The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
Clinical Risk Factors, DNA Variants, and the Development of Type 2 Diabetes
Open accessAbstract
BACKGROUND: Type 2 diabetes mellitus is thought to develop from an interaction between environmental and genetic factors. We examined whether clinical or genetic factors or both could predict progression to diabetes in two prospective cohorts. METHODS: We genotyped 16 single-nucleotide polymorphisms (SNPs) and examined clinical factors in 16,061 Swedish and 2770 Finnish subjects. Type 2 diabetes developed in 2201 (11.7%) of these subjects during a median follow-up period of 23.5 years. We also studied the effect of genetic variants on changes in insulin secretion and action over time. RESULTS: Strong predictors of diabetes were a family history of the disease, an increased body-mass index, elevated liver-enzyme levels, current smoking status, and reduced measures of insulin secretion and action. Variants in 11 genes (TCF7L2, PPARG, FTO, KCNJ11, NOTCH2, WFS1, CDKAL1, IGF2BP2, SLC30A8, JAZF1, and HHEX) were significantly associated with the risk of type 2 diabetes independently of clinical risk factors; variants in 8 of these genes were associated with impaired beta-cell function. The addition of specific genetic information to clinical factors slightly improved the prediction of future diabetes, with a slight increase in the area under the receiver-operating-characteristic curve from 0.74 to 0.75; however, the magnitude of the increase was significant (P=1.0x10(-4)). The discriminative power of genetic risk factors improved with an increasing duration of follow-up, whereas that of clinical risk factors decreased. CONCLUSIONS: As compared with clinical risk factors alone, common genetic variants associated with the risk of diabetes had a small effect on the ability to predict the future development of type 2 diabetes. The value of genetic factors increased with an increasing duration of follow-up.
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.
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?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Together these genes do not appear to explain much of the genetic basis of the disease with the use of genotype risk scores only slightly improving prediction of subsequent diabetes compared to common clinical risk factors22,23.”
From Pathophysiology and treatment of type 2 diabetes: perspectives on the past, present, and future (2014) · cites this paper as [23]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“In prospective studies, decreases in the disposition index, the product of insulin secretion and SI, are a strong predictor of diabetes onset and worsening of metabolic function pre- and postdiagnosis (53).”
From Sleep Restriction for 1 Week Reduces Insulin Sensitivity in Healthy Men (2010) · cites this paper as [53]
Experimental — accuracy not yet measuredIn the citing paper
“Indeed, the relative risks associated with elevated amino acids were comparable to, or higher than, those associated with higher age, fasting glucose, or body mass index in prior population-based studies28.”
From Metabolite profiles and the risk of developing diabetes (2011) · cites this paper as [28]
In the citing paper
“There has been interest in genetic risk prediction, but the known diabetes polymorphisms add modestly to risk assessment27,28. For instance, known polymorphisms are only associated with 5% to 37% increases in the relative risk of diabetes”
From Metabolite profiles and the risk of developing diabetes (2011) · cites this paper as [28]
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
Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences
Cohen, P., Cohen, P., West, S. G., & Aiken, L. S. (2014) · Psychology Press eBooks · doi:10.4324/9781410606266 · Cited by 20,870
Pencina, M. J., D' Agostino, R. B., D' Agostino, R. B., & Vasan, R. S. (2008) · Statistics in Medicine · doi:10.1002/sim.2929 · Cited by 6,493
Matsuda, M., & DeFronzo, R. A. (1999) · Diabetes Care · doi:10.2337/diacare.22.9.1462 · Cited by 5,621
