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1.  Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes 
Morris, Andrew P | Voight, Benjamin F | Teslovich, Tanya M | Ferreira, Teresa | Segrè, Ayellet V | Steinthorsdottir, Valgerdur | Strawbridge, Rona J | Khan, Hassan | Grallert, Harald | Mahajan, Anubha | Prokopenko, Inga | Kang, Hyun Min | Dina, Christian | Esko, Tonu | Fraser, Ross M | Kanoni, Stavroula | Kumar, Ashish | Lagou, Vasiliki | Langenberg, Claudia | Luan, Jian'an | Lindgren, Cecilia M | Müller-Nurasyid, Martina | Pechlivanis, Sonali | Rayner, N William | Scott, Laura J | Wiltshire, Steven | Yengo, Loic | Kinnunen, Leena | Rossin, Elizabeth J | Raychaudhuri, Soumya | Johnson, Andrew D | Dimas, Antigone S | Loos, Ruth J F | Vedantam, Sailaja | Chen, Han | Florez, Jose C | Fox, Caroline | Liu, Ching-Ti | Rybin, Denis | Couper, David J | Kao, Wen Hong L | Li, Man | Cornelis, Marilyn C | Kraft, Peter | Sun, Qi | van Dam, Rob M | Stringham, Heather M | Chines, Peter S | Fischer, Krista | Fontanillas, Pierre | Holmen, Oddgeir L | Hunt, Sarah E | Jackson, Anne U | Kong, Augustine | Lawrence, Robert | Meyer, Julia | Perry, John RB | Platou, Carl GP | Potter, Simon | Rehnberg, Emil | Robertson, Neil | Sivapalaratnam, Suthesh | Stančáková, Alena | Stirrups, Kathleen | Thorleifsson, Gudmar | Tikkanen, Emmi | Wood, Andrew R | Almgren, Peter | Atalay, Mustafa | Benediktsson, Rafn | Bonnycastle, Lori L | Burtt, Noël | Carey, Jason | Charpentier, Guillaume | Crenshaw, Andrew T | Doney, Alex S F | Dorkhan, Mozhgan | Edkins, Sarah | Emilsson, Valur | Eury, Elodie | Forsen, Tom | Gertow, Karl | Gigante, Bruna | Grant, George B | Groves, Christopher J | Guiducci, Candace | Herder, Christian | Hreidarsson, Astradur B | Hui, Jennie | James, Alan | Jonsson, Anna | Rathmann, Wolfgang | Klopp, Norman | Kravic, Jasmina | Krjutškov, Kaarel | Langford, Cordelia | Leander, Karin | Lindholm, Eero | Lobbens, Stéphane | Männistö, Satu | Mirza, Ghazala | Mühleisen, Thomas W | Musk, Bill | Parkin, Melissa | Rallidis, Loukianos | Saramies, Jouko | Sennblad, Bengt | Shah, Sonia | Sigurðsson, Gunnar | Silveira, Angela | Steinbach, Gerald | Thorand, Barbara | Trakalo, Joseph | Veglia, Fabrizio | Wennauer, Roman | Winckler, Wendy | Zabaneh, Delilah | Campbell, Harry | van Duijn, Cornelia | Uitterlinden89-, Andre G | Hofman, Albert | Sijbrands, Eric | Abecasis, Goncalo R | Owen, Katharine R | Zeggini, Eleftheria | Trip, Mieke D | Forouhi, Nita G | Syvänen, Ann-Christine | Eriksson, Johan G | Peltonen, Leena | Nöthen, Markus M | Balkau, Beverley | Palmer, Colin N A | Lyssenko, Valeriya | Tuomi, Tiinamaija | Isomaa, Bo | Hunter, David J | Qi, Lu | Shuldiner, Alan R | Roden, Michael | Barroso, Ines | Wilsgaard, Tom | Beilby, John | Hovingh, Kees | Price, Jackie F | Wilson, James F | Rauramaa, Rainer | Lakka, Timo A | Lind, Lars | Dedoussis, George | Njølstad, Inger | Pedersen, Nancy L | Khaw, Kay-Tee | Wareham, Nicholas J | Keinanen-Kiukaanniemi, Sirkka M | Saaristo, Timo E | Korpi-Hyövälti, Eeva | Saltevo, Juha | Laakso, Markku | Kuusisto, Johanna | Metspalu, Andres | Collins, Francis S | Mohlke, Karen L | Bergman, Richard N | Tuomilehto, Jaakko | Boehm, Bernhard O | Gieger, Christian | Hveem, Kristian | Cauchi, Stephane | Froguel, Philippe | Baldassarre, Damiano | Tremoli, Elena | Humphries, Steve E | Saleheen, Danish | Danesh, John | Ingelsson, Erik | Ripatti, Samuli | Salomaa, Veikko | Erbel, Raimund | Jöckel, Karl-Heinz | Moebus, Susanne | Peters, Annette | Illig, Thomas | de Faire, Ulf | Hamsten, Anders | Morris, Andrew D | Donnelly, Peter J | Frayling, Timothy M | Hattersley, Andrew T | Boerwinkle, Eric | Melander, Olle | Kathiresan, Sekar | Nilsson, Peter M | Deloukas, Panos | Thorsteinsdottir, Unnur | Groop, Leif C | Stefansson, Kari | Hu, Frank | Pankow, James S | Dupuis, Josée | Meigs, James B | Altshuler, David | Boehnke, Michael | McCarthy, Mark I
Nature genetics  2012;44(9):981-990.
