Development of the Diabetic Health Impact Indicator (DHII) as Composite Biomarker Based Score for Diabetes Health Assessment
DOI:
https://doi.org/10.15395/mkb.v58.4876Keywords:
Composite health indicator, diabetes mellitus, exploratory factor analysis, gaussian mixture model, risk stratificationAbstract
Diabetes mellitus is a global significant health concern that affects various body systems. Current assessment tools mostly rely on individual biomarkers, which do not fully capture the overall health burden of the disease. Therefore, a comprehensive and understandable measure is needed to evaluate the health impact associated with diabetes. This study aimed to develop the Diabetic Health Impact Indicator (DHII), a multidimensional, biomarker-based summary score designed to reflect the overall effects of diabetes, by employing a cross-sectional design with secondary data from 1,000 individuals, including diabetic, prediabetic, and non-diabetic participants. The dataset encompassed biochemical, physiological, and demographic variables related to diabetes. Exploratory Factor Analysis (EFA) was utilized to identify underlying health domains, with factor loadings used to assign weights. The empirical cumulative distribution function (ECDF) scaled the composite scores within a 0-10 range. Gaussian mixture modelling showed that DHII followed heterogeneous distribution patterns across subgroups, with K-CGMD providing the best fit in most cases. Predictive validation using logistic regression showed that DHII alone achieved an AUC of 0.721, while a baseline model with age, sex, and BMI achieved an AUC of 0.961. Adding DHII to the baseline model resulted in a marginal increase in AUC to 0.968, which was not statistically significant (DeLong’s test, p=0.33). In summary, DHII is a data-driven composite index with multiple biomarker domains that demonstrates moderate discriminatory ability for diabetes status. Further validation in independent populations is required to assess its clinical use.
Downloads
References
1. International Diabetes Federation. IDF Diabetes Atlas. 10th ed. Brussels: IDF; 2021.
2. Saeedi P, Petersohn I, Salpea P, Malanda B, Karuranga S, Unwin N, et al. Global and regional diabetes prevalence estimates for 2019 and projections. Diabetes Res Clin Pract. 2019;157:107843. doi:10.1016/j.diabres.2019.107843
3. O’Hearn M, Lauren BN, Wong JB, Kim DD, Mozaffarian D. Trends and disparities in cardiometabolic health among U.S. adults, 1999–2018. J Am Coll Cardiol. 2022;80(2):138–51. doi:10.1016/j.jacc.2022.04.046
4. Rawshani A, Rawshani A, Franzén S, Sattar N, Eliasson B, Svensson AM, et al. Risk factors, mortality, and cardiovascular outcomes in patients with type 2 diabetes. N Engl J Med. 2018;379(7):633–44. doi:10.1056/NEJMoa1800256
5. Zheng Y, Ley SH, Hu FB. Global aetiology and epidemiology of type 2 diabetes mellitus. Nat Rev Endocrinol. 2018;14(2):88–98. doi:10.1038/nrendo.2017.151
6. Pearson ER. Type 2 diabetes: a multifaceted disease. Diabetologia. 2019;62(7):1107–12. doi:10.1007/s00125–019–4909-y
7. Jolliffe IT, Cadima J. Principal component analysis: a review and recent developments. Philos Trans A Math Phys Eng Sci. 2016;374(2065):20150202. doi:10.1098/rsta.2015.0202
8. Scrucca L, Fop M, Murphy TB, Raftery AE. mclust 5: clustering, classification and density estimation using Gaussian finite mixture models. R J. 2016;8(1):289–317. doi:10.32614/RJ-2016–021
9. Ahlqvist E, Storm P, Käräjämäki A, Martinell M, Dorkhan M, Carlsson A, et al. Novel subgroups of adult-onset diabetes and their association with outcomes: a data-driven cluster analysis. Lancet Diabetes Endocrinol. 2018;6(5):361–9. doi:10.1016/S2213–8587(18)30051–2
10. Collins GS, Reitsma JB, Altman DG, Moons KG. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMJ. 2015;350:g7594. doi:10.1136/bmj.g7594
11. Wolff RF, Moons KGM, Riley RD, Whiting PF, Westwood M, Collins GS, et al. PROBAST: a tool to assess risk of bias and applicability of prediction model studies. Ann Intern Med. 2019;170(1):51–8. doi:10.7326/M18–1376.
12. Steyerberg EW, Harrell FE Jr. Prediction models need appropriate internal, internal-external, and external validation. J Clin Epidemiol. 2016;69:245–7. doi:10.1016/j.jclinepi.2015.04.005.
13. Van Calster B, McLernon DJ, van Smeden M, Wynants L, Steyerberg EW. Calibration: the Achilles heel of predictive analytics. BMC Med. 2019;17(1):230. doi:10.1186/s12916–019–1466–7
14. Wynants L, Van Calster B, Collins GS, Riley RD, Heinze G, Schuit E, et al. Prediction models for diagnosis and prognosis of COVID-19 infection: systematic review and critical appraisal. BMJ. 2020;369:m1328. doi:10.1136/bmj.m1328.
15. Riley RD, van der Windt D, Croft P, Moons KGM. Prognosis research strategy (PROGRESS) framework. BMJ. 2019;364:l459. doi:10.1136/bmj.l459
16. Grundy SM. Metabolic syndrome update. Circulation. 2016;134(1):e10–e19. doi:10.1161/CIRCULATIONAHA.115.018585
17. Contreras I, Vehi J. Artificial intelligence for diabetes management and decision support. J Med Internet Res. 2018;20(5):e10775. doi:10.2196/10775
18. Dennis JM, Shields BM, Henley WE, Jones AG, Hattersley AT. Disease progression and treatment response in data-driven subgroups of type 2 diabetes compared with models based on simple clinical features: an analysis using clinical trial data. Lancet Diabetes Endocrinol. 2019;7(6):442–51. doi:10.1016/S2213–8587(19)30087–7
19. Gregg EW, Sattar N, Ali MK. The changing face of diabetes complications. Lancet Diabetes Endocrinol. 2016;4(6):537–47. doi:10.1016/S2213–8587(16)30010–9
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Majalah Kedokteran Bandung
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).
Under this license, authors retain copyright and grant Majalah Kedokteran Bandung the right of first publication. Others may share, remix, and adapt the work for non-commercial purposes, provided that appropriate credit is given to the author and to Majalah Kedokteran Bandung as the initial place of publication.
For details, please see: https://creativecommons.org/licenses/by-nc/4.0/
