Prediction of Diabetes and Prediabetes among the Saudi Population Using a Non-Invasive Tool (AUSDRISK)
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Setting and Study Duration
2.2. Study Design and Sampling Technique
2.3. Sample Size and Study Population
2.4. Data Collection
2.5. Study Outcome
2.6. Ethical Consideration
2.7. Statistical Analysis
3. Results
4. Discussion
The Main Findings of the Study
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Yan, Y.; Wu, T.; Zhang, M.; Li, C.; Liu, Q.; Li, F. Prevalence, awareness and control of type 2 diabetes mellitus and risk factors in Chinese elderly population. BMC Public Health 2022, 22, 1382. [Google Scholar] [CrossRef] [PubMed]
- Atlas, D. International diabetes federation. In IDF Diabetes Atlas, 7th ed.; International Diabetes Federation: Brussels, Belgium, 2015; Volume 33. [Google Scholar]
- Sun, H.; Saeedi, P.; Karuranga, S.; Pinkepank, M.; Ogurtsova, K.; Duncan, B.B.; Stein, C.; Basit, A.; Chan, J.C.; Mbanya, J.C. IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. Diabetes Res. Clin. Pract. 2022, 183, 109119. [Google Scholar] [CrossRef] [PubMed]
- Centers for Disease Control and Prevention. Diabetes Fast Facts. Available online: https://www.cdc.gov/diabetes/basics/quick-facts.html (accessed on 22 September 2023).
- Galicia-Garcia, U.; Benito-Vicente, A.; Jebari, S.; Larrea-Sebal, A.; Siddiqi, H.; Uribe, K.B.; Ostolaza, H.; Martín, C. Pathophysiology of Type 2 Diabetes Mellitus. Int. J. Mol. Sci. 2020, 21, 6275. [Google Scholar] [CrossRef]
- Davies, M.J.; Aroda, V.R.; Collins, B.S.; Gabbay, R.A.; Green, J.; Maruthur, N.M.; Rosas, S.E.; Del Prato, S.; Mathieu, C.; Mingrone, G.; et al. Management of Hyperglycemia in Type 2 Diabetes, 2022. A Consensus Report by the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD). Diabetes Care 2022, 45, 2753–2786. [Google Scholar] [CrossRef] [PubMed]
- International Diabetes Federation. Diabetes in Saudi Arabia. 2021. Available online: https://idf.org/our-network/regions-and-members/middle-east-and-north-africa/members/saudi-arabia/ (accessed on 22 September 2023).
- Alqurashi, K.A.; Aljabri, K.S.; Bokhari, S.A. Prevalence of diabetes mellitus in a Saudi community. Ann. Saudi Med. 2011, 31, 19–23. [Google Scholar] [CrossRef]
- Al-Hariri, M.T.; Al-Enazi, A.S.; Alshammari, D.M.; Bahamdan, A.S.; Al-Khtani, S.M.; Al-Abdulwahab, A.A. Descriptive study on the knowledge, attitudes and practices regarding the diabetic foot. J. Taibah Univ. Med. Sci. 2017, 12, 492–496. [Google Scholar] [CrossRef] [PubMed]
- Goweda, R.; Shatla, M.; Alzaidi, A.; Alzaidi, A.; Aldhawani, B.; Alharbi, H.; Sultan, N.; Alnemari, D.; Rawa, B. Assessment of knowledge and practices of diabetic patients regarding diabetic foot care, in Makkah, Saudi Arabia. J. Fam. Med. Health Care 2017, 3, 17. [Google Scholar] [CrossRef]
