Dataset Information

Utilization of genetic data can improve the prediction of type 2 diabetes incidence in a Swedish cohort.

ABSTRACT: The aim of this study was to measure the impact of genetic data in improving the prediction of type 2 diabetes (T2D) in the Malmö Diet and Cancer Study cohort. The current study was performed in 3,426 Swedish individuals and utilizes of a set of genetic and environmental risk data. We first validated our environmental risk model by comparing it to both the Finnish Diabetes Risk Score and the T2D risk model derived from the Framingham Offspring Study. The area under the curve (AUC) for our environmental model was 0.72 [95% CI, 0.69-0.74], which was significantly better than both the Finnish (0.64 [95% CI, 0.61-0.66], p-value < 1 x 10-4) and Framingham (0.69 [95% CI, 0.66-0.71], p-value = 0.0017) risk scores. We then verified that the genetic data has a statistically significant positive correlation with incidence of T2D in the studied population. We also verified that adding genetic data slightly but statistically increased the AUC of a model based only on environmental risk factors (RFs, AUC shift +1.0% from 0.72 to 0.73, p-value = 0.042). To study the dependence of the results on the environmental RFs, we divided the population into two equally sized risk groups based only on their environmental risk and repeated the same analysis within each subpopulation. While there is a statistically significant positive correlation between the genetic data and incidence of T2D in both environmental risk categories, the positive shift in the AUC remains statistically significant only in the category with the lower environmental risk. These results demonstrate that genetic data can be used to increase the accuracy of T2D prediction. Also, the data suggests that genetic data is more valuable in improving T2D prediction in populations with lower environmental risk. This suggests that the impact of genetic data depends on the environmental risk of the studied population and thus genetic association studies should be performed in light of the underlying environmental risk of the population.

SUBMITTER: Zarkoob H

PROVIDER: S-EPMC5507496 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Utilization of genetic data can improve the prediction of type 2 diabetes incidence in a Swedish cohort.

Zarkoob Hadi H Lewinsky Sarah S Almgren Peter P Melander Olle O Fakhrai-Rad Hossein H

PloS one 20170712 7

The aim of this study was to measure the impact of genetic data in improving the prediction of type 2 diabetes (T2D) in the Malmö Diet and Cancer Study cohort. The current study was performed in 3,426 Swedish individuals and utilizes of a set of genetic and environmental risk data. We first validated our environmental risk model by comparing it to both the Finnish Diabetes Risk Score and the T2D risk model derived from the Framingham Offspring Study. The area under the curve (AUC) for our enviro ...[more]

PMID: 28700623

Similar Datasets

Project description:AIMS/HYPOTHESIS:The workplace is a potentially important arena for prevention of type 2 diabetes and the first step is to identify occupations where the disease is common and/or risk is high. Therefore, our aim was to analyse incidence and prevalence of type 2 diabetes across all occupational groups in Sweden. METHODS:This nationwide study included all Swedish citizens born between 1937 and 1979 and gainfully employed between 2001 and 2013 (N = 4,550,892), and followed for a diagnosis of diabetes from 2006 to 2015 (n = 201,717) through national registers. Prevalence in 2013 (mean age 51 years; range 35-67) and age-standardised incidence (per 1000 person-years) were analysed across the 30 most common occupations among men and women. Information on BMI, physical fitness and smoking was obtained through the National Conscription (mean age 18) and Medical Birth Registers (mean age 29). RESULTS:Prevalence of type 2 diabetes was 5.2% in men and 3.2% in women; in men it was highest among motor vehicle drivers (8.8%) and in women it was highest among manufacturing workers (6.4%). Incidence varied dramatically across occupational groups. In men, it was highest among manufacturing workers (9.41) and professional drivers (9.32) and lowest among university teachers (3.44). In women, incidence was highest in manufacturing workers (7.20) and cleaners (6.18) and lowest in physiotherapists (2.20). We found major differences in the prevalence of being overweight and smoking and in the level of physical fitness across these occupational groups even at young ages. CONCLUSIONS/INTERPRETATION:Professional drivers, manufacturing workers and cleaners have a threefold increased risk of type 2 diabetes compared with university teachers and physiotherapists. These differences most likely reflect dramatic differences in the prevalence of lifestyle risk factors. If workplace interventions could reduce weight and increase physical activity among employees in these occupations, major health gains may be made.

Project description:ObjectivesTo 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.DesignWorkplace based prospective cohort study with three 5 yearly medical screenings.Participants5535 initially healthy people (mean age 49 years; 33% women), of whom 302 developed new onset type 2 diabetes over 10 years.Outcome measuresNon-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.ResultsA 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.ConclusionThe 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.

Dataset Information

Utilization of genetic data can improve the prediction of type 2 diabetes incidence in a Swedish cohort.

Publications

Utilization of genetic data can improve the prediction of type 2 diabetes incidence in a Swedish cohort.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets