Risk Assessment Tools for Identifying Individuals at Risk of Developing Type 2 Diabetes

Applied logistic regression

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Detection, evaluation, and treatment of high blood cholesterol in adults (Adult Treatment Panel III)

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European Guidelines on cardiovascular disease prevention in clinical practice (version 2012)

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Evaluating the added predictive ability of a new marker: From area under the ROC curve to reclassification and beyond

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Follow-up report on the diagnosis of diabetes mellitus

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The American Journal of Managed Care

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Acarbose for prevention of type 2 diabetes mellitus: the STOP-NIDDM randomised trial

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Preventing Type 2 Diabetes Mellitus: A Call for Personalized Intervention

Glauber, H. S., & Karnieli, E. (2013) · The Permanente Journal · doi:10.7812/tpp/12-143 · Cited by 24

A community-based primary prevention programme for type 2 diabetes mellitus integrating identification and lifestyle intervention for prevention: a cluster randomised controlled trial

Davies, M. J., Gray, L. J., Ahrabian, D., Carey, M., Farooqi, A., Gray, A. M., Goldby, S., Hill, S., Jones, K. L., Leal, J., et al. (2017) · Programme Grants for Applied Research · doi:10.3310/pgfar05020 · Cited by 26

Preconception risk of gestational diabetes: Development of a prediction model in nulliparous Australian women

Schoenaker, D. A. J. M., Vergouwe, Y., Soedamah‐Muthu, S. S., Callaway, L. K., & Mishra, G. D. (2018) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2018.09.021 · Cited by 22

Assessment of risk of developing diabetes mellitus among local government employees in Onitsha, south-eastern Nigeria

Agu, U., Agu, M. C., & Nnaji, G. A. (2015) · Epidemiology Reports · doi:10.7243/2054-9911-3-4 · Cited by 22

Screening for HbA1c-defined prediabetes and diabetes in an at-risk greek population: Performance comparison of random capillary glucose, the ADA diabetes risk test and skin fluorescence spectroscopy

Tentolouris, N., Lathouris, P., Lontou, S. P., Tzemos, K., & Maynard, J. (2013) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2013.01.002 · Cited by 24

A Hybrid Approach for Modeling Type 2 Diabetes Mellitus Progression

Perveen, S., Shahbaz, M., Ansari, M. S., Keshavjee, K., & Guergachi, A. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2019.01076 · Cited by 25

Risk prediction for type 2 diabetes in the German population with the updated German Diabetes Risk Score (GDRS).

Mühlenbruch, K., Joost, H., Boeing, H., & Schulze, M. B. (2014) · Cited by 22

Predicting Risk of Type 2 Diabetes by Using Data on Easy-to-Measure Risk Factors

Turi, K. N., Büchner, D. M., & Grigsby‐Toussaint, D. S. (2017) · Preventing Chronic Disease · doi:10.5888/pcd14.160244 · Cited by 23

Evaluation of the Finnish Diabetes Risk Score (FINDRISC) for diabetes screening in Kayseri, Turkey

Atayoğlu, A. T., İnanç, N., Başmısırlı, E., & Çapar, A. G. (2020) · Primary care diabetes · doi:10.1016/j.pcd.2020.01.002 · Cited by 22

Role of various indices derived from an oral glucose tolerance test in the prediction of conversion from prediabetes to type 2 diabetes

Kim, Y. A., Ku, E. J., Khang, A. R., Hong, E. S., Kim, K. M., Moon, J. H., Choi, S. H., Park, K. S., Jang, H. C., & Lim, S. K. (2014) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2014.08.014 · Cited by 20

Predictors of undiagnosed prevalent type 2 diabetes – The Danish General Suburban Population Study

Heltberg, A., Andersen, J. S., Sandholdt, H., Siersma, V., Kragstrup, J., & Ellervik, C. (2017) · Primary care diabetes · doi:10.1016/j.pcd.2017.08.005 · Cited by 22

Undetected dysglycaemia common in primary care patients treated for hypertension and/or dyslipidaemia: on the need for a screening strategy in clinical practice. A report from EUROASPIRE IV a registry from the EuroObservational Research Programme of the European Society of Cardiology

