Evaluating the Dietary Factors Most Closely Associated with Diabetes Mellitus Using a Decision-Making Tree Algorithm

Document Type : Research Paper


1 Department of Applied Mathematics, Ferdowsi University of Mashhad, Mashhad, Iran.

2 International UNESCO Center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran.

3 Department of Nutrition, Food Sciences and Clinical Biochemistry, School of Medicine, Social Determinants of Health Research Center, Gonabad University of Medical Science, Gonabad, Iran.

4 Department of Statistics, Faculty of Mathematics and Computer Sciences, Allameh Tabataba’i University, Tehran, Iran.

5 Student Research Committee, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.

6 Department of Biostatistics and Epidemiology California, Loma Linda University, USA.

7 Cardiovascular Research Center, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.

8 Department of Biology, Mashhad Branch, Islamic Azad University, Mashhad, Iran.

9 Division of Medical Education, Brighton & Sussex Medical School, Falmer, Brighton, Sussex, UK.

10 Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.


Introduction: The development of type 2 diabetes mellitus (T2DM) is associated with lifestyle factors, including dietary patterns. A diet rich in macro- and micronutrients has been reported to reduce the risk of T2DM. Therefore, this study aimed to identify the dietary factors most closely associated with T2DM in subjects within the MASHAD cohort using a decision tree algorithm. Methods: This cross-sectional study was conducted on 9704 individuals from the Mashhad Stroke and Heart Atherosclerotic Disorders (MASHAD), of whom 5936 participants completed a 24h dietary recall questionnaire. Macronutrients and micronutrients were estimated using Diet Plan 6 software. A decision tree algorithm was utilized to evaluate the most crucial dietary nutrient intakes concerning T2DM. Results: The algorithm showed a high specificity (81.34%) but low sensitivity (34.21%), which could predict T2DM with a low-to-moderate diagnostic ability (AUC=0.58). Based on the decision tree, eight features, including dietary potassium, total sugar, sucrose, riboflavin, thiamin, sodium, total nitrogen, and magnesium, were T2DM’s most critical dietary components. Conclusion: Based on the results, consuming sugar, salt, and vitamin B was the most critical related dietary intake to T2DM. Dietary interventions may be a cost-effective strategy for preventing T2DM.


Main Subjects

  1. Atlas ID. 9th edn [Internet]. International Diabetes Federation. 2019.
  2. Zhu Y, Sidell MA, Arterburn D, Daley MF, Desai J, Fitzpatrick SL, et al. Racial/ethnic disparities in the prevalence of diabetes and prediabetes by BMI: Patient Outcomes Research To Advance Learning (PORTAL) multisite cohort of adults in the US. Diabetes Care. 2019;42(12):2211-9.
  3. Fox CS, Golden SH, Anderson C, Bray GA, Burke LE, De Boer IH, et al. Update on prevention of cardiovascular disease in adults with type 2 diabetes mellitus in light of recent evidence: a scientific statement from the American Heart Association and the American Diabetes Association. Circulation. 2015;132(8):691-718.
  4. Schwingshackl L, Hoffmann G, Lampousi A-M, Knüppel S, Iqbal K, Schwedhelm C, et al. Food groups and risk of type 2 diabetes mellitus: a systematic review and meta-analysis of prospective studies. European Journal of Epidemiology. 2017;32(5):363-75.
  5. Association AD. 5. Prevention or delay of type 2 diabetes: standards of medical care in diabetes—2018. Diabetes Care. 2018;41(Supplement 1):S51-S4.
  6. Ramachandran A, Snehalatha C, Mary S, Mukesh B, Bhaskar A, Vijay V. The Indian Diabetes Prevention Programme shows that lifestyle modification and metformin prevent type 2 diabetes in Asian Indian subjects with impaired glucose tolerance (IDPP-1). Diabetologia. 2006;49(2):289-97.
  7. Evert AB, Boucher JL, Cypress M, Dunbar SA, Franz MJ, Mayer-Davis EJ, et al. Nutrition therapy recommendations for the management of adults with diabetes. Diabetes Care. 2014;37(Supplement 1):S120-S43.
  8. Kavakiotis I, Tsave O, Salifoglou A, Maglaveras N, Vlahavas I, Chouvarda I. Machine learning and data mining methods in diabetes research. Computational and Structural Biotechnology Journal. 2017;15:104-16.
  9. Saberi-Karimian M, Khorasanchi Z, Ghazizadeh H, Tayefi M, Saffar S, Ferns GA, et al. Potential value and impact of data mining and machine learning in clinical diagnostics. Critical Reviews in Clinical Laboratory Sciences. 2021;58(4):275-96.
  10. Doupe P, Faghmous J, Basu S. Machine learning for health services researchers. Value in Health. 2019;22(7):808-15.
  11. Esmaily H, Tayefi M, Doosti H, Ghayour-Mobarhan M, Nezami H, Amirabadizadeh A. A comparison between decision tree and random forest in determining the risk factors associated with type 2 diabetes. Journal of Research in Health Sciences. 2018;18(2):412.

