Benefit of Finnish Score As a Risk Assessment Tool for Predicting Type II DM Among Sudanese Population in North Sudan


Background: Diabetes mellitus is a major noncommunicable disease worldwide, and its prevalence is rapidly increasing. The Finish score helps in the prediction of the risk of future diabetes development, as well as in the identification of undiagnosed diabetes. The current study was conducted to identify people at risk of developing type II diabetes mellitus in River Nile State, Sudan.

Methods: This cross-sectional community-based study was conducted in River Nile state between 2019 October and 2020 March. Data were collected using a questionnaire that included the Finnish Diabetes Risk Score variables from 400 participants after an informed consent. Chi-square test was used to test the associations, with the P-value considered significant when <0.05.

Results: The majority of participants (257 [64.3%]) were <45 years old, and 229 (57.3%) were male. The risk of type II diabetes mellitus was found to be low in 187 (46.8%) people and high in 213 (53.2%). Moreover, 128 (32%) had a body mass index (BMI) between 25 and 30 kg/m2, while 46 (11.5%) had >30 kg/m2. A waist circumference of <94 cm was found in 147 (36.8%) males, while only 63 females (15.8%) had a waist circumference of <80 cm. Age, gender, BMI, daily activity, history of hypertension, history of hyperglycemia, and family history of diabetes were all significantly associated with the risk of developing diabetes mellitus (P < 0.001).

Conclusion: The Finnish Diabetes Risk Score was found to be useful in facilitating wider access to the risk of type II diabetes among the study population. More than half of the study population were at risk of developing diabetes mellitus.


Finnish score, diabetes risk assessment, type II diabetes risk factors, noncommunicable disease, Sudan

[1] IDF. (2021). IDF Diabetes Atlas 2021 (10th ed.). Available from:

[2] Danaei, G., Finucane, M., Lu, Y., Singh, G., Cowan, M., Paciorek, C., Lin, J., Farzadfar, F., Khang, Y., Stevens, G., & the Global Burden of Metabolic Risk Factors of Chronic Diseases Collaborating Group. (2011). Blood glucose. Lancet, 378(9785), 3140.

[3] IDF. (2013). IDF Diabetes Atlas (6th ed.). IDF.

[4] Mathers, C. D., & Loncar, D. (2006). Projections of global mortality and burden of disease from 2002 to 2030. PLoS Medicine, 3(11), e442.

[5] WHO. (2013). Global status report on noncommunicable diseases 2010. Geneva: World Health Organization, 2011.

[6] Icks, A., Haastert, B., Trautner, C., Giani, G., Glaeske, G., & Hoffmann, F. (2009). Incidence of lowerlimb amputations in the diabetic compared to the non-diabetic population. findings from nationwide insurance data, Germany, 2005-2007. Experimental and Clinical Endocrinology & Diabetes, 117(9), 500– 504.

[7] Noor, S. K. M., Bushara, S. O. E., Sulaiman, A. A., Elmadhoun, W. M. Y., & Ahmed, M. H. (2015, May 19). Undiagnosed diabetes mellitus in rural communities in Sudan: Prevalence and risk factors. Eastern Mediterranean Health Journal, 21(3), 164– 170.

[8] Eltom, M. A., Babiker Mohamed, A. H., Elrayah- Eliadarous, H., Yassin, K., Noor, S. K., Elmadhoun, W. M., & Ahmed, M. H. (2018, February). Increasing prevalence of type 2 diabetes mellitus and impact of ethnicity in north Sudan. Diabetes Research and Clinical Practice, 136, 93–99.

[9] Awadalla, H., Noor, S. K., Elmadhoun, W. M., Almobarak, A. O., Elmak, N. E., Abdelaziz, S. I., Sulaiman, A. A., & Ahmed, M. H. (2017, December). Diabetes complications in Sudanese individuals with type 2 diabetes: Overlooked problems in sub-Saharan Africa? Diabetes & Metabolic Syndrome, 11(Suppl 2), S1047–S1051.

[10] Abdallah, M., Sharbaji, S., Sharbaji, M., Daher, Z., Faour, T., Mansour, Z., & Hneino, M. (2020). Diagnostic accuracy of the Finnish Diabetes Risk Score for the prediction of undiagnosed type 2 diabetes, prediabetes, and metabolic syndrome in the Lebanese University. Diabetology & Metabolic Syndrome, 12, 84. 020-00590-8

[11] Rokhman, M. R., Arifin, B., Zulkarnain, Z., Satibi, S., Perwitasari, D. A., Boersma, C., Postma, M. J., & van der Schans, J. (2022, July 21). Translation and performance of the Finnish Diabetes Risk Score for detecting undiagnosed diabetes and dysglycaemia in the Indonesian population. PLoS One, 17(7), e0269853.

[12] Ishaque, A., Shahzad, F., Muhammad, F. H., Usman, Y., & Ishaque, Z. (2016). Diabetes risk assessment among squatter settlements in Pakistan: A crosssectional study. Malaysian Family Physician, 11(2–3), 9–15.

[13] Milovanovic, S., Silenzi, A., Kheiraoui, F., Ventriglia, G., Boccia, S., & Poscia, A. (2018). Detecting persons at risk for diabetes mellitus type 2 using FINDRISC: Results from a community pharmacy-based study. European Journal of Public Health, 28, 1127–1132. Advance online publication.

[14] Al-Shudifat, A.-E., Al-Shdaifat, A., Ali Al-Abdouh, A., Aburoman, M. I., Otoum, S. M., Sweedan, A. G., Khrais, I., Abdel-Hafez, I. H., & Johannessen, A. (2017). Diabetes risk score in a young student population in Jordan: A cross-sectional study. Journal of Diabetes Research, 2017, 8290710.

[15] Meijnikman, A. S., De Block, C. E. M., Verrijken, A., Mertens, I., Corthouts, B., & Van Gaal, L. F. (2016). Screening for type 2 diabetes mellitus in overweight and obese subjects made easy by the FINDRISC score. Journal of Diabetes and Its Complications, 30(6), 1043–1049.

[16] Martínez-Millana, A., Fico, G., Fernández-Llatas, C., & Traver, V. (2015). Performance assessment of a closed-loop system for diabetes management. Medical & Biological Engineering & Computing, 53(12), 1295–1303. 1245-3

[17] Makrilakis, K., Liatis, S., Grammatikou, S., Perrea, D., Stathi, C., Tsiligros, P., & Katsilambros, N. (2011). Validation of the Finnish diabetes risk score (FINDRISC) questionnaire for screening for undiagnosed type 2 diabetes, dysglycaemia and the metabolic syndrome in Greece. Diabetes & Metabolism, 37, 144–151.

[18] Lindström, J., & Tuomilehto, J. (2003). The diabetes risk score: A practical tool to predict type 2 diabetes risk. Diabetes Care, 26, 725–731.