Shahim, B., Gyberg, V., Bacquer, D. D., Kotseva, K., Backer, G. D., Schnell, O., Tuomilehto, J. O., Wood, D., & Rydén, L. (2018) · Cardiovascular Diabetology · doi:10.1186/s12933-018-0665-4 · Cited by 23

Development and Validation of a Risk Score Predicting Substantial Weight Gain over 5 Years in Middle-Aged European Men and Women

Steffen, A., Sørensen, T. I. A., Knüppel, S., Travier, N., Sánchez, M., Huerta, J. M., Quirós, J. R., Ardanáz, E., Dorronsoro, M., Teucher, B., et al. (2013) · PLoS ONE · doi:10.1371/journal.pone.0067429 · Cited by 20

Driving Type 2 Diabetes Risk Scores into Clinical Practice: Performance Analysis in Hospital Settings

Martínez-Millana, A., Argente, M., Valdivieso, B., Traver, V., & Merino-Torres, J. F. (2019) · Journal of Clinical Medicine · doi:10.3390/jcm8010107 · Cited by 22

Epidemiology of Diabetes

Schulze, M. B., & Hu, F. B. (2013) · Springer eBooks · doi:10.1007/978-0-387-09834-0_66 · Cited by 20

Consensus on the detection and management of prediabetes. Consensus and Clinical Guidelines Working Group of the Spanish Diabetes Society

Mata‐Cases, M., Artola, S., Escalada, J., Loiola, P. E., Ferrer-García, J. C., Fornos, J. A., Girbés, J., & Rica, I. (2015) · Revista Clínica Española (English Edition) · doi:10.1016/j.rceng.2014.10.001 · Cited by 19

Development and validation of risk models to predict the 7‐year risk of type 2 diabetes: The Japan Epidemiology Collaboration on Occupational Health Study

Hu, H., Nakagawa, T., Yamamoto, S., Honda, T., Okazaki, H., Uehara, A., Yamamoto, M., Miyamoto, T., Kochi, T., Eguchi, M., et al. (2018) · Journal of Diabetes Investigation · doi:10.1111/jdi.12809 · Cited by 21

External validation of the KORA S4/F4 prediction models for the risk of developing type 2 diabetes in older adults: the PREVEND study

Abbasi, A., Corpeleijn, E., Peelen, L. M., Gansevoort, R. T., de Jong, P. E., Gans, R. O. B., Rathmann, W., Kowall, B., Meisinger, C., Hillege, H. L., et al. (2012) · European Journal of Epidemiology · doi:10.1007/s10654-011-9648-4 · Cited by 18

Derivation and validation of the prediabetes self-assessment screening score after acute pancreatitis (PERSEUS)

Soo, D. H. E., Pendharkar, S. A., Jivanji, C. J., Gillies, N. A., Windsor, J. A., & Petrov, M. S. (2017) · Digestive and Liver Disease · doi:10.1016/j.dld.2017.05.021 · Cited by 18

Favourable understandability, but poor actionability: An evaluation of online type 2 diabetes risk calculators

Fajardo, M. A., Balthazaar, G., Zalums, A., Trevena, L. J., & Bonner, C. (2018) · Patient Education and Counseling · doi:10.1016/j.pec.2018.10.014 · Cited by 18

The development and validation of the Portuguese risk score for detecting type 2 diabetes and impaired fasting glucose

Gray, L. J., Barros, H., Raposo, L., Khunti, K., Davies, M. J., & Santos, A. C. (2013) · Primary care diabetes · doi:10.1016/j.pcd.2013.01.003 · Cited by 18

Primary care physicians’ perspectives on computer-based health risk assessment tools for chronic diseases: a mixed methods study

Voruganti, T., O’Brien, M. A., Straus, S. E., McLaughlin, J. R., & Grunfeld, E. (2015) · Journal of Innovation in Health Informatics · doi:10.14236/jhi.v22i3.153 · Cited by 18

Validation of the Choking Risk Assessment and Pneumonia Risk Assessment for adults with Intellectual and Developmental Disability (IDD)

Sheppard, J. J., Malandraki, G. A., Pifer, P., Cuff, J., Troche, M. S., Hemsley, B., Balandin, S., Mishra, A., & Hochman, R. (2017) · Research in Developmental Disabilities · doi:10.1016/j.ridd.2017.07.016 · Cited by 17

