. 2015 May;64(5):1830-40.

doi: 10.2337/db14-1504. Epub 2014 Dec 4.

Sixty-five common genetic variants and prediction of type 2 diabetes

Philippa J Talmud¹, Jackie A Cooper², Richard W Morris³, Frank Dudbridge⁴, Tina Shah⁵, Jorgen Engmann⁵, Caroline Dale⁴, Jon White⁶, Stela McLachlan⁷, Delilah Zabaneh⁶, Andrew Wong⁸, Ken K Ong⁹, Tom Gaunt¹⁰, Michael V Holmes¹¹, Debbie A Lawlor¹⁰, Marcus Richards⁸, Rebecca Hardy⁸, Diana Kuh⁸, Nicholas Wareham¹², Claudia Langenberg¹³, Yoav Ben-Shlomo¹⁴, S Goya Wannamethee³, Mark W J Strachan¹⁵, Meena Kumari⁵, John C Whittaker¹⁶, Fotios Drenos¹⁷, Mika Kivimaki⁵, Aroon D Hingorani¹⁸, Jacqueline F Price⁷, Steve E Humphries²; UCLEB Consortium

Collaborators, Affiliations

Collaborators

UCLEB Consortium:
Philippa J Talmud, Steve E Humphries, Jackie A Cooper, Richard Morris, Peter Whincup, Goya Wannamethee, Barbara Jefferis, Caroline Dale, Antoinette Amuzu, Tom Gaunt, Teri-Louise Davies, Debbie A Lawlor, Ian N Day, Andrew Wong, Ken Ong, Marcus Richards, Rebecca Hardy, Diana Kuh, Mika Kivimaki, Meena Kumari, Claudia Langenberg, Meena Kumari, Christine Power, Elina Hypponen, Yoav Ben-Shlomo, Ian N Day, Stela McLachlan, Mark W J Strachan, Jacqueline Price, Tina Shah, Jordan Engmann, Jon White, Claudia Giambartolomei, Delilah Zabaneh, Michael V Holmes, Daniel I Swerdlow, Reecha Sofat, Mark Caulfield, Shah Ebrahim, Nicholas Wareham, Vincent Plagnol, Frank Dudbridge, John C Whittaker, Juan P Casas, Aroon D Hingorani

Affiliations

¹ Centre for Cardiovascular Genetics, Institute of Cardiovascular Science, University College London, London, U.K. p.talmud@ucl.ac.uk.
² Centre for Cardiovascular Genetics, Institute of Cardiovascular Science, University College London, London, U.K.
³ Department of Primary Care and Population Health, University College London, Royal Free Campus, London, U.K.
⁴ Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, U.K.
⁵ Department of Epidemiology and Public Health, University College London Institute of Epidemiology and Health Care, University College London, London, U.K.
⁶ University College London Genetics Institute, Department of Genetics, Environment and Evolution, London, U.K.
⁷ Centre for Population Health Sciences, University of Edinburgh, Edinburgh, U.K.
⁸ Medical Research Council Unit for Lifelong Health and Ageing at University College London, London, U.K.
⁹ Medical Research Council Unit for Lifelong Health and Ageing at University College London, London, U.K. Medical Research Council Epidemiology Unit, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, U.K.
¹⁰ School of Social and Community Medicine, University of Bristol, Bristol, U.K. Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, U.K.
¹¹ Department of Epidemiology and Public Health, University College London Institute of Epidemiology and Health Care, University College London, London, U.K. Division of Transplant Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA.
¹² Medical Research Council Epidemiology Unit, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, U.K.
¹³ Department of Epidemiology and Public Health, University College London Institute of Epidemiology and Health Care, University College London, London, U.K. Medical Research Council Epidemiology Unit, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, U.K.
¹⁴ Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, U.K.
¹⁵ Metabolic Unit, Western General Hospital, Edinburgh, U.K.
¹⁶ Genetics Division, Research and Development, GlaxoSmithKline, Harlow, U.K.
¹⁷ Centre for Cardiovascular Genetics, Institute of Cardiovascular Science, University College London, London, U.K. Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, U.K.
¹⁸ Department of Epidemiology and Public Health, University College London Institute of Epidemiology and Health Care, University College London, London, U.K. Centre for Clinical Pharmacology, University College London, London, U.K.

