Genotype score in addition to common risk factors for prediction of type 2 diabetes
Open accessAbstract
BACKGROUND: Multiple genetic loci have been convincingly associated with the risk of type 2 diabetes mellitus. We tested the hypothesis that knowledge of these loci allows better prediction of risk than knowledge of common phenotypic risk factors alone. METHODS: We genotyped single-nucleotide polymorphisms (SNPs) at 18 loci associated with diabetes in 2377 participants of the Framingham Offspring Study. We created a genotype score from the number of risk alleles and used logistic regression to generate C statistics indicating the extent to which the genotype score can discriminate the risk of diabetes when used alone and in addition to clinical risk factors. RESULTS: There were 255 new cases of diabetes during 28 years of follow-up. The mean (+/-SD) genotype score was 17.7+/-2.7 among subjects in whom diabetes developed and 17.1+/-2.6 among those in whom diabetes did not develop (P<0.001). The sex-adjusted odds ratio for diabetes was 1.12 per risk allele (95% confidence interval, 1.07 to 1.17). The C statistic was 0.534 without the genotype score and 0.581 with the score (P=0.01). In a model adjusted for sex and self-reported family history of diabetes, the C statistic was 0.595 without the genotype score and 0.615 with the score (P=0.11). In a model adjusted for age, sex, family history, body-mass index, fasting glucose level, systolic blood pressure, high-density lipoprotein cholesterol level, and triglyceride level, the C statistic was 0.900 without the genotype score and 0.901 with the score (P=0.49). The genotype score resulted in the appropriate risk reclassification of, at most, 4% of the subjects. CONCLUSIONS: A genotype score based on 18 risk alleles predicted new cases of diabetes in the community but provided only a slightly better prediction of risk than knowledge of common risk factors alone.
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Models to combine multiple markers into a cumulative risk score are often flawed and are usually no better than traditional risk factors such as family history, demographics and non-genetic clinical phenotypes.
“models to combine multiple markers into a cumulative risk score are often flawed and are usually no better than traditional risk factors such as family history, demographics and non-genetic clinical phenotypes.”
From Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (2015) · cites this paper as [55]
The Discussion reports that this 18-allele genotype score did not substantially improve type 2 diabetes prediction beyond common phenotypic risk factors measured in adulthood. It does not establish that cumulative risk-score models are often flawed or usually perform no better across diseases or settings. (LLM, conf=0.88)
Experimental — accuracy not yet measuredThe risk alleles in these loci all have relatively small effects and do not significantly enhance our ability to predict the risk of type 2 diabetes.
“However, the risk alleles in these loci all have relatively small effects and do not significantly enhance our ability to predict the risk of type 2 diabetes (396,397).”
From Guidelines and Recommendations for Laboratory Analysis in the Diagnosis and Management of Diabetes Mellitus (2023) · cites this paper as [397]
The Discussion reports small individual per-allele effects and says the 18-allele score did not substantially improve prediction beyond common phenotypic risk factors measured in adulthood. The claim omits that condition: the paper also reports modest discriminatory ability when only factors known in youth were considered. (LLM, conf=0.86)
Experimental — accuracy not yet measuredThe risk alleles at these loci have small effects (odds ratios 1.1-1.3) and do not substantially improve type 2 diabetes risk prediction.
“The risk alleles in these loci all have relatively small effects (odds ratios of 1. 1 to 1.3), however, and do not significantly enhance our ability to predict the risk of type 2 diabetes (274).”
From Guidelines and recommendations for laboratory analysis in the diagnosis and management of diabetes mellitus (2011) · cites this paper as [274]
Together these genes do not explain much of the genetic basis of type 2 diabetes; genotype risk scores only slightly improve prediction compared to common clinical risk factors.
“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 [22]
Known diabetes polymorphisms add modestly to risk assessment, being associated with 5% to 37% increases in relative risk.
“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 [27]
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Demler, O., Pencina, M. J., & D’Agostino, R. B. (2012) · Statistics in Medicine · doi:10.1002/sim.5328 · Cited by 313 · see citing works
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 · see citing works
“Few prospective studies have investigated the value of multiple genetic variants in type 2 diabetes prediction (23, 55, 68, 79, 89, 92, 94).”
