The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
The Diabetes Risk Score
Open accessAbstract
OBJECTIVE—Interventions to prevent type 2 diabetes should be directed toward individuals at increased risk for the disease. To identify such individuals without laboratory tests, we developed the Diabetes Risk Score. RESEARCH DESIGN AND METHODS—A random population sample of 35- to 64-year-old men and women with no antidiabetic drug treatment at baseline were followed for 10 years. New cases of drug-treated type 2 diabetes were ascertained from the National Drug Registry. Multivariate logistic regression model coefficients were used to assign each variable category a score. The Diabetes Risk Score was composed as the sum of these individual scores. The validity of the score was tested in an independent population survey performed in 1992 with prospective follow-up for 5 years. RESULTS—Age, BMI, waist circumference, history of antihypertensive drug treatment and high blood glucose, physical activity, and daily consumption of fruits, berries, or vegetables were selected as categorical variables. Complete baseline risk data were found in 4,435 subjects with 182 incident cases of diabetes. The Diabetes Risk Score value varied from 0 to 20. To predict drug-treated diabetes, the score value ≥9 had sensitivity of 0.78 and 0.81, specificity of 0.77 and 0.76, and positive predictive value of 0.13 and 0.05 in the 1987 and 1992 cohorts, respectively. CONCLUSIONS—The Diabetes Risk Score is a simple, fast, inexpensive, noninvasive, and reliable tool to identify individuals at high risk for type 2 diabetes.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
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
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
Self-citationPan, X.-R., Li, G.-W., Hu, Y.-H., Wang, J.-X., Yang, W.-Y., An, Z.-X., Hu, Z.-X., Juan-Lin, Xiao, J.-Z., Cao, H.-B., et al. (1997) · Diabetes Care · doi:10.2337/diacare.20.4.537 · Cited by 4,320
Diet, lifestyle, and the risk of type 2 diabetes mellitus in women.
Hu, F. B., Manson, J. E., Stampfer, M. J., Colditz, G., Liu, S., Solomon, C. G., & Willett, W. C. (2001) · New England Journal of Medicine · doi:10.1056/nejmoa010492 · Cited by 2,901
Harris, M. I., Flegal, K. M., Cowie, C. C., Eberhardt, M. S., Goldstein, D. E., Little, R. R., Wiedmeyer, H.-M., & Byrd-Holt, D. D. (1998) · Diabetes Care · doi:10.2337/diacare.21.4.518 · Cited by 2,703
Waist circumference as a measure for indicating need for weight management
Lean, M. E. J., Han, T. S., & Morrison, C. E. (1995) · BMJ · doi:10.1136/bmj.311.6998.158 · Cited by 1,869
Cited by
European Guidelines on cardiovascular disease prevention in clinical practice (version 2012)
Authors/Task Force Members:, Perk, J., De Backer, G., Gohlke, H., Graham, I., Reiner, Ž., Verschuren, M., Albus, C., Benlian, P., Boysen, G., et al. (2012) · European Heart Journal · doi:10.1093/eurheartj/ehs092 · Cited by 8,490
2016 European Guidelines on cardiovascular disease prevention in clinical practice
Piepoli, M. F., Hoes, A. W., Agewall, S., Albus, C., Brotons, C., Catapano, A. L., Cooney, M.-T., Corrà, U., Cosyns, B., Deaton, C., et al. (2016) · European Heart Journal · doi:10.1093/eurheartj/ehw106 · Cited by 6,457
