The DOI matches its Crossref record; 46 of the 49 references it cites were found in a registry or an index.
Validation of the Finnish diabetes risk score (FINDRISC) questionnaire for screening for undiagnosed type 2 diabetes, dysglycaemia and the metabolic syndrome in Greece
Abstract
Aim The present study aimed to validate the Finnish Type 2 Diabetes Risk Score (FINDRISC) questionnaire for its ability to predict the presence of any glucose homoeostasis abnormalities and the metabolic syndrome (MetS) in the Greek population. Methods Validation was performed on a sample of individuals who had agreed to participate in a screening program for type 2 diabetes (T2D) prevention (the Greek part of the DE-PLAN study), using both FINDRISC and oral glucose tolerance tests (OGTT). Impaired fasting glucose (IFG) was defined as a fasting plasma glucose level of 6.1-6.9 mmol/L, and impaired glucose tolerance (IGT) as a 2-h plasma glucose of 7.8-11.0 mmol/L. The predictive value of the FINDRISC was cross-sectionally evaluated using the area under the receiver operating characteristic (AUROC) curve method. Results A total of 869 individuals (379 men, aged 56.2 ± 10.8 years) were screened from the general population living in the city and suburbs of Athens. OGTT revealed the presence of unknown diabetes in 94 cases (10.8%), IFG in 85 (9.8%) and IGT in 109 (12.6%). The sensitivity of a FINDRISC score greater or equal to 15 (45% of the population) to predict unknown diabetes was 81.9% and its specificity was 59.7%. The AUROC curve for detecting unknown diabetes was 0.724 (95% CI: 0.677-0.770). For any dysglycaemia, the AUROC curve was 0.716 (0.680-0.752) while, for detection of the MetS, it was 0.733 (0.699-0.767). Conclusion The FINDRISC questionnaire performed well as a sc…
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Moreover, FINDRISC is the most commonly evaluated diabetes risk score worldwide [17,18,19,20,21,22,23,24,25] and has been recommended as the best available risk assessment tool for use in the clinical practice by a systematic review [11].”
From A Novel Testing Model for Opportunistic Screening of Pre-Diabetes and Diabetes among U.S. Adults (2015) · cites this paper as [19]
Citations
Cites
Friedewald, W. T., Levy, R. I., & Fredrickson, D. S. (1972) · Clinical Chemistry · doi:10.1093/clinchem/18.6.499 · Cited by 32,423
Grundy, S. M., Bilheimer, D. W., Chait, A., Clark, L. T., Denke, M. A., Havel, R. J., Hazzard, W. R., Hulley, S. B., Hunninghake, D. B., Kreisberg, R. A., et al. (1993) · JAMA · doi:10.1001/jama.269.23.3015 · Cited by 27,944
Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin
(2002) · New England Journal of Medicine · doi:10.1056/nejmoa012512 · Cited by 19,011
Diagnosis and Classification of Diabetes Mellitus
American Diabetes Association (2010) · Diabetes Care · doi:10.2337/dc11-s062 · Cited by 14,094
Tuomilehto, J., Lindström, J., Eriksson, J. G., Valle, T. T., Hämäläinen, H., Ilanne-Parikka, P., Keinänen-Kiukaanniemi, S., Laakso, M., Louheranta, A., Rastas, M., et al. (2001) · New England Journal of Medicine · doi:10.1056/nejm200105033441801 · Cited by 10,873 · cited 2×
“[3] Tuomilehto J, Lindstrom J, Eriksson JG, Valle TT, Hamalainen H, IlanneParikka P, et al.”