Diabetes Risk Assessment in Latinas: Effectiveness of a Brief Diabetes Risk Questionnaire for Detecting Prediabetes in a Community-Based Sample

Scanlan, A. B., Maia, C. M., Perez, A., Homko, C. J., & O’Brien, M. J. (2017) · Diabetes Spectrum · doi:10.2337/ds16-0051 · Cited by 19

Independent external validation and comparison of prevalent diabetes risk prediction models in a mixed-ancestry population of South Africa

Masconi, K. L., Matsha, T. E., Erasmus, R. T., & Kengne, A. P. (2015) · Diabetology & Metabolic Syndrome · doi:10.1186/s13098-015-0039-y · Cited by 18

Screening for people with abnormal glucose metabolism in the European DE-PLAN project

Cos, X., Barengo, N. C., Costa, B., Mundet, X., Lindström, J., & Tuomilehto, J. O. (2015) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2015.04.016 · Cited by 19

An evaluation of the performance of the NHS Health Check programme in identifying people at high risk of developing type 2 diabetes

Smith, S. A., Waterall, J., & Burden, A. C. (2013) · BMJ Open · doi:10.1136/bmjopen-2012-002219 · Cited by 16

Individual- and Area-Level SES in Diabetes Risk Prediction: The Multi-Ethnic Study of Atherosclerosis

Christine, P. J., Young, R. L., Adar, S. D., Bertoni, A. G., Heisler, M., Carnethon, M. R., Hayward, R. A., & Roux, A. V. D. (2017) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2017.04.019 · Cited by 16

Screening of undiagnosed hyperglycaemia in the dental setting: The DiabetRisk study. A field trial

Montero, E., Matesanz, P., Nobili, A., Herrera-Pombo, J. L., Sanz, M., Guerrero, A., Bujaldón, A., & Herrera, D. (2020) · Journal Of Clinical Periodontology · doi:10.1111/jcpe.13408 · Cited by 16

Simple non‐laboratory‐ and laboratory‐based risk assessment algorithms and nomogram for detecting undiagnosed diabetes mellitus

Wong, C. K. H., Siu, S., Wan, E. Y. F., Jiao, F., Yu, E. Y. T., Fung, C. S. C., Wong, K.-W., Leung, A. Y. M., & Lam, C. L. K. (2015) · Journal of Diabetes · doi:10.1111/1753-0407.12310 · Cited by 18

Diabetes risk scores for Hispanics living in the United States: A systematic review

Juarez, L., Gonzalez, J. S., Agne, A. A., Kulczycki, A., Pavela, G., Carson, A. P., Shelley, J. P., & Cherrington, A. (2018) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2018.05.009 · Cited by 17

A diabetes risk score for Qatar utilizing a novel mathematical modeling approach to identify individuals at high risk for diabetes

Awad, S. F., Dargham, S. R., Toumi, A. A., Dumit, E. M., El-Nahas, K. G., Al-Hamaq, A. O. A. A., Critchley, J., Tuomilehto, J. O., Al‐Thani, M. H., & Abu-Raddad, L. J. (2021) · Scientific Reports · doi:10.1038/s41598-021-81385-3 · Cited by 18

Consenso sobre la detección y el manejo de la prediabetes. Grupo de Trabajo de Consensos y Guías Clínicas de la Sociedad Española de Diabetes

Mata‐Cases, M., Artola, S., Escalada, J., Loiola, P. E., Ferrer-García, J. C., Fornos, J. A., Girbés, J., & Rica, I. (2014) · Revista Clínica Española · doi:10.1016/j.rce.2014.10.012 · Cited by 16

Ten-year Diabetes Risk Forecast in the Capital of Jordan

Alghadir, A. H., Alghwiri, A. A., Awad, H., & Anwer, S. (2016) · Medicine · doi:10.1097/md.0000000000003181 · Cited by 14

Diabetes risk 10 years forecast in the capital of Saudi Arabia: CanadianDiabetes Risk Assessment Questionnaire (CANRISK) perspective.