Cosentino, F., Grant, P. J., Aboyans, V., Bailey, C. J., Ceriello, A., Delgado, V., Federici, M., Filippatos, G., Grobbee, D. E., Hansen, T. B., et al. (2020) · European Heart Journal · doi:10.1093/eurheartj/ehz486 · Cited by 4,763
2013 ESC guidelines on the management of stable coronary artery disease
Task Force Members, Montalescot, G., Sechtem, U., Achenbach, S., Andreotti, F., Arden, C., Budaj, A., Bugiardini, R., Crea, F., Cuisset, T., et al. (2013) · European Heart Journal · doi:10.1093/eurheartj/eht296 · Cited by 4,510
Follow-up report on the diagnosis of diabetes mellitus
The Expert Committee on the Diagnosis and Classification of Diabetes Mellitus* (2003) · Diabetes Care · doi:10.2337/diacare.26.11.3160 · Cited by 3,784
DeFronzo, R. A., Ferrannini, E., Groop, L., Henry, R. R., Herman, W. H., Holst, J. J., Hu, F. B., Kahn, C. R., Raz, I., Shulman, G. I., et al. (2015) · Nature Reviews Disease Primers · doi:10.1038/nrdp.2015.19 · Cited by 2,611
Moons, K. G. M., de Groot, J. A. H., Bouwmeester, W., Vergouwe, Y., Mallett, S., Altman, D. G., Reitsma, J. B., & Collins, G. S. (2014) · PLoS Medicine · doi:10.1371/journal.pmed.1001744 · Cited by 2,018
“Prognostic models may involve models for individuals with a particular health condition, such as prediction of recurrence or death after diagnosis of breast cancer [8] or mortality after cardiac surgery [9], but also includes models for predicting the occurrence of future outcomes in apparently healthy individuals suc…”
BackgroundGarvey, W. T., Mechanick, J. I., Brett, E. M., Garber, A. J., Hurley, D. L., Jastreboff, A. M., Nadolsky, K., Pessah-Pollack, R., & Plodkowski, R. (2016) · Endocrine Practice · doi:10.4158/ep161365.gl · Cited by 1,914
Lindström, J., Ilanne-Parikka, P., Peltonen, M., Aunola, S., Eriksson, J. G., Hemiö, K., Hämäläinen, H., Härkönen, P., Keinänen-Kiukaanniemi, S., Laakso, M., et al. (2006) · The Lancet · doi:10.1016/s0140-6736(06)69701-8 · Cited by 1,799
Self-citationGraham (2007) · doi:10.1097/01.hjr.0000277983.23934.c9 · Cited by 1,647
Prediction of Incident Diabetes Mellitus in Middle-aged Adults: The Framingham Offspring Study
Wilson, P. W. F. (2007) · Archives of Internal Medicine · doi:10.1001/archinte.167.10.1068 · Cited by 4
Piepoli, M. F., Corrà, U., Benzer, W., Bjarnason-Wehrens, B., Dendale, P., Gaita, D., McGee, H., Mendes, M., Niebauer, J., Zwisler, A.-D. O., et al. (2010) · European Journal of Cardiovascular Prevention & Rehabilitation · doi:10.1097/hjr.0b013e3283313592 · Cited by 796
International Diabetes Federation: a consensus on Type 2 diabetes prevention
Alberti, K. G. M. M., Zimmet, P., & Shaw, J. (2007) · Diabetic Medicine · doi:10.1111/j.1464-5491.2007.02157.x · Cited by 778
Sanz, M., Ceriello, A., Buysschaert, M., Chapple, I., Demmer, R. T., Graziani, F., Herrera, D., Jepsen, S., Lione, L., Madianos, P., et al. (2018) · Journal of Clinical Periodontology · doi:10.1111/jcpe.12808 · Cited by 738
Risk models and scores for type 2 diabetes: systematic review
Noble, D., Mathur, R., Dent, T., Meads, C., & Greenhalgh, T. (2011) · BMJ · doi:10.1136/bmj.d7163 · Cited by 551
Ascaso, J. F., Pardo, S., Real, J. T., Lorente, R. I., Priego, A., & Carmena, R. (2003) · Diabetes Care · doi:10.2337/diacare.26.12.3320 · Cited by 490
Collins, G. S., Mallett, S., Omar, O., & Yu, L.-M. (2011) · BMC Medicine · doi:10.1186/1741-7015-9-103 · Cited by 484
Metabolomic profiles predict individual multidisease outcomes