BackgroundDiagnosis and classification of diabetes mellitus
American Diabetes Association (2010) · Diabetes Care · doi:10.2337/dc10-s062 · Cited by 5,491
Acarbose for prevention of type 2 diabetes mellitus: the STOP-NIDDM randomised trial
Chiasson, J.-L., Josse, R. G., Gomis, R., Hanefeld, M., Karasik, A., & Laakso, M. (2002) · The Lancet · doi:10.1016/s0140-6736(02)08905-5 · Cited by 2,543
Kahn, S. E. (2003) · Diabetologia · doi:10.1007/s00125-002-1009-0 · Cited by 2,166
Lindström, J., & Tuomilehto, J. O. (2003) · Diabetes Care · doi:10.2337/diacare.26.3.725 · Cited by 1,937
Cited by
Sorted by citations
Items 101–198 of 198
Mahmood, L., Moreno, L. A., Flores‐Barrantes, P., Mavrogianni, C., Schwarz, P. E. H., Makrilakis, K., Liatis, S., Cardon, G., Willems, R. P., Rurik, I., et al. (2022) · Public Health Nutrition · doi:10.1017/s1368980022002245 · Cited by 8
Self-citationPrediction of hypercholesterolemia using machine learning techniques
Moradifar, P., & Amiri, M. (2022) · Journal of Diabetes & Metabolic Disorders · doi:10.1007/s40200-022-01125-w · Cited by 8
Diabetes and the risk of cirrhosis and HCC: An analysis of the UK Biobank
Ye, F., Chen, L., & Zheng, X. (2023) · Hepatology Communications · doi:10.1097/hc9.0000000000000280 · Cited by 9
Limited Effectiveness of Diabetes Risk Assessment Tools in Seniors’ Facility Residents
Featherstone, T. R., Eurich, D. T., & Simpson, S. H. (2016) · Value in Health · doi:10.1016/j.jval.2016.09.2403 · Cited by 9
Böhme, P., Luc, A., Gillet, P., & Thilly, N. (2019) · European Journal of Clinical Nutrition · doi:10.1038/s41430-019-0472-3 · Cited by 8
Early prediction of diabetes by applying data mining techniques: A retrospective cohort study
Yousef, M. Z. A., Yasky, A. F., Shammari, R. A., & Ferwana, M. S. (2022) · Medicine · doi:10.1097/md.0000000000029588 · Cited by 7
Elshebiny, A., Alrashed, A., Albuwaydi, Z., Aljassim, S., Alhammad, F., & Alhajji, R. (2022) · Cureus · doi:10.7759/cureus.32034 · Cited by 8
Azzouz, M., Boudiba, A., Guerchani, M., Lyes, Y., Hannachi, R., Baghous, H., Meftah, A., & Mimouni, S. (2014) · Médecine des Maladies Métaboliques · doi:10.1016/s1957-2557(14)70877-6 · Cited by 9
Recalibration in Validation Studies of Diabetes Risk Prediction Models: A Systematic Review
Masconi, K. L., Matsha, T. E., Erasmus, R. T., & Kengne, A. P. (2015) · International Journal of Statistics in Medical Research · doi:10.6000/1929-6029.2015.04.04.5 · Cited by 7
A demonstration area for type 2 diabetes prevention in Barranquilla and Juan Mina (Colombia)
Acosta, T., Barengo, N. C., Arrieta, A., Ricaurte, C., & Tuomilehto, J. O. (2018) · Medicine · doi:10.1097/md.0000000000009285 · Cited by 7
Ture, N., Emecen, A. N., & Unal, B. (2025) · Primary Health Care Research and Development · doi:10.1017/s1463423625000180 · Cited by 2