- Peer, N.; Balakrishna, Y.; Durao, S. Screening for type 2 diabetes mellitus. Cochrane Database Syst. Rev. 2020, 5, Cd005266. [Google Scholar] [PubMed]
- Ortiz-Martínez, M.; González-González, M.; Martagón, A.J.; Hlavinka, V.; Willson, R.C.; Rito-Palomares, M. Recent Developments in Biomarkers for Diagnosis and Screening of Type 2 Diabetes Mellitus. Curr. Diabetes Rep. 2022, 22, 95–115. [Google Scholar] [CrossRef]
- Woo, Y.C.; Gao, B.; Lee, C.H.; Fong, C.H.; Lui, D.T.; Ming, J.; Wang, L.; Yeung, K.M.; Cheung, B.M.; Lam, T.H.; et al. Three-component non-invasive risk score for undiagnosed diabetes in Chinese people: Development, validation and longitudinal evaluation. J. Diabetes Investig. 2020, 11, 341–348. [Google Scholar] [CrossRef]
- Abdallah, M.; Sharbaji, S.; Sharbaji, M.; Daher, Z.; Faour, T.; Mansour, Z.; Hneino, M. Diagnostic accuracy of the Finnish Diabetes Risk Score for the prediction of undiagnosed type 2 diabetes, prediabetes, and metabolic syndrome in the Lebanese University. Diabetol. Metab. Syndr. 2020, 12, 84. [Google Scholar] [CrossRef] [PubMed]
- Chen, L.; Magliano, D.J.; Balkau, B.; Colagiuri, S.; Zimmet, P.Z.; Tonkin, A.M.; Mitchell, P.; Phillips, P.J.; Shaw, J.E. AUSDRISK: An Australian Type 2 Diabetes Risk Assessment Tool based on demographic, lifestyle and simple anthropometric measures. Med. J. Aust. 2010, 192, 197–202. [Google Scholar] [CrossRef]
- Farag, H.F.M.; Sultan, E.A.; Elrewany, E.; Abdel-Aziz, B.F. Arabic version of the Australian type 2 diabetes risk assessment tool (AUSDRISK): Translation and validation. BMC Res. Notes 2022, 15, 303. [Google Scholar] [CrossRef] [PubMed]
- Charokopou, M.; Sabater, F.; Townsend, R.; Roudaut, M.; McEwan, P.; Verheggen, B. Methods applied in cost-effectiveness models for treatment strategies in type 2 diabetes mellitus and their use in Health Technology Assessments: A systematic review of the literature from 2008 to 2013. Curr. Med. Res. Opin. 2016, 32, 207–218. [Google Scholar] [CrossRef]
- Farag, H.F.M.; Elrewany, E.; Abdel-Aziz, B.F.; Sultan, E.A. Prevalence and predictors of undiagnosed type 2 diabetes and pre-diabetes among adult Egyptians: A community-based survey. BMC Public Health 2023, 23, 949. [Google Scholar] [CrossRef]
- Zierle-Ghosh, A.; Jan, A. Physiology, Body Mass Index; StatPearls Publishing: Treasure Island, FL, USA, 2018. [Google Scholar]
- Alqahtani, S.A.M.; Alsaleem, M.A.; Ghazy, R.M. Association between serum ferritin level and lipid profile among diabetic patients: A retrospective cohort study. Medicine 2024, 103, e37631. [Google Scholar] [CrossRef] [PubMed]
- Hameed, I.; Masoodi, S.R.; Mir, S.A.; Nabi, M.; Ghazanfar, K.; Ganai, B.A. Type 2 diabetes mellitus: From a metabolic disorder to an inflammatory condition. World J. Diabetes 2015, 6, 598–612. [Google Scholar] [CrossRef]
- Noble, D.; Mathur, R.; Dent, T.; Meads, C.; Greenhalgh, T. Risk models and scores for type 2 diabetes: Systematic review. BMJ 2011, 343, d7163. [Google Scholar] [CrossRef]