BackgroundInfluentialTalmud, P. J., Hingorani, A. D., Cooper, J. A., Marmot, M. G., Brunner, E. J., Meena Kumari, B., Mika Kivimaki, B., & Humphries, S. E. (2010) · BMJ · doi:10.1136/bmj.b4838 · Cited by 294 · see citing works
“rs3792267 was associated with significant differences in concentrations of total cholesterol and low density lipoprotein cholesterol (P=0.01). We found no other associations between genotypes and phenotypes included in either of the non-genetic risk models at phase 3 or phase 7 (web tables D-J). The point estimates of…”
ResultInfluentialTrépo, E., Romeo, S., Zucman‐Rossi, J., & Nahon, P. (2016) · Journal of Hepatology · doi:10.1016/j.jhep.2016.03.011 · Cited by 293 · see citing works
Oxidative stress, inflammation, endothelial dysfunction and incidence of type 2 diabetes
Odegaard, A. O., Jacobs, D. R., Sanchez, O. A., Goff, D. C., Reiner, A. P., & Gross, M. D. (2016) · Cardiovascular Diabetology · doi:10.1186/s12933-016-0369-6 · Cited by 286 · see citing works
“Lastly, we examined whether these different biomarkers improved the prediction of diabetes relative to a common risk score utilized in research and clinical settings [33].”
BackgroundGenetic Predisposition, Clinical Risk Factor Burden, and Lifetime Risk of Atrial Fibrillation
Weng, L.-C. C., Preis, S. R., Hulme, O. L., Larson, M. G., Choi, S. H., Wang, B., Trinquart, L., McManus, D. D., Stærk, L., Lin, H., et al. (2017) · Circulation · doi:10.1161/circulationaha.117.031431 · Cited by 271 · see citing works
Genetic Risk Scores for Diabetes Diagnosis and Precision Medicine
Udler, M. S., McCarthy, M. I., Florez, J. C., & Mahajan, A. (2019) · Endocrine Reviews · doi:10.1210/er.2019-00088 · Cited by 302 · see citing works
“constructed an rsPS from the 16-18 T2D risk variant s k own at the time [30-32] and compared its predictive performance to that of clin i al T2D-risk factors.”
BackgroundResultSelf-citationHolloway, J. W., Yang, I. A., & Holgate, S. T. (2010) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2009.10.071 · Cited by 271 · see citing works
“Recent analyses of the power of genetic testing to predict the risk of type II diabetes (for which many more genetic risk factors have been identified through genome-wide approaches than for allergic disease at this stage) demonstrate that the inclusion of common genetic variants has only a small effect on the ability…”
BackgroundKrishnan, E., Pandya, B. J., Chung, L. S., Hariri, A., & Dabbous, O. H. (2012) · American Journal of Epidemiology · doi:10.1093/aje/kws002 · Cited by 267 · see citing works
Gene-Lifestyle Interaction and Type 2 Diabetes: The EPIC InterAct Case-Cohort Study
Langenberg, C., Sharp, S. J., Franks, P. W., Scott, R. A., Deloukas, P., Forouhi, N. G., Froguel, P., Groop, L., Hansen, T. F., Palla, L., et al. (2014) · PLoS Medicine · doi:10.1371/journal.pmed.1001647 · Cited by 237 · see citing works
“These studies have shown that information on common genetic variants associated with the risk of T2D offers little improvement for risk prediction over and above established T2D risk factors [28–31].”
Integrating Pathway Analysis and Genetics of Gene Expression for Genome-wide Association Studies
Zhong, H., Yang, X., Kaplan, L. M., Molony, C. M., & Schadt, E. E. (2010) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2010.02.020 · Cited by 234 · see citing works
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“Clinical risk models that incorporate fasting glucose level or other measures of insulin sensitivity have shown the least discriminative improvement with the addition of genetic information (19, 20).”