Traoré, S., Paré, B., Dabourou, D. L., Guira, O., Sagna, Y., Kamouni, J. P., Zoungrana, L., Bognounou, R., Tiéno, H., & Drabo, Y. (2021) · Open Journal of Internal Medicine · doi:10.4236/ojim.2021.112003 · Cited by 5
Chronic Non-Communicable Disease Risk Calculators – An Overview, Part II
Juchli, F., Zangger, M., Schueck, A., von Wolff, M., & Stute, P. (2020) · Maturitas · doi:10.1016/j.maturitas.2020.10.003 · Cited by 6
Detección de personas en riesgo de padecer diabetes en farmacias comunitarias españolas
Fornos-Pérez, J. A., Rodríguez, N. F. A., Lorenzo-Veiga, B., Royo, J. H., Vivar-Fernández, M., Andrés-Iglesias, J. C., & García-Rodríguez, P. (2015) · Farmacéuticos Comunitarios · doi:10.5672/fc.2173-9218.(2015/vol7).002.03 · Cited by 6
Automatic Diagnosis Metabolic Syndrome via a $$k-$$ Nearest Neighbour Classifier
Behadada, O., Abi-Ayad, M., Kontonatsios, G., & Trovati, M. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-57186-7_45 · Cited by 6
Alfageme-García, P., Basilio-Fernández, B., Ramírez-Durán, M. D. V., Gómez-Luque, A., Jiménez-Cano, V. M., Fabregat-Fernández, J., Alonso, V. R., Clavijo-Chamorro, M. Z., & Ruiz, S. H. (2024) · Journal of Personalized Medicine · doi:10.3390/jpm14020146 · Cited by 8
Diabetes mellitus risk among Jordanians in a dental setting: a cross-sectional study
Alazzam, M., Darwazeh, A. M.-G., Hassona, Y. M., & Khader, Y. S. (2020) · International Dental Journal · doi:10.1111/idj.12591 · Cited by 5
Mahmoodzadeh, S., Jahani, Y., Najafipour, H., Sanjari, M., Shadkam-Farokhi, M., & Shahesmaeili, A. (2022) · Intern J Endocrinol Metab · doi:10.5812/ijem-127114 · Cited by 5
Predicted nine-year risk of diabetes among professional divers: a prospective study
Pougnet, R., Uguen, M., Verdier, G., Pougnet, L., Lucas, D., Loddé, B., & Dewitte, J. (2015) · International Maritime Health · doi:10.5603/imh.2015.0021 · Cited by 5
Asencio-Barrientos, C., García-Rodas, O., Chang, C. E., Torres-Salazar, L. C., Cifuentes-Alvarado, M. E., & Barrios-Lupitou, L. C. (2022) · Ciencia Tecnología y Salud · doi:10.36829/63cts.v9i1.812 · Cited by 5
Porchia, L. M., Lara-Solís, B., Torres‐Rasgado, E., González-Mejía, M. E., Ruiz‐Vivanco, G., & Pérez-Fuentes, R. (2019) · Revista Panamericana de Salud Pública · doi:10.26633/rpsp.2019.9 · Cited by 5
Diabetes Risk Assessment Among the City Population in Azad Kashmir: A Cross-sectional Study
Javaeed, A., Lone, U. M., Sadiq, S., Ghauri, S. K., & Wajid, Z. (2019) · Cureus · doi:10.7759/cureus.4580 · Cited by 5
Cahyaningsih, I., Rokhman, M. R., Sudikno, S., Postma, M. J., & van der Schans, J. (2025) · PLoS ONE · doi:10.1371/journal.pone.0314824 · Cited by 4
Montes, Y. D., Arrieta, L. A. A., de la Hoz, J. J. E., Acosta, T., Acosta-Reyes, J. L., Lozano, K. C. F., Molina, R. T., Aschner, P. J., Acosta, S. M. R., & Barengo, N. C. (2025) · Primary care diabetes · doi:10.1016/j.pcd.2025.03.010 · Cited by 4
How can we identify candidates at highest risk – to screen or not to screen?
