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1.  Circulating Biomarkers of One-Carbon Metabolism in Relation to Renal Cell Carcinoma Incidence and Survival 
Background
The etiology of renal cell carcinoma (RCC) is only partially understood, but a metabolic component appears likely. We investigated biomarkers of one-carbon metabolism and RCC onset and survival.
Methods
The European Prospective Investigation into Cancer and Nutrition (EPIC) recruited 385747 participants with blood samples between 1992 and 2000, and this analysis included 556 RCC case-control pairs. A subsequent replication study included 144 case-control pairs nested within the Melbourne Collaborative Cohort Study (MCCS). Plasma concentrations of vitamin B2, vitamin B6, folate, vitamin B12, methionine and homocysteine were measured in prediagnostic samples and evaluated with respect to RCC risk using conditional and unconditional logistic regression models, and to all-cause mortality in RCC cases using Cox regression models. All statistical tests were two-sided.
Results
EPIC participants with higher plasma concentrations of vitamin B6 had lower risk of RCC, the odds ratio comparing the 4th and 1st quartiles (OR4vs1) being 0.40 95% confidence interval [CI] = 0.28 to 0.57, P trend < .001. We found similar results after adjusting for potential confounders (adjusted P trend < .001). In survival analysis, the hazard ratio for all-cause mortality in RCC cases when comparing the 4th and 1st quartiles (HR4vs1) of vitamin B6 was 0.57 (95% CI = 0.37 to 0.87, P trend < .001).
Subsequent replication of these associations within the MCCS yielded very similar results for both RCC risk (OR4vs1 = 0.47, 95% CI = 0.23 to 0.99, P trend = .07) and all-cause mortality (HR4vs1 = 0.56, 95% CI = 0.27 to 1.17, P trend = .02). No association was evident for the other measured biomarkers.
Conclusion
Study participants with higher circulating concentrations of vitamin B6 had lower risk of RCC and improved survival following diagnosis in two independent cohorts.
doi:10.1093/jnci/dju327
PMCID: PMC4273895  PMID: 25376861
2.  Estimation and selection of complex covariate effects in pooled nested case–control studies with heterogeneity 
Biostatistics (Oxford, England)  2013;14(4):682-694.
A major challenge in cancer epidemiologic studies, especially those of rare cancers, is observing enough cases. To address this, researchers often join forces by bringing multiple studies together to achieve large sample sizes, allowing for increased power in hypothesis testing, and improved efficiency in effect estimation. Combining studies, however, renders the analysis difficult owing to the presence of heterogeneity in the pooled data. In this article, motivated by a collaborative nested case–control (NCC) study of ovarian cancer in three cohorts from United States, Sweden, and Italy, we investigate the use of penalty regularized partial likelihood estimation in the context of pooled NCC studies to achieve two goals. First, we propose an adaptive group lasso (gLASSO) penalized approach to simultaneously identify important variables and estimate their effects. Second, we propose a composite agLASSO penalized approach to identify variables with heterogeneous effects. Both methods are readily implemented with the group coordinate gradient decent algorithm and shown to enjoy the oracle property. We conduct simulation studies to evaluate the performance of our proposed approaches in finite samples under various heterogeneity settings, and apply them to the pooled ovarian cancer study.
doi:10.1093/biostatistics/kxt015
PMCID: PMC3841381  PMID: 23632625
Cox's proportional hazards model; Group penalty; Heterogeneity; Nested case–control sampling; Ovarian cancer; Pooled studies; Shrinkage estimation
3.  Polymorphisms in genes related to one-carbon metabolism are not related to pancreatic cancer in PanScan and PanC4 
Cancer causes & control : CCC  2013;24(3):595-602.
Purpose
The evidence of a relation between folate intake and one-carbon metabolism (OCM) with pancreatic cancer (PanCa) is inconsistent. In this study, the association between genes and single-nucleotide polymorphisms (SNPs) related to OCM and PanCa was assessed.
Methods
Using biochemical knowledge of the OCM pathway, we identified thirty-seven genes and 834 SNPs to examine in association with PanCa. Our study included 1,408 cases and 1,463 controls nested within twelve cohorts (PanScan). The ten SNPs and five genes with lowest p values (<0.02) were followed up in 2,323 cases and 2,340 controls from eight case-control studies (PanC4) that participated in PanScan2. The correlation of SNPs with metabolite levels was assessed for 649 controls from the European Prospective Investigation into Cancer and Nutrition.
Results
When both stages were combined, we observed suggestive associations with PanCa for rs10887710 (MAT1A) (OR 1.13, 95%CI 1.04-1.23), rs1552462 (SYT9) (OR 1.27, 95%CI 1.02-1.59), and rs7074891 (CUBN) (OR 1.91, 95%CI 1.12-3.26). After correcting for multiple comparisons, no significant associations were observed in either the first or second stage. The three suggested SNPs showed no correlations with one-carbon biomarkers.
Conclusions
This is the largest genetic study to date to examine the relation between germline variations in OCM-related genes polymorphisms and the risk of PanCa. Suggestive evidence for an association between polymorphisms and PanCa was observed among the cohort-nested studies, but this did not replicate in the case-control studies. Our results do not strongly support the hypothesis that genes related to OCM play a role in pancreatic carcinogenesis.
doi:10.1007/s10552-012-0138-0
PMCID: PMC4127987  PMID: 23334854
Pancreatic cancer; One-carbon metabolism; Polymorphisms; Biomarkers; Epidemiology
4.  Evaluation of Human Papillomavirus Antibodies and Risk of Subsequent Head and Neck Cancer 
Journal of Clinical Oncology  2013;31(21):2708-2715.
Purpose
Human papillomavirus type 16 (HPV16) infection is causing an increasing number of oropharyngeal cancers in the United States and Europe. The aim of our study was to investigate whether HPV antibodies are associated with head and neck cancer risk when measured in prediagnostic sera.
Methods
We identified 638 participants with incident head and neck cancers (patients; 180 oral cancers, 135 oropharynx cancers, and 247 hypopharynx/larynx cancers) and 300 patients with esophageal cancers as well as 1,599 comparable controls from within the European Prospective Investigation Into Cancer and Nutrition cohort. Prediagnostic plasma samples from patients (collected, on average, 6 years before diagnosis) and control participants were analyzed for antibodies against multiple proteins of HPV16 as well as HPV6, HPV11, HPV18, HPV31, HPV33, HPV45, and HPV52. Odds ratios (ORs) of cancer and 95% CIs were calculated, adjusting for potential confounders. All-cause mortality was evaluated among patients using Cox proportional hazards regression.
Results
HPV16 E6 seropositivity was present in prediagnostic samples for 34.8% of patients with oropharyngeal cancer and 0.6% of controls (OR, 274; 95% CI, 110 to 681) but was not associated with other cancer sites. The increased risk of oropharyngeal cancer among HPV16 E6 seropositive participants was independent of time between blood collection and diagnosis and was observed more than 10 years before diagnosis. The all-cause mortality ratio among patients with oropharyngeal cancer was 0.30 (95% CI, 0.13 to 0.67), for patients who were HPV16 E6 seropositive compared with seronegative.
Conclusion
HPV16 E6 seropositivity was present more than 10 years before diagnosis of oropharyngeal cancers.
doi:10.1200/JCO.2012.47.2738
PMCID: PMC3709056  PMID: 23775966
5.  Genetic Determinants of Long-Term Changes in Blood Lipid Concentrations: 10-Year Follow-Up of the GLACIER Study 
PLoS Genetics  2014;10(6):e1004388.
Recent genome-wide meta-analyses identified 157 loci associated with cross-sectional lipid traits. Here we tested whether these loci associate (singly and in trait-specific genetic risk scores [GRS]) with longitudinal changes in total cholesterol (TC) and triglyceride (TG) levels in a population-based prospective cohort from Northern Sweden (the GLACIER Study). We sought replication in a southern Swedish cohort (the MDC Study; N = 2,943). GLACIER Study participants (N = 6,064) were genotyped with the MetaboChip array. Up to 3,495 participants had 10-yr follow-up data available in the GLACIER Study. The TC- and TG-specific GRSs were strongly associated with change in lipid levels (β = 0.02 mmol/l per effect allele per decade follow-up, P = 2.0×10−11 for TC; β = 0.02 mmol/l per effect allele per decade follow-up, P = 5.0×10−5 for TG). In individual SNP analysis, one TC locus, apolipoprotein E (APOE) rs4420638 (β = 0.12 mmol/l per effect allele per decade follow-up, P = 2.0×10−5), and two TG loci, tribbles pseudokinase 1 (TRIB1) rs2954029 (β = 0.09 mmol/l per effect allele per decade follow-up, P = 5.1×10−4) and apolipoprotein A-I (APOA1) rs6589564 (β = 0.31 mmol/l per effect allele per decade follow-up, P = 1.4×10−8), remained significantly associated with longitudinal changes for the respective traits after correction for multiple testing. An additional 12 loci were nominally associated with TC or TG changes. In replication analyses, the APOE rs4420638, TRIB1 rs2954029, and APOA1 rs6589564 associations were confirmed (P≤0.001). In summary, trait-specific GRSs are robustly associated with 10-yr changes in lipid levels and three individual SNPs were strongly associated with 10-yr changes in lipid levels.