- Bernabe-Ortiz, A.; Perel, P.; Miranda, J.J.; Smeeth, L. Diagnostic accuracy of the Finnish Diabetes Risk Score (FINDRISC) for undiagnosed T2DM in Peruvian population. Prim. Care Diabetes 2018, 12, 517–525. [Google Scholar] [CrossRef]
- Bernabe-Ortiz, A.; Smeeth, L.; Gilman, R.H.; Sanchez-Abanto, J.R.; Checkley, W.; Miranda, J.J.; Study Group, C.C. Development and Validation of a Simple Risk Score for Undiagnosed Type 2 Diabetes in a Resource-Constrained Setting. J. Diabetes Res. 2016, 2016, 8790235. [Google Scholar] [CrossRef]
- Kilkenny, M.F.; Johnson, R.; Andrew, N.E.; Purvis, T.; Hicks, A.; Colagiuri, S.; Cadilhac, D.A. Comparison of two methods for assessing diabetes risk in a pharmacy setting in Australia. BMC Public Health 2014, 14, 1227. [Google Scholar] [CrossRef] [PubMed]
- Alharbi, N.S.; Almutari, R.; Jones, S.; Al-Daghri, N.; Khunti, K.; De Lusignan, S. Trends in the prevalence of type 2 diabetes mellitus and obesity in the Arabian Gulf States: Systematic review and meta-analysis. Diabetes Res. Clin. Pract. 2014, 106, e30–e33. [Google Scholar] [CrossRef] [PubMed]
- Alshaikhi, S.A.; Alamri, A.M.; Alzilai, I.Y.; Alghanimi, A.A.; Alrufaidi, A.M.; Alrufaidi, A.M.; Bader, A.E.; Abdelmoniem, A.A.; Alshaikh, A.A.; Alshaikhi, O.A.; et al. Diabetes and prediabetes prevalence through a community-based screening initiative in Alqunfudah, Saudi Arabia. Future Sci. OA 2024, 10, FSO946. [Google Scholar] [CrossRef]
- Meo, S.A. Prevalence and future prediction of type 2 diabetes mellitus in the Kingdom of Saudi Arabia: A systematic review of published studies. J. Pak. Med. Assoc. 2016, 66, 722–725. [Google Scholar]
- Boutayeb, A.; Boutayeb, W.; Lamlili, M.E.; Boutayeb, S. Estimation of the direct cost of diabetes in the Arab region. Mediterr. J. Nutr. Metab. 2014, 7, 21–32. [Google Scholar] [CrossRef]
- Farag Mohamed, H.; Allam, M.M.; Hamdy, N.A.; Ghazy, R.M.; Emara, R.H. A Community Pharmacy-Based Intervention in the Matrix of Type 2 Diabetes Mellitus Outcomes (CPBI-T2DM): A Cluster Randomized Controlled Trial. Clin. Med. Insights Endocrinol. Diabetes 2021, 14, 11795514211056307. [Google Scholar] [CrossRef]
- Alshaikh, A.A.; Alqarni, H.M.; Assiri, H.A.H.; Shlwan, M.A.; AlJebreel, M.A.; Almuaddi, A.S.; Asiri, M.A.; Almuidh, F.N.A.; Al Qasim, N.Y.; Alshahrani, O.A. Knowledge, Attitude, and Practice of Diabetic Foot Ulcer Care in Asser Region: A Cross-Sectional Study. Cureus 2023, 15, e42807. [Google Scholar] [CrossRef]
- Alqahtani, B.; Elnaggar, R.K.; Alshehri, M.M.; Khunti, K.; Alenazi, A. National and regional prevalence rates of diabetes in Saudi Arabia: Analysis of national survey data. Int. J. Diabetes Dev. Ctries. 2023, 43, 392–397. [Google Scholar] [CrossRef]
- Mahfouz, A.A.; Alsaleem, S.A.; Alsaleem, M.A.; Ghazy, R.M. Prevalence of Obesity and Associated Dietary Habits among Medical Students at King Khalid University, Southwestern Saudi Arabia. Medicina 2024, 60, 347. [Google Scholar] [CrossRef]