Barengo, N. C., & Tuomilehto, J. O. (2016) · Herz · doi:10.1007/s00059-016-4417-5 · Cited by 4
Chronic non-communicable disease risk calculators – An overview, part I
Juchli, F., Zangger, M., Schueck, A., von Wolff, M., & Stute, P. (2020) · Maturitas · doi:10.1016/j.maturitas.2020.07.009 · Cited by 4
MALINDISA, E. K., Balandya, E., Mashili, F. L., & Njelekela, M. A. (2021) · Diabetology · doi:10.3390/diabetology2040022 · Cited by 4
Vitoratou, D.-I., Mavrogianni, C., Karatzi, K., Cardon, G., Iotova, V., Tsochev, Κ., Lindström, J., Wikström, K., González‐Gil, E. M., Moreno, L. A., et al. (2022) · Nutrition · doi:10.1016/j.nut.2022.111900 · Cited by 4
Self-citationRisks of obesity and diabetes development in the population of the Ivano-Frankivsk region in Ukraine
Lushchak, V. I., Covașă, M., Abrat, O., Mykytyn, T., Твердохліб, І. З., Storey, K. B., & Semchyshyn, H. M. (2023) · EXCLI journal · doi:10.17179/excli2023-6296 · Cited by 4
Katoh, S., Peltonen, M., Zeniya, M., Kaji, M., Sakamoto, Y., Utsunomiya, K., & Tuomilehto, J. (2016) · Primary Care Diabetes · doi:10.1016/j.pcd.2015.07.006 · Cited by 4
Transcultural Lifestyle Medicine
Nieto-Martínez, R. E., & Gonzalez-Rivas, J. P. (2020) · Springer eBooks · doi:10.1007/978-3-030-48088-2_19 · Cited by 4
Effect of Vitamin D on Retinoblastoma Protein in Prediabetic Individuals
Hardatt, D., Devi, M., Vyas, S., Singh, G., Jain, J., Gupta, S., & Dhanawat, M. (2023) · Current Diabetes Reviews · doi:10.2174/1573399819666230331083715 · Cited by 3
Noor, S. K. M., Farah, A. M. F., Karar, N. A. A., Bushara, S. O. E., Farah, S. I. S., Osman, M. S. E. H. M., Osman, M. M. A., Ali, A. O., Kaba, O. A. A. H., & Badi, S. (2024) · Sudan Journal of Medical Sciences · doi:10.18502/sjms.v19i1.15766 · Cited by 3
Keinänen, J., Eskelinen, S., From, T., Laurikainen, H., Hietala, J. A., Murray, G. K., Suvisaari, J., & Perry, B. I. (2024) · Acta Psychiatrica Scandinavica · doi:10.1111/acps.13752 · Cited by 3
Cardiovascular disease risk factor profile of male Gaelic Athletic Association sports referees
O’Riordan, C. N., Newell, M., & Flaherty, G. T. (2018) · Irish Journal of Medical Science (1971 -) · doi:10.1007/s11845-018-1792-3 · Cited by 3
Amelia, D., & Kurniawati, D. M. (2018) · Malaysian Journal of Medical Research · doi:10.31674/mjmr.2018.v02i02.007 · Cited by 3
Feel4Diabetes-study, O. B. O. T., & Liatis, S. (2020) · BMC Endocrine Disorders · doi:10.1186/s12902-019-0470-4 · Cited by 3
Self-citationDiabetes screening revisited: issues related to implementation
Wong, M. C. S., Huang, J., & Kong, A. P. S. (2020) · Hong Kong Medical Journal · doi:10.12809/hkmj205103 · Cited by 3
Calcaterra, V., Labati, L., Campoy, C., Rossi, V., Fiore, G., Escudero-Marín, M., Vandoni, M., Verduci, E., Marin, L., Pagani, V., et al. (2025) · Pediatric Research · doi:10.1038/s41390-025-04603-y · Cited by 2
Matabuena, M., Vidal, J. C., Ghosal, R., & Onnela, J.-P. “. (2026) · Statistical Methods in Medical Research · doi:10.1177/09622802261442893 · Cited by 3
Cardon, G., CHASTIN, S. F. M., Stappen, V. V., Huys, N., Stefanova, T., Chakarova, N. Y., Kivelä, J., Moreno, L. A., Radó, S. I., Androutsos, O., et al. (2022) · Health Promotion International · doi:10.1093/heapro/daac092 · Cited by 2
Kondakis, K., Grammatikaki, E., Kondakis, M., Molnár, D., Gómez-Martínez, S., González-Gross, M., Kafatos, A., Manios, Y., Pavón, D. J., Gottrand, F., et al. (2022) · Journal of Pediatric Endocrinology & Metabolism (JPEM) · doi:10.1515/jpem-2022-0265 · Cited by 1
[Diagnostic performance of the FINDRISC questionnaire to identify insulin resistance in adults].