BackgroundThanassoulis, G., Peloso, G. M., Pencina, M. J., Hoffmann, U., Fox, C. S., Cupples, L. A., Levy, D., D'Agostino, R. B., Hwang, S.-J., & O'Donnell, C. J. (2012) · Circulation: Cardiovascular Genetics · doi:10.1161/circgenetics.111.961342 · Cited by 211 · see citing works
Self-citationInduced pluripotent stem cells: at the heart of cardiovascular precision medicine
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“Furthermore, combining these variants does not enable prediction of type 2 diabetes [144, 145] or obesity [45, 146].”
BackgroundReview of methods for detecting glycemic disorders
Bergman, M., Abdul‐Ghani, M. A., DeFronzo, R. A., Manco, M., Sesti, G., Fiorentino, T. V., Ceriello, A., Rhee, M. K., Phillips, L. S., Chung, S. T., et al. (2020) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2020.108233 · Cited by 202 · see citing works
“6 higher odds of developing T2D than did those with rsPG ≤ 15 (~25% of the cohort) [116].”
BackgroundPrato, S. D. (2009) · Diabetic Medicine · doi:10.1111/j.1464-5491.2009.02847.x · Cited by 198 · see citing works
Development of a clinical polygenic risk score assay and reporting workflow
Hao, L., Kraft, P., Berriz, G. F., Hynes, E. D., Koch, C., Kumar, P. K. V., Parpattedar, S. S., Steeves, M. A., Yu, W., Antwi, A. A., et al. (2022) · Nature Medicine · doi:10.1038/s41591-022-01767-6 · Cited by 180 · see citing works
Hivert, M.-F., Jablonski, K. A., Perreault, L., Saxena, R., McAteer, J. B., Franks, P. W., Hamman, R. F., Kahn, S. E., Haffner, S., the DIAGRAM Consortium, et al. (2011) · Diabetes · doi:10.2337/db10-1119 · Cited by 191 · see citing works
“This is in accordance with previous reports from general population cohorts showing that even if a GRS is associated with diabetes incidence, it adds little to the common known clinical risk factors (8,9).”
MethodResultSelf-citationCardiovascular Disease Risk Factors, Type 2 Diabetes Mellitus, and the Framingham Heart Study
Fox, C. S. (2010) · Trends in Cardiovascular Medicine · doi:10.1016/j.tcm.2010.08.001 · Cited by 191 · see citing works
“We found that knowledge of an individual's genotype did not add to information above and beyond the simple clinical model that was previously developed in the FHS (Meigs et al. 2008).”
Self-citationHan, B., Diogo, D., Eyre, S., Källberg, H., Zhernakova, A., Bowes, J., Padyukov, L., Okada, Y., González-Gay, M. Á., Rantapää‐Dahlqvist, S., et al. (2014) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2014.02.013 · Cited by 180 · see citing works
Genetics of type 2 diabetes: pathophysiologic and clinical relevance
Herder, C., & Roden, M. (2010) · European Journal of Clinical Investigation · doi:10.1111/j.1365-2362.2010.02454.x · Cited by 174 · see citing works
de Miguel-Yanes, J. M., Shrader, P. J., Pencina, M. J., Fox, C. S., Manning, A. K., Grant, R. W., Dupuis, J., Florez, J. C., D’Agostino, R. B., Cupples, L. A., et al. (2010) · Diabetes Care · doi:10.2337/dc10-1265 · Cited by 172 · see citing works
Self-citationGe, T., Irvin, M. R., Patki, A., Srinivasasainagendra, V., Lin, K., Tiwari, H. K., Armstrong, N. D., Benoit, B., Chen, C., Choi, K. W., et al. (2022) · Genome Medicine · doi:10.1186/s13073-022-01074-2 · Cited by 170 · see citing works
Self-citationMajithia, A. R., Flannick, J. A., Shahinian, P., Guo, M. H., Bray, M., Fontanillas, P., Gabriel, S., NHGRI JHS/FHS Allelic Spectrum Project, Rosen, E. D., Altshuler, D., et al. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1410428111 · Cited by 171 · see citing works
“6-fold increased risk in individuals in high-score versus low-score groups (12); our data suggests that functional variants in PPARG may have effects larger than fivefold.”
BackgroundSelf-citationThirty-One Novel Biomarkers as Predictors for Clinically Incident Diabetes
Salomaa, V. V., Havulinna, A. S., Saarela, O., Zeller, T., Jousilahti, P., Jula, A. M., Münzel, T. F., Aromaa, A., Evans, A., Kuulasmaa, K., et al. (2010) · PLoS ONE · doi:10.1371/journal.pone.0010100 · Cited by 169 · see citing works
“Several risk equations based on lifestyle factors [8], classic clinical risk factors [9] and genetic factors [10], [11] have been proposed and tested for the prediction of diabetes.”
BackgroundBiomarkers for the Prediction of Type 2 Diabetes and Cardiovascular Disease
Herder, C., Karakas, M., & Köenig, W. (2011) · Clinical Pharmacology & Therapeutics · doi:10.1038/clpt.2011.93 · Cited by 168 · see citing works
Ahlqvist, E., Ahluwalia, T. S., & Groop, L. (2011) · Clinical Chemistry · doi:10.1373/clinchem.2010.157016 · Cited by 166 · see citing works
Forouhi, N. G., & Wareham, N. J. (2010) · Medicine · doi:10.1016/j.mpmed.2010.08.007 · Cited by 166 · see citing works
Genetic Screening for the Risk of Type 2 Diabetes
Lyssenko, V., & Laakso, M. (2013) · Diabetes Care · doi:10.2337/dcs13-2009 · Cited by 165 · see citing works
“901 in the Framingham Offspring study (20), and from 0.”
BackgroundGenetics of type 2 diabetes: the GWAS era and future perspectives [Review]
Imamura, M., & Maeda, S. (2011) · Endocrine Journal · doi:10.1507/endocrj.ej11-0113 · Cited by 164 · see citing works
Polygenic Type 2 Diabetes Prediction at the Limit of Common Variant Detection
Vassy, J. L., Hivert, M., Porneala, B., Dauriz, M., Florez, J. C., Dupuis, J., Siscovick, D. S., Fornage, M., Rasmussen‐Torvik, L. J., Bouchard, C., et al. (2014) · Diabetes · doi:10.2337/db13-1663 · Cited by 153 · see citing works
“A 17-SNP GRSt was published only in FOS (4); 38-SNPs were used in CARDIA (9) and 40-SNPs in FOS (16).”
BackgroundMethodInfluentialSelf-citationGene-Environment and Gene-Treatment Interactions in Type 2 Diabetes
Franks, P. W., Pearson, E. R., & Florez, J. C. (2013) · Diabetes Care · doi:10.2337/dc12-2211 · Cited by 154 · see citing works
Self-citationSacks, D. B., Arnold, M. A., Bakris, G. L., Bruns, D. E., Horvath, A. R., Lernmark, Å., Metzger, B. E., Nathan, D. M., & Kirkman, M. S. (2023) · Clinical Chemistry · doi:10.1093/clinchem/hvad080 · Cited by 157 · see citing works
“3), however, and do not significantly enhance our ability to predict the risk of type 2 diabetes (274).”
Opportunities and Challenges Associated with Clinical Diagnostic Genome Sequencing
Schrijver, I., Aziz, N., Farkas, D. H., Furtado, M. R., Gonzalez, A. F., Greiner, T. C., Grody, W. W., Hambuch, T. M., Kalman, L. V., Kant, J. A., et al. (2012) · Journal of Molecular Diagnostics · doi:10.1016/j.jmoldx.2012.04.006 · Cited by 148 · see citing works
Genomic insights into the causes of type 2 diabetes
Langenberg, C., & Lotta, L. A. (2018) · The Lancet · doi:10.1016/s0140-6736(18)31132-2 · Cited by 142 · see citing works
Zhang, X., Wang, Y., Hao, F., Zhou, X., Han, X., Tang, H., & Ji, L. (2009) · Journal of Proteome Research · doi:10.1021/pr900524z · Cited by 147 · see citing works
The emerging genetics of type 2 diabetes
Bonnefond, A., Froguel, P., & Vaxillaire, M. (2010) · Trends in Molecular Medicine · doi:10.1016/j.molmed.2010.06.004 · Cited by 146 · see citing works
Genome-based prediction of common diseases: methodological considerations for future research
Janssens, A. C. J. W., & van Duijn, C. M. (2009) · Genome Medicine · doi:10.1186/gm20 · Cited by 146 · see citing works
“First, they may calculate risk allele scores or genotype scores obtained by counting the number of risk alleles across all variants [10,11,14-23].”