Frayling, T. M., Timpson, N. J., Weedon, M. N., Zeggini, E., Freathy, R. M., Lindgren, C. M., Perry, J. R. B., Elliott, K. S., Lango, H., Rayner, N. W., et al. (2007) · Science · doi:10.1126/science.1141634 · Cited by 4,531
A genome-wide association study identifies novel risk loci for type 2 diabetes
Sladek, R., Rocheleau, G., Rung, J., Dina, C., Shen, L., Serre, D., Boutin, P., Vincent, D., Belisle, A., Hadjadj, S., et al. (2007) · Nature · doi:10.1038/nature05616 · Cited by 2,993
Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels
Saxena, R., Voight, B. F., Lyssenko, V., Burtt, N. P., de Bakker, P. I. W., Chen, H., Roix, J. J., Kathiresan, S., Hirschhorn, J. N., Daly, M. J., et al. (2007) · Science · doi:10.1126/science.1142358 · Cited by 2,843
Self-citationA genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants
Scott, L. J., Mohlke, K. L., Bonnycastle, L. L., Willer, C. J., Li, Y., Duren, W. L., Erdos, M. R., Stringham, H. M., Chines, P. S., Jackson, A. U., et al. (2007) · Science · doi:10.1126/science.1142382 · Cited by 2,753
Variant of transcription factor 7-like 2 (TCF7L2) gene confers risk of type 2 diabetes
Grant, S. F. A., Thorleifsson, G., Reynisdottir, I., Benediktsson, R., Manolescu, A., Sainz, J., Helgason, A., Stefansson, H., Emilsson, V., Helgadottir, A., et al. (2006) · Nature Genetics · doi:10.1038/ng1732 · Cited by 2,287
Replication of Genome-Wide Association Signals in UK Samples Reveals Risk Loci for Type 2 Diabetes
Zeggini, E., Weedon, M. N., Lindgren, C. M., Frayling, T. M., Elliott, K. S., Lango, H., Timpson, N. J., Perry, J. R. B., Rayner, N. W., Freathy, R. M., et al. (2007) · Science · doi:10.1126/science.1142364 · Cited by 2,194
D'Agostino, R. B., Grundy, S., Sullivan, L. M., Wilson, P., & for the CHD Risk Prediction Group (2001) · JAMA · doi:10.1001/jama.286.2.180 · Cited by 2,108
Cited by
Sorted by citations
Items 801–900 of 939
Update Typ-2-Diabetes anhand ausgewählter aktueller Publikationen
Kellerer, M. (2009) · Der Diabetologe · doi:10.1007/s11428-008-0368-7
Le récepteur 4 aux mélanocortines, un paradigme de la génétique de l'obésité
Stutzmann, F. (2009) · OpenGrey (Institut de l'Information Scientifique et Technique)
Translating Advances in Our Understanding of the Genetics of Diabetes into the Clinic
Gardner, D. S.-L., Owen, K. R., & Gloyn, A. L. (2014) · Frontiers in diabetes · doi:10.1159/000362476
de Sousa, A. G. P. (2011) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/t.5.2011.tde-18022011-161431
Juurinen, L. (2014) · Työväentutkimus Vuosikirja
Ng, M. C. Y., & Cox, N. J. (2010) · Elsevier eBooks · doi:10.1016/b978-0-12-374934-5.00052-0
Genetic and Metabolic Markers for the Development of Diabetes after Gestational Diabetes Mellitus
Ekelund, M. (2010) · Lund University Publications (Lund University)
Leósdóttir, M. (2012) · Lund University Publications (Lund University)
Lindberg, L. (2011) · KTH Publication Database DiVA (KTH Royal Institute of Technology)
Role of WFS1 in Regulating Endoplasmic Reticulum Stress Signaling: A Dissertation
Fonseca, S. G. (2009) · The Journal of the American Medical Association (JAMA) Network (American Medical Association) · doi:10.13028/hwk3-1w56
Exploring the Grey Zone between Type 1 and Type 2 Diabetes
Bakhtadze, E. (2009) · Lund University Publications (Lund University)
Integrating bioinformatics and physiology to describe genetic effects in complex polygenic diseases
Parikh, H. M. (2009) · Lund University Publications (Lund University)
Predictive value of genomics in the screening of type 2 diabetes: limitations and current status
Mohan, & Radha, V. (2014) · Advances in Genomics and Genetics · doi:10.2147/agg.s18705
2型糖尿病患者における薬物療法に関する実態調査(V)―経口血糖降下薬とインスリンの併用療法:投与薬剤と臨床像の特徴―(JDDM19)
東, 金., 紘一, 川., 紘一, 平., まり子, 大., 正, 小., & 糖尿病データマネジメント研究会 (2010) · 糖尿病 · doi:10.11213/tonyobyo.53.737
Deleskog, A. (2013) · Figshare
Prédisposition génétique de développer une intolérance au glucose chez des jeunes obèses
Cauchi, S. (2014) · Médecine des Maladies Métaboliques · doi:10.1016/s1957-2557(14)70760-6
Novel genetic findings applied to the clinic in type 2 diabetes
Florez, J. C. (2009) · Endocrinología y Nutrición · doi:10.1016/s1575-0922(09)73512-0
Génétique du diabète de type 2 et de l’obésité : où en sommes-nous ? Que pouvons-nous en attendre ?
