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Genetic predisposition to long-term nondiabetic deteriorations in glucose homeostasis: Ten-year follow-up of the GLACIER study.

Renström F, Shungin D, Johansson I, MAGIC InvestigatorsFlorez JC, Hallmans G, Hu FB, Franks PW - Diabetes (2010)

Bottom Line: Sixteen fasting glucose-raising loci were genotyped in middle-aged adults from the Gene x Lifestyle interactions And Complex traits Involved in Elevated disease Risk (GLACIER) Study, a population-based prospective cohort study from northern Sweden.Genotypes were tested for association with baseline fasting and 2-h postchallenge glycemia (N = 16,330), and for changes in these glycemic traits during a 10-year follow-up period (N = 4,059).Our findings imply that genetic profiling might facilitate the early detection of persons who are genetically susceptible to deteriorating glucose control; studies of incident type 2 diabetes and discrete cardiovascular end points will help establish whether the magnitude of these changes is clinically relevant.

View Article: PubMed Central - PubMed

Affiliation: Department of Public Health and Clinical Medicine, Umeå University Hospital, Sweden.

ABSTRACT

Objective: To assess whether recently discovered genetic loci associated with hyperglycemia also predict long-term changes in glycemic traits.

Research design and methods: Sixteen fasting glucose-raising loci were genotyped in middle-aged adults from the Gene x Lifestyle interactions And Complex traits Involved in Elevated disease Risk (GLACIER) Study, a population-based prospective cohort study from northern Sweden. Genotypes were tested for association with baseline fasting and 2-h postchallenge glycemia (N = 16,330), and for changes in these glycemic traits during a 10-year follow-up period (N = 4,059).

Results: Cross-sectional directionally consistent replication with fasting glucose concentrations was achieved for 12 of 16 variants; 10 variants were also associated with impaired fasting glucose (IFG) and 7 were independently associated with 2-h postchallenge glucose concentrations. In prospective analyses, the effect alleles at four loci (GCK rs4607517, ADRA2A rs10885122, DGKB-TMEM195 rs2191349, and G6PC2 rs560887) were nominally associated with worsening fasting glucose concentrations during 10-years of follow-up. MTNR1B rs10830963, which was predictive of elevated fasting glucose concentrations in cross-sectional analyses, was associated with a protective effect on postchallenge glucose concentrations during follow-up; however, this was only when baseline fasting and 2-h glucoses were adjusted for. An additive effect of multiple risk alleles on glycemic traits was observed: a weighted genetic risk score (80th vs. 20th centiles) was associated with a 0.16 mmol/l (P = 2.4 × 10⁻⁶) greater elevation in fasting glucose and a 64% (95% CI: 33-201%) higher risk of developing IFG during 10 years of follow-up.

Conclusions: Our findings imply that genetic profiling might facilitate the early detection of persons who are genetically susceptible to deteriorating glucose control; studies of incident type 2 diabetes and discrete cardiovascular end points will help establish whether the magnitude of these changes is clinically relevant.

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Related in: MedlinePlus

Association between the weighted genetic risk score and change in fasting glucose concentrations during 10 years of follow-up. The weighted genetic risk score (wGRS) was constructed as described in the research design and methods section. Data are mean (95% CI). Δ glucose levels (follow-up minus baseline) per quintile of the wGRS are adjusted for baseline age, sex, baseline fasting glucose, fasting time at baseline and follow-up, and follow-up time (N = 4,059).
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Figure 1: Association between the weighted genetic risk score and change in fasting glucose concentrations during 10 years of follow-up. The weighted genetic risk score (wGRS) was constructed as described in the research design and methods section. Data are mean (95% CI). Δ glucose levels (follow-up minus baseline) per quintile of the wGRS are adjusted for baseline age, sex, baseline fasting glucose, fasting time at baseline and follow-up, and follow-up time (N = 4,059).

Mentions: The effect of multiple genetic risk loci on the glycemic traits was studied by constructing two different types of genetic risk score (GRS) for each study participant. The first assumed an equal magnitude of effect for each risk allele and was generated for each participant by summing the number of risk alleles at each of the 16 SNP loci. Thus, because these are all biallelic loci, the GRS has a maximum possible value of 32 and a minimum possible value of 0. To calculate the second GRS, we used published effect sizes for each SNP (3) to weight the contribution of each risk allele. The weighted alleles were subsequently summed into a single score (wGRS). The maximum value for the wGRS for fasting and postchallenge glucoses are 0.948 and 1.548, respectively (i.e., 32 risk alleles × the relevant β coefficient for each allele). To facilitate the interpretation of the results, each individual's wGRS was divided by the maximum possible wGRS and multiplied by 32 (the maximal number of risk alleles) (25). The purpose of undertaking this last step is to create a variable which is expressed on the same scale as the unweighted GRS, thus facilitating comparisons between these two scores. Missing genotypes were imputed as previously described (26) by replacing each missing genotype with its mean value, which was derived from the fraction of the cohort in which the genotype data were available. Analyses were performed using the wGRS on the continuous scale and on a categorical scale (wGRS quintiles). We used the latter to compare the magnitude of the effects between the top and bottom quintiles of the wGRS. The purpose of this comparison is to illustrate the extent to which having a relatively high genetic burden (i.e., >80% of the wGRS distribution) verses a relatively low genetic burden (i.e., <20% of the wGRS distribution) influences glucose homeostasis. The cut points were chosen because they allow for the comparison of effects for genetically distinct subgroups of the population, while ensuring these subgroups are sufficiently prevalent to be reasonably generalizable. There was no biologically informed reason for choosing these cut points as the relationship of the wGRS with glucose levels is linear (as illustrated in Fig. 1).


