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Utility of genetic and non-genetic risk factors in prediction of type 2 diabetes: Whitehall II prospective cohort study.

Talmud PJ, Hingorani AD, Cooper JA, Marmot MG, Brunner EJ, Kumari M, Kivimäki M, Humphries SE - BMJ (2010)

Bottom Line: Cases of incident type 2 diabetes were defined on the basis of a standard oral glucose tolerance test, self report of a doctor's diagnosis, or the use of anti-diabetic drugs.A genetic score based on the number of risk alleles carried (range 0-40; area under receiver operating characteristics curve 0.54, 95% confidence interval 0.50 to 0.58) and a genetic risk function in which carriage of risk alleles was weighted according to the summary odds ratios of their effect from meta-analyses of genetic studies (area under receiver operating characteristics curve 0.55, 0.51 to 0.59) did not effectively discriminate cases of diabetes.Adding genetic information to phenotype based risk models did not improve discrimination and provided only a small improvement in model calibration and a modest net reclassification improvement of about 5% when added to the Cambridge risk score but not when added to the Framingham offspring risk score.

View Article: PubMed Central - PubMed

Affiliation: Centre of Cardiovascular Genetics, Department of Medicine, University College London, London WC1E 6JF. p.talmud@ucl.ac.uk

ABSTRACT

Objectives: To assess the performance of a panel of common single nucleotide polymorphisms (genotypes) associated with type 2 diabetes in distinguishing incident cases of future type 2 diabetes (discrimination), and to examine the effect of adding genetic information to previously validated non-genetic (phenotype based) models developed to estimate the absolute risk of type 2 diabetes.

Design: Workplace based prospective cohort study with three 5 yearly medical screenings.

Participants: 5535 initially healthy people (mean age 49 years; 33% women), of whom 302 developed new onset type 2 diabetes over 10 years.

Outcome measures: Non-genetic variables included in two established risk models-the Cambridge type 2 diabetes risk score (age, sex, drug treatment, family history of type 2 diabetes, body mass index, smoking status) and the Framingham offspring study type 2 diabetes risk score (age, sex, parental history of type 2 diabetes, body mass index, high density lipoprotein cholesterol, triglycerides, fasting glucose)-and 20 single nucleotide polymorphisms associated with susceptibility to type 2 diabetes. Cases of incident type 2 diabetes were defined on the basis of a standard oral glucose tolerance test, self report of a doctor's diagnosis, or the use of anti-diabetic drugs.

Results: A genetic score based on the number of risk alleles carried (range 0-40; area under receiver operating characteristics curve 0.54, 95% confidence interval 0.50 to 0.58) and a genetic risk function in which carriage of risk alleles was weighted according to the summary odds ratios of their effect from meta-analyses of genetic studies (area under receiver operating characteristics curve 0.55, 0.51 to 0.59) did not effectively discriminate cases of diabetes. The Cambridge risk score (area under curve 0.72, 0.69 to 0.76) and the Framingham offspring risk score (area under curve 0.78, 0.75 to 0.82) led to better discrimination of cases than did genotype based tests. Adding genetic information to phenotype based risk models did not improve discrimination and provided only a small improvement in model calibration and a modest net reclassification improvement of about 5% when added to the Cambridge risk score but not when added to the Framingham offspring risk score.

Conclusion: The phenotype based risk models provided greater discrimination for type 2 diabetes than did models based on 20 common independently inherited diabetes risk alleles. The addition of genotypes to phenotype based risk models produced only minimal improvement in accuracy of risk estimation assessed by recalibration and, at best, a minor net reclassification improvement. The major translational application of the currently known common, small effect genetic variants influencing susceptibility to type 2 diabetes is likely to come from the insight they provide on causes of disease and potential therapeutic targets.

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Fig 1 Receiver operating characteristics curves for gene count score alone (area under curve 0.54, 95% CI 0.50 to 0.58), Framingham offspring risk score (area under curve 0.78, 0.75 to 0.82), and gene count score incorporated into Framingham offspring risk score (area under curve 0.78, 0.75 to 0.81)
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fig1: Fig 1 Receiver operating characteristics curves for gene count score alone (area under curve 0.54, 95% CI 0.50 to 0.58), Framingham offspring risk score (area under curve 0.78, 0.75 to 0.82), and gene count score incorporated into Framingham offspring risk score (area under curve 0.78, 0.75 to 0.81)

Mentions: The odds ratio for developing diabetes was 8.4 (95% confidence interval 5.1 to 13.9) for people in the top fifth of the Cambridge risk score distribution compared with those in the bottom fifth, and the area under the receiver operating characteristics curve was 0.72 (95% confidence interval 0.69 to 0.76) at phase 7 (table 1). The odds ratio for type 2 diabetes in participants in the top fifth of the Framingham offspring risk score distribution compared with those in the bottom fifth was 18.2 (9.8 to 33.8), with a significantly greater area under the receiver operating characteristics curve of 0.78 (0.75 to 0.82) (P=0.01) (table 1 and fig 1). Detection rates (sensitivity) for a 5% false positive rate were 19.7 (95% confidence interval 14.1 to 25.3) for the Cambridge risk score (cut-off point 0.34) and 30.6 (24.1 to 37.1) for the Framingham offspring risk score (cut-off point 0.08). The respective detection rates for a 10% false positive rate were 34.2 (27.5 to 40.9; cut-off point 0.26) and 43.0 (36.0 to 50.0; cut-off point 0.05) (table 1).