Author Summary
Although large cross-sectional studies have proven highly successful in identifying gene variants related to lipid levels and other cardiometabolic traits, very few examples of well-designed longitudinal studies exist where associations between genotypes and long-term changes in lipids have been assessed. Here we undertook analyses in the GLACIER Study to determine whether the 157 previously identified lipid-associated genes variants associate with changes in blood lipid levels over 10-yr follow-up. We identified a variant in APOE that is robustly associated with total cholesterol change and two variants in TRIB1 and APOA1 respectively that are robustly associated with triglyceride change. We replicated these findings in a second Swedish cohort (the MDC Study). The identified genes had previously been associated with cardiovascular traits such as myocardial infarction or coronary heart disease; hence, these novel lipid associations provide additional insight into the pathogenesis of atherosclerotic heart and large vessel disease. By incorporating all 157 established variants into gene scores, we also observed strong associations with 10-yr lipid changes, illustrating the polygenic nature of blood lipid deterioration.
doi:10.1371/journal.pgen.1004388
PMCID: PMC4055682  PMID: 24922540
6.  Known glioma risk loci are associated with glioma with a family history of brain tumours - a case-control gene association study 
Familial cancer can be used to leverage genetic association studies. Recent genome-wide association studies have reported independent associations between seven single nucleotide polymorphisms (SNPs) and risk of glioma. The aim of this study was to investigate whether glioma cases with a positive family history of brain tumours, defined as having at least one first or second degree relative with a history of brain tumour, are associated with known glioma risk loci. 1431 glioma cases and 2868 cancer-free controls were identified from four case-control studies and two prospective cohorts from USA, Sweden, and Denmark and genotyped for seven SNPs previously reported to be associated with glioma risk in case-control designed studies. Odds ratios were calculated by unconditional logistic regression. In analyses including glioma cases with a family history of brain tumours (n=104) and control subjects free of glioma at baseline, three out of seven SNPs were associated with glioma risk; rs2736100 (5p15.33, TERT), rs4977756 (9p21.3, CDKN2A-CDKN2B), and rs6010620 (20q13.33, RTEL1). After Bonferroni correction for multiple comparisons, only one marker was statistically significantly associated with glioma risk, rs6010620 (ORtrend for the minor (A) allele, 0.39; 95% CI, 0.25–0.61; Bonferroni adjusted ptrend, 1.7×10−4). In conclusion, as previously shown for glioma regardless of family history of brain tumours, rs6010620 (RTEL1) was associated with an increased risk of glioma when restricting to cases with family history of brain tumours. These findings require confirmation in further studies with a larger number of glioma cases with a family history of brain tumours.
doi:10.1002/ijc.27922
PMCID: PMC3586297  PMID: 23115063
Glioma; brain tumours; genome-wide association study; single nucleotide polymorphism
7.  Discovery and Refinement of Loci Associated with Lipid Levels 
Willer, Cristen J. | Schmidt, Ellen M. | Sengupta, Sebanti | Peloso, Gina M. | Gustafsson, Stefan | Kanoni, Stavroula | Ganna, Andrea | Chen, Jin | Buchkovich, Martin L. | Mora, Samia | Beckmann, Jacques S. | Bragg-Gresham, Jennifer L. | Chang, Hsing-Yi | Demirkan, Ayşe | Den Hertog, Heleen M. | Do, Ron | Donnelly, Louise A. | Ehret, Georg B. | Esko, Tõnu | Feitosa, Mary F. | Ferreira, Teresa | Fischer, Krista | Fontanillas, Pierre | Fraser, Ross M. | Freitag, Daniel F. | Gurdasani, Deepti | Heikkilä, Kauko | Hyppönen, Elina | Isaacs, Aaron | Jackson, Anne U. | Johansson, Åsa | Johnson, Toby | Kaakinen, Marika | Kettunen, Johannes | Kleber, Marcus E. | Li, Xiaohui | Luan, Jian’an | Lyytikäinen, Leo-Pekka | Magnusson, Patrik K.E. | Mangino, Massimo | Mihailov, Evelin | Montasser, May E. | Müller-Nurasyid, Martina | Nolte, Ilja M. | O’Connell, Jeffrey R. | Palmer, Cameron D. | Perola, Markus | Petersen, Ann-Kristin | Sanna, Serena | Saxena, Richa | Service, Susan K. | Shah, Sonia | Shungin, Dmitry | Sidore, Carlo | Song, Ci | Strawbridge, Rona J. | Surakka, Ida | Tanaka, Toshiko | Teslovich, Tanya M. | Thorleifsson, Gudmar | Van den Herik, Evita G. | Voight, Benjamin F. | Volcik, Kelly A. | Waite, Lindsay L. | Wong, Andrew | Wu, Ying | Zhang, Weihua | Absher, Devin | Asiki, Gershim | Barroso, Inês | Been, Latonya F. | Bolton, Jennifer L. | Bonnycastle, Lori L | Brambilla, Paolo | Burnett, Mary S. | Cesana, Giancarlo | Dimitriou, Maria | Doney, Alex S.F. | Döring, Angela | Elliott, Paul | Epstein, Stephen E. | Ingi Eyjolfsson, Gudmundur | Gigante, Bruna | Goodarzi, Mark O. | Grallert, Harald | Gravito, Martha L. | Groves, Christopher J. | Hallmans, Göran | Hartikainen, Anna-Liisa | Hayward, Caroline | Hernandez, Dena | Hicks, Andrew A. | Holm, Hilma | Hung, Yi-Jen | Illig, Thomas | Jones, Michelle R. | Kaleebu, Pontiano | Kastelein, John J.P. | Khaw, Kay-Tee | Kim, Eric | Klopp, Norman | Komulainen, Pirjo | Kumari, Meena | Langenberg, Claudia | Lehtimäki, Terho | Lin, Shih-Yi | Lindström, Jaana | Loos, Ruth J.F. | Mach, François | McArdle, Wendy L | Meisinger, Christa | Mitchell, Braxton D. | Müller, Gabrielle | Nagaraja, Ramaiah | Narisu, Narisu | Nieminen, Tuomo V.M. | Nsubuga, Rebecca N. | Olafsson, Isleifur | Ong, Ken K. | Palotie, Aarno | Papamarkou, Theodore | Pomilla, Cristina | Pouta, Anneli | Rader, Daniel J. | Reilly, Muredach P. | Ridker, Paul M. | Rivadeneira, Fernando | Rudan, Igor | Ruokonen, Aimo | Samani, Nilesh | Scharnagl, Hubert | Seeley, Janet | Silander, Kaisa | Stančáková, Alena | Stirrups, Kathleen | Swift, Amy J. | Tiret, Laurence | Uitterlinden, Andre G. | van Pelt, L. Joost | Vedantam, Sailaja | Wainwright, Nicholas | Wijmenga, Cisca | Wild, Sarah H. | Willemsen, Gonneke | Wilsgaard, Tom | Wilson, James F. | Young, Elizabeth H. | Zhao, Jing Hua | Adair, Linda S. | Arveiler, Dominique | Assimes, Themistocles L. | Bandinelli, Stefania | Bennett, Franklyn | Bochud, Murielle | Boehm, Bernhard O. | Boomsma, Dorret I. | Borecki, Ingrid B. | Bornstein, Stefan R. | Bovet, Pascal | Burnier, Michel | Campbell, Harry | Chakravarti, Aravinda | Chambers, John C. | Chen, Yii-Der Ida | Collins, Francis S. | Cooper, Richard S. | Danesh, John | Dedoussis, George | de Faire, Ulf | Feranil, Alan B. | Ferrières, Jean | Ferrucci, Luigi | Freimer, Nelson B. | Gieger, Christian | Groop, Leif C. | Gudnason, Vilmundur | Gyllensten, Ulf | Hamsten, Anders | Harris, Tamara B. | Hingorani, Aroon | Hirschhorn, Joel N. | Hofman, Albert | Hovingh, G. Kees | Hsiung, Chao Agnes | Humphries, Steve E. | Hunt, Steven C. | Hveem, Kristian | Iribarren, Carlos | Järvelin, Marjo-Riitta | Jula, Antti | Kähönen, Mika | Kaprio, Jaakko | Kesäniemi, Antero | Kivimaki, Mika | Kooner, Jaspal S. | Koudstaal, Peter J. | Krauss, Ronald M. | Kuh, Diana | Kuusisto, Johanna | Kyvik, Kirsten O. | Laakso, Markku | Lakka, Timo A. | Lind, Lars | Lindgren, Cecilia M. | Martin, Nicholas G. | März, Winfried | McCarthy, Mark I. | McKenzie, Colin A. | Meneton, Pierre | Metspalu, Andres | Moilanen, Leena | Morris, Andrew D. | Munroe, Patricia B. | Njølstad, Inger | Pedersen, Nancy L. | Power, Chris | Pramstaller, Peter P. | Price, Jackie F. | Psaty, Bruce M. | Quertermous, Thomas | Rauramaa, Rainer | Saleheen, Danish | Salomaa, Veikko | Sanghera, Dharambir K. | Saramies, Jouko | Schwarz, Peter E.H. | Sheu, Wayne H-H | Shuldiner, Alan R. | Siegbahn, Agneta | Spector, Tim D. | Stefansson, Kari | Strachan, David P. | Tayo, Bamidele O. | Tremoli, Elena | Tuomilehto, Jaakko | Uusitupa, Matti | van Duijn, Cornelia M. | Vollenweider, Peter | Wallentin, Lars | Wareham, Nicholas J. | Whitfield, John B. | Wolffenbuttel, Bruce H.R. | Ordovas, Jose M. | Boerwinkle, Eric | Palmer, Colin N.A. | Thorsteinsdottir, Unnur | Chasman, Daniel I. | Rotter, Jerome I. | Franks, Paul W. | Ripatti, Samuli | Cupples, L. Adrienne | Sandhu, Manjinder S. | Rich, Stephen S. | Boehnke, Michael | Deloukas, Panos | Kathiresan, Sekar | Mohlke, Karen L. | Ingelsson, Erik | Abecasis, Gonçalo R.
