iran: STEPs 2016. Archives of I3ranian medicine. 2017;20(9):608-16.
19. Prevention of Noncommunicable Diseases Department. Global Physical Activity Questionnaire
(GPAQ); Analysis Guide Geneva , Switzerland: World Health Organization; [cited 2024 May 14].
Available from: https://www.who.int/docs/default-source/ncds/ncd-surveillance/gpaq-analysis-guide.
pdf?sfvrsn=1e83d571_2.
20. World Health Organization. WHO package of essential noncommunicable (PEN) disease
interventions for primary health care [Internet]. Geneva: World Health Organization;2020 (cited 2025
Apr 30]. Available from; https://www.who.int/publications-detail-redirect/who-package-of-essential-
noncommunicable-(pen)-disease-interventions-for-primary-health-care
21. Alberti KG, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, et al. Harmonizing
the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task
Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American
Heart Association; World Heart Federation; International Atherosclerosis Society; and International
Association for the Study of Obesity. Circulation. 2009;120(16):1640-5.
22. Fung TT, Chiuve SE, McCullough ML, Rexrode KM, Logroscino G, Hu FB. Adherence
to a DASH-style diet and risk of coronary heart disease and stroke in women. Arch Intern Med.
2008;168(7):713-20.
23. Mirmiran P, Esfahani FH, Mehrabi Y, Hedayati M, Azizi F. Reliability and relative validity of
an FFQ for nutrients in the Tehran lipid and glucose study. Public health nutrition. 2010;13(5):654-62.
24. Nylund KL, Asparouhov T, Muthén BO. Deciding on the Number of Classes in Latent Class
Analysis and Growth Mixture Modeling: A Monte Carlo Simulation Study. Structural Equation Modeling: A Multidisciplinary Journal. 2007;14(4):535-69.
25. Sadeghian S, Graili P, Salarifar M, Karimi AA, Darvish S, Abbasi SH. Opium consumption in
men and diabetes mellitus in women are the most important risk factors of premature coronary artery
disease in Iran. Int J Cardiol. 2010;141(1):116-8.
26. Roayaei P, Aminorroaya A, Vasheghani-Farahani A, Oraii A, Sadeghian S, Poorhosseini H, et
al. Opium and cardiovascular health: A devil or an angel? Indian Heart J. 2020;72(6):482-90.
27. Sadr Bafghi S, Rafiei M, Bahadorzadeh L, Namayeh S, Soltani M, M M, et al. Is opium
addiction a risk factor for acute myocardial infarction? Acta Medica Iranica. 2005;43(3).218-222
28. Jousilahti P, Vartiainen E, Tuomilehto J, Puska P. Sex, age, cardiovascular risk factors, and
coronary heart disease: a prospective follow-up study of 14 786 middle-aged men and women in
Finland. Circulation. 1999;99(9):1165-72.
29. Pradhan AD. Sex Differences in the Metabolic Syndrome: Implications for Cardiovascular
Health in Women. Clinical Chemistry. 2014;60(1):44-52.
30. Huxley R, Barzi F, Woodward M. Excess risk of fatal coronary heart disease associated with
diabetes in men and women: meta-analysis of 37 prospective cohort studies. Bmj. 2006;332(7533):73-
8.
31. Koirala B, Turkson-Ocran RA, Baptiste D, Koirala B, Francis L, Davidson P, et al. Heterogeneity
of Cardiovascular Disease Risk Factors Among Asian Immigrants: Insights From the 2010 to 2018
National Health Interview Survey. J Am Heart Assoc. 2021;10(13):e020408.
32. Tian F, Chen L, Qian Z, Xia H, Zhang Z, Zhang J, et al. Ranking age-specific modifiable risk
factors for cardiovascular disease and mortality: evidence from a population-based longitudinal study.
eClinicalMedicine. 2023;64.
33. Sharif Nia H, Sivarajan-Froelicher E, Haghdoost AA, Moosazadeh M, Huak-Chan Y, Farsavian
AA, et al. The estimate of average age at the onset of acute myocardial infarction in Iran: A systematic
review and meta-analysis study. ARYA Atheroscler. 2018;14(5):225-32.
34. De Luca L, Marini M, Gonzini L, Boccanelli A, Casella G, Chiarella F, et al. Contemporary
Trends and Age-Specific Sex Differences in Management and Outcome for Patients With ST-Segment
Elevation Myocardial Infarction. J Am Heart Assoc. 2016;5(12).
35. Mehta LS, Beckie TM, DeVon HA, Grines CL, Krumholz HM, Johnson MN, et al. Acute
Myocardial Infarction in Women: A Scientific Statement From the American Heart Association.
Circulation. 2016;133(9):916-47.
36. Joshi P, Islam S, Pais P, Reddy S, Dorairaj P, Kazmi K, et al. Risk factors for early myocardial
infarction in South Asians compared with individuals in other countries. Jama. 2007;297(3):286-94.