12.Tayefi M, Esmaeily H, Ghayour-Mobarhan M, Amirabadizadeh AR. Comparing three data mining algorithms for identifying associated risk factors of Type 2 Diabetes. Medical Technologies Journal. 2017;1(4):133-4.

  1. Esmaeily H, Tayefi M, Doosti H, Ghayour-Mobarhan M, Amirabadizadeh AR. Applying decision tree for detection of a low risk population for type 2 diabetes: A population based study. Medical Technologies Journal. 2017;1(4):132-.
  2. Tayefi M, Esmaeili H, Karimian MS, Zadeh AA, Ebrahimi M, Safarian M, et al. The application of a decision tree to establish the parameters associated with hypertension. Computer Methods and Programs in Biomedicine. 2017;139:83-91.
  3. Tayefi M, Saberi-Karimian M, Esmaeili H, Zadeh AA, Ebrahimi M, Mohebati M, et al. Evaluating of associated risk factors of metabolic syndrome by using decision tree. Comparative Clinical Pathology. 2018;27(1):215-23.
  4. Tayefi M, Tajfard M, Saffar S, Hanachi P, Amirabadizadeh AR, Esmaeily H, et al. hs-CRP is strongly associated with coronary heart disease (CHD): A data mining approach using decision tree algorithm. Computer Methods and Programs in Biomedicine . 2017;141:105-9.
  5. Soflaei SS, Shamsara E, Sahranavard T, Esmaily H, Moohebati M, Shabani N, et al. Dietary protein is the strong predictor of coronary artery disease; a data mining approach. Clinical Nutrition ESPEN. 2021;43:442-7.
  6. Shamsara E, Soflaei SS, Tajfard M, Yamshchikov I, Esmaily H, Saberi-Karimian M, et al. Artificial neural network models for coronary artery disease. Current Bioinformatics. 2021;16(4):610-23.
  7. Gonoodi K, Tayefi M, Saberi-Karimian M, Darroudi S, Farahmand SK, Abasalti Z, et al. An assessment of the risk factors for vitamin D deficiency using a decision tree model. Diabetes & Metabolic Syndrome: Clinical Research & Reviews. 2019;13(3):1773-7.
  8. Gonoodi K, Tayefi M, Bahrami A, Amirabadi Zadeh A, Ferns GA, Mohammadi F, et al. Determinants of the magnitude of response to vitamin D supplementation in adolescent girls identified using a decision tree algorithm. BioFactors. 2019;45(5):795-802.
  9. Amiri Z, Nosrati M, Sharifan P, Saffar Soflaei S, Darroudi S, Ghazizadeh H, Mohammadi Bajgiran M, Moafian F, Tayefi M, Hasanzade E, Rafiee M. Factors determining the serum 25‐hydroxyvitamin D response to vitamin D supplementation: Data mining approach. BioFactors. 2021 Sep;47(5):828-36.
  10. Ghayour-Mobarhan M, Moohebati M, Esmaily H, Ebrahimi M, Parizadeh SMR, Heidari-Bakavoli AR, et al. Mashhad stroke and heart atherosclerotic disorder (MASHAD) study: design, baseline characteristics and 10-year cardiovascular risk estimation. International Journal of Public Health. 2015;60(5):561-72.
  11. Hu FB, Stampfer MJ, Rimm E, Ascherio A, Rosner BA, Spiegelman D, et al. Dietary fat and coronary heart disease: a comparison of approaches for adjusting for total energy intake and modeling repeated dietary measurements. American Journal of Epidemiology. 1999;149(6):531-40.
  12. Mackerras D. Energy adjustment: the concepts underlying the debate. Journal of Clinical Epidemiology. 1996;49(9):957-62.
  13. Nazeminezhad R, Tajfard M, Latiff L, Mouhebati M, Esmaeily H, Ferns G, et al. Dietary intake of patients with angiographically defined coronary artery disease and that of healthy controls in Iran. European Journal of Clinical Nutrition. 2014;68(1):109-13.
