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Common variants in 40 genes assessed for diabetes incidence and response to metformin and lifestyle intervention in the diabetes prevention program

Jablonski, K A; McAteer, J B; de Bakker, P I; Franks, Paul LU ; Pollin, T I; Hanson, R L; Saxena, R; Fowler, S; Shuldiner, A R and Knowler, W C, et al. (2010) In Diabetes Oct(59(10)). p.2672-2681
Abstract
OBJECTIVE: Genome-wide association studies have begun to elucidate the genetic architecture of type 2 diabetes. We examined whether single nucleotide polymorphisms (SNPs) identified through targeted complementary approaches affect diabetes incidence in the at-risk population of the Diabetes Prevention Program (DPP) and whether they influence a response to preventive interventions.



RESEARCH DESIGN AND METHODS: We selected SNPs identified by prior genome-wide association studies for type 2 diabetes and related traits, or capturing common variation in 40 candidate genes previously associated with type 2 diabetes, implicated in monogenic diabetes, encoding type 2 diabetes drug targets or drug-metabolizing/transporting... (More)
OBJECTIVE: Genome-wide association studies have begun to elucidate the genetic architecture of type 2 diabetes. We examined whether single nucleotide polymorphisms (SNPs) identified through targeted complementary approaches affect diabetes incidence in the at-risk population of the Diabetes Prevention Program (DPP) and whether they influence a response to preventive interventions.



RESEARCH DESIGN AND METHODS: We selected SNPs identified by prior genome-wide association studies for type 2 diabetes and related traits, or capturing common variation in 40 candidate genes previously associated with type 2 diabetes, implicated in monogenic diabetes, encoding type 2 diabetes drug targets or drug-metabolizing/transporting enzymes, or involved in relevant physiological processes. We analyzed 1,590 SNPs for association with incident diabetes and their interaction with response to metformin or lifestyle interventions in 2,994 DPP participants. We controlled for multiple hypothesis testing by assessing false discovery rates.



RESULTS: We replicated the association of variants in the metformin transporter gene SLC47A1 with metformin response and detected nominal interactions in the AMP kinase (AMPK) gene STK11, the AMPK subunit genes PRKAA1 and PRKAA2, and a missense SNP in SLC22A1, which encodes another metformin transporter. The most significant association with diabetes incidence occurred in the AMPK subunit gene PRKAG2 (hazard ratio 1.24, 95% CI 1.09-1.40, P = 7 × 10(-4)). Overall, there were nominal associations with diabetes incidence at 85 SNPs and nominal interactions with the metformin and lifestyle interventions at 91 and 69 mostly nonoverlapping SNPs, respectively. The lowest P values were consistent with experiment-wide 33% false discovery rates.



CONCLUSIONS: We have identified potential genetic determinants of metformin response. These results merit confirmation in independent samples. (Less)
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organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Diabetes
volume
Oct
issue
59(10)
pages
2672 - 2681
publisher
American Diabetes Association
external identifiers
  • wos:000283205700042
  • scopus:77957601724
ISSN
1939-327X
DOI
10.2337/db10-0543
language
English
LU publication?
yes
id
edc659dc-548b-4514-9559-c5543dada12d (old id 1715830)
alternative location
http://diabetes.diabetesjournals.org/content/59/10/2672.short
date added to LUP
2010-11-11 11:49:40
date last changed
2017-05-21 04:47:03
@article{edc659dc-548b-4514-9559-c5543dada12d,
  abstract     = {OBJECTIVE: Genome-wide association studies have begun to elucidate the genetic architecture of type 2 diabetes. We examined whether single nucleotide polymorphisms (SNPs) identified through targeted complementary approaches affect diabetes incidence in the at-risk population of the Diabetes Prevention Program (DPP) and whether they influence a response to preventive interventions.<br/><br>
<br/><br>
RESEARCH DESIGN AND METHODS: We selected SNPs identified by prior genome-wide association studies for type 2 diabetes and related traits, or capturing common variation in 40 candidate genes previously associated with type 2 diabetes, implicated in monogenic diabetes, encoding type 2 diabetes drug targets or drug-metabolizing/transporting enzymes, or involved in relevant physiological processes. We analyzed 1,590 SNPs for association with incident diabetes and their interaction with response to metformin or lifestyle interventions in 2,994 DPP participants. We controlled for multiple hypothesis testing by assessing false discovery rates.<br/><br>
<br/><br>
RESULTS: We replicated the association of variants in the metformin transporter gene SLC47A1 with metformin response and detected nominal interactions in the AMP kinase (AMPK) gene STK11, the AMPK subunit genes PRKAA1 and PRKAA2, and a missense SNP in SLC22A1, which encodes another metformin transporter. The most significant association with diabetes incidence occurred in the AMPK subunit gene PRKAG2 (hazard ratio 1.24, 95% CI 1.09-1.40, P = 7 × 10(-4)). Overall, there were nominal associations with diabetes incidence at 85 SNPs and nominal interactions with the metformin and lifestyle interventions at 91 and 69 mostly nonoverlapping SNPs, respectively. The lowest P values were consistent with experiment-wide 33% false discovery rates.<br/><br>
<br/><br>
CONCLUSIONS: We have identified potential genetic determinants of metformin response. These results merit confirmation in independent samples.},
  author       = {Jablonski, K A and McAteer, J B and de Bakker, P I and Franks, Paul and Pollin, T I and Hanson, R L and Saxena, R and Fowler, S and Shuldiner, A R and Knowler, W C and Altshuler, D and Florez, J C},
  issn         = {1939-327X},
  language     = {eng},
  number       = {59(10)},
  pages        = {2672--2681},
  publisher    = {American Diabetes Association},
  series       = {Diabetes},
  title        = {Common variants in 40 genes assessed for diabetes incidence and response to metformin and lifestyle intervention in the diabetes prevention program},
  url          = {http://dx.doi.org/10.2337/db10-0543},
  volume       = {Oct},
  year         = {2010},
}