BackgroundMethodInfluentialGenetic Signatures of Exceptional Longevity in Humans
Sebastiani, P., Solovieff, N., Puca, A., Hartley, S. W., Melista, E., Andersen, S., Dworkis, D. A., Wilk, J. B., Myers, R. H., Steinberg, M. H., et al. (2010) · Science · doi:10.1126/science.1190532 · Cited by 141 · see citing works
“The specificity of our classification rule is 60–61% in the independent sets and is comparable to other genetic studies of complex traits [76,77,78].”
ResultRetractedPersonalized Genetic Risk Counseling to Motivate Diabetes Prevention
Grant, R. W., OʼBrien, K., Waxler, J. L., Vassy, J. L., Delahanty, L. M., Bissett, L., Green, R. C., Stember, K. G., Guiducci, C., Park, E. R., et al. (2012) · Diabetes Care · doi:10.2337/dc12-0884 · Cited by 142 · see citing works
“In FOS, 17.9% patients in the top quartile of genetic risk score distribution (.38) developed diabetes (46% relative increase compared with middle two average quartiles), whereas 9.9% in the bottom quartile (scores ,34) developed diabetes (18% relative decrease compared with “average” risk).”
MethodInfluentialSelf-citationSchulze, M. B., Weikert, C., Pischon, T., Bergmann, M. M., Al‐Hasani, H., Schleicher, E. D., Fritsche, A., Häring, H., Boeing, H., & Joost, H. (2009) · Diabetes Care · doi:10.2337/dc09-0197 · Cited by 141 · see citing works
Polygenic Risk, Rapid Childhood Growth, and the Development of Obesity
Belsky, D. W., Moffitt, T. E., Houts, R., Bennett, G. G., Biddle, A. K., Blumenthal, J. A., Evans, J. P., Harrington, H., Sugden, K., Williams, B. S., et al. (2012) · Archives of Pediatrics and Adolescent Medicine · doi:10.1001/archpediatrics.2012.131 · Cited by 135 · see citing works
Li, Y., Ley, S. H., Tobias, D. K., Chiuve, S. E., VanderWeele, T. J., Rich-Edwards, J. W., Curhan, G. C., Willett, W. C., Manson, J. E., Hu, F. B., et al. (2015) · BMJ · doi:10.1136/bmj.h3672 · Cited by 134 · see citing works
Pathomechanisms of Type 2 Diabetes Genes
Staiger, H., Machicao, F. E., Fritsche, A., & Häring, H.-U. (2009) · Endocrine Reviews · doi:10.1210/er.2009-0017 · Cited by 133 · see citing works
“pressure, and plasma triglycerides) alone (34, 388, 389),”
Validity of polygenic risk scores: are we measuring what we think we are?
Janssens, A. C. J. W. (2019) · Human Molecular Genetics · doi:10.1093/hmg/ddz205 · Cited by 129 · see citing works
“Examples include baseline glucose level to predict type 2 diabetes later in life (40), social impairments to predict psychosis (41), childhood obesity to predict adult obesity (42) and education at younger ages to predict highest educational attainment (43).”
BackgroundMetabolomics in diabetes research
Friedrich, N. (2012) · Journal of Endocrinology · doi:10.1530/joe-12-0120 · Cited by 132 · see citing works
Pancreatitis-Diabetes-Pancreatic Cancer
Andersen, D. K., Andrén‐Sandberg, Å., Duell, E. J., Goggins, M., Korc, M., Petersen, G. M., Smith, J. P., & Whitcomb, D. C. (2013) · Pancreas · doi:10.1097/mpa.0b013e3182a9ad9d · Cited by 132 · see citing works
“Research to date suggests that susceptibility to DM is explained by a polygenic risk model, with each genetic variant having a small effect, and none of which significantly improve risk assessment over common risk factors such as age, sex, family history, BMI, and clinical measures [51].”