  14. World Health Organization. Definition and diagnosis of diabetes mellitus and intermediate hyperglycaemia: report of a WHO/IDF consultation. 2006.
  15. Koloverou E, Esposito K, Giugliano D, Panagiotakos D. The effect of Mediterranean diet on the development of type 2 diabetes mellitus: a meta-analysis of 10 prospective studies and 136,846 participants. Metabolism. 2014;63(7):903-11.
  16. Esposito K, Maiorino MI, Bellastella G, Panagiotakos DB, Giugliano D. Mediterranean diet for type 2 diabetes: cardiometabolic benefits. Endocrine. 2017;56(1):27-32.
  17. Anand SS, Hawkes C, De Souza RJ, Mente A, Dehghan M, Nugent R, et al. Food consumption and its impact on cardiovascular disease: importance of solutions focused on the globalized food system: a report from the workshop convened by the World Heart Federation. Journal of the American College of Cardiology. 2015;66(14):1590-614.
  18. Helderman JH, Elahi D, Andersen DK, Raizes GS, Tobin JD, Shocken D, et al. Prevention of the Glucose Intolerance of Thiazide Diuretics by Maintenance of Body-Potassium. Diuretika III. Springer Berlin Heidelberg. 1986; 98-109.
  19. Elliott WJ, Meyer PM. Incident diabetes in clinical trials of antihypertensive drugs: a network meta-analysis. The Lancet. 2007;369(9557):201-7.
  20. Taylor EN, Hu FB, Curhan GC. Antihypertensive medications and the risk of incident type 2 diabetes. Diabetes Care. 2006;29(5):1065-70.
  21. Shafi T, Appel LJ, Miller III ER, Klag MJ, Parekh RS. Changes in serum potassium mediate thiazide-induced diabetes. Hypertension. 2008;52(6):1022-9.
  22. Zillich AJ, Garg J, Basu S, Bakris GL, Carter BL. Thiazide diuretics, potassium, and the development of diabetes: a quantitative review. Hypertension. 2006;48(2):219-24.
  23. Electrolytes IoMPoDRIf, Water. DRI, dietary reference intakes for water, potassium, sodium, chloride, and sulfate: National Academy Press; 2004.
  24. Chatterjee R, Colangelo L, Yeh H, Anderson C, Daviglus M, Liu K, et al. Potassium intake and risk of incident type 2 diabetes mellitus: the Coronary Artery Risk Development in Young Adults (CARDIA) study. Diabetologia. 2012;55(5):1295-303.
  25. Tsilas CS, de Souza RJ, Mejia SB, Mirrahimi A, Cozma AI, Jayalath VH, et al. Relation of total sugars, fructose and sucrose with incident type 2 diabetes: a systematic review and meta-analysis of prospective cohort studies. CMAJ. 2017;189(20):E711-E20.
  26. Brisbois TD, Marsden SL, Anderson GH, Sievenpiper JL. Estimated intakes and sources of total and added sugars in the Canadian diet. Nutrients. 2014;6(5):1899-912.
  27. Marriott BP, Cole N, Lee E. National estimates of dietary fructose intake increased from 1977 to 2004 in the United States. The Journal of Nutrition. 2009;139(6):1228S-35S.
  28. Aune D, Norat T, Romundstad P, Vatten LJ. Dairy products and the risk of type 2 diabetes: a systematic review and dose-response meta-analysis of cohort studies. The American journal of clinical nutrition. 2013;98(4):1066-83.
  29. Li M, Fan Y, Zhang X, Hou W, Tang Z. Fruit and vegetable intake and risk of type 2 diabetes mellitus: meta-analysis of prospective cohort studies. BMJ Open. 2014;4(11):e005497.
  30. Provenzano LF, Stark S, Steenkiste A, Piraino B, Sevick MA. Dietary sodium intake in type 2 diabetes. Clinical Diabetes. 2014;32(3):106-12.