The use of genetic information in the prediction of Type 2 diabetes

Effects of the interaction between glycated haemoglobin genetic risk score and postpartum weight reduction on glycaemic changes: A gene-weight interaction analysis

AIM

To investigate the effects of the interaction between glycated haemoglobin (HbA1c) genetic risk score and weight changes during and after pregnancy (postpartum weight reduction and gestational weight gain) on long-term glycaemic changes in the largest cohort of women with a history of gestational diabetes mellitus (GDM).

METHODS

This was a retrospective cohort using the baseline data from the Tianjin Gestational Diabetes Mellitus Prevention Programme. A genetic risk score was established by combining 10 HbA1c-related single-nucleotide polymorphisms, which were identified by genome-wide association studies. General linear regression models were applied to evaluate the effect of interaction between HbA1c genetic risk score and weight changes during and after pregnancy (postpartum weight reduction and gestational weight gain) on glycaemic changes.

RESULTS

'A total of 1156 women with a history of GDM were included in this respective cohort study. Statistical differences in pre-pregnancy weight, pre-delivery weight and postpartum weight were evidenced across different groups of postpartum weight reduction. After adjusting for covariates, statistical significance for changes in HbA1c level was only observed in the postpartum weight reduction <5 kg/y group (P = 0.002), and a significant effect of interaction between HbA1c genetic risk score and postpartum weight reduction on long-term changes in HbA1c was evidenced (P interaction = 0.01). In women with postpartum weight reduction ≥8 kg/y, those with a lower HbA1c genetic risk score had a greater decrease in HbA1c level.

CONCLUSIONS

HbA1c genetic risk score interacts with postpartum weight reduction to affect long-term changes in HbA1c levels among women with a history of GDM.

Assessment of the clinical utility of adding common single nucleotide polymorphism genetic scores to classical risk factor algorithms in coronary heart disease risk prediction in UK men

Background:

Risk prediction algorithms for coronary heart disease (CHD) are recommended for clinical use. However, their predictive ability remains modest and the inclusion of genetic risk may improve their performance.

Methods:

QRISK2 was used to assess CHD risk using conventional risk factors (CRFs). The performance of a 19 single nucleotide polymorphism (SNP) gene score (GS) for CHD including variants identified by genome-wide association study and candidate gene studies (weighted using the results from the CARDIoGRAMplusC4D meta-analysis) was assessed using the second Northwick Park Heart Study (NPHSII) of 2775 healthy UK men (284 cases). To improve the GS, five SNPs with weak evidence of an association with CHD were removed and replaced with seven robustly associated SNPs – giving a 21-SNP GS.

Results:

The weighted 19 SNP GS was associated with lipid traits (p<0.05) and CHD after adjustment for CRFs, (OR=1.31 per standard deviation, p=0.03). Addition of the 19 SNP GS to QRISK2 showed improved discrimination (area under the receiver operator characteristic curve 0.68 vs. 0.70 p=0.02), a positive net reclassification index (0.07, p=0.04) compared to QRISK2 alone and maintained good calibration (p=0.17). The 21-SNP GS was also associated with CHD after adjustment for CRFs (OR=1.39 per standard deviation, 1.42×10

Conclusions:

The 19-SNP GS is robustly associated with CHD and showed potential clinical utility in the UK population.

Challenges of implementing personalized (precision) medicine: a focus on diabetes

The concept of personalized (precision) medicine (PM) emphasizes the scientific and technological innovations that enable the physician to tailor disease prediction, diagnosis and treatment to the individual patient, based on a personalized data-driven approach. The major challenge for the medical systems is to translate the molecular and genomic advances into clinical available means. Patients and healthcare providers, the pharmaceutical and diagnostic industries manifest a growing interest in PM. Multiple stakeholders need adaptation and re-engineering for successful clinical implementation of PM. Drawing primarily from the field of ‘diabetes’, this article will summarize the main challenges to implementation of PM into current medical practice and some of the approaches currently being implemented to overcome these challenges.