Awad, H., Al-Eisa, E. S., & Alghwiri, A. A. (2014) · Biomedical Research-tokyo · Cited by 15

HAPT2D: high accuracy of prediction of T2D with a model combining basic and advanced data depending on availability

Camillo, B. D., Hakaste, L., Sambo, F., Gabriel, R., Kravić, J., Isomaa, B., Tuomilehto, J. O., Alonso, M., Longato, E., Facchinetti, A., et al. (2018) · European Journal of Endocrinology · doi:10.1530/eje-17-0921 · Cited by 16

Selecting the optimal risk threshold of diabetes risk scores to identify high-risk individuals for diabetes prevention: a cost-effectiveness analysis

Mühlenbruch, K., Zhuo, X., Bardenheier, B. H., Shao, H., Laxy, M., Icks, A., Zhang, P., Gregg, E. W., & Schulze, M. B. (2019) · Acta Diabetologica · doi:10.1007/s00592-019-01451-1 · Cited by 16

Use of Machine Learning to Predict the Onset of Diabetes

Mishra, V., Samuel, C., & Sharma, S. K. (2015) · International Journal of Recent advances in Mechanical Engineering · doi:10.14810/ijmech.2015.4202 · Cited by 15

Dose, dose, dose, but where is the patient dose?

Rehani, M. M., & Xu, X. G. (2024) · Radiation Protection Dosimetry · doi:10.1093/rpd/ncae137 · Cited by 14

Consenso sobre la detección y el manejo de la prediabetes. Grupo de Trabajo de Consensos y Guías Clínicas de la Sociedad Española de Diabetes

Mata‐Cases, M., Artola, S., Escalada, J., Loiola, P. E., Ferrer-García, J. C., Fornos, J. A., Girbés, J., & Rica, I. (2015) · Medicina de Familia SEMERGEN · doi:10.1016/j.semerg.2014.12.001 · Cited by 16

Development and Evaluation of a Web-Based Tool to Estimate Type 2 Diabetes Risk: Diab_Alert

Barreda-Pérez, M., Díez, I. D. L. T., López-Coronado, M., Rodrigues, J. J. P. C., & de la Iglesia, T. G. (2013) · Telemedicine Journal and e-Health · doi:10.1089/tmj.2012.0110 · Cited by 14

Risk distribution and its influence on the population targets for diabetes prevention

Rosella, L. C., Lebenbaum, M., Li, Y., Wang, J., & Manuel, D. G. (2013) · Preventive Medicine · doi:10.1016/j.ypmed.2013.10.007 · Cited by 18

Predictive models for type 2 diabetes onset in middle-aged subjects with the metabolic syndrome

Ozery-Flato, M., Parush, N., El‐Hay, T., Visockienė, Ž., Ryliškytė, L., Badarienė, J., Solovjova, S., Kovaitė, M., Navickas, R., & Laucevičius, A. (2013) · Diabetology & Metabolic Syndrome · doi:10.1186/1758-5996-5-36 · Cited by 13

Baseline Fasting Glucose Level, Age, Sex, and Body Mass Index and the Development of Diabetes in US Adults

Egan, A. M., Wood‐Wentz, C. M., Mohan, S., Bailey, K. R., & Vella, A. V. (2025) · JAMA Network Open · doi:10.1001/jamanetworkopen.2024.56067 · Cited by 13

Hemoglobin A1c in combination with fasting plasma glucose trumps fasting plasma glucose alone as predictive indicators for diabetes mellitus: an ambidirectional cohort study of Thai people with impaired fasting glucose

Thamakaison, S., Anothaisintawee, T., Sukhato, K., Unwanatham, N., Rattanasiri, S., Reutrakul, S., & Thakkinstian, A. (2021) · BMJ Open Diabetes Research & Care · doi:10.1136/bmjdrc-2021-002427 · Cited by 13

Validation of a nomogram for predicting regression from impaired fasting glucose to normoglycaemia to facilitate clinical decision making

Guo, V. Y., Yu, E. Y. T., Wong, C. K. H., Sit, R. W. S., Wang, J. H., Ho, S., & Lam, C. L. K. (2016) · Family Practice · doi:10.1093/fampra/cmw031 · Cited by 12

Derivation and external validation of a clinical version of the German Diabetes Risk Score (GDRS) including measures of HbA1c

Mühlenbruch, K., Paprott, R., Joost, H., Boeing, H., Heidemann, C., & Schulze, M. B. (2018) · BMJ Open Diabetes Research & Care · doi:10.1136/bmjdrc-2018-000524 · Cited by 13