Buergel, T., Steinfeldt, J., Ruyoga, G., Pietzner, M., Bizzarri, D., Vojinovic, D., Upmeier zu Belzen, J., Loock, L., Kittner, P., Christmann, L., et al. (2022) · Nature Medicine · doi:10.1038/s41591-022-01980-3 · Cited by 406
Kempf, K., Herder, C., Erlund, I., Kolb, H., Martin, S., Carstensen, M., Koenig, W., Sundvall, J., Bidel, S., Kuha, S., et al. (2010) · The American Journal of Clinical Nutrition · doi:10.3945/ajcn.2009.28548 · Cited by 364
What is the best predictor of future type 2 diabetes
Abdul-Ghani, M. A., Williams, K., DeFronzo, R. A., & Stern, M. (2007) · Diabetes Care · doi:10.2337/dc06-1331 · Cited by 355
Association between age at diabetes onset and subsequent risk of dementia
Barbiellini Amidei, C., Fayosse, A., Dumurgier, J., Machado-Fragua, M. D., Tabak, A. G., van Sloten, T., Kivimäki, M., Dugravot, A., Sabia, S., & Singh-Manoux, A. (2021) · JAMA · doi:10.1001/jama.2021.4001 · Cited by 355
Abbasi, A., Peelen, L. M., Corpeleijn, E., van der Schouw, Y. T., Stolk, R. P., Spijkerman, A. M. W., van der A, D. L., Moons, K. G. M., Navis, G., Bakker, S. J. L., et al. (2012) · BMJ · doi:10.1136/bmj.e5900 · Cited by 321
Lifestyle Intervention for Prevention of Type 2 Diabetes in Primary Health Care
Saaristo, T., Moilanen, L., Korpi-Hyövälti, E., Vanhala, M., Saltevo, J., Niskanen, L., Jokelainen, J., Peltonen, M., Oksa, H., Tuomilehto, J., et al. (2010) · Diabetes Care · doi:10.2337/dc10-0410 · Cited by 302
Self-citationRisk assessment tools for identifying individuals at risk of developing type 2 diabetes
Buijsse, B., Simmons, R. K., Griffin, S. J., & Schulze, M. B. (2011) · Epidemiologic Reviews · doi:10.1093/epirev/mxq019 · Cited by 297
Jarczok, M. N., Weimer, K., Braun, C., Williams, D. P., Thayer, J. F., Gündel, H. O., & Balint, E. M. (2022) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2022.104907 · Cited by 246
Guo, F., Moellering, D. R., & Garvey, W. T. (2014) · Obesity · doi:10.1002/oby.20585 · Cited by 202
Wu, P., Gulati, M., Kwok, C. S., Wong, C. W., Narain, A., O'Brien, S., Chew‐Graham, C. A., Verma, G., Kadam, U. T., & Mamas, M. A. (2018) · Journal of the American Heart Association · doi:10.1161/jaha.117.007809 · Cited by 204
Makrilakis, K., Liatis, S., Grammatikou, S., Perrea, D., Stathi, C., Tsiligros, P., & Katsilambros, N. (2011) · Diabetes & Metabolism · doi:10.1016/j.diabet.2010.09.006 · Cited by 199
Knowler (2011) · doi:10.1001/archinternmed.2011.367 · Cited by 194
Carrión, R. E., Cornblatt, B. A., Burton, C. Z., Tso, I. F., Auther, A. M., Adelsheim, S., Calkins, R., Carter, C. S., Niendam, T., Sale, T. G., et al. (2016) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2016.15121565 · Cited by 170
“A number of risk calculators are now freely available to the public and can estimate the risk, for example, of prostate, ovarian, breast, pancreatic, and colorectal cancer(4–6), type 2 diabetes(7, 8), and cardiovascular disease (9–11).”
BackgroundF. Piepoli, M. (2017) · International Journal of Behavioral Medicine · doi:10.1007/s12529-016-9583-6 · Cited by 162
Franciosi, M., De Berardis, G., Rossi, M. C. E., Sacco, M., Belfiglio, M., Pellegrini, F., Tognoni, G., Valentini, M., Nicolucci, A., & for the IGLOO Study Group (2005) · Diabetes Care · doi:10.2337/diacare.28.5.1187 · Cited by 161
Novel biochemical risk factors for type 2 diabetes: pathogenic insights or prediction possibilities?