Genetic predisposition to long-term nondiabetic deteriorations in glucose homeostasis: Ten-year follow-up of the GLACIER study.

Renström F, Shungin D, Johansson I, MAGIC InvestigatorsFlorez JC, Hallmans G, Hu FB, Franks PW - Diabetes (2010)

Association between the weighted genetic risk score and change in fasting glucose concentrations during 10 years of follow-up. The weighted genetic risk score (wGRS) was constructed as described in the research design and methods section. Data are mean (95% CI). Δ glucose levels (follow-up minus baseline) per quintile of the wGRS are adjusted for baseline age, sex, baseline fasting glucose, fasting time at baseline and follow-up, and follow-up time (N = 4,059).
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3012192&req=5

Figure 1: Association between the weighted genetic risk score and change in fasting glucose concentrations during 10 years of follow-up. The weighted genetic risk score (wGRS) was constructed as described in the research design and methods section. Data are mean (95% CI). Δ glucose levels (follow-up minus baseline) per quintile of the wGRS are adjusted for baseline age, sex, baseline fasting glucose, fasting time at baseline and follow-up, and follow-up time (N = 4,059).
Mentions: The effect of multiple genetic risk loci on the glycemic traits was studied by constructing two different types of genetic risk score (GRS) for each study participant. The first assumed an equal magnitude of effect for each risk allele and was generated for each participant by summing the number of risk alleles at each of the 16 SNP loci. Thus, because these are all biallelic loci, the GRS has a maximum possible value of 32 and a minimum possible value of 0. To calculate the second GRS, we used published effect sizes for each SNP (3) to weight the contribution of each risk allele. The weighted alleles were subsequently summed into a single score (wGRS). The maximum value for the wGRS for fasting and postchallenge glucoses are 0.948 and 1.548, respectively (i.e., 32 risk alleles × the relevant β coefficient for each allele). To facilitate the interpretation of the results, each individual's wGRS was divided by the maximum possible wGRS and multiplied by 32 (the maximal number of risk alleles) (25). The purpose of undertaking this last step is to create a variable which is expressed on the same scale as the unweighted GRS, thus facilitating comparisons between these two scores. Missing genotypes were imputed as previously described (26) by replacing each missing genotype with its mean value, which was derived from the fraction of the cohort in which the genotype data were available. Analyses were performed using the wGRS on the continuous scale and on a categorical scale (wGRS quintiles). We used the latter to compare the magnitude of the effects between the top and bottom quintiles of the wGRS. The purpose of this comparison is to illustrate the extent to which having a relatively high genetic burden (i.e., >80% of the wGRS distribution) verses a relatively low genetic burden (i.e., <20% of the wGRS distribution) influences glucose homeostasis. The cut points were chosen because they allow for the comparison of effects for genetically distinct subgroups of the population, while ensuring these subgroups are sufficiently prevalent to be reasonably generalizable. There was no biologically informed reason for choosing these cut points as the relationship of the wGRS with glucose levels is linear (as illustrated in Fig. 1).

Bottom Line: Sixteen fasting glucose-raising loci were genotyped in middle-aged adults from the Gene x Lifestyle interactions And Complex traits Involved in Elevated disease Risk (GLACIER) Study, a population-based prospective cohort study from northern Sweden.Genotypes were tested for association with baseline fasting and 2-h postchallenge glycemia (N = 16,330), and for changes in these glycemic traits during a 10-year follow-up period (N = 4,059).Our findings imply that genetic profiling might facilitate the early detection of persons who are genetically susceptible to deteriorating glucose control; studies of incident type 2 diabetes and discrete cardiovascular end points will help establish whether the magnitude of these changes is clinically relevant.

View Article: PubMed Central - PubMed

Affiliation: Department of Public Health and Clinical Medicine, Umeå University Hospital, Sweden.

ABSTRACT

Objective: To assess whether recently discovered genetic loci associated with hyperglycemia also predict long-term changes in glycemic traits.

Research design and methods: Sixteen fasting glucose-raising loci were genotyped in middle-aged adults from the Gene x Lifestyle interactions And Complex traits Involved in Elevated disease Risk (GLACIER) Study, a population-based prospective cohort study from northern Sweden. Genotypes were tested for association with baseline fasting and 2-h postchallenge glycemia (N = 16,330), and for changes in these glycemic traits during a 10-year follow-up period (N = 4,059).

Results: Cross-sectional directionally consistent replication with fasting glucose concentrations was achieved for 12 of 16 variants; 10 variants were also associated with impaired fasting glucose (IFG) and 7 were independently associated with 2-h postchallenge glucose concentrations. In prospective analyses, the effect alleles at four loci (GCK rs4607517, ADRA2A rs10885122, DGKB-TMEM195 rs2191349, and G6PC2 rs560887) were nominally associated with worsening fasting glucose concentrations during 10-years of follow-up. MTNR1B rs10830963, which was predictive of elevated fasting glucose concentrations in cross-sectional analyses, was associated with a protective effect on postchallenge glucose concentrations during follow-up; however, this was only when baseline fasting and 2-h glucoses were adjusted for. An additive effect of multiple risk alleles on glycemic traits was observed: a weighted genetic risk score (80th vs. 20th centiles) was associated with a 0.16 mmol/l (P = 2.4 × 10⁻⁶) greater elevation in fasting glucose and a 64% (95% CI: 33-201%) higher risk of developing IFG during 10 years of follow-up.

Conclusions: Our findings imply that genetic profiling might facilitate the early detection of persons who are genetically susceptible to deteriorating glucose control; studies of incident type 2 diabetes and discrete cardiovascular end points will help establish whether the magnitude of these changes is clinically relevant.

Show MeSH
Related in: MedlinePlus