Tanaka, F., Yamamoto, K., Suzuki, S., Inoue, H., Tsurumaru, M., Kajiyama, Y., Kato, H., Igaki, H., Furuta, K., Fujita, H., et al. (2010) · Gut · doi:10.1136/gut.2009.205724 · Cited by 128 · see citing works
“To answer these questions, it is necessary to conduct a prospective study in genetically at-risk populations with or without drinking and/or smoking habits, as recently performed for type 2 diabetes.(21) 22 In summary, this study disclosed a significant genetic-environmental interaction, with very large ORs, associate…”
BackgroundRosenzweig, J. L., Bakris, G. L., Berglund, L., Hivert, M., Horton, E. S., Kalyani, R. R., Murad, M. H., & Vergès, B. L. (2019) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2019-01338 · Cited by 131 · see citing works
Pathophysiology of Type 2 Diabetes in Children and Adolescents
Valaiyapathi, B., Gower, B. A., & Ashraf, A. P. (2019) · Current Diabetes Reviews · doi:10.2174/1573399814666180608074510 · Cited by 151 · see citing works
Genome-wide association studies and type 2 diabetes
Wheeler, E., & Barroso, I. (2011) · Briefings in Functional Genomics · doi:10.1093/bfgp/elr008 · Cited by 119 · see citing works
Risk Factors and Management of Gestational Diabetes
Chen, P., Wang, S., Ji, J., Ge, A., Chen, C., Zhu, Y., Xie, N., & Wang, Y. (2014) · Cell Biochemistry and Biophysics · doi:10.1007/s12013-014-0248-2 · Cited by 119 · see citing works
“This is a very impressive area of active research, with studies focussing on using genotypic risk scores in accurately predicting T2DM in the population of GDM women [17, 18].”
BackgroundLin, X., Song, K. S., Lim, N., Yuan, X., Johnson, T., Abderrahmani, A., Vollenweider, P., Stirnadel-Farrant, H. A., Sundseth, S. S., Lai, E., et al. (2009) · Diabetologia · doi:10.1007/s00125-008-1254-y · Cited by 118 · see citing works
“The present population-based cross-sectional study is in line with two very recently published prospective studies [19, 20].”
BackgroundMethodResultInfluentialIntegrating Genetics and Social Science: Genetic Risk Scores
Belsky, D. W., & Israel, S. (2014) · Biodemography and Social Biology · doi:10.1080/19485565.2014.946591 · Cited by 114 · see citing works
“2012), “SNP score” (Vrieze, McGue, and Iacono 2012), “genotype score” (Meigs et al. 2008), and “genetic prediction score” (Zhao et al.”
The Current and Future Use of Ridge Regression for Prediction in Quantitative Genetics
de Vlaming, R., & Groenen, P. J. F. (2015) · BioMed Research International · doi:10.1155/2015/143712 · Cited by 113 · see citing works
Genetics of Type 2 Diabetes: Insights into the Pathogenesis and Its Clinical Application
Sun, X., Yu, W., & Hu, C. (2014) · BioMed Research International · doi:10.1155/2014/926713 · Cited by 111 · see citing works
Patel, C. J., Chen, R., Kodama, K., Ioannidis, J. P. A., & Butte, A. J. (2013) · Human Genetics · doi:10.1007/s00439-012-1258-z · Cited by 109 · see citing works
Genetics of Type 2 Diabetes: Implications from Large-Scale Studies
DeForest, N., & Majithia, A. R. (2022) · Current Diabetes Reports · doi:10.1007/s11892-022-01462-3 · Cited by 112 · see citing works
Cumulative association of 22 genetic variants with seropositive rheumatoid arthritis risk
Karlson, E. W., Chibnik, L. B., Kraft, P., Cui, J., Keenan, B. T., Ding, B., Raychaudhuri, S., Klareskog, L., Alfredsson, L. S., & Plenge, R. M. (2010) · Annals of the Rheumatic Diseases · doi:10.1136/ard.2009.120170 · Cited by 108 · see citing works
Progress in the genetics of common obesity and type 2 diabetes
Vimaleswaran, K. S., & Loos, R. J. F. (2010) · Expert Reviews in Molecular Medicine · doi:10.1017/s1462399410001389 · Cited by 104 · see citing works
Blackman, S. M., Hsu, S., Ritter, S. E., Naughton, K., Wright, F. A., Drumm, M. L., Knowles, M. R., & Cutting, G. R. (2009) · Diabetologia · doi:10.1007/s00125-009-1436-2 · Cited by 123 · see citing works
“[38], Lyssenko et al.”
