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Joint Effects of Common Genetic Variants on the Risk for Type 2 Diabetes in U.S. Men and Women of European Ancestry

Marilyn C. Cornelis, PhD; Lu Qi, MD, PhD; Cuilin Zhang, MD, PhD; Peter Kraft, PhD; JoAnn Manson, MD, DPH; Tianxi Cai, PhD; David J. Hunter, MBBS, ScD; and Frank B. Hu, MD, PhD
[+] Article and Author Information

From Harvard School of Public Health, Channing Laboratory, and Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, and the Eunice Kennedy Shriver National Institute of Child Health and Human Development, Rockville, Maryland.


Acknowledgment: The authors thank Patrice Soule and Dr. Hardeep Ranu of the Dana Farber/Harvard Cancer Center Genotyping Core for sample preparation and genotyping and the participants in the NHS and HPFS for their dedication and commitment.

Grant Support: By the National Institutes of Health (grants DK58845 and CA87969). Dr. Cornelis is a recipient of a Canadian Institutes of Health Research Fellowship. Dr. Qi is a recipient of the American Heart Association Scientist Development Award. Dr. Zhang is supported by the Intramural Research Program of the Eunice Kennedy Shriver National Institute of Child Health and Human Development.

Potential Financial Conflicts of Interest: None disclosed.

Reproducible Research Statement:Study protocol and data set: Not available. Statistical code: Available from Dr. Cornelis (e-mail, mcorneli@hsph.harvard.edu).

Requests for Single Reprints: Frank B. Hu, MD, PhD, Department of Nutrition, Harvard School of Public Health, Building II, 665 Huntington Avenue, Boston, MA 02115; e-mail, frank.hu@channing.harvard.edu.

Current Author Addresses: Drs. Cornelis, Qi, Kraft, Cai, Hunter, and Hu: Harvard School of Public Health, Building II, 665 Huntington Avenue, Boston, MA 02115.

Dr. Zhang: Division of Epidemiology, Statistics, and Prevention Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD 20892.

Dr. Manson: 900 Commonwealth Avenue East, Boston, MA 02215.

Author Contributions: Conception and design: L. Qi, F.B. Hu.

Analysis and interpretation of the data: M.C. Cornelis, L. Qi, C. Zhang, P. Kraft, D.J. Hunter, F.B. Hu.

Drafting of the article: M.C. Cornelis, L. Qi.

Critical revision of the article for important intellectual content: M.C. Cornelis, L. Qi, C. Zhang, P. Kraft, J. Manson, D.J. Hunter, F.B. Hu.

Final approval of the article: L. Qi, C. Zhang, P. Kraft, J. Manson, D.J. Hunter, F.B. Hu.

Provision of study materials or patients: F.B. Hu.

Statistical expertise: M.C. Cornelis, L. Qi, P. Kraft, D.J. Hunter.

Obtaining of funding: L. Qi, F.B. Hu.

Administrative, technical, or logistic support: J. Manson, F.B. Hu.

Collection and assembly of data: L. Qi, J. Manson, F.B. Hu.


Ann Intern Med. 2009;150(8):541-550. doi:10.7326/0003-4819-150-8-200904210-00008
Text Size: A A A

Background: Genome-wide association studies have identified novel type 2 diabetes loci, each of which has a modest impact on risk.

Objective: To examine the joint effects of several type 2 diabetes risk variants and their combination with conventional risk factors on type 2 diabetes risk in 2 prospective cohorts.

Design: Nested case–control study.

Setting: United States.

Participants: 2809 patients with type 2 diabetes and 3501 healthy control participants of European ancestry from the Health Professionals Follow-up Study and Nurses' Health Study.

Measurements: A genetic risk score (GRS) was calculated on the basis of 10 polymorphisms in 9 loci.

Results: After adjustment for age and body mass index (BMI), the odds ratio for type 2 diabetes with each point of GRS, corresponding to 1 risk allele, was 1.19 (95% CI, 1.14 to 1.24) and 1.16 (CI, 1.12 to 1.20) for men and women, respectively. Persons with a BMI of 30 kg/m2 or greater and a GRS in the highest quintile had an odds ratio of 14.06 (CI, 8.90 to 22.18) compared with persons with a BMI less than 25 kg/m2 and a GRS in the lowest quintile after adjustment for age and sex. Persons with a positive family history of diabetes and a GRS in the highest quintile had an odds ratio of 9.20 (CI, 5.50 to 15.40) compared with persons without a family history of diabetes and with a GRS in the lowest quintile. The addition of the GRS to a model of conventional risk factors improved discrimination by 1% (P < 0.001).

