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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.
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“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.
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“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]
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“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]
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“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]
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Citations
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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
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Qin, J., Li, Y., Cai, Z., Li, S., Zhu, J., Zhang, F., Liang, S., Zhang, W., Guan, Y., Shen, D., et al. (2012) · Nature · doi:10.1038/nature11450 · Cited by 6,762
Metabolite profiles and the risk of developing diabetes
Wang, T. J., Larson, M. G., Vasan, R. S., Cheng, S., Rhee, E. P., McCabe, E., Lewis, G. D., Fox, C. S., Jacques, P. F., Fernandez, C., et al. (2011) · Nature Medicine · doi:10.1038/nm.2307 · Cited by 3,143
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
Self-citationStatins and risk of incident diabetes: A collaborative meta-analysis of randomised statin trials
Sattar, N., Preiss, D., Murray, H. M., Welsh, P., Buckley, B. M., de Craen, A. J., Seshasai, S. R. K., McMurray, J. J., Freeman, D. J., Jukema, J. W., et al. (2010) · The Lancet · doi:10.1016/s0140-6736(09)61965-6 · Cited by 2,436
The worldwide epidemiology of type 2 diabetes mellitus—present and future perspectives
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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
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