Varela-Vega, Y., Roy-García, I. A., Pérez‐Rodríguez, M., & López, L. V. (2023) · PubMed Central · Cited by 2
Diabetes Risk Score of Adult Applications to Primary Health Care Center: A Cross-Sectional Study
EVCİMEN, H., AYYILDIZ, N. İ., & Doğan, U. (2023) · Van Sağlık Bilimleri Dergisi · doi:10.52976/vansaglik.1162785 · Cited by 2
Eldakhakhny, B. M., Enani, S. M., Jambi, H., Ajabnoor, G., Al-Ahmadi, J., Al-Raddadi, R. M., Alsheikh, L., Abdulaal, W. H., Gad, H., Borai, A., et al. (2023) · Biomedicines · doi:10.3390/biomedicines11123242 · Cited by 3
Üniversite Çalışanlarında Tip 2 Diyabet Riskinin ve İlişkili Faktörlerin Değerlendirilmesi
Biçer, E. K., Çekiç, M., & AYVAZOĞLU, G. (2024) · İstanbul Gelişim Üniversitesi Sağlık Bilimleri Dergisi · doi:10.38079/igusabder.1241694 · Cited by 2
Metabolic syndrome index measurement tool (MSI): scale development, reliability and validity study
Akeren, Z., & Apaydın, E. (2025) · BMC Public Health · doi:10.1186/s12889-025-21304-7 · Cited by 2
Martínez, W. A. R., Salazar-Solarte, A. M., Sánchez-Machado, D. M., Figueroa, L., Pacheco, R., Bolaños-Moreno, Y., & Casanova, M. (2025) · Cardiovascular Diabetology – Endocrinology Reports · doi:10.1186/s40842-025-00224-w · Cited by 2
The Atrial Fibrillation Risk Score for Hyperthyroidism Patients
Derevitskii, I. V., Savitskaya, D. A., Babenko, A. Y., & Kovalchuk, S. V. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-50423-6_37 · Cited by 2
Predicting Risk of Diabetes using a Model based on Multilayer Perceptron and Features Extraction
Fallucchi, F., & Cabroni, A. (2021) · Journal of Computer Science · doi:10.3844/jcssp.2021.748.761 · Cited by 2
Tıp Fakültesi Öğrencilerinin Metabolik Sendrom Farkındalık Düzeylerinin İncelenmesi
BULUT, M. M., Bozkurt, S., Yabacı, A., & Yücesan, E. (2022) · Acta Medica Nicomedia · doi:10.53446/actamednicomedia.1092330 · Cited by 2
Ramírez-Durán, M. D. V., Basilio-Fernández, B., Gómez-Luque, A., Alfageme-García, P., Clavijo-Chamorro, M., Jiménez-Cano, V. M., Fabregat-Fernández, J., Robles-Alonso, V., & Hidalgo-Ruíz, S. (2022) · Journal of Personalized Medicine · doi:10.3390/jpm12101669 · Cited by 2
A Preliminary Report of Type 2 Diabetes Risk and Related Factors
Büyüksoy, G. D., & Öz, H. (2020) · SN Comprehensive Clinical Medicine · doi:10.1007/s42399-020-00567-8
Kourpas, E., Makrilakis, K., Dafoulas, G., Iotova, V., Tsochev, Κ., Dimova, R. B., Cardon, G., González‐Gil, E. M., Moreno, L. A., Kivelä, J., et al. (2024) · Diabetic Medicine · doi:10.1111/dme.15368 · Cited by 1
Self-citationKoistinen, V., Manninen, S. M., Tuomainen, M. H., Aittola, K., Järvelä‐Reijonen, E., Tilles‐Tirkkonen, T., Männikkö, R., Lintu, N., Karhunen, L. J., Kolehmainen, M., et al. (2025) · Scientific Reports · doi:10.1038/s41598-025-25749-z · Cited by 1
Screening Tools for Early Identification of Adults at High Risk of Type 2 Diabetes: A Scoping Review