Practical challenges in integrating genomic data into the electronic health record
Kho, A. N., Rasmussen, L. V., Connolly, J. J., Peissig, P. L., Starren, J., Hakonarson, H., & Hayes, M. G. (2013) · Genetics in Medicine · doi:10.1038/gim.2013.131 · Cited by 108 · see citing works
“16,17 However, family history may be infrequently or incompletely captured in EHRs.”
BackgroundAdvanced research on risk factors of type 2 diabetes
Bi, Y., Wang, T., Xu, M., Xu, Y., Li, M., Lu, J., Zhu, X., & Ning, G. (2012) · Diabetes/Metabolism Research and Reviews · doi:10.1002/dmrr.2352 · Cited by 107 · see citing works
Katz, J., Gong, Y., Salmasinia, D., Hou, W., Burkley, B., Ferreira, P., Casanova, O. B., Langaee, T. Y., & Moreb, J. S. (2011) · International Journal of Oral and Maxillofacial Surgery · doi:10.1016/j.ijom.2011.02.002 · Cited by 105 · see citing works
- Authors
- Meigs, J. B., Shrader, P., Sullivan, L. M., McAteer, J. B., Fox, C. S., Dupuis, J., Manning, A. K., Florez, J. C., Wilson, P. W. F., D'Agostino, R. B., & Cupples, L. A.
- Affiliations
- Massachusetts General Hospital (US)
- Boston University (US)
- Broad Institute (US)
- Brigham and Women's Hospital (US)
- ORCID
- Venue
- New England Journal of Medicine
- Publisher
- Massachusetts Medical Society
- Published
- 2008-11-20
- Volume
- 359
- Issue
- 21
- Pages
- 2208-2219
- ISSN
- 0028-4793 (ISSN-L)
- 1533-4406
- PMC
- PMC2746946
- OpenAlex
- W2101359494
- Cited by
- 759
- References
- 41
- Citations by year
- 2026: 5 · 2025: 14 · 2024: 13 · 2023: 20 · 2022: 20 · 2021: 26 · 2020: 16 · 2019: 37 · 2018: 37 · 2017: 42 · 2016: 47 · 2015: 41 · 2014: 58 · 2013: 72 · 2012: 85
- FWCI
- 42.93
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- New England Journal of Medicine (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (n01hc25195-41-0-1)
- National Heart, Lung, and Blood Institute (N01‐HC25195, N01-HC-025195)
- National Institute of Diabetes and Digestive and Kidney Diseases (K23 DK65978, R01DK078616, K24DK080140, K23DK065978)
- National Center for Research Resources (1S10 RR163736-01A1)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Nutrition, Genetics, and Disease
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes, Cardiovascular Risks, and Lipoproteins
- Keywords
- type 2 diabetes, genotype score, genetic susceptibility loci, single nucleotide polymorphisms, diabetes risk prediction, Framingham Offspring Study, family history of diabetes, risk alleles
- MeSH terms
- Adult, Diabetes Mellitus, Type 2, Female, Follow-Up Studies, Gene Frequency, Genotype, Humans, Male, Middle Aged, Phenotype, Risk Factors, Logistic Models, Genetic Predisposition to Disease, Polymorphism, Single Nucleotide
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