Bonnefond, A., & Froguel, P. (2011) · Médecine des Maladies Métaboliques · doi:10.1016/s1957-2557(11)70296-6 · Cited by 1
How useful are hypertension guidelines for diabetes?
Nilsson, P. M. (2013) · Diabetes Management · doi:10.2217/dmt.12.89
Self-citationSwen, J. J., Guchelaar, H., Baak-Pablo, R. F., Assendelft, W. J., & Wessels, J. A. M. (2012) · Nederlands Tijdschrift voor Diabetologie · doi:10.1007/s12467-012-0004-4
What are the practical implications of developments in genetics?
Pearson, E. R. (2010) · The Journal of the Royal College of Physicians of Edinburgh · doi:10.4997/jrcpe.2010.s02
What implications do biomarkers have for the prediction and treatment of Type 2 diabetes?
Rosengren, A. H. (2013) · Clinical Practice · doi:10.2217/cpr.13.1
Genome-wide association studies of Type 2 diabetes: are these ready to make an impact in the clinic?
Thanabalasingham, G., Gloyn, A. L., & Owen, K. R. (2011) · Diabetes Management · doi:10.2217/dmt.11.30
Shahrjerdi, A., Jadhav, B. L., & Jamkhedkar, S. (2013) · Journal of Pharmacy Research · doi:10.1016/j.jopr.2013.02.013
Pürnak, T. (2009) · Hepatology · doi:10.1002/hep.22985
Screening for Prediabetes and Diabetes
Tirosh, A. (2012) · Springer eBooks · doi:10.1007/978-1-4614-3314-9_7
Translating type 2 diabetes genetics: what does it add to clinical practice?
Thanabalasingham, G., & Owen, K. (2010) · Practical Diabetes · doi:10.1002/pdi.1499
Cauchi, S. (2014) · Médecine des Maladies Métaboliques · doi:10.1016/s1957-2557(14)70763-1
Electrophoresis of Large DNA with a Sparse Zinc Oxide Nanowire Array
Araki, N. (2010) · University of Minnesota Digital Conservancy (University of Minnesota)
Population Genomics of Heart Diseases and Risk Factors: Going Wider and Deeper
Huang, J., Tang, W., & Wang, L. (2011) · North American Journal of Medicine and Science · doi:10.7156/v4i4p249
Njølstad, P. R., Molven, A., & Groop, L. (2009) · PubMed · Cited by 1
Self-citationImpact of Candidate Genes on Obesity and Type 2 Diabetes
Långberg, E. (2011) · Figshare
Sharma, S. P., Khatiwada, B., Dhakal, B., & Timilsina, U. (2014)
Translating pharmacogenetics to primary care
Swen, J. J. (2011) · Leiden Repository (Leiden University)
Lyssenko, V., & Laakso, M. (2013)
Self-citationOn Glucose Metabolism in Patients with the m.3243A>G Mutation
Lindroos, M. M. (2013) · UTUPub (University of Turku)
Shrader, P. J., Pencina, M. J., Fox, C. S., Li, M., & Grant, R. W. (2016)
Immune Response to Helminth Infections and Its Role in Treatment for Autoimmune Disorders
Bowcutt, R., Wolff, M. J., & Loke, P. (2015) · Springer eBooks · doi:10.1007/978-1-4939-2911-5_8
Finnström, H. (2015) · Työväentutkimus Vuosikirja
Cui, W., Zhang, C., & Song, Y. (2009) · North American Journal of Medicine and Science · doi:10.7156/v2i1p006
Clinical Risk Factors, DNA Variants, and the Development of Type 2 Diabetes
Schinner, S. (2009) · Yearbook of Endocrinology · doi:10.1016/s0084-3741(09)79282-0
Genomics of Type 2 Diabetes - Review
Deepak, S., & Marita, R. (2015)
Gim, J., Kim, W., Kwak, S. H., Park, K. S., & Won, S. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/041335
