Relationship between health behavior and metabolic syndrome progression: The parallel latent growth curve model

Article Sidebar

Somkiat Tonphu
PDF
Published: Feb 26, 2022
DOI: https://doi.org/10.31524/bkkmedj.2022.11.001

Main Article Content

Somkiat Tonphu, MBA, BPH, BA, BSc
Graduate School of Applied Statistics,Business Analytics and Intelligence, National Institute of Development Administration, Bangkok, Thailand.
Sumith Tanaprakob, MD
Health Promotion Center 2, Bangkok Hospital, Bangkok Hospital Group, Bangkok, Thailand.
Phunlerd Piyaraj, MD, PhD
Department of Parasitology, Phramongkutklao College of Medicine, Bangkok, Thailand. Bangkok Health Research Center, Bangkok Dusit Medical Services Public Company Limited, Bangkok, Thailand.
Arnond Sakworawich, PhD, MBA, MA, BBA
Graduate School of Applied Statistics, Business Analytics and Intelligence, National Institute of Development Administration, Bangkok, Thailand.

Abstract

OBJECTIVES: This research aims to develop a measurement model to assess an relationship between Metabolic Syndrome (MetS) progression and health behavior changes. 


MATERIAL AND METHODS: Medical records of selected patients attending checkup clinics of a private hospital from 2006 - 2017 was reviewed. Clinical and questionnaire data that assessed exercise (EXE), smoking (SMK), and failure to control weight (FCW) including waist circumference (WC) and BMI as well as laboratory results i.e. Blood pressure, high density lipoprotein (HDL), low density lipoprotein (LDL), triglycerides (TG), and fasting blood sugar (FBS) were retrieved.  The NCEP ATP III was applied to diagnose Metabolic Syndrome (MetS) and a parallel latent growth curve model was used to assess relationships between MetS progression and health-related behaviors using R-code with lavaan (latent variable analysis) version 0.6-1 package.  χ2/df, CFI, TLI, GFI, SRMR, RMSEA and Analysis of Covariate (ANCOVA) were used to determine goodness of fit and influence of sex and age as co-variates of the model.  


RESULT: Among 1,296 patients, 61 (4.7%), 64 (4.9%), 70 (5.4%), 73 (5.6%) and 87 (6.7%) patients had MetS each year.  FCW had strong effects on prevalence (coefficient 0.69) and trend (0.57) of MetS progression while EXE had small negative (-0.05 and -0.07) and SMK had small and positive (0.01 and 0.03) effects.  Age and gender contributed to MetS indirectly through FCW.  Decomposition of effects revealed high relationship between FCW and prevalence (0.69) and trend (0.57) of MetS.  SMK had indirect effect on TG (0.66) and HDL (-0.61) but these effects were diluted off after controlled for effects of other variables.    


CONCLUSION: 


We have brought to light the hidden (latent) aspect that age and sex result in MetS through FCW.  With the significance of FCW, which subsequently increases risks for several NCDs, healthy eating should be the most important health promotion topic to avoid MetS progression. The LGC model can be used to supplement the diagnostic and prognostic scores for both physicians and health teams because it provides more detailed information of hidden (latent) relationships. Mobile phone applications using this model should be developed in order to promote self-regulation among MetS patients. Future research should be conducted for revision, calibration and validation of the ATP III criteria and the current research risk scores.

Article Details

How to Cite
1.
Tonphu, MBA, BPH, BA, BSc S, Tanaprakob, MD S, Piyaraj, MD, PhD P, Sakworawich, PhD, MBA, MA, BBA A. Relationship between health behavior and metabolic syndrome progression: The parallel latent growth curve model. BKK Med J [Internet]. 2022 Feb. 26 [cited 2024 Apr. 26];18(1):1. Available from: https://he02.tci-thaijo.org/index.php/bkkmedj/article/view/252432
Issue
Vol. 18 No. 1 (2022): February
Section
Original Article
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