Christakis, C., Saliari, D., Zampelas, A., & Androutsos, O. (2026) · Healthcare · doi:10.3390/healthcare14070839 · Cited by 1
Glandt, M., & Bloomgarden, Z. T. (2012) · Springer eBooks · doi:10.1007/978-1-4614-3314-9_11 · Cited by 1
DEMİRAĞ, H., & Boyraz, S. (2021) · Clinical and Experimental Health Sciences · doi:10.33808/clinexphealthsci.655688 · Cited by 1
Ugwueze, C. V., Nnolim, B. I., Anikpo, N. C., Onyekachi, K. E., Onah, C. K., Chukwu, O. E., Abonyi, M. C., Ezeokpo, B. C., & Modebe, O. (2022) · Nigerian Journal of Medicine · doi:10.4103/njm.njm_79_22 · Cited by 1
Riebeling, R. D. S. G., & Álvarez, L. (2024) · Psic-Obesidad · doi:10.22201/fesz.20075502e.2024.14.53.89450 · Cited by 1
Hamedi, A., Seif, M., Sharifi, M. H., Rezaianzadeh, A., & Hassanzadeh, J. (2025) · Primary care diabetes · doi:10.1016/j.pcd.2025.07.001 · Cited by 1
Siri, S. R. A. (2014) · NORA - Norwegian Open Research Archives · Cited by 1
Moreira‐Lucas, T. (2018) · TSpace (University of Toronto) · Cited by 1
Amirian, P., & Zarpoosh, M. (2025) · medRxiv · doi:10.1101/2025.09.05.25335151 · Cited by 1
Marzoog, B. A. (2025) · medRxiv · doi:10.1101/2025.11.01.25339311 · Cited by 1
Prevention of cardiovascular disease and diabetes on a population level
Gyberg, V. (2014) · Figshare
Identification of Target Populations for Intervention
Valensi, P. E., & Cosson, E. (2013) · Wiley eBooks · doi:10.1002/9781118661321.ch2
Tsaousis, K. T. (2014) · Europe PMC (PubMed Central)
Diabetes mellitus management in the Greek financial crisis as an opportunity to steer recovery
Zafeiris, P. (2014) · Indian Journal of Endocrinology and Metabolism · doi:10.4103/2230-8210.126596
Screening for Prediabetes and Diabetes
Tirosh, A. (2012) · Springer eBooks · doi:10.1007/978-1-4614-3314-9_7
Importance of Rational Screening for Type 2 Diabetes
V, E. (2016) · Journal of Diabetes Metabolic Disorders & Control · doi:10.15406/jdmdc.2016.03.00074
Screening of Prediabetes and Type 2 Diabetes Mellitus in Rabigh, Saudi Arabia
Abdelrahman, A. S., Gazzaz, Z. J., Khabaz, M. N., Bakarman, M. A., Yaseen, A., Butt, N. S., Alhabib, M., Majnuni, A., Almousa, A. A., Alkayal, M., et al. (2018) · Global Journal of Health Science · doi:10.5539/gjhs.v10n3p161
Screening for diabetes in dental settings
(2014) · Dental Abstracts · doi:10.1016/j.denabs.2014.03.039
Pharmacist Led Diabetes Disease State Management in Residential Care Facilities
Featherstone, T. R. (2016) · ERA: Education and Research Archive (University of Alberta) · doi:10.7939/r3tm72b6c
Alieva, A., Ismailov, S., & Нишановна, Р. Г. (2020) · International Journal of Biomedicine · doi:10.21103/article10(1)_oa3
Diabetes Risk Profile for an Argeş County Adult Sample – Findrisc Score Characteristics
Roşescu, R., Cînpeanu, O. C., Teodorescu, C., & Tarcea, M. (2020) · Acta Medica Transilvanica · doi:10.2478/amtsb-2020-0040