University of Washington; 2019 [cited 2023 July 05]. Available from: http://vizhub.healthdata.org/
gbd-compare.
2. Benjamin EJ, Blaha MJ, Chiuve SE, Cushman M, Das SR, Deo R, et al. Heart Disease
and Stroke Statistics-2017 Update: A Report From the American Heart Association. Circulation.
2017;135(10):e146-e603.
3. Rodrigues da Silva TP, Matozinhos FP, Gratão LHA, Rocha LL, Vilela LA, Oliveira T, et al. Coexistence of risk factors for cardiovascular diseases among Brazilian adolescents: Individual
characteristics and school environment. PLoS One. 2021;16(7):e0254838.
4. Brunzell JD, Davidson M, Furberg CD, Goldberg RB, Howard BV, Stein JH, et al. Lipoprotein
management in patients with cardiometabolic risk: consensus conference report from the American
Diabetes Association and the American College of Cardiology Foundation. J Am Coll Cardiol.
2008;51(15):1512-24.
5. Mahmood SS, Levy D, Vasan RS, Wang TJ. The Framingham Heart Study and the epidemiology
of cardiovascular disease: a historical perspective. Lancet (London, England). 2014;383(9921):999-
1008.
6. Ho FK, Gray SR, Welsh P, Gill JMR, Sattar N, Pell JP, et al. Ethnic differences in cardiovascular
risk: examining differential exposure and susceptibility to risk factors. BMC Med. 2022;20(1):149.
7. Toms R, Bonney A, Mayne DJ, Feng X, Walsan R. Geographic and area-level socioeconomic
variation in cardiometabolic risk factor distribution: a systematic review of the literature. Int J Health
Geogr. 2019;18(1):1.
8. Muilwijk M, Ho F, Waddell H, Sillars A, Welsh P, Iliodromiti S, et al. Contribution of type
2 diabetes to all-cause mortality, cardiovascular disease incidence and cancer incidence in white
Europeans and South Asians: findings from the UK Biobank population-based cohort study. BMJ
Open Diabetes Res Care. 2019;7(1):e000765.
9. Weller BE, Bowen NK, Faubert SJ. Latent Class Analysis: A Guide to Best Practice. Journal
of Black Psychology. 2020;46(4):287-311.
10. Kim S, Cho S, Nah EH. The patterns of lifestyle, metabolic status, and obesity among
hypertensive Korean patients: a latent class analysis. Epidemiology and health. 2020;42:e2020061.
11. Liberali R, Del Castanhel F, Kupek E, Assis MAA. Latent Class Analysis of Lifestyle Risk
Factors and Association with Overweight and/or Obesity in Children and Adolescents: Systematic
Review. Childhood obesity (Print). 2021;17(1):2-15.
12. Ahanchi NS, Hadaegh F, Alipour A, Ghanbarian A, Azizi F, Khalili D. Application of Latent
Class Analysis to Identify Metabolic Syndrome Components Patterns in adults: Tehran Lipid and
Glucose study. Scientific reports. 2019;9(1):1572-80
13. Jahangiry L, Abbasalizad Farhangi M, Najafi M, Sarbakhsh P. Clusters of the Risk Markers
and the Pattern of Premature Coronary Heart Disease: An Application of the Latent Class Analysis.
Front Cardiovasc Med. 2021;8:707070.
14. Ju E, Choi J. [Identifying Latent Classes of Risk Factors for Coronary Artery Disease].
2017;47(6):817-27. Article in Korea
15. Nylund-Gibson K, Garber AC, Carter DB, Chan M, Arch DAN, Simon O, et al. Ten frequently
asked questions about latent transition analysis. Psychol Methods. 2023;28(2):284-300.
16. Armstrong T, Bull F. Development of the World Health Organization Global Physical Activity
Questionnaire (GPAQ). Journal of Public Health. 2006;14(2):66-70.
17. Bull FC, Maslin TS, Armstrong T. Global physical activity questionnaire (GPAQ): nine country
reliability and validity study. Journal of physical activity & health. 2009;6(6):790-804.
18. Djalalinia S, Modirian M, Sheidaei A, Yoosefi M, Zokaiee H, Damirchilu B, et al. Protocol
Design for Large-Scale Cross-Sectional Studies of Surveillance of Risk Factors of Non-Communicable
Diseases in Iran: STEPs 2016. Archives of I3ranian medicine. 2017;20(9):608-16.
19. Prevention of Noncommunicable Diseases Department. Global Physical Activity Questionnaire
(GPAQ); Analysis Guide Geneva , Switzerland: World Health Organization; [cited 2024 May 14].
Available from: https://www.who.int/docs/default-source/ncds/ncd-surveillance/gpaq-analysis-guide.
pdf?sfvrsn=1e83d571_2.