Nature genetics  2013;45(11):10.1038/ng.2797.
Low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol, triglycerides, and total cholesterol are heritable, modifiable, risk factors for coronary artery disease. To identify new loci and refine known loci influencing these lipids, we examined 188,578 individuals using genome-wide and custom genotyping arrays. We identify and annotate 157 loci associated with lipid levels at P < 5×10−8, including 62 loci not previously associated with lipid levels in humans. Using dense genotyping in individuals of European, East Asian, South Asian, and African ancestry, we narrow association signals in 12 loci. We find that loci associated with blood lipids are often associated with cardiovascular and metabolic traits including coronary artery disease, type 2 diabetes, blood pressure, waist-hip ratio, and body mass index. Our results illustrate the value of genetic data from individuals of diverse ancestries and provide insights into biological mechanisms regulating blood lipids to guide future genetic, biological, and therapeutic research.
doi:10.1038/ng.2797
PMCID: PMC3838666  PMID: 24097068
8.  Common variants associated with plasma triglycerides and risk for coronary artery disease 
Do, Ron | Willer, Cristen J. | Schmidt, Ellen M. | Sengupta, Sebanti | Gao, Chi | Peloso, Gina M. | Gustafsson, Stefan | Kanoni, Stavroula | Ganna, Andrea | Chen, Jin | Buchkovich, Martin L. | Mora, Samia | Beckmann, Jacques S. | Bragg-Gresham, Jennifer L. | Chang, Hsing-Yi | Demirkan, Ayşe | Den Hertog, Heleen M. | Donnelly, Louise A. | Ehret, Georg B. | Esko, Tõnu | Feitosa, Mary F. | Ferreira, Teresa | Fischer, Krista | Fontanillas, Pierre | Fraser, Ross M. | Freitag, Daniel F. | Gurdasani, Deepti | Heikkilä, Kauko | Hyppönen, Elina | Isaacs, Aaron | Jackson, Anne U. | Johansson, Åsa | Johnson, Toby | Kaakinen, Marika | Kettunen, Johannes | Kleber, Marcus E. | Li, Xiaohui | Luan, Jian'an | Lyytikäinen, Leo-Pekka | Magnusson, Patrik K.E. | Mangino, Massimo | Mihailov, Evelin | Montasser, May E. | Müller-Nurasyid, Martina | Nolte, Ilja M. | O'Connell, Jeffrey R. | Palmer, Cameron D. | Perola, Markus | Petersen, Ann-Kristin | Sanna, Serena | Saxena, Richa | Service, Susan K. | Shah, Sonia | Shungin, Dmitry | Sidore, Carlo | Song, Ci | Strawbridge, Rona J. | Surakka, Ida | Tanaka, Toshiko | Teslovich, Tanya M. | Thorleifsson, Gudmar | Van den Herik, Evita G. | Voight, Benjamin F. | Volcik, Kelly A. | Waite, Lindsay L. | Wong, Andrew | Wu, Ying | Zhang, Weihua | Absher, Devin | Asiki, Gershim | Barroso, Inês | Been, Latonya F. | Bolton, Jennifer L. | Bonnycastle, Lori L | Brambilla, Paolo | Burnett, Mary S. | Cesana, Giancarlo | Dimitriou, Maria | Doney, Alex S.F. | Döring, Angela | Elliott, Paul | Epstein, Stephen E. | Eyjolfsson, Gudmundur Ingi | Gigante, Bruna | Goodarzi, Mark O. | Grallert, Harald | Gravito, Martha L. | Groves, Christopher J. | Hallmans, Göran | Hartikainen, Anna-Liisa | Hayward, Caroline | Hernandez, Dena | Hicks, Andrew A. | Holm, Hilma | Hung, Yi-Jen | Illig, Thomas | Jones, Michelle R. | Kaleebu, Pontiano | Kastelein, John J.P. | Khaw, Kay-Tee | Kim, Eric | Klopp, Norman | Komulainen, Pirjo | Kumari, Meena | Langenberg, Claudia | Lehtimäki, Terho | Lin, Shih-Yi | Lindström, Jaana | Loos, Ruth J.F. | Mach, François | McArdle, Wendy L | Meisinger, Christa | Mitchell, Braxton D. | Müller, Gabrielle | Nagaraja, Ramaiah | Narisu, Narisu | Nieminen, Tuomo V.M. | Nsubuga, Rebecca N. | Olafsson, Isleifur | Ong, Ken K. | Palotie, Aarno | Papamarkou, Theodore | Pomilla, Cristina | Pouta, Anneli | Rader, Daniel J. | Reilly, Muredach P. | Ridker, Paul M. | Rivadeneira, Fernando | Rudan, Igor | Ruokonen, Aimo | Samani, Nilesh | Scharnagl, Hubert | Seeley, Janet | Silander, Kaisa | Stančáková, Alena | Stirrups, Kathleen | Swift, Amy J. | Tiret, Laurence | Uitterlinden, Andre G. | van Pelt, L. Joost | Vedantam, Sailaja | Wainwright, Nicholas | Wijmenga, Cisca | Wild, Sarah H. | Willemsen, Gonneke | Wilsgaard, Tom | Wilson, James F. | Young, Elizabeth H. | Zhao, Jing Hua | Adair, Linda S. | Arveiler, Dominique | Assimes, Themistocles L. | Bandinelli, Stefania | Bennett, Franklyn | Bochud, Murielle | Boehm, Bernhard O. | Boomsma, Dorret I. | Borecki, Ingrid B. | Bornstein, Stefan R. | Bovet, Pascal | Burnier, Michel | Campbell, Harry | Chakravarti, Aravinda | Chambers, John C. | Chen, Yii-Der Ida | Collins, Francis S. | Cooper, Richard S. | Danesh, John | Dedoussis, George | de Faire, Ulf | Feranil, Alan B. | Ferrières, Jean | Ferrucci, Luigi | Freimer, Nelson B. | Gieger, Christian | Groop, Leif C. | Gudnason, Vilmundur | Gyllensten, Ulf | Hamsten, Anders | Harris, Tamara B. | Hingorani, Aroon | Hirschhorn, Joel N. | Hofman, Albert | Hovingh, G. Kees | Hsiung, Chao Agnes | Humphries, Steve E. | Hunt, Steven C. | Hveem, Kristian | Iribarren, Carlos | Järvelin, Marjo-Riitta | Jula, Antti | Kähönen, Mika | Kaprio, Jaakko | Kesäniemi, Antero | Kivimaki, Mika | Kooner, Jaspal S. | Koudstaal, Peter J. | Krauss, Ronald M. | Kuh, Diana | Kuusisto, Johanna | Kyvik, Kirsten O. | Laakso, Markku | Lakka, Timo A. | Lind, Lars | Lindgren, Cecilia M. | Martin, Nicholas G. | März, Winfried | McCarthy, Mark I. | McKenzie, Colin A. | Meneton, Pierre | Metspalu, Andres | Moilanen, Leena | Morris, Andrew D. | Munroe, Patricia B. | Njølstad, Inger | Pedersen, Nancy L. | Power, Chris | Pramstaller, Peter P. | Price, Jackie F. | Psaty, Bruce M. | Quertermous, Thomas | Rauramaa, Rainer | Saleheen, Danish | Salomaa, Veikko | Sanghera, Dharambir K. | Saramies, Jouko | Schwarz, Peter E.H. | Sheu, Wayne H-H | Shuldiner, Alan R. | Siegbahn, Agneta | Spector, Tim D. | Stefansson, Kari | Strachan, David P. | Tayo, Bamidele O. | Tremoli, Elena | Tuomilehto, Jaakko | Uusitupa, Matti | van Duijn, Cornelia M. | Vollenweider, Peter | Wallentin, Lars | Wareham, Nicholas J. | Whitfield, John B. | Wolffenbuttel, Bruce H.R. | Altshuler, David | Ordovas, Jose M. | Boerwinkle, Eric | Palmer, Colin N.A. | Thorsteinsdottir, Unnur | Chasman, Daniel I. | Rotter, Jerome I. | Franks, Paul W. | Ripatti, Samuli | Cupples, L. Adrienne | Sandhu, Manjinder S. | Rich, Stephen S. | Boehnke, Michael | Deloukas, Panos | Mohlke, Karen L. | Ingelsson, Erik | Abecasis, Goncalo R. | Daly, Mark J. | Neale, Benjamin M. | Kathiresan, Sekar
Nature genetics  2013;45(11):1345-1352.
Triglycerides are transported in plasma by specific triglyceride-rich lipoproteins; in epidemiologic studies, increased triglyceride levels correlate with higher risk for coronary artery disease (CAD). However, it is unclear whether this association reflects causal processes. We used 185 common variants recently mapped for plasma lipids (P<5×10−8 for each) to examine the role of triglycerides on risk for CAD. First, we highlight loci associated with both low-density lipoprotein cholesterol (LDL-C) and triglycerides, and show that the direction and magnitude of both are factors in determining CAD risk. Second, we consider loci with only a strong magnitude of association with triglycerides and show that these loci are also associated with CAD. Finally, in a model accounting for effects on LDL-C and/or high-density lipoprotein cholesterol, a polymorphism's strength of effect on triglycerides is correlated with the magnitude of its effect on CAD risk. These results suggest that triglyceride-rich lipoproteins causally influence risk for CAD.
doi:10.1038/ng.2795
PMCID: PMC3904346  PMID: 24097064
9.  Metabolic risk factors for esophageal squamous cell carcinoma and adenocarcinoma: a prospective study of 580 000 subjects within the Me-Can project 
BMC Cancer  2014;14:103.