Limitation: The study focused only on persons of European ancestry; whether GRS is associated with type 2 diabetes in other ethnic groups remains unknown.

Conclusion: Although its discriminatory value is currently limited, a GRS that combines information from multiple genetic variants might be useful for identifying subgroups with a particularly high risk for type 2 diabetes.

Primary Funding Source: National Institutes of Health.

Figures

Grahic Jump Location
Figure 1.
Association of reported loci and risk for type 2 diabetes in pooled analysis of men and women.

Odds ratios (95% CIs) are adjusted for age (quintiles), sex, and body mass index (quintiles). SNP = single nucleotide polymorphism.

Grahic Jump Location
Grahic Jump Location
Figure 2.
Genetic risk score and risk for type 2 diabetes.

Results are based on the count genetic risk score for pooled data from men and women. Adjusted for age (quintiles), sex, and body mass index (quintiles).

Grahic Jump Location
Grahic Jump Location
Figure 3.
Joint effects of conventional risk factors and genetic risk score on risk for type 2 diabetes.

Values on bars indicate sample size. Top. Joint effects of body mass index and count genetic risk score (adjusted for age and sex) for pooled data from men and women. Bottom. Joint effects of family history of diabetes and count genetic risk score (adjusted for age, sex, and body mass index) for pooled data from men and women.

Grahic Jump Location
Grahic Jump Location
Figure 4.
Receiver-operating characteristic curves for type 2 diabetes.

The curves are based on logistic regression models incorporating conventional risk factors (age, sex, body mass index, family history of diabetes, smoking, alcohol intake, and physical activity) with and without the count GRS. AUC = area under the curve; GRS = genetic risk score.

Grahic Jump Location

Tables

References

Letters

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Comments

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More Thoughts on Multivariate Regression
Posted on May 9, 2009
Jongoh Kim
Albert Einstein Medical Center
Conflict of Interest: None Declared

Many studies have been done to evaluate risks of diabetes associated with comorbidities or behavioral factors (conventional risk factors) (1) and recently also with some genetic loci (2,3). Cornelis et al. presented combined effects of conventional risk factors and genetic loci for the first time. However, the presented data was not enough to grasp the whole picture of the combined effects. The authors did not give information on how genetic loci and conventional risk factors were related and how the effects of genetic loci were adjusted by conventional risk factors in detail. Above all, they did not show how adding BMI adjusted the effects of genetic loci. Obesity is the most important risk factor for diabetes [1]. In this regard, careful examination of the patterns of adjustment of the effect of each and joint genetic loci by BMI could have given clues on how these genetic loci contribute to the development of diabetes.

Secondly, on discussion of the AUC of having diabetes, the authors mentioned a possibility of collinearity between conventional risk factors and GRS (Genetic Risk Score) as an explanation for marginal contribution of adding GRS. However, the author should have calculated correlation between these if they thought that it was a possibility. And as another explanation, the authors also suggested that the effects of GRS could have been mediated through conventional risk factors. But as a matter of fact, this is opposite to their findings in the results where they already showed that the effects of GRS were significant after adjustment of age and BMI and minimally adjusted by adding other risk factors.

References

1. Perry IJ, Wannamethee SG, Walker MK, Thomson AG, Whincup PH, Shaper AG. Prospective study of risk factors for development of non- insulin dependent diabetes in middle aged British men. British Medical Journal 1995;310(6979):560-4.

2. Sladek R, Rocheleau G, Rung J, et al. A genome-wide association study identifies novel risk loci for type 2 diabetes. Nature 2007;445:881- 5.

3. Zeggini E, Weedon MN, Lindgren CM, et al. Replication of genome- wide association signals in UK samples reveals risk loci for type 2 diabetes. Science 2007;316(5829):1336.

Conflict of Interest:

None declared

Re: More Thoughts on Multivariate Regression
Posted on June 16, 2009
Marilyn C. Cornelis
Harvard School of Public Health
Conflict of Interest: None Declared

In Response

 

We appreciate Dr. Kim's comments which highlight some of the challenges inherent to combining genetic and conventional risk factors. Such challenges will persist as new loci with modest effects on risk continue to be discovered.

Limited space did not allow us to provide details on how genetic loci and conventional risk factors were related. As Dr. Kim alludes to, obesity is an important risk factor to attend to in understanding the mechanism by which these loci contribute to diabetes development. None of the 10 loci included in the genetic risk score (GRS) were related to body mass index (BMI) or the remaining conventional risk factors (age, smoking, alcohol, physical activity, menopausal status, family history of diabetes)(data not shown). In the women, adjusting for BMI slightly strengthened the association for GRS, but additional adjustment for other covariates did not appreciably alter the results (Table 1).  Similar results were observed for men (data not shown). The study design as well as the modest effect of each of these loci on risk impedes our ability to postulate potential mechanisms by which each locus contributes to risk. Nevertheless, the primary purpose of our study was not to uncover mechanisms, but rather to evaluate the combined effects of these loci and conventional risk factors on risk of the disease and our ability to discriminate between diabetics and non-diabetics.