Utility of genetic and non-genetic risk factors in prediction of type 2 diabetes: Whitehall II prospective cohort study.

Talmud PJ, Hingorani AD, Cooper JA, Marmot MG, Brunner EJ, Kumari M, Kivimäki M, Humphries SE - BMJ (2010)

Fig 1 Receiver operating characteristics curves for gene count score alone (area under curve 0.54, 95% CI 0.50 to 0.58), Framingham offspring risk score (area under curve 0.78, 0.75 to 0.82), and gene count score incorporated into Framingham offspring risk score (area under curve 0.78, 0.75 to 0.81)
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC2806945&req=5

fig1: Fig 1 Receiver operating characteristics curves for gene count score alone (area under curve 0.54, 95% CI 0.50 to 0.58), Framingham offspring risk score (area under curve 0.78, 0.75 to 0.82), and gene count score incorporated into Framingham offspring risk score (area under curve 0.78, 0.75 to 0.81)
Mentions: The odds ratio for developing diabetes was 8.4 (95% confidence interval 5.1 to 13.9) for people in the top fifth of the Cambridge risk score distribution compared with those in the bottom fifth, and the area under the receiver operating characteristics curve was 0.72 (95% confidence interval 0.69 to 0.76) at phase 7 (table 1). The odds ratio for type 2 diabetes in participants in the top fifth of the Framingham offspring risk score distribution compared with those in the bottom fifth was 18.2 (9.8 to 33.8), with a significantly greater area under the receiver operating characteristics curve of 0.78 (0.75 to 0.82) (P=0.01) (table 1 and fig 1). Detection rates (sensitivity) for a 5% false positive rate were 19.7 (95% confidence interval 14.1 to 25.3) for the Cambridge risk score (cut-off point 0.34) and 30.6 (24.1 to 37.1) for the Framingham offspring risk score (cut-off point 0.08). The respective detection rates for a 10% false positive rate were 34.2 (27.5 to 40.9; cut-off point 0.26) and 43.0 (36.0 to 50.0; cut-off point 0.05) (table 1).

Bottom Line: Cases of incident type 2 diabetes were defined on the basis of a standard oral glucose tolerance test, self report of a doctor's diagnosis, or the use of anti-diabetic drugs.A genetic score based on the number of risk alleles carried (range 0-40; area under receiver operating characteristics curve 0.54, 95% confidence interval 0.50 to 0.58) and a genetic risk function in which carriage of risk alleles was weighted according to the summary odds ratios of their effect from meta-analyses of genetic studies (area under receiver operating characteristics curve 0.55, 0.51 to 0.59) did not effectively discriminate cases of diabetes.Adding genetic information to phenotype based risk models did not improve discrimination and provided only a small improvement in model calibration and a modest net reclassification improvement of about 5% when added to the Cambridge risk score but not when added to the Framingham offspring risk score.

View Article: PubMed Central - PubMed

Affiliation: Centre of Cardiovascular Genetics, Department of Medicine, University College London, London WC1E 6JF. p.talmud@ucl.ac.uk

ABSTRACT

Objectives: To assess the performance of a panel of common single nucleotide polymorphisms (genotypes) associated with type 2 diabetes in distinguishing incident cases of future type 2 diabetes (discrimination), and to examine the effect of adding genetic information to previously validated non-genetic (phenotype based) models developed to estimate the absolute risk of type 2 diabetes.

Design: Workplace based prospective cohort study with three 5 yearly medical screenings.

Participants: 5535 initially healthy people (mean age 49 years; 33% women), of whom 302 developed new onset type 2 diabetes over 10 years.

Outcome measures: Non-genetic variables included in two established risk models-the Cambridge type 2 diabetes risk score (age, sex, drug treatment, family history of type 2 diabetes, body mass index, smoking status) and the Framingham offspring study type 2 diabetes risk score (age, sex, parental history of type 2 diabetes, body mass index, high density lipoprotein cholesterol, triglycerides, fasting glucose)-and 20 single nucleotide polymorphisms associated with susceptibility to type 2 diabetes. Cases of incident type 2 diabetes were defined on the basis of a standard oral glucose tolerance test, self report of a doctor's diagnosis, or the use of anti-diabetic drugs.

Results: A genetic score based on the number of risk alleles carried (range 0-40; area under receiver operating characteristics curve 0.54, 95% confidence interval 0.50 to 0.58) and a genetic risk function in which carriage of risk alleles was weighted according to the summary odds ratios of their effect from meta-analyses of genetic studies (area under receiver operating characteristics curve 0.55, 0.51 to 0.59) did not effectively discriminate cases of diabetes. The Cambridge risk score (area under curve 0.72, 0.69 to 0.76) and the Framingham offspring risk score (area under curve 0.78, 0.75 to 0.82) led to better discrimination of cases than did genotype based tests. Adding genetic information to phenotype based risk models did not improve discrimination and provided only a small improvement in model calibration and a modest net reclassification improvement of about 5% when added to the Cambridge risk score but not when added to the Framingham offspring risk score.

Conclusion: The phenotype based risk models provided greater discrimination for type 2 diabetes than did models based on 20 common independently inherited diabetes risk alleles. The addition of genotypes to phenotype based risk models produced only minimal improvement in accuracy of risk estimation assessed by recalibration and, at best, a minor net reclassification improvement. The major translational application of the currently known common, small effect genetic variants influencing susceptibility to type 2 diabetes is likely to come from the insight they provide on causes of disease and potential therapeutic targets.

Show MeSH
Related in: MedlinePlus