Wang, S., Hu, F. B., & Dupuis, J. (2016) · Springer eBooks · doi:10.1007/978-3-319-01574-3_20
From Association to Function: KCNJ11 and ABCC8
Weedon, M. N., & Light, P. E. (2016) · Springer eBooks · doi:10.1007/978-3-319-01574-3_17
López-Jaramillo, P. A., Rey, J. J., Rodríguez, Y. A., & López-López, J. (2010)
Community based diabetes prevention programme in older adults in a Greek rural area
Matte, M., Makrilakis, K., Polychronopoulos, E., Sakellaropoulos, G. C., Braoudakis, P., Fokas, S., Koukios, K., & Velonakis, E. (2016) · International Journal of Community Medicine and Public Health · doi:10.18203/2394-6040.ijcmph20161406
Genetic Variants and Risk of Diabetes
Lyssenko, V. (2016) · Elsevier eBooks · doi:10.1016/b978-0-12-801585-8.00020-8
Self-citationSchofield, C. J. (2011) · OpenGrey (Institut de l'Information Scientifique et Technique)
Effects of the Mediterranean Diet on Type 2 Diabetes' Incidence and Treatment: A Systematic Review
Kailatzi, P., Matte, M., & Tziaferi, S. (2014)
Yu, W. (2013) · TSpace (University of Toronto)
Christopher, P. J., Brinda, B., Selvaraj, S., Redleene, B. M., & Madhu, C. (2016) · Journal of Evidence Based Medicine and Healthcare · doi:10.18410/jebmh/2016/871
Social Contexts in Postsecondary Pathophysiology Textbooks: How Type 2 Diabetes is Understood
McCleave, S. (2013) · TSpace (University of Toronto)
The Role of p21-activated Protein Kinase 1 in Metabolic Homeostasis
Chiang, Y. P. (2014) · TSpace (University of Toronto)
The Role of Transcription Factor 7-Like 2 in Hepatic Glucose Metabolism
Ip, W. (2014) · TSpace (University of Toronto)
Begum, M. (2016) · Biometrics & Biostatistics International Journal · doi:10.15406/bbij.2016.04.00091
Towards a personalized assessment of pancreatic function in diabetes
Sheu, C., & Paramithiotis, E. (2017) · Expert Review of Precision Medicine and Drug Development · doi:10.1080/23808993.2017.1385391
Obesity and Type 2 Diabetes in Youths
Santoro, N., Giannini, C., & Caprio, S. (2012) · Elsevier eBooks · doi:10.1016/b978-0-12-385083-6.00006-1
Mungrue, K. (2017) · Current Research in Diabetes & Obesity Journal · doi:10.19080/crdoj.2017.02.555583
Genetics of Diabetes and Diabetic Complications
Prasad, R. B., Ahlqvist, E., & Groop, L. (2018) · Endocrinology · doi:10.1007/978-3-319-27317-4_6-1
Self-citationA novel nomogram of naïve Bayesian model for prevalence of cardiovascular disease
Kang, E. J., Kim, H. J., & Lee, J. Y. (2018) · Communications for Statistical Applications and Methods · doi:10.29220/csam.2018.25.3.297
Brzozowska, M. M., & Allen, R. (2018) · Preprints.org · doi:10.20944/preprints201806.0373.v1
Study of the association of genetic polymorphisms with the consumption of some nutrients
Baturin, A. K., Sorokina, E. Y., Keshabyants, E. E., & Kobelkova, I. V. (2018) · Russian Journal of Preventive Medicine · doi:10.17116/profmed20182106136
Al-Ghamdi, S. H., Ahmad, G., Ali, A. H. A., Bahakim, N. O., Alomran, S., Alhowikan, W., Almutairi, S., Basalem, T., & Aljuaid, F. F. (2019) · BMC Family Practice · doi:10.1186/s12875-019-1043-3
RetractedDemographic Influences and Health Disparities
