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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 2 Percentage of participants in each gene count score category among those who developed type 2 diabetes and those who remained free from diabetes. Risk of developing diabetes according to gene count shown as fitted line from regression analysis
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fig2: Fig 2 Percentage of participants in each gene count score category among those who developed type 2 diabetes and those who remained free from diabetes. Risk of developing diabetes according to gene count shown as fitted line from regression analysis

Mentions: Figure 2 shows the distribution of the gene count score in people who developed type 2 diabetes and those who remained free of diabetes; the web figure shows the equivalent data for genetic risk function. Fourteen per cent of people with type 2 diabetes were in the top fifth of the gene count score compared with 13.9% of those without type 2 diabetes (web table K). The odds ratio for type 2 diabetes for participants in the top fifth of the score was 1.6 (0.9 to 2.5) compared with those in the bottom fifth. Twenty-five per cent of people with type 2 diabetes were in the top fifth of the genetic risk function, compared with 20% of those without diabetes, giving an odds ratio for type 2 diabetes of 2.3 (1.5 to 3.8) (web table K). The mean gene count score was 21.1 (SD 2.6) in people with type 2 diabetes and 20.5 (2.7) in those without type 2 diabetes (web table K). The simple gene count and the genetic risk function gave very similar discrimination, with areas under the receiver operating characteristics curve of 0.54 (0.50 to 0.58) (fig 1) and 0.55 (0.51 to 0.59) (table 1). Table 1 shows the detection rates at 5% and 10% false positive rates.


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 2 Percentage of participants in each gene count score category among those who developed type 2 diabetes and those who remained free from diabetes. Risk of developing diabetes according to gene count shown as fitted line from regression analysis
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig2: Fig 2 Percentage of participants in each gene count score category among those who developed type 2 diabetes and those who remained free from diabetes. Risk of developing diabetes according to gene count shown as fitted line from regression analysis
Mentions: Figure 2 shows the distribution of the gene count score in people who developed type 2 diabetes and those who remained free of diabetes; the web figure shows the equivalent data for genetic risk function. Fourteen per cent of people with type 2 diabetes were in the top fifth of the gene count score compared with 13.9% of those without type 2 diabetes (web table K). The odds ratio for type 2 diabetes for participants in the top fifth of the score was 1.6 (0.9 to 2.5) compared with those in the bottom fifth. Twenty-five per cent of people with type 2 diabetes were in the top fifth of the genetic risk function, compared with 20% of those without diabetes, giving an odds ratio for type 2 diabetes of 2.3 (1.5 to 3.8) (web table K). The mean gene count score was 21.1 (SD 2.6) in people with type 2 diabetes and 20.5 (2.7) in those without type 2 diabetes (web table K). The simple gene count and the genetic risk function gave very similar discrimination, with areas under the receiver operating characteristics curve of 0.54 (0.50 to 0.58) (fig 1) and 0.55 (0.51 to 0.59) (table 1). Table 1 shows the detection rates at 5% and 10% false positive rates.

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