20. World Health Organization. WHO package of essential noncommunicable (PEN) disease
interventions for primary health care [Internet]. Geneva: World Health Organization;2020 (cited 2025
Apr 30]. Available from; https://www.who.int/publications-detail-redirect/who-package-of-essential-
noncommunicable-(pen)-disease-interventions-for-primary-health-care
21. Alberti KG, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, et al. Harmonizing
the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task
Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American
Heart Association; World Heart Federation; International Atherosclerosis Society; and International
Association for the Study of Obesity. Circulation. 2009;120(16):1640-5.
22. Fung TT, Chiuve SE, McCullough ML, Rexrode KM, Logroscino G, Hu FB. Adherence
to a DASH-style diet and risk of coronary heart disease and stroke in women. Arch Intern Med.
2008;168(7):713-20.
23. Mirmiran P, Esfahani FH, Mehrabi Y, Hedayati M, Azizi F. Reliability and relative validity of
an FFQ for nutrients in the Tehran lipid and glucose study. Public health nutrition. 2010;13(5):654-62.
24. Nylund KL, Asparouhov T, Muthén BO. Deciding on the Number of Classes in Latent Class
Analysis and Growth Mixture Modeling: A Monte Carlo Simulation Study. Structural Equation Modeling: A Multidisciplinary Journal. 2007;14(4):535-69.
25. Sadeghian S, Graili P, Salarifar M, Karimi AA, Darvish S, Abbasi SH. Opium consumption in
men and diabetes mellitus in women are the most important risk factors of premature coronary artery
disease in Iran. Int J Cardiol. 2010;141(1):116-8.
26. Roayaei P, Aminorroaya A, Vasheghani-Farahani A, Oraii A, Sadeghian S, Poorhosseini H, et
al. Opium and cardiovascular health: A devil or an angel? Indian Heart J. 2020;72(6):482-90.
27. Sadr Bafghi S, Rafiei M, Bahadorzadeh L, Namayeh S, Soltani M, M M, et al. Is opium
addiction a risk factor for acute myocardial infarction? Acta Medica Iranica. 2005;43(3).218-222
28. Jousilahti P, Vartiainen E, Tuomilehto J, Puska P. Sex, age, cardiovascular risk factors, and
coronary heart disease: a prospective follow-up study of 14 786 middle-aged men and women in
Finland. Circulation. 1999;99(9):1165-72.
29. Pradhan AD. Sex Differences in the Metabolic Syndrome: Implications for Cardiovascular
Health in Women. Clinical Chemistry. 2014;60(1):44-52.
30. Huxley R, Barzi F, Woodward M. Excess risk of fatal coronary heart disease associated with
diabetes in men and women: meta-analysis of 37 prospective cohort studies. Bmj. 2006;332(7533):73-
8.
31. Koirala B, Turkson-Ocran RA, Baptiste D, Koirala B, Francis L, Davidson P, et al. Heterogeneity
of Cardiovascular Disease Risk Factors Among Asian Immigrants: Insights From the 2010 to 2018
National Health Interview Survey. J Am Heart Assoc. 2021;10(13):e020408.
32. Tian F, Chen L, Qian Z, Xia H, Zhang Z, Zhang J, et al. Ranking age-specific modifiable risk
factors for cardiovascular disease and mortality: evidence from a population-based longitudinal study.
eClinicalMedicine. 2023;64.
33. Sharif Nia H, Sivarajan-Froelicher E, Haghdoost AA, Moosazadeh M, Huak-Chan Y, Farsavian
AA, et al. The estimate of average age at the onset of acute myocardial infarction in Iran: A systematic
review and meta-analysis study. ARYA Atheroscler. 2018;14(5):225-32.
34. De Luca L, Marini M, Gonzini L, Boccanelli A, Casella G, Chiarella F, et al. Contemporary
Trends and Age-Specific Sex Differences in Management and Outcome for Patients With ST-Segment
Elevation Myocardial Infarction. J Am Heart Assoc. 2016;5(12).
35. Mehta LS, Beckie TM, DeVon HA, Grines CL, Krumholz HM, Johnson MN, et al. Acute
Myocardial Infarction in Women: A Scientific Statement From the American Heart Association.
Circulation. 2016;133(9):916-47.
36. Joshi P, Islam S, Pais P, Reddy S, Dorairaj P, Kazmi K, et al. Risk factors for early myocardial
infarction in South Asians compared with individuals in other countries. Jama. 2007;297(3):286-94.
| Files | ||
| Issue | Vol 11 No 3 (2025): . | |
| Section | Articles | |
| Keywords | ||
| Latent class analysis Acute coronary syndrome Risk factors Metabolic syndrome Behavioral risk factors | ||
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How to Cite
1.
Shakiba M, Salari A, Masudi S, Nikfarjam S, mahdavi Roshan marjan, abhari elnaz, borgheie yasaman. Latent Class Analysis of Behavioral and Metabolic Risk Factors Among Patients with Acute Coronary Syndrome. JBE. 11(3):268-281.
