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Towards a Parsimonious Pathway Model of Modifiable and Mediating Risk Factors Leading to Diabetes Risk

Author

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  • Yi-Ching Lynn Ho

    (Office of Regional Health, Singapore Health Services, 167 Jalan Bukit Merah, Singapore 150167, Singapore
    Programme in Health Services and Systems Research, Duke-NUS Medical School, 8 College Rd., Singapore 169857, Singapore
    Co-first authors.)

  • Vivian Shu Yi Lee

    (Office of Regional Health, Singapore Health Services, 167 Jalan Bukit Merah, Singapore 150167, Singapore
    Co-first authors.)

  • Moon-Ho Ringo Ho

    (School of Social Sciences, Nanyang Technological University, 48 Nanyang Ave., Singapore 639818, Singapore)

  • Gladis Jing Lin

    (Office of Regional Health, Singapore Health Services, 167 Jalan Bukit Merah, Singapore 150167, Singapore
    Programme in Health Services and Systems Research, Duke-NUS Medical School, 8 College Rd., Singapore 169857, Singapore)

  • Julian Thumboo

    (Office of Regional Health, Singapore Health Services, 167 Jalan Bukit Merah, Singapore 150167, Singapore
    Department of Rheumatology and Immunology, Singapore General Hospital, Outram Rd., Singapore 169608, Singapore
    Medicine Academic Clinical Programme, Duke-NUS Medical School, 8 College Rd., Singapore 169857, Singapore)

Abstract

Modifiable risk factors are of interest for chronic disease prevention. Few studies have assessed the system of modifiable and mediating pathways leading to diabetes mellitus. We aimed to develop a pathway model for Diabetes Risk with modifiable Lifestyle Risk factors as the start point and Physiological Load as the mediator. As there are no standardised risk thresholds for lifestyle behaviour, we derived a weighted composite for Lifestyle Risk. Physiological Load was based on an index using clinical thresholds. Sociodemographics are non-modifiable risk factors and were specified as covariates. We used structural equation modeling to test the model, first using 2014/2015 data from the Indonesian Family Life Survey. Next, we fitted a smaller model with longitudinal data (2007/2008 to 2014/2015), given limited earlier data. Both models showed the indirect effects of Lifestyle Risk on Diabetes Risk via the mediator of Physiological Load, whereas the direct effect was only supported in the cross-sectional analysis. Specifying Lifestyle Risk as an observable, composite variable incorporates the cumulative effect of risk behaviour and differentiates this study from previous studies assessing it as a latent construct. The parsimonious model groups the multifarious risk factors and illustrates modifiable pathways that could be applied in chronic disease prevention efforts.

Suggested Citation

  • Yi-Ching Lynn Ho & Vivian Shu Yi Lee & Moon-Ho Ringo Ho & Gladis Jing Lin & Julian Thumboo, 2021. "Towards a Parsimonious Pathway Model of Modifiable and Mediating Risk Factors Leading to Diabetes Risk," IJERPH, MDPI, vol. 18(20), pages 1-20, October.
  • Handle: RePEc:gam:jijerp:v:18:y:2021:i:20:p:10907-:d:658305
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    References listed on IDEAS

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    1. Eric A Finkelstein & Junxing Chay & Shailendra Bajpai, 2014. "The Economic Burden of Self-Reported and Undiagnosed Cardiovascular Diseases and Diabetes on Indonesian Households," PLOS ONE, Public Library of Science, vol. 9(6), pages 1-8, June.
    2. Ram D. Joshi & Chandra K. Dhakal, 2021. "Predicting Type 2 Diabetes Using Logistic Regression and Machine Learning Approaches," IJERPH, MDPI, vol. 18(14), pages 1-17, July.
    3. Seccareccia, F. & Pannozzo, F. & Dima, F. & Minoprio, A. & Menditto, A. & Noce, C.L. & Giampaoli, S., 2001. "Heart rate as a predictor of mortality: The MATISS project," American Journal of Public Health, American Public Health Association, vol. 91(8), pages 1258-1263.
    4. Manal Alghamdi & Mouaz Al-Mallah & Steven Keteyian & Clinton Brawner & Jonathan Ehrman & Sherif Sakr, 2017. "Predicting diabetes mellitus using SMOTE and ensemble machine learning approach: The Henry Ford ExercIse Testing (FIT) project," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-15, July.
    5. Steven E. Kahn & Rebecca L. Hull & Kristina M. Utzschneider, 2006. "Mechanisms linking obesity to insulin resistance and type 2 diabetes," Nature, Nature, vol. 444(7121), pages 840-846, December.
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