Sattar, N., Wannamethee, S. G., & Forouhi, N. G. (2008) · Diabetologia · doi:10.1007/s00125-008-0954-7 · Cited by 143
Zhang, L., Zhang, Z., Zhang, Y., Hu, G., & Chen, L. (2014) · PLoS ONE · doi:10.1371/journal.pone.0097865 · Cited by 130
A unique plasma microRNA profile defines type 2 diabetes progression
de Candia, P., Spinetti, G., Specchia, C., Sangalli, E., La Sala, L., Uccellatore, A., Lupini, S., Genovese, S., Matarese, G., & Ceriello, A. (2017) · PLOS ONE · doi:10.1371/journal.pone.0188980 · Cited by 105
Zhao, Q., Zhang, K., Li, Y., Zhen, Q., Shi, J., Yu, Y., Tao, Y., Cheng, Y., & Liu, Y. (2018) · Diabetic Medicine · doi:10.1111/dme.13787 · Cited by 105
Tankova, T., Chakarova, N., Atanassova, I., & Dakovska, L. (2011) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2010.12.020 · Cited by 101
Cichosz, S. L., Johansen, M. D., & Hejlesen, O. (2016) · Journal of Diabetes Science and Technology · doi:10.1177/1932296815611680 · Cited by 101
Parker, A. R., Byham-Gray, L., Denmark, R., & Winkle, P. J. (2014) · Journal of the Academy of Nutrition and Dietetics · doi:10.1016/j.jand.2014.07.020 · Cited by 92
Mattila, E., Orsama, A.-L., Ahtinen, A., Hopsu, L., Leino, T., & Korhonen, I. (2013) · JMIR mhealth and uhealth · doi:10.2196/mhealth.2557 · Cited by 93
Soriguer, F., Valdés, S., Tapia, M. J., Esteva, I., Ruiz de Adana, M. S., Almaraz, M. C., Morcillo, S., García Fuentes, E., Rodríguez, F., & Rojo-Martinez, G. (2012) · Medicina Clínica · doi:10.1016/j.medcli.2011.05.025 · Cited by 87
Luo, G. (2016) · Health Information Science and Systems · doi:10.1186/s13755-016-0015-4 · Cited by 85
Korkiakangas, E. E., Alahuhta, M. A., Husman, P. M., Keinänen‐Kiukaanniemi, S., Taanila, A. M., & Laitinen, J. H. (2011) · Scandinavian Journal of Caring Sciences · doi:10.1111/j.1471-6712.2010.00791.x · Cited by 81
Rathmann, W., Kowall, B., Heier, M., Herder, C., Holle, R., Thorand, B., Strassburger, K., Peters, A., Wichmann, H. ‐., Giani, G., et al. (2010) · Diabetic Medicine · doi:10.1111/j.1464-5491.2010.03065.x · Cited by 74
Yates, T., Davies, M. J., Gray, L. J., Webb, D., Henson, J., Gill, J. M. R., Sattar, N., & Khunti, K. (2010) · Preventive Medicine · doi:10.1016/j.ypmed.2010.06.011 · Cited by 70
Kadowaki, S., Okamura, T., Hozawa, A., Kadowaki, T., Kadota, A., Murakami, Y., Nakamura, K., Saitoh, S., Nakamura, Y., Hayakawa, T., et al. (2008) · Diabetologia · doi:10.1007/s00125-007-0915-6 · Cited by 69
Lin, J.-W., Chang, Y.-C., Li, H.-Y., Chien, Y.-F., Wu, M.-Y., Tsai, R.-Y., Hsieh, Y.-C., Chen, Y.-J., Hwang, J.-J., & Chuang, L.-M. (2009) · Diabetes Care · doi:10.2337/dc09-0694 · Cited by 60
Ziemer, D. C., Kolm, P., Foster, J. K., Weintraub, W. S., Vaccarino, V., Rhee, M. K., Varughese, R. M., Tsui, C. W., Koch, D. D., Twombly, J. G., et al. (2008) · Journal of General Internal Medicine · doi:10.1007/s11606-008-0524-1 · Cited by 56
Penn, L., Dombrowski, S. U., Sniehotta, F. F., & White, M. (2013) · BMJ Open · doi:10.1136/bmjopen-2013-002949 · Cited by 55
Diabetes risk score in the diagnostic categories of polycystic ovary syndrome
Moran, L. J., Strauss, B. J., & Teede, H. J. (2011) · Fertility and Sterility · doi:10.1016/j.fertnstert.2011.01.133 · Cited by 41
A Novel Testing Model for Opportunistic Screening of Pre-Diabetes and Diabetes among U.S. Adults
Zhang, Y., Hu, G., Zhang, L., Mayo, R., & Chen, L. (2015) · PLOS ONE · doi:10.1371/journal.pone.0120382 · Cited by 41
“FINDRISC was originally developed in a prospective cohort study in a Finnish population to predict the 10 year diabetes incidence [27].”
