https://doi.org/10.4081/itjm.2023.1642
Predictive model for diabetes mellitus occurrence in Iran’s southeastern region: a study based on American diabetes association guidelines
Published: 26 September 2023
Abstract Views: 385
PDF: 143
HTML: 2
HTML: 2
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Authors
To control diabetes in a society, risk assessment tools are used to predict disease risk. We aimed to assess the value of different risk factors for diabetes mellitus in a remarkable community in the city of Kerman, one of the vast areas in the southeast of Iran, with the final goal of designing a predictive model for diabetes in this region. This study was a cross-sectional study with the aim of investigating the predictive value of risk factors indicating the presence of diabetes in the population of Kerman City based on the guidelines of the American Diabetes Association (ADA) risk assessment tool. The information of 4000 people participating in the comprehensive screening plan for cardiovascular risk factors in Kerman City was extracted by reviewing the relevant data registry. According to the ADA guideline, 32.5% of participants were at risk for diabetes mellitus. The hazard ratio of diabetes mellitus in the subgroup with the ADA final score ≥5 as compared to those with a lower final score was 1.9. Advanced age, history of gestational diabetes, family history of diabetes mellitus, history of hypertension, low physical activity, and higher body mass index were the main determinants of diabetes mellitus. According to ADA guidelines and the diabetes mellitus risk assessment tool, 32.5% of the population residents in Kerman City are potentially at risk for diabetes mellitus that can be successfully predicted aide by the ADA risk assessment tool.
Magliano DJ, Islam RM, Barr ELM, et al. Trends in incidence of total or type 2 diabetes: systematic review. BMJ 2019;366:l5003.
DOI: https://doi.org/10.1136/bmj.l5003
Harding JL, Pavkov ME, Magliano DJ, et al. Global trends in diabetes complications: a review of current evidence. Diabetologia 2019;62:3-16.
DOI: https://doi.org/10.1007/s00125-018-4711-2
Ali MK, Pearson-Stuttard J, Selvin E, et al. Interpreting global trends in type 2 diabetes complications and mortality. Diabetologia 2022;65:3-13.
DOI: https://doi.org/10.1007/s00125-021-05585-2
Reed J, Bain S, Kanamarlapudi V. A Review of Current Trends with Type 2 Diabetes Epidemiology, Aetiology, Pathogenesis, Treatments and Future Perspectives. Diabetes Metab Syndr Obes 2021;14:3567-602.
DOI: https://doi.org/10.2147/DMSO.S319895
Gruss SM, Nhim K, Gregg E, et al. Public Health Approaches to Type 2 Diabetes Prevention: the US National Diabetes Prevention Program and Beyond. Curr Diab Rep 2019;19:78.
DOI: https://doi.org/10.1007/s11892-019-1200-z
Ritchie ND, Baucom KJW, Sauder KA. Current Perspectives on the Impact of the National Diabetes Prevention Program: Building on Successes and Overcoming Challenges. Diabetes Metab Syndr Obes 2020;13:2949-57.
DOI: https://doi.org/10.2147/DMSO.S218334
Hill-Briggs F. The American Diabetes Association in the Era of Health Care Transformation. Diabetes Spectr 2019;32:61-8.
DOI: https://doi.org/10.2337/ds18-0071
Ndjaboue R, Farhat I, Ferlatte CA, et al. Predictive models of diabetes complications: protocol for a scoping review. Syst Rev 2020;9:137.
DOI: https://doi.org/10.1186/s13643-020-01391-w
Lai H, Huang H, Keshavjee K, et al. Predictive models for diabetes mellitus using machine learning techniques. BMC Endocr Disord 2019;19:101.
DOI: https://doi.org/10.1186/s12902-019-0436-6
Krishnamoorthi R, Joshi S, Almarzouki HZ, et al. A Novel Diabetes Healthcare Disease Prediction Framework Using Machine Learning Techniques. J Healthc Eng 2022;2022:1684017.
DOI: https://doi.org/10.1155/2022/1684017
Hasandokht T, Joukar F, Maroufizadeh S, et al. Detection of high risk people for diabetes by American diabetes association risk score in PERSIAN Guilan cohort study. BMC Endocr Disord 2023;23:12
DOI: https://doi.org/10.1186/s12902-022-01248-4
Ye J, Wu Y, Yang S, et al. The global, regional and national burden of type 2 diabetes mellitus in the past, present and future: a systematic analysis of the Global Burden of Disease Study 2019. Front Endocrinol (Lausanne) 2023;14:1192629.
DOI: https://doi.org/10.3389/fendo.2023.1192629
Blonde L, Umpierrez GE, Reddy SS, et al. American Association of Clinical Endocrinology Clinical Practice Guideline: Developing a Diabetes Mellitus Comprehensive Care Plan-2022 Update. Endocr Pract 2022;28:923-1049.
DOI: https://doi.org/10.1016/j.eprac.2022.08.002
Kautzky-Willer A, Harreiter J, Pacini G. Sex and Gender Differences in Risk, Pathophysiology and Complications of Type 2 Diabetes Mellitus. Endocr Rev 2016;37:278-316.
DOI: https://doi.org/10.1210/er.2015-1137
Ciarambino T, Crispino P, Leto G, et al. Influence of Gender in Diabetes Mellitus and Its Complication. Int J Mol Sci 2022;23:8850
DOI: https://doi.org/10.3390/ijms23168850
You H, Hu J, Liu Y, et al. Risk of type 2 diabetes mellitus after gestational diabetes mellitus: A systematic review & meta-analysis. Indian J Med Res 2021;154:62-77.