Background
Obesity is associated with an increased risk of esophageal adenocarcinoma (EAC) and a decreased risk of esophageal squamous cell carcinoma (ESCC). However, little is known about the risk of EAC and ESCC related to other metabolic risk factors. We aimed to examine the risk of EAC and ESCC in relation to metabolic risk factors, separately and combined in a prospective cohort study.
Methods
The Metabolic Syndrome and Cancer cohort includes prospective cohorts in Austria, Norway and Sweden, with blood pressure, lipids, glucose and BMI available from 578 700 individuals. Relative risk (RR) for EAC and ESCC was calculated using Cox’s proportional hazards analysis for metabolic risk factors categorized into quintiles and transformed into z-scores. The standardized sum of all z-scores was used as a composite score for the metabolic syndrome (MetS).
Results
In total, 324 histologically verified cases of esophageal cancer were identified (114 EAC, 184 ESCC and 26 with other histology). BMI was associated with an increased risk of EAC (RR 7.34 (95% confidence interval, 2.88-18.7) top versus bottom quintile) and negatively associated with the risk of ESCC (RR 0.38 (0.23-0.62)). The mean value of systolic and diastolic blood pressure (mid blood pressure) was associated with the risk of ESCC (RR 1.77 (1.37-2.29)). The composite MetS score was associated with the risk of EAC (RR 1.56 (1.19-2.05) per one unit increase of z-score) but not ESCC.
Conclusions
In accordance with previous studies, high BMI was associated with an increased risk of EAC and a decreased risk of ESCC. An association between high blood pressure and risk of ESCC was observed but alcohol consumption is a potential confounding factor that we were not able to adjust for in the analysis. The MetS was associated with EAC but not ESCC. However this association was largely driven by the strong association between BMI and EAC. We hypothesize that this association is more likely to be explained by factors directly related to obesity than the metabolic state of the MetS, considering that no other metabolic factor than BMI was associated with EAC.
doi:10.1186/1471-2407-14-103
PMCID: PMC3929907  PMID: 24548688
Esophageal cancer; Esophageal adenocarcinoma; Esophageal squamous cell carcinoma; Obesity; Hypertension
10.  Diabetes and risk of pancreatic cancer: a pooled analysis from the pancreatic cancer cohort consortium 
Cancer causes & control : CCC  2012;24(1):13-25.
Purpose
Diabetes is a suspected risk factor for pancreatic cancer, but questions remain about whether it is a risk factor or a result of the disease. This study prospectively examined the association between diabetes and the risk of pancreatic adenocarcinoma in pooled data from the NCI pancreatic cancer cohort consortium (PanScan).
Methods
The pooled data included 1,621 pancreatic adenocarcinoma cases and 1,719 matched controls from twelve cohorts using a nested case–control study design. Subjects who were diagnosed with diabetes near the time (<2 years) of pancreatic cancer diagnosis were excluded from all analyses. All analyses were adjusted for age, race, gender, study, alcohol use, smoking, BMI, and family history of pancreatic cancer.
Results
Self-reported diabetes was associated with a forty percent increased risk of pancreatic cancer (OR = 1.40, 95 % CI: 1.07, 1.84). The association differed by duration of diabetes; risk was highest for those with a duration of 2–8 years (OR = 1.79, 95 % CI: 1.25, 2.55); there was no association for those with 9+ years of diabetes (OR = 1.02, 95 % CI: 0.68, 1.52).
Conclusions
These findings provide support for a relationship between diabetes and pancreatic cancer risk. The absence of association in those with the longest duration of diabetes may reflect hypoinsulinemia and warrants further investigation.
doi:10.1007/s10552-012-0078-8
PMCID: PMC3529822  PMID: 23112111
Diabetes; Risk factor; Cohort consortium; Pancreatic cancer
11.  Genome-Wide Association Study of Survival in Patients with Pancreatic Adenocarcinoma 
Gut  2012;63(1):10.1136/gutjnl-2012-303477.
Objective
Survival of patients with pancreatic adenocarcinoma is limited and few prognostic factors are known. We conducted a two-stage genome-wide association study (GWAS) to identify germline variants associated with survival in patients with pancreatic adenocarcinoma.
Design
We analyzed overall survival in relation to single nucleotide polymorphisms (SNPs) among 1,005 patients from two large GWAS datasets, PanScan I and ChinaPC. Cox proportional hazards regression was used in an additive genetic model with adjustment for age, sex, clinical stage and the top four principal components of population stratification. The first stage included 642 cases of European ancestry (PanScan), from which the top SNPs (P≤10−5) were advanced to a joint analysis with 363 additional patients from China (ChinaPC).
Results
In the first stage of cases of European descent, the top-ranked loci were at chromosomes 11p15.4, 18p11.21, and 1p36.13, tagged by rs12362504 (P=1.63×10−7), rs981621 (P=1.65×10−7), and rs16861827 (P=3.75×10−7), respectively. One-hundred thirty-one SNPs with P ≤ 10−5 were advanced to a joint analysis with cases from the ChinaPC study. In the joint analysis, the top-ranked SNP was rs10500715 (minor allele frequency, 0.37; P=1.72×10−7) on chromosome 11p15.4, which is intronic to the SET binding factor 2 (SBF2) gene. The hazard ratio (95% CI) for death was 0.74 (0.66–0.84) in PanScan I, 0.79 (0.65–0.97) in ChinaPC, and 0.76 (0.68–0.84) in the joint analysis.
Conclusion
Germline genetic variation in the SBF2 locus was associated with overall survival in patients with pancreatic adenocarcinoma of European and Asian ancestry. This association should be investigated in additional large patient cohorts.
doi:10.1136/gutjnl-2012-303477
PMCID: PMC3816124  PMID: 23180869
Pancreatic cancer; GWAS; single nucleotide polymorphism; SET binding factor 2
12.  Circulating Carotenoids and Risk of Breast Cancer: Pooled Analysis of Eight Prospective Studies 
Background
Carotenoids, micronutrients in fruits and vegetables, may reduce breast cancer risk. Most, but not all, past studies of circulating carotenoids and breast cancer have found an inverse association with at least one carotenoid, although the specific carotenoid has varied across studies.
Methods
We conducted a pooled analysis of eight cohort studies comprising more than 80% of the world’s published prospective data on plasma or serum carotenoids and breast cancer, including 3055 case subjects and 3956 matched control subjects. To account for laboratory differences and examine population differences across studies, we recalibrated participant carotenoid levels to a common standard by reassaying 20 plasma or serum samples from each cohort together at the same laboratory. Using conditional logistic regression, adjusting for several breast cancer risk factors, we calculated relative risks (RRs) and 95% confidence intervals (CIs) using quintiles defined among the control subjects from all studies. All P values are two-sided.
Results
Statistically significant inverse associations with breast cancer were observed for α-carotene (top vs bottom quintile RR = 0.87, 95% CI = 0.71 to 1.05, Ptrend = .04), β-carotene (RR = 0.83, 95% CI = 0.70 to 0.98, Ptrend = .02), lutein+zeaxanthin (RR = 0.84, 95% CI = 0.70 to 1.01, Ptrend = .05), lycopene (RR = 0.78, 95% CI = 0.62 to 0.99, Ptrend = .02), and total carotenoids (RR = 0.81, 95% CI = 0.68 to 0.96, Ptrend = .01). β-Cryptoxanthin was not statistically significantly associated with risk. Tests for heterogeneity across studies were not statistically significant. For several carotenoids, associations appeared stronger for estrogen receptor negative (ER−) than for ER+ tumors (eg, β-carotene: ER−: top vs bottom quintile RR = 0.52, 95% CI = 0.36 to 0.77, Ptrend = .001; ER+: RR = 0.83, 95% CI = 0.66 to 1.04, Ptrend = .06; Pheterogeneity = .01).
Conclusions
This comprehensive prospective analysis suggests women with higher circulating levels of α-carotene, β-carotene, lutein+zeaxanthin, lycopene, and total carotenoids may be at reduced risk of breast cancer.
doi:10.1093/jnci/djs461
PMCID: PMC3525817  PMID: 23221879
13.  High-fiber rye diet increases ileal excretion of energy and macronutrients compared with low-fiber wheat diet independent of meal frequency in ileostomy subjects 
Food & Nutrition Research  2013;57:10.3402/fnr.v57i0.18519.
Background
Whole-grain foods and cereal dietary fiber intake is associated with lower body weight. This may partly result from lower energy utilization of high-fiber diets.
Objective
In the present study, the impact on ileal excretion of energy and macronutrients in response to a rye bread high-fiber diet compared to a refined wheat low-fiber diet was investigated. Furthermore, the effect of meal frequency on apparent absorption of nutrients was studied for the first time.
Design
Ten participants that had undergone ileostomy consumed standardized iso-caloric diets, including low-fiber wheat bread (20 g dietary fiber per day) for 2 weeks followed by high-fiber rye bread (52 g dietary fiber per day) for 2 weeks. The diets were consumed in an ordinary (three meals per day) and a nibbling (seven meals per day) meal frequency in a cross-over design. Ileal effluents were collected during 24 h at the third day of each of the four dietary periods and analyzed for gross energy and nutrient contents.
Results
The results showed that intake of rye bread high-fiber diet compared to the refined wheat low-fiber diet caused an increase in ileal excretion of energy and macronutrients. The effect was independent of meal frequency. This suggests that a high intake of rye may result in lower availability of macronutrients for small intestinal digestion and absorption. A regular intake of rye may therefore have implications for weight management.
doi:10.3402/fnr.v57i0.18519
PMCID: PMC3862981  PMID: 24358035
rye bread; refined wheat; meal frequency; ileal excretion; ileostomy
14.  Pooled cohort study on height and risk of cancer and cancer death 
Cancer Causes & Control  2013;25(2):151-159.