Matabuena, M., Félix, P., Meijide-Garcia, C., & Gudé, F. (2020) · arXiv (Cornell University) · doi:10.13140/rg.2.2.14674.99529
Mugume, I. B., Wafula, S. T., Kadengye, D., & van Olmen, J. V. (2021) · Research Square · doi:10.21203/rs.3.rs-414395/v1
Nedyalkova, M., Romanova, J., Naneva, L., & Simeonov, V. (2022) · Physical Sciences Reviews · doi:10.1515/psr-2021-0158
Riesgo cardiovascular en pacientes con FINDRISC-C mayor o igual a 12
Santana, K. L. C., Cantillo, H. J. M., Romero, D. F. O., Restrepo, H. K. C., Fernández, E. A. T., & Caballero, H. G. Y. (2023) · Revista Repertorio de Medicina y Cirugía · doi:10.31260/repertmedcir.01217372.1028
Chiang, W. L., My, B. N., Azrina, A. N., Irmi, Z. I., & Zalina, A. Z. (2022) · Proceeding B-ICON · doi:10.33088/bicon.v1i1.25 · Cited by 1
The Relationship of Diabetes Mellitus Risk with Well-Being, and Physical Activity among Young Adults
Öcalan, D., Ceylantekin, Y., Kunduracılar, Z., & Doğan, T. (2021) · INTERNATIONAL JOURNAL OF DIABETES & METABOLIC SYNDROME · doi:10.52106/2771-1331.1007
Body physique rating as a factor to identify at-risk Mexicans for Metabolic Syndrome
Herrera-Fomperosa, O., Bustamante-Villagomez, S. K., Vásquez-Alvarez, S., Vazquez-Marroquin, G., Porchia, L. M., Torres‐Rasgado, E., Pérez-Fuentes, R., & González-Mejía, M. E. (2023) · Human Nutrition & Metabolism · doi:10.1016/j.hnm.2023.200206
Bilgehan, T., & VAR, E. Ç. (2024) · Sağlık Bilimleri Üniversitesi Hemşirelik Dergisi · doi:10.48071/sbuhemsirelik.1436414
Hamurcu, P., & Batar, N. (2024) · Revista de Nutrição · doi:10.1590/1678-9865202437e230070
Nkpozi, M. O., Onwuchekwa, U., Ukoma, B. C. U., Okezie, F. C., Madu, G. O., Ohanusi, P. O., & Barrah, J. E. (2025) · European Journal of Medical and Health Research · doi:10.59324/ejmhr.2025.3(1).13
Holm, T. F., Kronborg, T., Jensen, M. H., & Hangaard, S. (2025) · Journal of Diabetes Science and Technology · doi:10.1177/19322968251376380
Stappen, V. V., Cardon, G., Craemer, M. D., Mavrogianni, C., Usheva, N., Kivelä, J., Wikström, K., Miguel‐Etayo, P. D., González‐Gil, E. M., Radó, A. S., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14897450
Stappen, V. V., Cardon, G., Craemer, M. D., Mavrogianni, C., Usheva, N., Kivelä, J., Wikström, K., Miguel‐Etayo, P. D., González‐Gil, E. M., Radó, A. S., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14897444
Stappen, V. V., Cardon, G., Craemer, M. D., Mavrogianni, C., Usheva, N., Kivelä, J., Wikström, K., Miguel‐Etayo, P. D., González‐Gil, E. M., Radó, A. S., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14897447
Associations between glycated albumin and current measures of glycaemic control in Saudi adults
Bahijri, S., Sabban, A., Enani, S. M., Alqahtani, M. S., Malibary, M., Alhashmi, M. H., & Tuomileto, J. (2026) · Frontiers in Endocrinology · doi:10.3389/fendo.2025.1731945