A Risk Assessment Model for Type 2 Diabetes in Chinese

Luo, S., Han, L., Zeng, P., Chen, F., Pan, L., Wang, S., & Zhang, T. (2014) · PLoS ONE · doi:10.1371/journal.pone.0104046 · Cited by 13

Longitudinal changes in blood biomarkers and their ability to predict type 2 diabetes mellitus—The Tromsø study

Allaoui, G., Rylander, C., Averina, M., Wilsgaard, T., Fuskevåg, O., & Berg, V. (2022) · Endocrinology Diabetes & Metabolism · doi:10.1002/edm2.325 · Cited by 11

10-year risk for atherosclerotic cardiovascular disease and coronary heart disease among Korean adults: Findings from the Korean National Health and Nutrition Examination Survey 2009–2010

Lee, K. (2014) · International Journal of Cardiology · doi:10.1016/j.ijcard.2014.07.101 · Cited by 12

Life’s Essential 8 and osteoporosis in adults aged 50 years or older: data from the National Health and Nutrition Examination Survey

Tang, Y., Dong, W., Shen, J., Jiang, G., Wang, Q., Hao, J., & Hu, Z. (2024) · Archives of Osteoporosis · doi:10.1007/s11657-024-01368-5 · Cited by 11

Random glucose is useful for individual prediction of type 2 diabetes: Results of the Study of Health in Pomerania (SHIP)

Kowall, B., Rathmann, W., Giani, G., Schipf, S., Baumeister, S. E., Wallaschofski, H., Nauck, M. A., & Völzke, H. (2012) · Primary care diabetes · doi:10.1016/j.pcd.2012.12.001 · Cited by 10

A family history of type 2 diabetes as a predictor of fatty liver disease in diabetes-free individuals with excessive body weight

Pergola, G. D., Castellana, F., Zupo, R., Nucci, S. D., Panza, F., Castellana, M., Lampignano, L., Chito, M. D., Triggiani, V., Sardone, R., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-03583-3 · Cited by 11

Impaired fasting glucose: Pro-diabetic, “atheroprotective” and modified by metabolic syndrome

Onat, A., Aydın, M., Can, G., Çakmak, H. A., Köroğlu, B., Kaya, A., & Ademoğlu, E. (2013) · World Journal of Diabetes · doi:10.4239/wjd.v4.i5.210 · Cited by 11

Biomarkers in Type 2 diabetes: improving risk stratification with the PreDx® Diabetes Risk Score

Kolberg, J. A., Gerwien, R. W., Watkins, S. M., Wuestehube, L. J., & Urdea, M. S. (2011) · Expert Review of Molecular Diagnostics · doi:10.1586/erm.11.63 · Cited by 11

Systematic review of the development, implementation and availability of smart‐phone applications for assessing Type 2 diabetes risk

Gray, L. J., Leigh, T., Davies, M. J., Patel, N., Stone, M. A., Bonar, M., Badge, R. M., & Khunti, K. (2013) · Diabetic Medicine · doi:10.1111/dme.12115 · Cited by 13

Urinary N‐Telopeptide as Predictor of Onset of Menopause‐Related Bone Loss in Pre‐ and Perimenopausal Women

Shieh, A., Greendale, G. A., Cauley, J. A., Karvonen-Gutierrez, C. A., Lo, J. C.-M., & Karlamangla, A. S. (2018) · JBMR Plus · doi:10.1002/jbm4.10116 · Cited by 11

Risk scores for diabetes prediction: The International Diabetes Federation PREDICT-2 project

Lee, C., & Colagiuri, S. (2013) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2013.01.024 · Cited by 11

Assessment of risk and prevention of type 2 diabetes in primary health care

Saaristo, T. (2011) · Cited by 10

Family Involvement to Stop the Conversion of Prediabetes to Diabetes

Prakoso, D. A., Mahendradhata, Y., & Istiono, W. (2023) · Korean Journal of Family Medicine · doi:10.4082/kjfm.23.0019 · Cited by 11

Validation of the German Diabetes Risk Score within a population‐based representative cohort

Hartwig, S., Kuß, O., Tiller, D., Greiser, K. H., Schulze, M. B., Dierkes, J., Werdan, K., Haerting, J., & Kluttig, A. (2013) · Diabetic Medicine · doi:10.1111/dme.12216 · Cited by 12