- Alshaikh, A.A.; Alqahtani, A.S.; AlShehri, F.A.; Al Hadi, A.M.; Alqahtani, M.M.M.; Alshahrani, O.M.; Albraik, M.A.; Alamri, S.A.; Ghazy, R.M.; Alshahrani, O.M., Jr. Examining the Impact of Socioeconomic Factors and Lifestyle Habits on Obesity Prevalence Among Male and Female Adolescent Students in Asser, Saudi Arabia. Cureus 2023, 15, e43918. [Google Scholar] [CrossRef]
- Lushniak, B.D.; Samet, J.M.; Pechacek, T.F.; Norman, L.A.; Taylor, P.A. The Health Consequences of Smoking—50 Years of Progress: A Report of the Surgeon General; US Department of Health and Human Services, Centers for Disease: Atlanta, GA, USA, 2014. [Google Scholar]
- Cai, X.; Chen, Y.; Yang, W.; Gao, X.; Han, X.; Ji, L. The association of smoking and risk of diabetic retinopathy in patients with type 1 and type 2 diabetes: A meta-analysis. Endocrine 2018, 62, 299–306. [Google Scholar] [CrossRef] [PubMed]
- Kirsti Vik, H.; Jo, S.S.; Tom, I.L.N. Adiposity, physical activity and risk of diabetes mellitus: Prospective data from the population-based HUNT study, Norway. BMJ Open 2017, 7, e013142. [Google Scholar]
- Al-Baghli, N.A.; Al-Ghamdi, A.; Al-Turki, K.; Al Elq, A.; El-Zubaier, A.; Bahnassy, A. Prevalence of diabetes mellitus and impaired fasting glucose levels in the Eastern Province of Saudi Arabia: Results of a screening campaign. Singap. Med. J. 2010, 51, 923. [Google Scholar]
- Al-Zahrani, J.M.; Aldiab, A.; Aldossari, K.K.; Al-Ghamdi, S.; Batais, M.A.; Javad, S.; Nooruddin, S.; Zahid, N.; Razzak, H.A.; El-Metwally, A. Prevalence of Prediabetes, Diabetes and Its Predictors among Females in Alkharj, Saudi Arabia: A Cross-Sectional Study. Ann. Glob. Health 2019, 85, 109. [Google Scholar] [CrossRef]
Question | Points | ||
---|---|---|---|
1 | Age | ||
Less than 35 years | 0 | ||
35–44 years | 2 | ||
45–54 years | 4 | ||
55–64 years | 6 | ||
65 years or older | 8 | ||
2 | Gender | ||
Female | 0 | ||
Male | 3 | ||
3 | Has either of your parents or siblings been diagnosed with diabetes (type 1 or type 2)? | ||
No | 0 | ||
Yes | 3 | ||
4 | Have you ever had high blood sugar levels (e.g., during a health checkup, illness, or pregnancy)? | ||
No | 0 | ||
Yes | 6 | ||
5 | Currently, are you taking medication for high blood pressure? | ||
No | 0 | ||
Yes | 2 | ||
6 | Currently, do you smoke cigarettes or any other tobacco product daily? | ||
No | 0 | ||
Yes | 2 | ||
7 | Typically, how often do you consume vegetables or fruits? | ||
Every day | 0 | ||
Not every day | 1 | ||
8 | On average, do you believe you engage in physical activity for at least 2.5 h per week (e.g., 30 min daily for 5 days or more per week)? | ||
Yes | 0 | ||
No | 2 | ||
9 | Waist measurement at the bottom of the ribs (usually at the level of the navel and while standing): | ||