Purpose
To assess the association between height and risk of cancer and cancer death.
Methods
The metabolic syndrome and cancer project is a prospective pooled cohort study of 585,928 participants from seven cohorts in Austria, Norway, and Sweden. Hazard ratios (HRs) and 95 % confidence intervals (CIs) for cancer incidence and death were estimated in height categories and per 5-cm increment for each cancer site using Cox proportional hazards model.
Results
During a mean follow-up of 12.7 years (SD = 7.2), 38,862 participants were diagnosed with cancer and 13,547 participants died of cancer. Increased height (per 5-cm increment) was associated with an increased overall cancer risk in women, HR 1.07 (95 % CI 1.06–1.09), and in men, HR 1.04 (95 % CI 1.03–1.06). The highest HR was seen for malignant melanoma in women, HR 1.17 (95 % CI 1.11–1.24), and in men HR 1.12 (95 % CI 1.08–1.19). Height was also associated with increased risk of cancer death in women, HR 1.03 (95 % CI 1.01–1.16), and in men, HR 1.03 (95 % CI 1.01–1.05). The highest HR was observed for breast cancer death in postmenopausal women (>60 years), HR 1.10 (95 % CI 1.00–1.21), and death from renal cell carcinoma in men, HR 1.18 (95 % CI 1.07–1.30). All these associations were independent of body mass index.
Conclusion
Height was associated with risk of cancer and cancer death indicating that factors related to height such as hormonal and genetic factors stimulate both cancer development and progression.
doi:10.1007/s10552-013-0317-7
PMCID: PMC3929024  PMID: 24173535
Body stature; Body height; Epidemiology; Cancer risk; Cohort study
15.  Selected Polymorphisms in Sex Hormone-Related Genes, Circulating Sex Hormones and Risk of Endometrial Cancer 
Cancer epidemiology  2012;36(5):445-452.
Background
The role of estrogen and progesterone in the development of endometrial cancer is well documented. Few studies have examined the association of genetic variants in sex hormone-related genes with endometrial cancer risk.
Methods
We conducted a case-control study nested within three cohorts to examine the association of endometrial cancer risk with polymorphisms in hormone-related genes among 391 cases (92% postmenopausal at diagnosis) and 712 individually-matched controls. We also examined the association of these polymorphisms with circulating levels of sex hormones and SHBG in a cross-sectional analysis including 596 healthy postmenopausal women at blood donation (controls from this nested case-control study and from a nested case-control study of breast cancer in one of the three cohorts).
Results
Adjusting for endometrial cancer risk factors, the A allele of rs4775936 in CYP19 was significantly associated (ORper allele = 1.22, 95% CI = 1.01–1.47, ptrend = 0.04), while the T allele of rs10046 was marginally associated with increased risk of endometrial cancer (ORper allele = 1.20, 95% CI = 0.99 – 1.45, ptrend = 0.06). PGR rs1042838 was also marginally associated with risk (ORper allele = 1.25, 95% CI = 0.96–1.61, ptrend = 0.09). No significant association was found for the other polymorphisms, i.e. CYP1B1 rs1800440 and rs1056836, UGT1A1 rs8175347, SHBG rs6259 and ESR1 rs2234693. Rs8175347 was significantly associated with postmenopausal levels of estradiol, free estradiol and estrone and rs6259 with SHBG and estradiol.
Conclusion
Our findings support an association between genetic variants in CYP19, and possibly PGR, and risk of endometrial cancer.
doi:10.1016/j.canep.2012.04.006
PMCID: PMC3663487  PMID: 22633539
endometrial cancer; estrogen; sex hormone-binding globulin; progesterone receptor; single nucleotide polymorphism
16.  An Absolute Risk Model to Identify Individuals at Elevated Risk for Pancreatic Cancer in the General Population 
PLoS ONE  2013;8(9):e72311.
Purpose
We developed an absolute risk model to identify individuals in the general population at elevated risk of pancreatic cancer.
Patients and Methods
Using data on 3,349 cases and 3,654 controls from the PanScan Consortium, we developed a relative risk model for men and women of European ancestry based on non-genetic and genetic risk factors for pancreatic cancer. We estimated absolute risks based on these relative risks and population incidence rates.
Results
Our risk model included current smoking (multivariable adjusted odds ratio (OR) and 95% confidence interval: 2.20 [1.84–2.62]), heavy alcohol use (>3 drinks/day) (OR: 1.45 [1.19–1.76]), obesity (body mass index >30 kg/m2) (OR: 1.26 [1.09–1.45]), diabetes >3 years (nested case-control OR: 1.57 [1.13–2.18], case-control OR: 1.80 [1.40–2.32]), family history of pancreatic cancer (OR: 1.60 [1.20–2.12]), non-O ABO genotype (AO vs. OO genotype) (OR: 1.23 [1.10–1.37]) to (BB vs. OO genotype) (OR 1.58 [0.97–2.59]), rs3790844(chr1q32.1) (OR: 1.29 [1.19–1.40]), rs401681(5p15.33) (OR: 1.18 [1.10–1.26]) and rs9543325(13q22.1) (OR: 1.27 [1.18–1.36]). The areas under the ROC curve for risk models including only non-genetic factors, only genetic factors, and both non-genetic and genetic factors were 58%, 57% and 61%, respectively. We estimate that fewer than 3/1,000 U.S. non-Hispanic whites have more than a 5% predicted lifetime absolute risk.
Conclusion
Although absolute risk modeling using established risk factors may help to identify a group of individuals at higher than average risk of pancreatic cancer, the immediate clinical utility of our model is limited. However, a risk model can increase awareness of the various risk factors for pancreatic cancer, including modifiable behaviors.
doi:10.1371/journal.pone.0072311
PMCID: PMC3772857  PMID: 24058443
17.  Gene × Physical Activity Interactions in Obesity: Combined Analysis of 111,421 Individuals of European Ancestry 
PLoS Genetics  2013;9(7):e1003607.
Numerous obesity loci have been identified using genome-wide association studies. A UK study indicated that physical activity may attenuate the cumulative effect of 12 of these loci, but replication studies are lacking. Therefore, we tested whether the aggregate effect of these loci is diminished in adults of European ancestry reporting high levels of physical activity. Twelve obesity-susceptibility loci were genotyped or imputed in 111,421 participants. A genetic risk score (GRS) was calculated by summing the BMI-associated alleles of each genetic variant. Physical activity was assessed using self-administered questionnaires. Multiplicative interactions between the GRS and physical activity on BMI were tested in linear and logistic regression models in each cohort, with adjustment for age, age2, sex, study center (for multicenter studies), and the marginal terms for physical activity and the GRS. These results were combined using meta-analysis weighted by cohort sample size. The meta-analysis yielded a statistically significant GRS × physical activity interaction effect estimate (Pinteraction = 0.015). However, a statistically significant interaction effect was only apparent in North American cohorts (n = 39,810, Pinteraction = 0.014 vs. n = 71,611, Pinteraction = 0.275 for Europeans). In secondary analyses, both the FTO rs1121980 (Pinteraction = 0.003) and the SEC16B rs10913469 (Pinteraction = 0.025) variants showed evidence of SNP × physical activity interactions. This meta-analysis of 111,421 individuals provides further support for an interaction between physical activity and a GRS in obesity disposition, although these findings hinge on the inclusion of cohorts from North America, indicating that these results are either population-specific or non-causal.
Author Summary
We undertook analyses in 111,421 adults of European descent to examine whether physical activity diminishes the genetic risk of obesity predisposed by 12 single nucleotide polymorphisms, as previously reported in a study of 20,000 UK adults (Li et al, PLoS Med. 2010). Although the study by Li et al is widely cited, the original report has not been replicated to our knowledge. Therefore, we sought to confirm or refute the original study's findings in a combined analysis of 111,421 adults. Our analyses yielded a statistically significant interaction effect (Pinteraction = 0.015), confirming the original study's results; we also identified an interaction between the FTO locus and physical activity (Pinteraction = 0.003), verifying previous analyses (Kilpelainen et al, PLoS Med., 2010), and we detected a novel interaction between the SEC16B locus and physical activity (Pinteraction = 0.025). We also examined the power constraints of interaction analyses, thereby demonstrating that sources of within- and between-study heterogeneity and the manner in which data are treated can inhibit the detection of interaction effects in meta-analyses that combine many cohorts with varying characteristics. This suggests that combining many small studies that have measured environmental exposures differently may be relatively inefficient for the detection of gene × environment interactions.
doi:10.1371/journal.pgen.1003607
PMCID: PMC3723486  PMID: 23935507
18.  Genetic Variants in Hormone-Related Genes and Risk of Breast Cancer 
PLoS ONE  2013;8(7):e69367.
Sex hormones play a key role in the development of breast cancer. Certain polymorphic variants (SNPs and repeat polymorphisms) in hormone-related genes are associated with sex hormone levels. However, the relationship observed between these genetic variants and breast cancer risk has been inconsistent. We conducted a case-control study nested within two prospective cohorts to assess the relationship between specific genetic variants in hormone-related genes and breast cancer risk. In total, 1164 cases and 2111 individually-matched controls were included in the study. We did not observe an association between potential functional genetic polymorphisms in the estrogen pathway, SHBG rs6259, ESR1 rs2234693, CYP19 rs10046 and rs4775936, and UGT1A1 rs8175347, or the progesterone pathway, PGR rs1042838, with the risk of breast cancer. Our results suggest that these genetic variants do not have a strong effect on breast cancer risk.
doi:10.1371/journal.pone.0069367
PMCID: PMC3720532  PMID: 23935996
19.  Pathway analysis of genome-wide association study data highlights pancreatic development genes as susceptibility factors for pancreatic cancer 
Carcinogenesis  2012;33(7):1384-1390.
Four loci have been associated with pancreatic cancer through genome-wide association studies (GWAS). Pathway-based analysis of GWAS data is a complementary approach to identify groups of genes or biological pathways enriched with disease-associated single-nucleotide polymorphisms (SNPs) whose individual effect sizes may be too small to be detected by standard single-locus methods. We used the adaptive rank truncated product method in a pathway-based analysis of GWAS data from 3851 pancreatic cancer cases and 3934 control participants pooled from 12 cohort studies and 8 case–control studies (PanScan). We compiled 23 biological pathways hypothesized to be relevant to pancreatic cancer and observed a nominal association between pancreatic cancer and five pathways (P < 0.05), i.e. pancreatic development, Helicobacter pylori lacto/neolacto, hedgehog, Th1/Th2 immune response and apoptosis (P = 2.0 × 10−6, 1.6 × 10−5, 0.0019, 0.019 and 0.023, respectively). After excluding previously identified genes from the original GWAS in three pathways (NR5A2, ABO and SHH), the pancreatic development pathway remained significant (P = 8.3 × 10−5), whereas the others did not. The most significant genes (P < 0.01) in the five pathways were NR5A2, HNF1A, HNF4G and PDX1 for pancreatic development; ABO for H. pylori lacto/neolacto; SHH for hedgehog; TGFBR2 and CCL18 for Th1/Th2 immune response and MAPK8 and BCL2L11 for apoptosis. Our results provide a link between inherited variation in genes important for pancreatic development and cancer and show that pathway-based approaches to analysis of GWAS data can yield important insights into the collective role of genetic risk variants in cancer.
doi:10.1093/carcin/bgs151
PMCID: PMC3405651  PMID: 22523087
20.  Sex-stratified Genome-wide Association Studies Including 270,000 Individuals Show Sexual Dimorphism in Genetic Loci for Anthropometric Traits 
Randall, Joshua C. | Winkler, Thomas W. | Kutalik, Zoltán | Berndt, Sonja I. | Jackson, Anne U. | Monda, Keri L. | Kilpeläinen, Tuomas O. | Esko, Tõnu | Mägi, Reedik | Li, Shengxu | Workalemahu, Tsegaselassie | Feitosa, Mary F. | Croteau-Chonka, Damien C. | Day, Felix R. | Fall, Tove | Ferreira, Teresa | Gustafsson, Stefan | Locke, Adam E. | Mathieson, Iain | Scherag, Andre | Vedantam, Sailaja | Wood, Andrew R. | Liang, Liming | Steinthorsdottir, Valgerdur | Thorleifsson, Gudmar | Dermitzakis, Emmanouil T. | Dimas, Antigone S. | Karpe, Fredrik | Min, Josine L. | Nicholson, George | Clegg, Deborah J. | Person, Thomas | Krohn, Jon P. | Bauer, Sabrina | Buechler, Christa | Eisinger, Kristina | Bonnefond, Amélie | Froguel, Philippe | Hottenga, Jouke-Jan | Prokopenko, Inga | Waite, Lindsay L. | Harris, Tamara B. | Smith, Albert Vernon | Shuldiner, Alan R. | McArdle, Wendy L. | Caulfield, Mark J. | Munroe, Patricia B. | Grönberg, Henrik | Chen, Yii-Der Ida | Li, Guo | Beckmann, Jacques S. | Johnson, Toby | Thorsteinsdottir, Unnur | Teder-Laving, Maris | Khaw, Kay-Tee | Wareham, Nicholas J. | Zhao, Jing Hua | Amin, Najaf | Oostra, Ben A. | Kraja, Aldi T. | Province, Michael A. | Cupples, L. Adrienne | Heard-Costa, Nancy L. | Kaprio, Jaakko | Ripatti, Samuli | Surakka, Ida | Collins, Francis S. | Saramies, Jouko | Tuomilehto, Jaakko | Jula, Antti | Salomaa, Veikko | Erdmann, Jeanette | Hengstenberg, Christian | Loley, Christina | Schunkert, Heribert | Lamina, Claudia | Wichmann, H. Erich | Albrecht, Eva | Gieger, Christian | Hicks, Andrew A. | Johansson, Åsa | Pramstaller, Peter P. | Kathiresan, Sekar | Speliotes, Elizabeth K. | Penninx, Brenda | Hartikainen, Anna-Liisa | Jarvelin, Marjo-Riitta | Gyllensten, Ulf | Boomsma, Dorret I. | Campbell, Harry | Wilson, James F. | Chanock, Stephen J. | Farrall, Martin | Goel, Anuj | Medina-Gomez, Carolina | Rivadeneira, Fernando | Estrada, Karol | Uitterlinden, André G. | Hofman, Albert | Zillikens, M. Carola | den Heijer, Martin | Kiemeney, Lambertus A. | Maschio, Andrea | Hall, Per | Tyrer, Jonathan | Teumer, Alexander | Völzke, Henry | Kovacs, Peter | Tönjes, Anke | Mangino, Massimo | Spector, Tim D. | Hayward, Caroline | Rudan, Igor | Hall, Alistair S. | Samani, Nilesh J. | Attwood, Antony Paul | Sambrook, Jennifer G. | Hung, Joseph | Palmer, Lyle J. | Lokki, Marja-Liisa | Sinisalo, Juha | Boucher, Gabrielle | Huikuri, Heikki | Lorentzon, Mattias | Ohlsson, Claes | Eklund, Niina | Eriksson, Johan G. | Barlassina, Cristina | Rivolta, Carlo | Nolte, Ilja M. | Snieder, Harold | Van der Klauw, Melanie M. | Van Vliet-Ostaptchouk, Jana V. | Gejman, Pablo V. | Shi, Jianxin | Jacobs, Kevin B. | Wang, Zhaoming | Bakker, Stephan J. L. | Mateo Leach, Irene | Navis, Gerjan | van der Harst, Pim | Martin, Nicholas G. | Medland, Sarah E. | Montgomery, Grant W. | Yang, Jian | Chasman, Daniel I. | Ridker, Paul M. | Rose, Lynda M. | Lehtimäki, Terho | Raitakari, Olli | Absher, Devin | Iribarren, Carlos | Basart, Hanneke | Hovingh, Kees G. | Hyppönen, Elina | Power, Chris | Anderson, Denise | Beilby, John P. | Hui, Jennie | Jolley, Jennifer | Sager, Hendrik | Bornstein, Stefan R. | Schwarz, Peter E. H. | Kristiansson, Kati | Perola, Markus | Lindström, Jaana | Swift, Amy J. | Uusitupa, Matti | Atalay, Mustafa | Lakka, Timo A. | Rauramaa, Rainer | Bolton, Jennifer L. | Fowkes, Gerry | Fraser, Ross M. | Price, Jackie F. | Fischer, Krista | KrjutÅ¡kov, Kaarel | Metspalu, Andres | Mihailov, Evelin | Langenberg, Claudia | Luan, Jian'an | Ong, Ken K. | Chines, Peter S. | Keinanen-Kiukaanniemi, Sirkka M. | Saaristo, Timo E. | Edkins, Sarah | Franks, Paul W. | Hallmans, Göran | Shungin, Dmitry | Morris, Andrew David | Palmer, Colin N. A. | Erbel, Raimund | Moebus, Susanne | Nöthen, Markus M. | Pechlivanis, Sonali | Hveem, Kristian | Narisu, Narisu | Hamsten, Anders | Humphries, Steve E. | Strawbridge, Rona J. | Tremoli, Elena | Grallert, Harald | Thorand, Barbara | Illig, Thomas | Koenig, Wolfgang | Müller-Nurasyid, Martina | Peters, Annette | Boehm, Bernhard O. | Kleber, Marcus E. | März, Winfried | Winkelmann, Bernhard R. | Kuusisto, Johanna | Laakso, Markku | Arveiler, Dominique | Cesana, Giancarlo | Kuulasmaa, Kari | Virtamo, Jarmo | Yarnell, John W. G. | Kuh, Diana | Wong, Andrew | Lind, Lars | de Faire, Ulf | Gigante, Bruna | Magnusson, Patrik K. E. | Pedersen, Nancy L. | Dedoussis, George | Dimitriou, Maria | Kolovou, Genovefa | Kanoni, Stavroula | Stirrups, Kathleen | Bonnycastle, Lori L. | Njølstad, Inger | Wilsgaard, Tom | Ganna, Andrea | Rehnberg, Emil | Hingorani, Aroon | Kivimaki, Mika | Kumari, Meena | Assimes, Themistocles L. | Barroso, Inês | Boehnke, Michael | Borecki, Ingrid B. | Deloukas, Panos | Fox, Caroline S. | Frayling, Timothy | Groop, Leif C. | Haritunians, Talin | Hunter, David | Ingelsson, Erik | Kaplan, Robert | Mohlke, Karen L. | O'Connell, Jeffrey R. | Schlessinger, David | Strachan, David P. | Stefansson, Kari | van Duijn, Cornelia M. | Abecasis, Gonçalo R. | McCarthy, Mark I. | Hirschhorn, Joel N. | Qi, Lu | Loos, Ruth J. F. | Lindgren, Cecilia M. | North, Kari E. | Heid, Iris M.
PLoS Genetics  2013;9(6):e1003500.