To extend understanding of the genetic architecture and molecular basis of type 2 diabetes (T2D), we conducted a meta-analysis of genetic variants on the Metabochip involving 34,840 cases and 114,981 controls, overwhelmingly of European descent. We identified ten previously unreported T2D susceptibility loci, including two demonstrating sex-differentiated association. Genome-wide analyses of these data are consistent with a long tail of further common variant loci explaining much of the variation in susceptibility to T2D. Exploration of the enlarged set of susceptibility loci implicates several processes, including CREBBP-related transcription, adipocytokine signalling and cell cycle regulation, in diabetes pathogenesis.
doi:10.1038/ng.2383
PMCID: PMC3442244  PMID: 22885922
2.  Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes 
Morris, Andrew P | Voight, Benjamin F | Teslovich, Tanya M | Ferreira, Teresa | Segré, Ayellet V | Steinthorsdottir, Valgerdur | Strawbridge, Rona J | Khan, Hassan | Grallert, Harald | Mahajan, Anubha | Prokopenko, Inga | Kang, Hyun Min | Dina, Christian | Esko, Tonu | Fraser, Ross M | Kanoni, Stavroula | Kumar, Ashish | Lagou, Vasiliki | Langenberg, Claudia | Luan, Jian’an | Lindgren, Cecilia M | Müller-Nurasyid, Martina | Pechlivanis, Sonali | Rayner, N William | Scott, Laura J | Wiltshire, Steven | Yengo, Loic | Kinnunen, Leena | Rossin, Elizabeth J | Raychaudhuri, Soumya | Johnson, Andrew D | Dimas, Antigone S | Loos, Ruth J F | Vedantam, Sailaja | Chen, Han | Florez, Jose C | Fox, Caroline | Liu, Ching-Ti | Rybin, Denis | Couper, David J | Kao, Wen Hong L | Li, Man | Cornelis, Marilyn C | Kraft, Peter | Sun, Qi | van Dam, Rob M | Stringham, Heather M | Chines, Peter S | Fischer, Krista | Fontanillas, Pierre | Holmen, Oddgeir L | Hunt, Sarah E | Jackson, Anne U | Kong, Augustine | Lawrence, Robert | Meyer, Julia | Perry, John R B | Platou, Carl G P | Potter, Simon | Rehnberg, Emil | Robertson, Neil | Sivapalaratnam, Suthesh | Stančáková, Alena | Stirrups, Kathleen | Thorleifsson, Gudmar | Tikkanen, Emmi | Wood, Andrew R | Almgren, Peter | Atalay, Mustafa | Benediktsson, Rafn | Bonnycastle, Lori L | Burtt, Noël | Carey, Jason | Charpentier, Guillaume | Crenshaw, Andrew T | Doney, Alex S F | Dorkhan, Mozhgan | Edkins, Sarah | Emilsson, Valur | Eury, Elodie | Forsen, Tom | Gertow, Karl | Gigante, Bruna | Grant, George B | Groves, Christopher J | Guiducci, Candace | Herder, Christian | Hreidarsson, Astradur B | Hui, Jennie | James, Alan | Jonsson, Anna | Rathmann, Wolfgang | Klopp, Norman | Kravic, Jasmina | Krjutškov, Kaarel | Langford, Cordelia | Leander, Karin | Lindholm, Eero | Lobbens, Stéphane | Männistö, Satu | Mirza, Ghazala | Mühleisen, Thomas W | Musk, Bill | Parkin, Melissa | Rallidis, Loukianos | Saramies, Jouko | Sennblad, Bengt | Shah, Sonia | Sigurðsson, Gunnar | Silveira, Angela | Steinbach, Gerald | Thorand, Barbara | Trakalo, Joseph | Veglia, Fabrizio | Wennauer, Roman | Winckler, Wendy | Zabaneh, Delilah | Campbell, Harry | van Duijn, Cornelia | Uitterlinden, Andre G | Hofman, Albert | Sijbrands, Eric | Abecasis, Goncalo R | Owen, Katharine R | Zeggini, Eleftheria | Trip, Mieke D | Forouhi, Nita G | Syvänen, Ann-Christine | Eriksson, Johan G | Peltonen, Leena | Nöthen, Markus M | Balkau, Beverley | Palmer, Colin N A | Lyssenko, Valeriya | Tuomi, Tiinamaija | Isomaa, Bo | Hunter, David J | Qi, Lu | Shuldiner, Alan R | Roden, Michael | Barroso, Ines | Wilsgaard, Tom | Beilby, John | Hovingh, Kees | Price, Jackie F | Wilson, James F | Rauramaa, Rainer | Lakka, Timo A | Lind, Lars | Dedoussis, George | Njølstad, Inger | Pedersen, Nancy L | Khaw, Kay-Tee | Wareham, Nicholas J | Keinanen-Kiukaanniemi, Sirkka M | Saaristo, Timo E | Korpi-Hyövälti, Eeva | Saltevo, Juha | Laakso, Markku | Kuusisto, Johanna | Metspalu, Andres | Collins, Francis S | Mohlke, Karen L | Bergman, Richard N | Tuomilehto, Jaakko | Boehm, Bernhard O | Gieger, Christian | Hveem, Kristian | Cauchi, Stephane | Froguel, Philippe | Baldassarre, Damiano | Tremoli, Elena | Humphries, Steve E | Saleheen, Danish | Danesh, John | Ingelsson, Erik | Ripatti, Samuli | Salomaa, Veikko | Erbel, Raimund | Jöckel, Karl-Heinz | Moebus, Susanne | Peters, Annette | Illig, Thomas | de Faire, Ulf | Hamsten, Anders | Morris, Andrew D | Donnelly, Peter J | Frayling, Timothy M | Hattersley, Andrew T | Boerwinkle, Eric | Melander, Olle | Kathiresan, Sekar | Nilsson, Peter M | Deloukas, Panos | Thorsteinsdottir, Unnur | Groop, Leif C | Stefansson, Kari | Hu, Frank | Pankow, James S | Dupuis, Josée | Meigs, James B | Altshuler, David | Boehnke, Michael | McCarthy, Mark I
Nature genetics  2012;44(9):981-990.