Mironovici, C., Kepper, M. M., Scribner, R. A., & Chalew, S. A. (2020) · Springer eBooks · doi:10.1007/978-3-030-33286-0_13
Physiopathologie des états diabétiques
(2010) · Elsevier eBooks · doi:10.1016/b978-2-294-70868-8.50002-5
Physiopathologie des états diabétiques
(2014) · Elsevier eBooks · doi:10.1016/b978-2-294-73954-5.00002-0
Emerging biomarkers in cardiometabolic disease
Molvin, J. (2020) · Lund University Publications (Lund University)
What your genes can (and can’t) tell you about BMI and diabetes
Ng, C. D., & Weiss, J. (2021) · Biodemography and Social Biology · doi:10.1080/19485565.2020.1806032
Quantitative assessment of the variation in IGF2BP2 gene and type 2 diabetes risk
Zhou, J., ZouShicheng, H., Liu, G., & Xu, L. L. (2012)
Risk Factors and Management of Gestational Diabetes
Ji, P. C. W., Zhu, A. G. C., & Wang, N. X. (2015)
Genetic risk factors for type 2 diabetes mellitus and response to sulfonylurea treatment
Swen, J. J., Wessels, J. A. M., Assendelft, W. J., & Guchelaar, H. (2012) · European Journal of Hospital Pharmacy · doi:10.1136/ejhpharm-2012-000074.272
On the Relationship of Diabetes and Sleep Apnea: Evolution and Epigenetics
Wilson, N. R. C. (2021) · ScholarsArchive (Brigham Young University)
Prediction, diagnosis, prevention and treatment: genetic-led care of patients with diabetes
Tangjittipokin, W., Borrisut, N., & Rujirawan, P. (2021) · Expert Review of Precision Medicine and Drug Development · doi:10.1080/23808993.2021.1970526
Branched-chain amino acids and trimethylamine N-oxide as biomarkers of cardiometabolic outcomes
Flores-Guerrero, J. L. (2021) · University of Groningen research database (University of Groningen / Centre for Information Technology) · doi:10.33612/diss.192215052
Association of Adenosine Deaminase (ADA) +22 G->A (rs73598374) Genotype in Latino Adult ObesePreprint
Martiniuk, F. T., Tchou-Wong, K.-M., Chen, A., Wong, A. H., Cribb, E., Smolenski, G., Yocum, K., Barangan, A., Lo, K., Watt, M. H., et al. (2021) · medRxiv · doi:10.1101/2021.11.11.21266231
(2009) · Current Opinion in Lipidology · doi:10.1097/mol.0b013e32832a7e09
Omics: Potential Role in Early Phase Drug Development
Grallert, H., Marzi, C., Hauck, S. M., & Gieger, C. (2019) · Springer eBooks · doi:10.1007/978-3-030-11748-1_12
(2009) · Current Opinion in Obstetrics & Gynecology · doi:10.1097/gco.0b013e32832f731f
Wnt Signaling in Pancreatic Islets
Habener, J. F., & Liu, Z. (2014) · Springer eBooks · doi:10.1007/978-94-007-6686-0_13
(2009) · Current Opinion in Endocrinology Diabetes and Obesity · doi:10.1097/med.0b013e328329fcc2
Dritsa, M., Economou, M. P., Perifanis, V., Teli, Α., & Christoforidis, A. (2022) · Acta Haematologica · doi:10.1159/000523874
Groop, L., & Rosengren, A. H. (2011) · Wiley eBooks · doi:10.1002/9780470015902.a0005512.pub2
Self-citationCornelis, M. C., & Hu, F. B. (2009) · Annals of Internal Medicine · doi:10.7326/0003-4819-151-11-200912010-00023
Genetics of Type 2 Diabetes: From Candidate Genes to Genome-Wide Association Analysis
Kleinberger, J. W., Brown, K. M., Silver, K. D., & Shuldiner, A. R. (2017) · Springer eBooks · doi:10.1007/978-3-319-18741-9_10