Detection and Early Lifestyle Intervention in Those at Risk of Type 2 Diabetes

Edwardson, C. L., Gray, L. J., Yates, T. E., Barber, S. R., Khunti, K., & Davies, M. J. (2014) · EMJ Diabetes · doi:10.33590/emjdiabet/10313999 · Cited by 11

Association of familial history of diabetes or myocardial infarction and stroke with risk of cardiovascular diseases in four German cohorts

Mühlenbruch, K., Menzel, J., Dörr, M., Ittermann, T., Meisinger, C., Peters, A., Kluttig, A., Medenwald, D., Bergmann, M. M., Boeing, H., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-72361-4 · Cited by 10

The global burden of early-onset type 2 diabetes mellitus caused by high body-mass index from 1990 to 2021 and projection to 2050

Liang, L., Lin, Y., Liu, Z., Jiang, Y. Y., Cheng, W., & Zhao, L. (2025) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2025.112368 · Cited by 9

A Comprehensive Analysis of Chinese, Japanese, Korean, US-PIMA Indian, and Trinidadian Screening Scores for Diabetes Risk Assessment and Prediction

Fitriyani, N. L., Syafrudin, M., Ulyah, S. M., Alfian, G., Qolbiyani, S. L., & Anshari, M. (2022) · Mathematics · doi:10.3390/math10214027 · Cited by 9

Genomic-based tools for the risk assessment, management, and prevention of type 2 diabetes

Taber, K. J., & Dickinson, B. D. (2015) · The Application of Clinical Genetics · doi:10.2147/tacg.s75583 · Cited by 10

Association between CETP gene polymorphism, insulin resistance and risk of diabetes mellitus in patients with vascular disease

Koopal, C., van der Graaf, Y., Asselbergs, F. W., Westerink, J., & Visseren, F. L. J. (2015) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2015.08.015 · Cited by 10

Screening for Type 2 Diabetes

Gray, L. J., Davies, M. J., & Khunti, K. (2013) · Springer eBooks · doi:10.1007/978-1-4471-2834-2_15 · Cited by 9

Which clinical and biochemical predictors should be used to screen for diabetes in patients with serious mental illness receiving antipsychotic medication? A large observational study

Mitchell, A. J., Vancampfort, D., Manu, P., Correll, C. U., Wampers, M., van Winkel, R., Yu, W., & Binder, M. D. (2019) · PLoS ONE · doi:10.1371/journal.pone.0210674 · Cited by 8

Comparison of the Finnish Diabetes Risk Score Model With the Metabolic Syndrome in a Shanghai Population

Jin, S., Chen, Q., Han, X., Liu, Y., Cai, M., Zheng, Y., & Lü, H. (2022) · Frontiers in Endocrinology · doi:10.3389/fendo.2022.725314 · Cited by 9

Integration of Risk Scores and Integration Capability in Electronic Patient Records

Heider, A.-K., & Mang, H. (2022) · Applied Clinical Informatics · doi:10.1055/s-0042-1756367 · Cited by 7

Consenso sobre la detección y el manejo de la prediabetes. Grupo de Trabajo de Consensos y Guías Clínicas de la Sociedad Española de Diabetes

Mata‐Cases, M., Artola, S., Escalada, J., Loiola, P. E., Ferrer-García, J. C., Fornos, J. A., Girbés, J., & Rica, I. (2015) · Atención Primaria · doi:10.1016/j.aprim.2014.12.002 · Cited by 11

Ethnicity Considerations in Diagnosing Glucose Disorders

Hare, M. J. L., & Shaw, J. E. (2015) · Current Diabetes Reviews · doi:10.2174/1573399811666150211112455 · Cited by 8

Screening for Diabetes and Prediabetes

Gray, L. J., Willis, A. M., Webb, D. R., Davies, M. J., & Khunti, K. (2018) · Endocrinology · doi:10.1007/978-3-319-45015-5_12 · Cited by 9

Machine Learning in Multi-Omics Data to Assess Longitudinal Predictors of Glycaemic HealthPreprint

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Abnormal skin in toe webs is a marker for abnormal glucose metabolism. A cross-sectional survey among 1,849 adults in Finland

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