Given the anthropometric differences between men and women and previous evidence of sex-difference in genetic effects, we conducted a genome-wide search for sexually dimorphic associations with height, weight, body mass index, waist circumference, hip circumference, and waist-to-hip-ratio (133,723 individuals) and took forward 348 SNPs into follow-up (additional 137,052 individuals) in a total of 94 studies. Seven loci displayed significant sex-difference (FDR<5%), including four previously established (near GRB14/COBLL1, LYPLAL1/SLC30A10, VEGFA, ADAMTS9) and three novel anthropometric trait loci (near MAP3K1, HSD17B4, PPARG), all of which were genome-wide significant in women (P<5×10−8), but not in men. Sex-differences were apparent only for waist phenotypes, not for height, weight, BMI, or hip circumference. Moreover, we found no evidence for genetic effects with opposite directions in men versus women. The PPARG locus is of specific interest due to its role in diabetes genetics and therapy. Our results demonstrate the value of sex-specific GWAS to unravel the sexually dimorphic genetic underpinning of complex traits.
Author Summary
Men and women differ substantially regarding height, weight, and body fat. Interestingly, previous work detecting genetic effects for waist-to-hip ratio, to assess body fat distribution, has found that many of these showed sex-differences. However, systematic searches for sex-differences in genetic effects have not yet been conducted. Therefore, we undertook a genome-wide search for sexually dimorphic genetic effects for anthropometric traits including 133,723 individuals in a large meta-analysis and followed promising variants in further 137,052 individuals, including a total of 94 studies. We identified seven loci with significant sex-difference including four previously established (near GRB14/COBLL1, LYPLAL1/SLC30A10, VEGFA, ADAMTS9) and three novel anthropometric trait loci (near MAP3K1, HSD17B4, PPARG), all of which were significant in women, but not in men. Of interest is that sex-difference was only observed for waist phenotypes, but not for height or body-mass-index. We found no evidence for sex-differences with opposite effect direction for men and women. The PPARG locus is of specific interest due to its link to diabetes genetics and therapy. Our findings demonstrate the importance of investigating sex differences, which may lead to a better understanding of disease mechanisms with a potential relevance to treatment options.
doi:10.1371/journal.pgen.1003500
PMCID: PMC3674993  PMID: 23754948
21.  Total and high-molecular weight adiponectin and risk of colorectal cancer: the European Prospective Investigation into Cancer and Nutrition Study 
Carcinogenesis  2012;33(6):1211-1218.
Adiponectin—an adipose tissue-derived protein—may provide a molecular link between obesity and colorectal cancer (CRC), but evidence from large prospective studies is limited. In particular, no epidemiological study explored high-molecular weight (HMW) and non-HMW adiponectin fractions in relation to CRC risk, despite them being hypothesized to have differential biological activities, i.e. regulating insulin sensitivity (HMW adiponectin) versus inflammatory response (non-HMW adiponectin). In a prospective, nested case–control study, we investigated whether prediagnostic serum concentrations of total, HMW and non-HMW adiponectin are associated with risk of CRC, independent of obesity and other known CRC risk factors. A total of 1206 incident cases (755 colon and 451 rectal) were matched to 1206 controls using incidence-density sampling. In conditional logistic regression, adjusted for dietary and lifestyle factors, total adiponectin and non-HMW adiponectin concentrations were inversely associated with risk of CRC [relative risk (RR) comparing highest versus lowest quintile = 0.71, 95% confidence interval (CI) = 0.53–0.95, P trend = 0.03 for total adiponectin and RR = 0.45, 95% CI = 0.34–0.61, P trend < 0.0001 for non-HMW adiponectin]. HMW adiponectin concentrations were not associated with CRC risk (RR = 0.91, 95% CI = 0.68–1.22, P trend = 0.55). Non-HMW adiponectin was associated with CRC risk even after adjustment for body mass index and waist circumference (RR = 0.39, 95% CI = 0.26–0.60, P trend < 0.0001), whereas the association with total adiponectin was no longer significant (RR = 0.81, 95% CI = 0.60–1.09, P trend = 0.23). When stratified by cancer site, non-HMW adiponectin was inversely associated with both colon and rectal cancer. These findings suggest an important role of the relative proportion of non-HMW adiponectin in CRC pathogenesis. Future studies are warranted to confirm these results and to elucidate the underlying mechanisms.
doi:10.1093/carcin/bgs133
PMCID: PMC3388489  PMID: 22431719
22.  Plasma folate, but not homocysteine, is associated with Apolipoprotein A1 levels in a non-fortified population 
Background
Elevated total plasma homocysteine (tHcy) in humans is associated with cardiovascular disease but prevention trials have failed to confirm causality. Reported reasons for this association have been that homocysteine and its major genetic determinant methylenetetrahydrofolate reductase (MTHFR) may have an effect on HDL and Apolipoprotein (Apo) A1 levels. We wanted to study if tHcy and its major determinants were correlated with Apo A1 levels in a large population without folate fortification.
Methods
This study was a prospective incident nested case-referent study within the Northern Sweden Health and Disease Study Cohort (NSHDSC), including 545 cases with first myocardial infarction and 1054 matched referents, median age at inclusion was 59 years. Univariate and multiple regression analyzes was used to study the associations between apolipoproteins Apo A1 and B, tHcy, folate and vitamin B12 in plasma as well as MTHFR polymorphisms 677C>T and 1298A>C.
Results
Apo A1 and Apo B were strongly associated with the risk of a first myocardial infarction. tHcy was not associated with Apo A1 levels. Instead, folate had an independent positive association with Apo A1 levels in univariate and multiple regression models. The associations were seen in all men and women, among referents but not among cases. MTHFR polymorphisms had no clear effect on Apo A1 levels.
Conclusions
Analyzing over 1500 subjects we found an independent positive association between plasma folate (major dietary determinant of tHcy) and Apo A1 levels among those who later did not develop a first myocardial infarction. No association was seen between tHcy and Apo A1.
doi:10.1186/1476-511X-12-74
PMCID: PMC3679998  PMID: 23697869
Apolipoprotein; Homocysteine; Myocardial infarction; Folate; Epidemiology
23.  Low-carbohydrate, high-protein diet score and risk of incident cancer; a prospective cohort study 
Nutrition Journal  2013;12:58.
Background
Although carbohydrate reduction of varying degrees is a popular and controversial dietary trend, potential long-term effects for health, and cancer in specific, are largely unknown.
Methods
We studied a previously established low-carbohydrate, high-protein (LCHP) score in relation to the incidence of cancer and specific cancer types in a population-based cohort in northern Sweden. Participants were 62,582 men and women with up to 17.8 years of follow-up (median 9.7), including 3,059 prospective cancer cases. Cox regression analyses were performed for a LCHP score based on the sum of energy-adjusted deciles of carbohydrate (descending) and protein (ascending) intake labeled 1 to 10, with higher scores representing a diet lower in carbohydrates and higher in protein. Important potential confounders were accounted for, and the role of metabolic risk profile, macronutrient quality including saturated fat intake, and adequacy of energy intake reporting was explored.
Results
For the lowest to highest LCHP scores, 2 to 20, carbohydrate intakes ranged from median 60.9 to 38.9% of total energy intake. Both protein (primarily animal sources) and particularly fat (both saturated and unsaturated) intakes increased with increasing LCHP scores. LCHP score was not related to cancer risk, except for a non-dose-dependent, positive association for respiratory tract cancer that was statistically significant in men. The multivariate hazard ratio for medium (9–13) versus low (2–8) LCHP scores was 1.84 (95% confidence interval: 1.05-3.23; p-trend = 0.38). Other analyses were largely consistent with the main results, although LCHP score was associated with colorectal cancer risk inversely in women with high saturated fat intakes, and positively in men with higher LCHP scores based on vegetable protein.
Conclusion
These largely null results provide important information concerning the long-term safety of moderate carbohydrate reduction and consequent increases in protein and, in this cohort, especially fat intakes. In order to determine the effects of stricter carbohydrate restriction, further studies encompassing a wider range of macronutrient intakes are warranted.
doi:10.1186/1475-2891-12-58
PMCID: PMC3654894  PMID: 23651548
Diet; Cancer; Macronutrients; Carbohydrate intake; Protein intake; Fat intake; Cohort study
24.  No Interactions Between Previously Associated 2-Hour Glucose Gene Variants and Physical Activity or BMI on 2-Hour Glucose Levels 
Scott, Robert A. | Chu, Audrey Y. | Grarup, Niels | Manning, Alisa K. | Hivert, Marie-France | Shungin, Dmitry | Tönjes, Anke | Yesupriya, Ajay | Barnes, Daniel | Bouatia-Naji, Nabila | Glazer, Nicole L. | Jackson, Anne U. | Kutalik, Zoltán | Lagou, Vasiliki | Marek, Diana | Rasmussen-Torvik, Laura J. | Stringham, Heather M. | Tanaka, Toshiko | Aadahl, Mette | Arking, Dan E. | Bergmann, Sven | Boerwinkle, Eric | Bonnycastle, Lori L. | Bornstein, Stefan R. | Brunner, Eric | Bumpstead, Suzannah J. | Brage, Soren | Carlson, Olga D. | Chen, Han | Chen, Yii-Der Ida | Chines, Peter S. | Collins, Francis S. | Couper, David J. | Dennison, Elaine M. | Dowling, Nicole F. | Egan, Josephine S. | Ekelund, Ulf | Erdos, Michael R. | Forouhi, Nita G. | Fox, Caroline S. | Goodarzi, Mark O. | Grässler, Jürgen | Gustafsson, Stefan | Hallmans, Göran | Hansen, Torben | Hingorani, Aroon | Holloway, John W. | Hu, Frank B. | Isomaa, Bo | Jameson, Karen A. | Johansson, Ingegerd | Jonsson, Anna | Jørgensen, Torben | Kivimaki, Mika | Kovacs, Peter | Kumari, Meena | Kuusisto, Johanna | Laakso, Markku | Lecoeur, Cécile | Lévy-Marchal, Claire | Li, Guo | Loos, Ruth J.F. | Lyssenko, Valeri | Marmot, Michael | Marques-Vidal, Pedro | Morken, Mario A. | Müller, Gabriele | North, Kari E. | Pankow, James S. | Payne, Felicity | Prokopenko, Inga | Psaty, Bruce M. | Renström, Frida | Rice, Ken | Rotter, Jerome I. | Rybin, Denis | Sandholt, Camilla H. | Sayer, Avan A. | Shrader, Peter | Schwarz, Peter E.H. | Siscovick, David S. | Stančáková, Alena | Stumvoll, Michael | Teslovich, Tanya M. | Waeber, Gérard | Williams, Gordon H. | Witte, Daniel R. | Wood, Andrew R. | Xie, Weijia | Boehnke, Michael | Cooper, Cyrus | Ferrucci, Luigi | Froguel, Philippe | Groop, Leif | Kao, W.H. Linda | Vollenweider, Peter | Walker, Mark | Watanabe, Richard M. | Pedersen, Oluf | Meigs, James B. | Ingelsson, Erik | Barroso, Inês | Florez, Jose C. | Franks, Paul W. | Dupuis, Josée | Wareham, Nicholas J. | Langenberg, Claudia
Diabetes  2012;61(5):1291-1296.