(2010) · Circulation · doi:10.1161/cir.0b013e3181e8e9d8
Mühlenbruch, K., Jeppesen, C., Joost, H., Boeing, H., & Schulze, M. B. (2013) · PLoS ONE · doi:10.1371/annotation/65bd3a11-b821-4f10-88d2-29b69a730f21
Obesity and Type 2 Diabetes in Youths
Santoro, N., Giannini, C., & Caprio, S. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-812019-4.00004-0
Physiopathologie des états diabétiques
(2019) · Elsevier eBooks · doi:10.1016/b978-2-294-75889-8.00002-6
Genetics of Type 2 Diabetes: From Candidate Genes to Genome-Wide Association Analysis
Kleinberger, J. W., Brown, K. M., Silver, K. D., & Shuldiner, A. R. (2017) · Springer eBooks · doi:10.1007/978-3-319-20797-1_10-2
Rhee, S. Y., & Kim, Y. S. (2009) · Korean Diabetes Journal · doi:10.4093/kdj.2009.33.1.1
Genetics of Type 2 Diabetes: From Candidate Genes to Genome-Wide Association Analysis
Kleinberger, J. W., Shuldiner, A. R., & Silver, K. D. (2016) · Springer eBooks · doi:10.1007/978-3-319-20797-1_10-1
(2009) · Hipertensión y Riesgo Vascular · doi:10.1016/s1889-1837(09)70513-6
Watanabe, R. M., & Hansen, T. F. (2016) · Springer eBooks · doi:10.1007/978-3-319-01574-3_9
Metadata
- Authors
- Lyssenko, V., Jonsson, A., Almgren, P., Pulizzi, N., Isomaa, B., Tuomi, T., Berglund, G., Altshuler, D., Nilsson, P., & Groop, L.
- Affiliations
- Lund University (SE)
- University of Helsinki (FI)
- Broad Institute (US)
- Venue
- New England Journal of Medicine
- Publisher
- Massachusetts Medical Society
- Published
- 2008-11-20
- Volume
- 359
- Issue
- 21
- Pages
- 2220-2232
- ISSN
- 0028-4793 (ISSN-L)
- 1533-4406
- OpenAlex
- W2165796078
- Cited by
- 938
- References
- 30
- Citations by year
- 2026: 10 · 2025: 12 · 2024: 19 · 2023: 25 · 2022: 36 · 2021: 33 · 2020: 29 · 2019: 33 · 2018: 34 · 2017: 37 · 2016: 55 · 2015: 54 · 2014: 76 · 2013: 90 · 2012: 106
- FWCI
- 55.04
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- New England Journal of Medicine (publishedVersion)
- PubMed (publishedVersion)
- Lund University Publications (Lund University) (submittedVersion)
- Research at the University of Copenhagen (University of Copenhagen) (submittedVersion)
- Funders
- Vetenskapsrådet
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Diabetes and associated disorders
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Nutrition, Genetics, and Disease
- Keywords
- type 2 diabetes, genetic risk factors, single nucleotide polymorphisms, clinical risk factors, diabetes prediction, TCF7L2, PPARγ, FTO, KCNJ11, Notch2, WFS1, CDKAL1, IGF2BP2, SLC30A8, JAZF1, HHEX, beta-cell function, insulin secretion, insulin action, family history (medicine), body mass index, smoking, liver enzymes, gene-environment interaction
- MeSH terms
- Insulin Secretion, Adult, Transaminases, Blood Glucose, Blood Pressure, Diabetes Mellitus, Type 2, Female, Finland, Follow-Up Studies, Genotype, Humans, Insulin, Male, Middle Aged, Proteins, Risk Factors, ROC Curve, Smoking, Sweden, Body Mass Index, Logistic Models, Odds Ratio, Genetic Predisposition to Disease, Polymorphism, Single Nucleotide
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