We believe Dr. Kim may have misinterpreted our discussion regarding the discriminative value of the GRS. We were not suggesting that collinearity may have explained the minimal improvement we observed. We were referring to previous studies (1, 2) which incorporated fasting glucose levels or other measures of insulin sensitivity in their clinical risk models and who observed the least discriminative improvement with the addition of genetic information. No correlation between the GRS and conventional risk factors were observed in our study. Although, the mechanism by which these loci contribute to risk have not been established, it remains possible that some act via similar pathways to those of conventional risk factors while others by novel pathways. Our GRS would not capture interactions between individual loci and conventional risk factors if they exist; a limitation we have highlighted in our paper. While a score which accounts for all possible interactions will certainly perform better than a score which does not; to design and validate such a score will be challenge at this time.

 

Table 1. Association of Candidate SNP Loci and Risk for Type 2 Diabetes Among Women

SNP

Model 1

OR (95% CI)

Model 2

OR (95% CI)

Model 3

OR (95% CI)

rs1111875

1.01 (1.00-1.02)

1.06 (0.95-1.17)

1.05 (0.94-1.17)

rs7756992

1.16 (1.04-1.28)

1.17 (1.04-1.31)

1.13 (1.00-1.28)

rs4402960

1.23 (1.00-1.02)

1.26 (1.12-1.40)

1.24 (1.10-1.39)

rs13266634

1.18 (1.06-1.30)

1.19 (1.06-1.33)

1.17 (1.04-1.32)

rs10010131

1.10 (1.00-1.22)

1.10 (0.99-1.22)

1.09 (0.97-1.21)

rs564398

1.09 (0.99-1.20)

1.12 (1.01-1.24)

1.11 (1.00-1.24)

rs10811661

1.20 (1.06-1.36)

1.14 (1.00-1.31)

1.12 (0.97-1.30)

rs12255372

1.32 (1.20-1.47)

1.36 (1.22-1.52)

1.35 (1.20-1.51)

rs1801282

1.18 (1.02-1.36)

1.19 (1.02-1.39)

1.17 (0.99-1.37)

rs5219

1.17 (1.06-1.29)

1.17 (1.05-1.30)

1.17 (1.04-1.31)

 

 

 

 

GRS

Continuous

1.15 (1.12-1.19)

1.16 (1.12-1.20)

1.15 (1.11-1.19)

 

 

 

 

Quintiles                1

1.00 (ref)

1.00 (ref)

1.00 (ref)

2

1.06 (0.86-1.33)

1.25 (0.97-1.60)

1.20 (0.92-1.56)

3

1.62 (1.31-2.01)

1.60 (1.25-2.04)

1.52 (1.17-1.96)

4

2.06 (1.69-2.52)

1.94 (1.53-2.46)

1.78 (1.39-2.29)

5

2.07 (1.70-2.51)

2.46 (1.95-3.10)

2.26 (1.77-2.90)

Model 1: Adjusted for age

Model 2: Adjusted for age and BMI (5 categories)

Model 3: adjusted for age, BMI (5 categories), family history of diabetes (yes, no), smoking (never, past, current), menopausal status [pre- or post-menopausal (never, past, or current hormone use); women only], alcohol (5 categories), and quintiles of physical activity (hours/wk).

 

References

1.         Lyssenko V, Jonsson A, Almgren P, et al. Clinical risk factors, DNA variants, and the development of type 2 diabetes. N Engl J Med. 2008;359(21):2220-32.

2.         Meigs JB, Shrader P, Sullivan LM, et al. Genotype score in addition to common risk factors for prediction of type 2 diabetes. N Engl J Med. 2008;359(21):2208-19.

 

 

Conflict of Interest:

None declared

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Summary for Patients

Using Genetic and Other Factors to Predict Risk for Type 2 Diabetes

The summary below is from the full report titled “Joint Effects of Common Genetic Variants on the Risk for Type 2 Diabetes in U.S. Men and Women of European Ancestry.” It is in the 21 April 2009 issue of Annals of Internal Medicine (volume 150, pages 541-550). The authors are M.C. Cornelis, L. Qi, C. Zhang, P. Kraft, J. Manson, T. Cai, D.J. Hunter, and F.B. Hu.

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