Gene–lifestyle interactions have been suggested to contribute to the development of type 2 diabetes. Glucose levels 2 h after a standard 75-g glucose challenge are used to diagnose diabetes and are associated with both genetic and lifestyle factors. However, whether these factors interact to determine 2-h glucose levels is unknown. We meta-analyzed single nucleotide polymorphism (SNP) × BMI and SNP × physical activity (PA) interaction regression models for five SNPs previously associated with 2-h glucose levels from up to 22 studies comprising 54,884 individuals without diabetes. PA levels were dichotomized, with individuals below the first quintile classified as inactive (20%) and the remainder as active (80%). BMI was considered a continuous trait. Inactive individuals had higher 2-h glucose levels than active individuals (β = 0.22 mmol/L [95% CI 0.13–0.31], P = 1.63 × 10−6). All SNPs were associated with 2-h glucose (β = 0.06–0.12 mmol/allele, P ≤ 1.53 × 10−7), but no significant interactions were found with PA (P > 0.18) or BMI (P ≥ 0.04). In this large study of gene–lifestyle interaction, we observed no interactions between genetic and lifestyle factors, both of which were associated with 2-h glucose. It is perhaps unlikely that top loci from genome-wide association studies will exhibit strong subgroup-specific effects, and may not, therefore, make the best candidates for the study of interactions.
doi:10.2337/db11-0973
PMCID: PMC3331745  PMID: 22415877
25.  A genome-wide approach accounting for body mass index identifies genetic variants influencing fasting glycemic traits and insulin resistance 
Manning, Alisa K. | Hivert, Marie-France | Scott, Robert A. | Grimsby, Jonna L. | Bouatia-Naji, Nabila | Chen, Han | Rybin, Denis | Liu, Ching-Ti | Bielak, Lawrence F. | Prokopenko, Inga | Amin, Najaf | Barnes, Daniel | Cadby, Gemma | Hottenga, Jouke-Jan | Ingelsson, Erik | Jackson, Anne U. | Johnson, Toby | Kanoni, Stavroula | Ladenvall, Claes | Lagou, Vasiliki | Lahti, Jari | Lecoeur, Cecile | Liu, Yongmei | Martinez-Larrad, Maria Teresa | Montasser, May E. | Navarro, Pau | Perry, John R. B. | Rasmussen-Torvik, Laura J. | Salo, Perttu | Sattar, Naveed | Shungin, Dmitry | Strawbridge, Rona J. | Tanaka, Toshiko | van Duijn, Cornelia M. | An, Ping | de Andrade, Mariza | Andrews, Jeanette S. | Aspelund, Thor | Atalay, Mustafa | Aulchenko, Yurii | Balkau, Beverley | Bandinelli, Stefania | Beckmann, Jacques S. | Beilby, John P. | Bellis, Claire | Bergman, Richard N. | Blangero, John | Boban, Mladen | Boehnke, Michael | Boerwinkle, Eric | Bonnycastle, Lori L. | Boomsma, Dorret I. | Borecki, Ingrid B. | Böttcher, Yvonne | Bouchard, Claude | Brunner, Eric | Budimir, Danijela | Campbell, Harry | Carlson, Olga | Chines, Peter S. | Clarke, Robert | Collins, Francis S. | Corbatón-Anchuelo, Arturo | Couper, David | de Faire, Ulf | Dedoussis, George V | Deloukas, Panos | Dimitriou, Maria | Egan, Josephine M | Eiriksdottir, Gudny | Erdos, Michael R. | Eriksson, Johan G. | Eury, Elodie | Ferrucci, Luigi | Ford, Ian | Forouhi, Nita G. | Fox, Caroline S | Franzosi, Maria Grazia | Franks, Paul W | Frayling, Timothy M | Froguel, Philippe | Galan, Pilar | de Geus, Eco | Gigante, Bruna | Glazer, Nicole L. | Goel, Anuj | Groop, Leif | Gudnason, Vilmundur | Hallmans, Göran | Hamsten, Anders | Hansson, Ola | Harris, Tamara B. | Hayward, Caroline | Heath, Simon | Hercberg, Serge | Hicks, Andrew A. | Hingorani, Aroon | Hofman, Albert | Hui, Jennie | Hung, Joseph | Jarvelin, Marjo Riitta | Jhun, Min A. | Johnson, Paul C.D. | Jukema, J Wouter | Jula, Antti | Kao, W.H. | Kaprio, Jaakko | Kardia, Sharon L. R. | Keinanen-Kiukaanniemi, Sirkka | Kivimaki, Mika | Kolcic, Ivana | Kovacs, Peter | Kumari, Meena | Kuusisto, Johanna | Kyvik, Kirsten Ohm | Laakso, Markku | Lakka, Timo | Lannfelt, Lars | Lathrop, G Mark | Launer, Lenore J. | Leander, Karin | Li, Guo | Lind, Lars | Lindstrom, Jaana | Lobbens, Stéphane | Loos, Ruth J. F. | Luan, Jian’an | Lyssenko, Valeriya | Mägi, Reedik | Magnusson, Patrik K. E. | Marmot, Michael | Meneton, Pierre | Mohlke, Karen L. | Mooser, Vincent | Morken, Mario A. | Miljkovic, Iva | Narisu, Narisu | O’Connell, Jeff | Ong, Ken K. | Oostra, Ben A. | Palmer, Lyle J. | Palotie, Aarno | Pankow, James S. | Peden, John F. | Pedersen, Nancy L. | Pehlic, Marina | Peltonen, Leena | Penninx, Brenda | Pericic, Marijana | Perola, Markus | Perusse, Louis | Peyser, Patricia A | Polasek, Ozren | Pramstaller, Peter P. | Province, Michael A. | Räikkönen, Katri | Rauramaa, Rainer | Rehnberg, Emil | Rice, Ken | Rotter, Jerome I. | Rudan, Igor | Ruokonen, Aimo | Saaristo, Timo | Sabater-Lleal, Maria | Salomaa, Veikko | Savage, David B. | Saxena, Richa | Schwarz, Peter | Seedorf, Udo | Sennblad, Bengt | Serrano-Rios, Manuel | Shuldiner, Alan R. | Sijbrands, Eric J.G. | Siscovick, David S. | Smit, Johannes H. | Small, Kerrin S. | Smith, Nicholas L. | Smith, Albert Vernon | Stančáková, Alena | Stirrups, Kathleen | Stumvoll, Michael | Sun, Yan V. | Swift, Amy J. | Tönjes, Anke | Tuomilehto, Jaakko | Trompet, Stella | Uitterlinden, Andre G. | Uusitupa, Matti | Vikström, Max | Vitart, Veronique | Vohl, Marie-Claude | Voight, Benjamin F. | Vollenweider, Peter | Waeber, Gerard | Waterworth, Dawn M | Watkins, Hugh | Wheeler, Eleanor | Widen, Elisabeth | Wild, Sarah H. | Willems, Sara M. | Willemsen, Gonneke | Wilson, James F. | Witteman, Jacqueline C.M. | Wright, Alan F. | Yaghootkar, Hanieh | Zelenika, Diana | Zemunik, Tatijana | Zgaga, Lina | Wareham, Nicholas J. | McCarthy, Mark I. | Barroso, Ines | Watanabe, Richard M. | Florez, Jose C. | Dupuis, Josée | Meigs, James B. | Langenberg, Claudia
Nature genetics  2012;44(6):659-669.
Recent genome-wide association studies have described many loci implicated in type 2 diabetes (T2D) pathophysiology and beta-cell dysfunction, but contributed little to our understanding of the genetic basis of insulin resistance. We hypothesized that genes implicated in insulin resistance pathways may be uncovered by accounting for differences in body mass index (BMI) and potential interaction between BMI and genetic variants. We applied a novel joint meta-analytical approach to test associations with fasting insulin (FI) and glucose (FG) on a genome-wide scale. We present six previously unknown FI loci at P<5×10−8 in combined discovery and follow-up analyses of 52 studies comprising up to 96,496non-diabetic individuals. Risk variants were associated with higher triglyceride and lower HDL cholesterol levels, suggestive of a role for these FI loci in insulin resistance pathways. The localization of these additional loci will aid further characterization of the role of insulin resistance in T2D pathophysiology.
doi:10.1038/ng.2274
PMCID: PMC3613127  PMID: 22581228

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