Dataset Information

The value of genetic information for diabetes risk prediction - differences according to sex, age, family history and obesity.

ABSTRACT:

Background

Genome-wide association studies have identified numerous single nucleotide polymorphisms associated with type 2 diabetes through the past years. In previous studies, the usefulness of these genetic markers for prediction of diabetes was found to be limited. However, differences may exist between substrata of the population according to the presence of major diabetes risk factors. This study aimed to investigate the added predictive value of genetic information (42 single nucleotide polymorphisms) in subgroups of sex, age, family history of diabetes, and obesity.

Methods

A case-cohort study (random subcohort N?=?1,968; incident cases: N?=?578) within the European Prospective Investigation into Cancer and Nutrition Potsdam study was used. Prediction models without and with genetic information were evaluated in terms of the area under the receiver operating characteristic curve and the integrated discrimination improvement. Stratified analyses included subgroups of sex, age (<50 or ?50 years), family history (positive if either father or mother or a sibling has/had diabetes), and obesity (BMI< or ?30 kg/m(2)).

Results

A genetic risk score did not improve prediction above classic and metabolic markers, but - compared to a non-invasive prediction model - genetic information slightly improved the area under the receiver operating characteristic curve (difference [95%-CI]: 0.007 [0.002-0.011]). Stratified analyses showed stronger improvement in the older age group (0.010 [0.002-0.018]), the group with a positive family history (0.012 [0.000-0.023]) and among obese participants (0.015 [-0.005-0.034]) compared to the younger participants (0.005 [-0.004-0.014]), participants with a negative family history (0.003 [-0.001-0.008]) and non-obese (0.007 [0.000-0.014]), respectively. No difference was found between men and women.

Conclusion

There was no incremental value of genetic information compared to standard non-invasive and metabolic markers. Our study suggests that inclusion of genetic variants in diabetes risk prediction might be useful for subgroups with already manifest risk factors such as older age, a positive family history and obesity.

SUBMITTER: Muhlenbruch K

PROVIDER: S-EPMC3658960 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Publications

The value of genetic information for diabetes risk prediction - differences according to sex, age, family history and obesity.

Mühlenbruch Kristin K Jeppesen Charlotte C Joost Hans-Georg HG Boeing Heiner H Schulze Matthias B MB

PloS one 20130520 5

<h4>Background</h4>Genome-wide association studies have identified numerous single nucleotide polymorphisms associated with type 2 diabetes through the past years. In previous studies, the usefulness of these genetic markers for prediction of diabetes was found to be limited. However, differences may exist between substrata of the population according to the presence of major diabetes risk factors. This study aimed to investigate the added predictive value of genetic information (42 single nucle ...[more]

PMID: 23700469

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Project description:BackgroundHypertension, an important risk factor for the health of human being, is often accompanied by various comorbidities. However, the incidence patterns of those comorbidities have not been widely studied.AimApplying big-data techniques on a large collection of electronic medical records, we investigated sex-specific and age-specific detection rates of some important comorbidities of hypertension, and sketched their relationships to reveal the risk for hypertension patients.MethodsWe collected a total of 6,371,963 hypertension-related medical records from 106 hospitals in 72 cities throughout China. Those records were reported to a National Center for Disease Control in China between 2011 and 2013. Based on the comprehensive and geographically distributed data set, we identified the top 20 comorbidities of hypertension, and disclosed the sex-specific and age-specific patterns of those comorbidities. A comorbidities network was constructed based on the frequency of co-occurrence relationships among those comorbidities.ResultsThe top four comorbidities of hypertension were coronary heart disease, diabetes, hyperlipemia, and arteriosclerosis, whose detection rates were 21.71% (21.49% for men vs 21.95% for women), 16.00% (16.24% vs 15.74%), 13.81% (13.86% vs 13.76%), and 12.66% (12.25% vs 13.08%), respectively. The age-specific detection rates of comorbidities showed five unique patterns and also indicated that nephropathy, uremia, and anemia were significant risks for patients under 39 years of age. On the other hand, coronary heart disease, diabetes, arteriosclerosis, hyperlipemia, and cerebral infarction were more likely to occur in older patients. The comorbidity network that we constructed indicated that the top 20 comorbidities of hypertension had strong co-occurrence correlations.ConclusionsHypertension patients can be aware of their risks of comorbidities based on our sex-specific results, age-specific patterns, and the comorbidity network. Our findings provide useful insights into the comorbidity prevention, risk assessment, and early warning for hypertension patients.

Dataset Information

The value of genetic information for diabetes risk prediction - differences according to sex, age, family history and obesity.

Background

Methods

Results

Conclusion

Publications

The value of genetic information for diabetes risk prediction - differences according to sex, age, family history and obesity.

Similar Datasets

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