PMID: 25475436
PMCID: PMC4407866
DOI: 10.2337/db14-1504

Sixty-five common genetic variants and prediction of type 2 diabetes

Philippa J Talmud et al. Diabetes. 2015 May.

. 2015 May;64(5):1830-40.

doi: 10.2337/db14-1504. Epub 2014 Dec 4.

Authors

Collaborators

UCLEB Consortium:
Philippa J Talmud, Steve E Humphries, Jackie A Cooper, Richard Morris, Peter Whincup, Goya Wannamethee, Barbara Jefferis, Caroline Dale, Antoinette Amuzu, Tom Gaunt, Teri-Louise Davies, Debbie A Lawlor, Ian N Day, Andrew Wong, Ken Ong, Marcus Richards, Rebecca Hardy, Diana Kuh, Mika Kivimaki, Meena Kumari, Claudia Langenberg, Meena Kumari, Christine Power, Elina Hypponen, Yoav Ben-Shlomo, Ian N Day, Stela McLachlan, Mark W J Strachan, Jacqueline Price, Tina Shah, Jordan Engmann, Jon White, Claudia Giambartolomei, Delilah Zabaneh, Michael V Holmes, Daniel I Swerdlow, Reecha Sofat, Mark Caulfield, Shah Ebrahim, Nicholas Wareham, Vincent Plagnol, Frank Dudbridge, John C Whittaker, Juan P Casas, Aroon D Hingorani

Affiliations

¹ Centre for Cardiovascular Genetics, Institute of Cardiovascular Science, University College London, London, U.K. p.talmud@ucl.ac.uk.
² Centre for Cardiovascular Genetics, Institute of Cardiovascular Science, University College London, London, U.K.
³ Department of Primary Care and Population Health, University College London, Royal Free Campus, London, U.K.
⁴ Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, U.K.
⁵ Department of Epidemiology and Public Health, University College London Institute of Epidemiology and Health Care, University College London, London, U.K.
⁶ University College London Genetics Institute, Department of Genetics, Environment and Evolution, London, U.K.
⁷ Centre for Population Health Sciences, University of Edinburgh, Edinburgh, U.K.
⁸ Medical Research Council Unit for Lifelong Health and Ageing at University College London, London, U.K.
⁹ Medical Research Council Unit for Lifelong Health and Ageing at University College London, London, U.K. Medical Research Council Epidemiology Unit, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, U.K.
¹⁰ School of Social and Community Medicine, University of Bristol, Bristol, U.K. Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, U.K.
¹¹ Department of Epidemiology and Public Health, University College London Institute of Epidemiology and Health Care, University College London, London, U.K. Division of Transplant Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA.
¹² Medical Research Council Epidemiology Unit, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, U.K.
¹³ Department of Epidemiology and Public Health, University College London Institute of Epidemiology and Health Care, University College London, London, U.K. Medical Research Council Epidemiology Unit, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, U.K.
¹⁴ Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, U.K.
¹⁵ Metabolic Unit, Western General Hospital, Edinburgh, U.K.
¹⁶ Genetics Division, Research and Development, GlaxoSmithKline, Harlow, U.K.
¹⁷ Centre for Cardiovascular Genetics, Institute of Cardiovascular Science, University College London, London, U.K. Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, U.K.
¹⁸ Department of Epidemiology and Public Health, University College London Institute of Epidemiology and Health Care, University College London, London, U.K. Centre for Clinical Pharmacology, University College London, London, U.K.

PMID: 25475436
PMCID: PMC4407866
DOI: 10.2337/db14-1504

Abstract

We developed a 65 type 2 diabetes (T2D) variant-weighted gene score to examine the impact on T2D risk assessment in a U.K.-based consortium of prospective studies, with subjects initially free from T2D (N = 13,294; 37.3% women; mean age 58.5 [38-99] years). We compared the performance of the gene score with the phenotypically derived Framingham Offspring Study T2D risk model and then the two in combination. Over the median 10 years of follow-up, 804 participants developed T2D. The odds ratio for T2D (top vs. bottom quintiles of gene score) was 2.70 (95% CI 2.12-3.43). With a 10% false-positive rate, the genetic score alone detected 19.9% incident cases, the Framingham risk model 30.7%, and together 37.3%. The respective area under the receiver operator characteristic curves were 0.60 (95% CI 0.58-0.62), 0.75 (95% CI 0.73 to 0.77), and 0.76 (95% CI 0.75 to 0.78). The combined risk score net reclassification improvement (NRI) was 8.1% (5.0 to 11.2; P = 3.31 × 10(-7)). While BMI stratification into tertiles influenced the NRI (BMI ≤24.5 kg/m(2), 27.6% [95% CI 17.7-37.5], P = 4.82 × 10(-8); 24.5-27.5 kg/m(2), 11.6% [95% CI 5.8-17.4], P = 9.88 × 10(-5); >27.5 kg/m(2), 2.6% [95% CI -1.4 to 6.6], P = 0.20), age categories did not. The addition of the gene score to a phenotypic risk model leads to a potentially clinically important improvement in discrimination of incident T2D.

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Figures

**Figure 1**
Forest plot showing the 10% detection rate for all seven studies for (A) the Framingham phenotypic score alone and (B) Framingham T2D score plus the externally weighted gene score. DR10, 10% detection rate.

**Figure 2**
The distribution of the 65-SNP gene score, weighted by the external published β-values in the combined studies. Superimposed are the log ORs for T2D.

**Figure 3**
The receiver operating curve for the seven combined studies.

**Figure 4**
Scatter plot showing the correlation between Framingham T2D score and Framingham T2D score plus the externally weighted gene score. A: Those who remained T2D free. B: Incident T2D cases.

See this image and copyright information in PMC

Comment in

Advances in risk prediction of type 2 diabetes: integrating genetic scores with Framingham risk models.
Keating BJ. Keating BJ. Diabetes. 2015 May;64(5):1495-7. doi: 10.2337/db15-0033. Diabetes. 2015. PMID: 25908872 Free PMC article. No abstract available.

References

1. Færch K, Witte DR, Tabák AG, et al. . Trajectories of cardiometabolic risk factors before diagnosis of three subtypes of type 2 diabetes: a post-hoc analysis of the longitudinal Whitehall II cohort study. Lancet Diabetes Endocrinol 2013;1:43–51 - PubMed
1. Sinharoy K, Mandal L, Chakrabarti S, Paul UK, Bandyopadhyay R, Basu AK. A study on clinical and biochemical profile of low body weight type 2 diabetes mellitus. J Indian Med Assoc 2008;106:747–750 - PubMed
1. Noble D, Mathur R, Dent T, Meads C, Greenhalgh T. Risk models and scores for type 2 diabetes: systematic review. BMJ 2011;343:d7163. - PMC - PubMed
1. Lyssenko V, Laakso M. Genetic screening for the risk of type 2 diabetes: worthless or valuable? Diabetes Care 2013;36(Suppl. 2):S120–S126 - PMC - PubMed
1. Wilson PW, Meigs JB, Sullivan L, Fox CS, Nathan DM, D’Agostino RB Sr. Prediction of incident diabetes mellitus in middle-aged adults: the Framingham Offspring Study. Arch Intern Med 2007;167:1068–1074 - PubMed

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Sixty-five common genetic variants and prediction of type 2 diabetes

Collaborators

Affiliations

Sixty-five common genetic variants and prediction of type 2 diabetes

Authors

Collaborators

Affiliations

Abstract

Figures

Comment in

References

Publication types

MeSH terms

Grants and funding

LinkOut - more resources

Full Text Sources

Other Literature Sources

Medical

Molecular Biology Databases