Waist measurement (cm) | |||
Men | Women | ||
Less than 102 cm | Less than 88 cm | 0 | |
102–110 cm | 88–100 cm | 4 | |
More than 110 cm | More than 100 cm | 7 |
Studied Variables | Overall (N = 652) | |
---|---|---|
Age | Mean (SD) | 32.0 ± 12.0 |
Median [Min, Max] | 28 [14, 92] | |
Sex | Female | 301 (46.2%) |
Male | 351 (53.8%) | |
Residence | Rural | 68 (10.4%) |
Urban | 584 (89.6%) | |
Marital status | Divorced | 24 (3.7%) |
Married | 258 (39.6%) | |
Single | 364 (55.8%) | |
Widow | 6 (0.9%) | |
Education | Secondary | 150 (23.0%) |
University | 453 (67.2%) | |
Postgraduate | 49 (7.5%) | |
Below 5000 SAR | 267 (41.0%) | |
Income (month) | 5000–10,000 SAR | 197 (30.2%) |
10,000–15,000 SAR | 104 (16.0%) | |
15,000–20,000 SAR | 55 (8.4%) | |
More than 20,000 SAR | 29 (4.4%) | |
Occupation | Do not work | 252 (38.7%) |
Governmental sector | 238 (36.5%) | |
Private | 127 (19.5%) | |
Retired | 35 (5.4%) | |
Nationality | Non-Saudi | 46 (7.1%) |
Saudi | 606 (92.9%) |
Studied Variables | Mean (SD) | |
---|---|---|
Body mass index | Median [Min, Max] | 26.0 [16.0, 22.0] |
Hypertension | 37 (5.7%) | |
Thyroid | 19 (2.9%) | |
Renal | 4 (0.6%) | |
Hepatic disease | 1 (0.2%) | |
Ischemic heart disease | 12 (1.8%) | |
Chronic obstructive airway disease | 6 (0.9%) | |
Cerebrovascular disease | 10 (1.5%) | |
Autoimmune disease | 6 (0.9%) | |
Family history of diabetes mellitus | 325 (49.2%) |
Items (N = 652) | Points | Overall | Female | Male | Test Statistics | |||
---|---|---|---|---|---|---|---|---|
N | % | N | % | |||||
Age | below 35 years | 0 | 426 (65.3%) | 223 | 74.1 | 203 | 57.8 | χ2 = 21.963, p = 0.001 |
35–44 years | 2 | 129 (19.8%) | 51 | 16.9 | 78 | 22.2 | ||
45–54 years | 4 | 64 (9.8%) | 18 | 6 | 46 | 13.1 | ||
55–64 years | 6 | 29 (4.4%) | 8 | 2.7 | 21 | 6 | ||
≥65 years | 8 | 4 (0.6%) | 1 | 0.3 | 3 | 0.9 | ||
Have you ever been found to have high blood glucose (sugar) | No | 0 | 579 (88.8%) | 273 | 90.7 | 306 | 87.2 | χ2 = 1.68, p = 0.195 |
Yes | 6 | 73 (11.2%) | 28 | 9.3 | 45 | 12.8 | ||
Have either of your parents or any of your brothers or sisters been diagnosed with diabetes | No | 0 | 325 (49.8%) | 157 | 52.2 | 157 | 44.7 | χ2 = 7.527, p = 0.006 |
Yes | 3 | 327(50.2%) | 194 | 64.5 | 194 | 55.3 | ||
Are you currently taking medication for high blood pressure? | No | 0 | 611 (93.7%) | 295 | 98 | 316 | 90 | χ2 = 16.18, p = 0.0001 |
Yes | 3 | 41 (6.3%) | 6 | 2 | 35 | 10 | ||
Do you currently smoke cigarettes or any other tobacco products daily? | No | 0 | 485 (74.4%) | 290 | 96.3 | 195 | 55.6 | χ2 = 139.37, p = 0.0001 |
Yes | 2 | 167 (25.6%) | 11 | 3.7 | 156 | 44.4 | ||
How often do you eat vegetables or fruits? | No | 0 | 138 (21.2%) | 58 | 19.3 | 80 | 22.8 | χ2 = 1.003, p = 0.316 |
Yes | 2 | 514 (78.8%) | 243 | 80.7 | 271 | 77.2 | ||
On average, would you say you do at least 2.5 h of physical activity per week? | No | 0 | 311 (47.7%) | 121 | 40.2 | 190 | 54.1 | χ2 = 12.06, p = 0.001 |