Zhang, J., Li, D. N., Wang, Y., Chai, X., Zhu, D., Yang, C., Chen, M., Xiang, X., Zhang, J., Yin, X., et al. (2026) · Trials · doi:10.1186/s13063-025-09400-w
Kontochristopoulou, A. M., Anastasiou, C. A., Moschonis, G., & Μanios, Y. (2026) · Nutrition and Health · doi:10.1177/02601060261421109
ALATAŞ, H., Arslan, N., Kurt, H., & Bentli, S. (2026) · Frontiers in Public Health · doi:10.3389/fpubh.2026.1778156
Ngô, V. L., 1, N. T. H., 1, N. T. H., Huế, 1. T. Đ. H. Y. D., Lạt, 2. T. T. Y. K. P. Đ., & Dong, L. (2025) · Tạp chí Tim Mạch Học Việt Nam · doi:10.58354/jvc.119.2025.1030
Economic Evaluation of Adopting a Risk Score for Diabetes Screening in Indonesia
Rokhman, M. R., Hanifa, R. S., Niamuzisilawati, E., Thobari, J. A., Postma, M. J., Boersma, C., & van der Schans, J. (2026) · Value in Health Regional Issues · doi:10.1016/j.vhri.2026.101632
Díaz, C., Álvarez-Ochoa, R., Faicán-Rocano, P. F., Cordero, G., & Barrera, J. M. L. (2026) · Clinical Epidemiology and Global Health · doi:10.1016/j.cegh.2026.102526
Metadata
- Authors
- Makrilakis, K., Liatis, S., Grammatikou, S., Perrea, D., Stathi, C., Tsiligros, P., & Katsilambros, N.
- Affiliations
- Laiko General Hospital of Athens (GR)
- National and Kapodistrian University of Athens (GR)
- Corresponding author
- Makrilakis, K.
- Venue
- Diabetes & Metabolism
- Publisher
- Elsevier BV
- Published
- 2011-04
- Volume
- 37
- Issue
- 2
- Pages
- 144-151
- ISSN
- 1262-3636 (ISSN-L)
- OpenAlex
- W2061002349
- Cited by
- 199
- References
- 49
- Citations by year
- 2026: 8 · 2025: 15 · 2024: 8 · 2023: 12 · 2022: 20 · 2021: 17 · 2020: 25 · 2019: 9 · 2018: 13 · 2017: 10 · 2016: 18 · 2015: 14 · 2014: 10 · 2013: 11 · 2012: 5
- FWCI
- 4.60
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Diabetes & Metabolism (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Bristol-Myers Squibb
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes, Cardiovascular Risks, and Lipoproteins
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Health Promotion and Cardiovascular Prevention
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Keywords
- FINDRISC questionnaire, type 2 diabetes prediction, undiagnosed type 2 diabetes, dysglycemia, impaired fasting glucose, impaired glucose tolerance, metabolic syndrome, diabetes screening, Greek population, questionnaire validation, oral glucose tolerance test, Athens, Greece, cross-cultural adaptation, sensitivity and specificity
- MeSH terms
- Adult, Aged, Blood Glucose, Cross-Sectional Studies, Diabetes Mellitus, Type 2, Fasting, Female, Finland, Glucose Tolerance Test, Greece, Humans, Male, Mass Screening, Middle Aged, Surveys and Questionnaires, Risk Factors, ROC Curve, Metabolic Syndrome, Glucose Metabolism Disorders
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