References

1. DeFronzo RA, Ferrannini E. Insulin resistance. A multifaceted syndrome responsible for NIDDM, obesity, hypertension, dyslipidemia, and atherosclerotic cardiovascular disease. Diabetes Care. 1991;14(3):173-94.
2. Koh KK, Han SH, Quon MJ. Inflammatory markers and the metabolic syndrome: insights from therapeutic interventions. J Am Coll Cardiol. 2005;46(11):1978-85.
3. Lindsay RS, Howard BV. Cardiovascular risk associated with the metabolic syndrome. Curr Diab Rep. 2004;4(1):63-8.
4. World Health O. A global brief on hypertension : silent killer, global public health crisis: World Health Day 2013. Geneva: World Health Organization; 2013. Contract No.: WHO/DCO/WHD/2013.2.
5. Organization WH. Global report on diabetes: executive summary. World Health Organization; 2016.
6. Dzau VJ, Balatbat CA. Future of Hypertension. Hypertension. 2019;74(3):450-7.
7. Organization WH. Hypertension care in Thailand: best practices and challenges, 2019. 2019.
8. Bassi N, Karagodin I, Wang S, Vassallo P, Priyanath A, Massaro E, et al. Lifestyle modification for metabolic syndrome: a systematic review. Am J Med. 2014;127(12):1242 e1-10.
9. Bozkurt B, Aguilar D, Deswal A, Dunbar SB, Francis GS, Horwich T, et al. Contributory Risk and Management of Comorbidities of Hypertension, Obesity, Diabetes Mellitus, Hyperlipidemia, and Metabolic Syndrome in Chronic Heart Failure: A Scientific Statement From the American Heart Association. Circulation. 2016;134(23):e535-e78.
10. Magkos F, Yannakoulia M, Chan JL, Mantzoros CS. Management of the metabolic syndrome and type 2 diabetes through lifestyle modification. Annu Rev Nutr. 2009;29:223-56.
11. Oh JD, Lee S, Lee JG, Kim YJ, Kim YJ, Cho BM. Health Behavior and Metabolic Syndrome. Korean J Fam Med. 2009;30(2):120-8.
12. Huang PL. A comprehensive definition for metabolic syndrome. Dis Model Mech. 2009;2(5-6):231-7.
13. Moy FM, Bulgiba A. The modified NCEP ATP III criteria maybe better than the IDF criteria in diagnosing Metabolic Syndrome among Malays in Kuala Lumpur. BMC Public Health. 2010;10(1):678.
14. Caruana EJ, Roman M, Hernandez-Sanchez J, Solli P. Longitudinal studies. J Thorac Dis. 2015;7(11):E537-40.
15. Byrne BM. Structural equation modeling with Mplus: Basic concepts, applications, and programming: routledge; 2013.
16. Curran PJ, Obeidat K, Losardo D. Twelve Frequently Asked Questions About Growth Curve Modeling. J Cogn Dev. 2010;11(2):121-36.
17. Grimm KJ, Ram N. Nonlinear Growth Models in Mplus and SAS. Structural Equation Modeling: A Multidisciplinary Journal. 2009;16(4):676-701.
18. Geiser C, Bishop J, Lockhart G, Shiffman S, Grenard JL. Analyzing latent state-trait and multiple-indicator latent growth curve models as multilevel structural equation models. Front Psychol. 2013;4:975.
19. Muthén BO. Beyond SEM: General latent variable modeling. Behaviormetrika. 2002;29(1):81-117.
20. Tabachnick BG, Fidell LS, Ullman JB. Using multivariate statistics: Pearson Boston, MA; 2007.
21. Kline RB. Principles and practice of structural equation modeling 2nd ed. New York: Guilford. 2005;3.
22. Wan TT. Evidence-based health care management: Multivariate modeling approaches: Springer Science & Business Media; 2002.
23. Bentler PM. Comparative fit indexes in structural models. Psychological bulletin. 1990;107(2):238.
24. Tucker LR, Lewis C. A reliability coefficient for maximum likelihood factor analysis. Psychometrika. 1973;38(1):1-10.
25. Shevlin M, Miles JNV. Effects of sample size, model specification and factor loadings on the GFI in confirmatory factor analysis. Personality and Individual Differences. 1998;25(1):85-90.
26. Hu Lt, Bentler PM. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural equation modeling: a multidisciplinary journal. 1999;6(1):1-55.
27. Chen FF. Sensitivity of goodness of fit indexes to lack of measurement invariance. Structural equation modeling: a multidisciplinary journal. 2007;14(3):464-504.
28. Consultation WHOE. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet. 2004;363(9403):157-63.
29. Grundy SM, Stone NJ, Bailey AL, Beam C, Birtcher KK, Blumenthal RS, et al. 2018 AHA/ACC/AACVPR/AAPA/ABC/ACPM/ADA/AGS/APhA/ASPC/NLA/PCNA Guideline on the Management of Blood Cholesterol: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. Circulation. 2019;139(25):e1082-e143.
30. MacCallum RC, Hong S. Power Analysis in Covariance Structure Modeling Using GFI and AGFI. Multivariate Behavioral Research. 1997;32(2):193-210.
31. Sharma S, Mukherjee S, Kumar A, Dillon WR. A simulation study to investigate the use of cutoff values for assessing model fit in covariance structure models. Journal of Business Research. 2005;58(7):935-43.
32. Park YW, Zhu S, Palaniappan L, Heshka S, Carnethon MR, Heymsfield SB. The metabolic syndrome: prevalence and associated risk factor findings in the US population from the Third National Health and Nutrition Examination Survey, 1988-1994. Arch Intern Med. 2003;163(4):427-36.
33. Miller M, Stone NJ, Ballantyne C, Bittner V, Criqui MH, Ginsberg HN, et al. Triglycerides and cardiovascular disease: a scientific statement from the American Heart Association. Circulation. 2011;123(20):2292-333.
34. Gennuso KP, Gangnon RE, Thraen-Borowski KM, Colbert LH. Dose-response relationships between sedentary behaviour and the metabolic syndrome and its components. Diabetologia. 2015;58(3):485-92.