Yes | 2 | 341 (52.3%) | 180 | 59.8 | 161 | 45.9 | ||
Waist circumference | Male < 102 cm/female < 88 cm | 0 | 509 (78.1%) | 260 | 86.4 | 249 | 70.9 | χ2 = 27.00, p = 0.0001 |
Male 102–110 cm/female 88–100 cm | 4 | 83 (12.7%) | 30 | 10 | 53 | 15.1 | ||
Male > 110 cm/female > 100 | 7 | 60 (9.2%) | 11 | 3.7 | 49 | 14 | ||
Total score | 8.0 (5.0–31.0) | 10.0 (7.0–16.0) | 9 | (7.0–14.0) | W = 16,294, p < 0.001 |
Dependent: Undiagnosed | AUSRISK ≥ 13 | AUSDRISK < 13 | p | |
---|---|---|---|---|
Marital status | Divorced | 8 (5.6) | 16 (3.1) | <0.001 |
Married | 95 (66.4) | 163 (32.0) | ||
Single | 37 (25.9) | 327 (64.2) | ||
Widow | 3 (2.1) | 3 (0.6) | ||
Illiterate | 0 (0.0) | 5 (1.0) | ||
Education | Primary | 1 (0.7) | 0 (0.0) | <0.001 |
Secondary | 50 (35.0) | 100 (19.6) | ||
University | 77 (53.8) | 370 (72.7) | ||
Postgraduate | 15 (10.5) | 34 (6.7) | ||
Income | Below 5000 SAR | 36 (25.2) | 231 (45.4) | <0.001 |
5000–10,000 SAR | 41 (28.7) | 156 (30.6) | ||
10,000–15,000 SAR | 31 (21.7) | 73 (14.3) | ||
15,000–20,000 SAR | 19 (13.3) | 36 (7.1) | ||
20,000 SAR | 16 (11.2) | 13 (2.6) | ||
Residence | Rural | 26 (18.2) | 42 (8.3) | 0.001 |
Urban | 117 (81.8) | 467 (91.7) | ||
Occupation | Do not work | 26 (18.2) | 226 (44.4) | <0.001 |
Governmental sector | 59 (41.3) | 179 (35.2) | ||
Private | 32 (22.4) | 95 (18.7) | ||
Retired | 26 (18.2) | 9 (1.8) | ||
Nationality | Non-Saudi | 11 (7.7) | 35 (6.9) | 0.879 |
Saudi | 132 (92.3) | 474 (93.1) | ||
Ischemic heart disease | No | 134 (93.7) | 506 (99.4) | <0.001 |
Yes | 9 (6.3) | 3 (0.6) | ||
Hyperlipidemia | Maybe | 45 (31.5) | 66 (13.0) | <0.001 |
No | 78 (54.5) | 417 (81.9) | ||
Yes | 20 (14.0) | 26 (5.1) |
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Alshaikh, A.A.; Al-Qahtani, F.S.; Taresh, H.M.N.; Hayaza, R.A.A.; Alqhtani, S.S.M.; Summan, S.I.; Al Mansour, S.A.; Alsultan, O.H.A.; Asiri, H.Y.M.; Alqahtani, Y.M.S.; et al. Prediction of Diabetes and Prediabetes among the Saudi Population Using a Non-Invasive Tool (AUSDRISK). Medicina 2024, 60, 775. https://doi.org/10.3390/medicina60050775
Alshaikh AA, Al-Qahtani FS, Taresh HMN, Hayaza RAA, Alqhtani SSM, Summan SI, Al Mansour SA, Alsultan OHA, Asiri HYM, Alqahtani YMS, et al. Prediction of Diabetes and Prediabetes among the Saudi Population Using a Non-Invasive Tool (AUSDRISK). Medicina. 2024; 60(5):775. https://doi.org/10.3390/medicina60050775
Chicago/Turabian StyleAlshaikh, Ayoub Ali, Faisal Saeed Al-Qahtani, Hassan Misfer N Taresh, Rand Abdullah A Hayaza, Sultan Saeed M Alqhtani, Sarah Ibrahim Summan, Sultan Abdullah Al Mansour, Omar Hezam A Alsultan, Hassan Yahya M Asiri, Yazeed Mohammed S Alqahtani, and et al. 2024. "Prediction of Diabetes and Prediabetes among the Saudi Population Using a Non-Invasive Tool (AUSDRISK)" Medicina 60, no. 5: 775. https://doi.org/10.3390/medicina60050775