DOI: https://doi.org/10.4103/ijmr.IJMR_852_18
Yang J, Qian F, Chavarro JE, et al. Modifiable risk factors and long term risk of type 2 diabetes among individuals with a history of gestational diabetes mellitus: prospective cohort study. BMJ 2022;378:e070312.
DOI: https://doi.org/10.1136/bmj-2022-070312
Ramezankhani A, Habibi-Moeini AS, Zadeh SST, et al. Effect of family history of diabetes and obesity status on lifetime risk of type 2 diabetes in the Iranian population. J Glob Health 2022;12:04068.
DOI: https://doi.org/10.7189/jogh.12.04068
Chobot A, Górowska-Kowolik K, Sokołowska M, et al. Obesity and diabetes-Not only a simple link between two epidemics. Diabetes Metab Res Rev 2018;34:e3042.
DOI: https://doi.org/10.1002/dmrr.3042
Roumie CL, Hung AM, Russell GB, et al. Blood Pressure Control and the Association With Diabetes Mellitus Incidence: Results From SPRINT Randomized Trial. Hypertension 2020;75:331-8.
DOI: https://doi.org/10.1161/HYPERTENSIONAHA.118.12572
Aldayel FA, Belal MA, Alsheikh AM. The Validity of the American Diabetes Association’s Diabetes Risk Test in a Saudi Arabian Population. Cureus 2021;13:e18018.
DOI: https://doi.org/10.7759/cureus.18018
Asgari S, Lotfaliany M, Fahimfar N, et al. The external validity and performance of the no-laboratory American Diabetes Association screening tool for identifying undiagnosed type 2 diabetes among the Iranian population. Prim Care Diabetes 2020;14:672-7.
DOI: https://doi.org/10.1016/j.pcd.2020.04.001
How to Cite
Khoshnazar, S. M., Najafipour, H., SoltaniNejad, L., Pezeshki, S., & Yousefzadeh, G. (2023). Predictive model for diabetes mellitus occurrence in Iran’s southeastern region: a study based on American diabetes association guidelines. Italian Journal of Medicine, 17(2). https://doi.org/10.4081/itjm.2023.1642
Copyright (c) 2023 the Author(s)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
PAGEPress has chosen to apply the Creative Commons Attribution NonCommercial 4.0 International License (CC BY-NC 4.0) to all manuscripts to be published.
Similar Articles
- Mauro Campanini, Giuliano Pinna, Roberto Nardi, on behalf of the FADOI Cardiovascular Prevention Position Statements Group, The FADOI (Federation of Associations of Hospital Doctors on Internal Medicine) position paper on cardiovascular prevention in the higher risk complex patients , Italian Journal of Medicine: Vol. 9 No. 4 (2015)
- Ada Maffettone, Massimo Rinaldi, Andrea Fontanella, Postprandial hyperglycemia: a new frontier in diabetes management? , Italian Journal of Medicine: Vol. 12 No. 2 (2018)
- Cristina Cenci, Tea Restuccia, Paola Gnerre, Irene Chiti, Laura Betti, Milena Micati, Claudio Tana, The management of the patient with acute pancreatitis: from evidence to clinical practice , Italian Journal of Medicine: Vol. 11 No. 3 (2017)
- Riccardo Gerloni, Luciano Mucci, Tiziana Ciarambino, Manuel Ventura, Valeria Baglio, Massimiliano Chiuch, Francesca Saladini, Stefania Dorigoni, Davide Tizzani, Paola Gnerre, Management of stable coronary artery disease: from evidence to clinical practice , Italian Journal of Medicine: Vol. 11 No. 2 (2017)
- Tiziana M. Attardo, Elena Magnani, Carlotta Casati, Danilo Cavalieri, Pietro Crispino, Federica Fascì Spurio, Stefano De Carli, Daniela Tirotta, Paola Gnerre, Management of celiac disease: from evidence to clinical practice , Italian Journal of Medicine: Vol. 11 No. 4 (2017)
- Michele Meschi, Ezio Degli Esposti, Luca Degli Esposti, Francesca Spagnoli, Pasquale Gianluca Giuri, Enrico Fiaccadori, Alberto Caiazza, Referral to sub-specialists: who have the most to gain from early specialist intervention among patients with markers of renal disease? , Italian Journal of Medicine: Vol. 8 No. 3 (2014)
- Federico Moroni, Vieri Vannucchi, Costanza Vinci, Simone Bianchi, Alessandra Giuello, Francesco Prosperi Iovi, Michele Lanigra, Angela Konze, Giancarlo Landini, A new stroke mimic prediction scale in a stroke center with a high thrombolysis rate , Italian Journal of Medicine: Vol. 18 No. 1 (2024)
- Riccardo Gerloni, Domenico Panuccio, Pregnancy and heart disease: what Internists should know , Italian Journal of Medicine: Vol. 11 No. 3 (2017)
- Paola Faggioli, Antonio Tamburello, Alba Sciascera, Adele Giulia Gilardi, Antonino Mazzone, Nailfold videocapillaroscopy in internal medicine , Italian Journal of Medicine: Vol. 9 No. 3 (2015)
You may also start an advanced similarity search for this article.