MethodZhang, L., Shang, X., Sreedharan, S., Yan, X., Liu, J., Keel, S., Wu, J., Peng, W., & He, M. (2020) · JMIR Medical Informatics · doi:10.2196/16850 · Cited by 40
Winkler, G., Hídvégi, T., Vándorfi, G., Balogh, S., & Jermendy, G. (2013) · Medical Science Monitor · doi:10.12659/msm.883747 · Cited by 36
Gholipour, K., Asghari-Jafarabadi, M., Iezadi, S., Jannati, A., & Keshavarz, S. (2018) · Eastern Mediterranean Health Journal · doi:10.26719/emhj.18.012 · Cited by 30
Wennehorst, K., Mildenstein, K., Saliger, B., Tigges, C., Diehl, H., Keil, T., & Englert, H. (2016) · Prevention Science · doi:10.1007/s11121-015-0623-2 · Cited by 28
Lévesque, V., Poirier, P., Després, J.-P., & Alméras, N. (2017) · The American Journal of Cardiology · doi:10.1016/j.amjcard.2017.08.008 · Cited by 25
Makrilakis, K., Liatis, S., Tsiakou, A., Stathi, C., Papachristoforou, E., Perrea, D., Katsilambros, N., Kontodimopoulos, N., & Niakas, D. (2018) · BMC Endocrine Disorders · doi:10.1186/s12902-018-0261-3 · Cited by 27
Mozaffary, A., Asgari, S., Tohidi, M., Kazempour-Ardebili, S., Azizi, F., & Hadaegh, F. (2016) · BMJ Open · doi:10.1136/bmjopen-2015-010889 · Cited by 27
Buccheri, E., Dell'Aquila, D., & Russo, M. (2021) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2021.108722 · Cited by 24
Updated risk factors should be used to predict development of diabetes
Bethel, M. A., Hyland, K. A., Chacra, A. R., Deedwania, P., Fulcher, G. R., Holman, R. R., Jenssen, T., Levitt, N. S., McMurray, J. J. V., Boutati, E., et al. (2017) · Journal of Diabetes and its Complications · doi:10.1016/j.jdiacomp.2017.02.012 · Cited by 12
Van Stappen, V., De Lepeleere, S., Huys, N., Latomme, J., Verloigne, M., Cardon, G., Androutsos, O., Manios, Y., De Bourdeaudhuij, I., & De Craemer, M. (2019) · PLOS ONE · doi:10.1371/journal.pone.0226131 · Cited by 9
van Olmen, J., Absetz, P., Mayega, R. W., Timm, L., Delobelle, P., Alvesson, H. M., Naggayi, G., Kasujja, F., Hassen, M., de Man, J., et al. (2022) · BMJ Open Diabetes Research & Care · doi:10.1136/bmjdrc-2022-002902 · Cited by 5
Metadata
- Authors
- Lindström, J., & Tuomilehto, J.
- Venue
- Diabetes Care
- Publisher
- American Diabetes Association
- Published
- 2003-03-01
- Volume
- 26
- Issue
- 3
- Pages
- 725-731
- ISSN
- 0149-5992 (ISSN-L)
- 1935-5548
- OpenAlex
- W2103556204
- Cited by
- 1,862
- References
- 6
- FWCI
- 13.89
- Type
- journal article
- Language
- en
- Open access
- bronze
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
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.