To extend understanding of the genetic architecture and molecular basis of type 2 diabetes (T2D), we conducted a meta-analysis of genetic variants on the Metabochip involving 34,840 cases and 114,981 controls, overwhelmingly of European descent. We identified ten previously unreported T2D susceptibility loci, including two demonstrating sex-differentiated association. Genome-wide analyses of these data are consistent with a long tail of further common variant loci explaining much of the variation in susceptibility to T2D. Exploration of the enlarged set of susceptibility loci implicates several processes, including CREBBP-related transcription, adipocytokine signalling and cell cycle regulation, in diabetes pathogenesis.
doi:10.1038/ng.2383
PMCID: PMC3442244  PMID: 22885922
3.  Stratifying Type 2 Diabetes Cases by BMI Identifies Genetic Risk Variants in LAMA1 and Enrichment for Risk Variants in Lean Compared to Obese Cases 
Perry, John R. B. | Voight, Benjamin F. | Yengo, Loïc | Amin, Najaf | Dupuis, Josée | Ganser, Martha | Grallert, Harald | Navarro, Pau | Li, Man | Qi, Lu | Steinthorsdottir, Valgerdur | Scott, Robert A. | Almgren, Peter | Arking, Dan E. | Aulchenko, Yurii | Balkau, Beverley | Benediktsson, Rafn | Bergman, Richard N. | Boerwinkle, Eric | Bonnycastle, Lori | Burtt, Noël P. | Campbell, Harry | Charpentier, Guillaume | Collins, Francis S. | Gieger, Christian | Green, Todd | Hadjadj, Samy | Hattersley, Andrew T. | Herder, Christian | Hofman, Albert | Johnson, Andrew D. | Kottgen, Anna | Kraft, Peter | Labrune, Yann | Langenberg, Claudia | Manning, Alisa K. | Mohlke, Karen L. | Morris, Andrew P. | Oostra, Ben | Pankow, James | Petersen, Ann-Kristin | Pramstaller, Peter P. | Prokopenko, Inga | Rathmann, Wolfgang | Rayner, William | Roden, Michael | Rudan, Igor | Rybin, Denis | Scott, Laura J. | Sigurdsson, Gunnar | Sladek, Rob | Thorleifsson, Gudmar | Thorsteinsdottir, Unnur | Tuomilehto, Jaakko | Uitterlinden, Andre G. | Vivequin, Sidonie | Weedon, Michael N. | Wright, Alan F. | Hu, Frank B. | Illig, Thomas | Kao, Linda | Meigs, James B. | Wilson, James F. | Stefansson, Kari | van Duijn, Cornelia | Altschuler, David | Morris, Andrew D. | Boehnke, Michael | McCarthy, Mark I. | Froguel, Philippe | Palmer, Colin N. A. | Wareham, Nicholas J. | Groop, Leif | Frayling, Timothy M. | Cauchi, Stéphane
PLoS Genetics  2012;8(5):e1002741.
Common diseases such as type 2 diabetes are phenotypically heterogeneous. Obesity is a major risk factor for type 2 diabetes, but patients vary appreciably in body mass index. We hypothesized that the genetic predisposition to the disease may be different in lean (BMI<25 Kg/m2) compared to obese cases (BMI≥30 Kg/m2). We performed two case-control genome-wide studies using two accepted cut-offs for defining individuals as overweight or obese. We used 2,112 lean type 2 diabetes cases (BMI<25 kg/m2) or 4,123 obese cases (BMI≥30 kg/m2), and 54,412 un-stratified controls. Replication was performed in 2,881 lean cases or 8,702 obese cases, and 18,957 un-stratified controls. To assess the effects of known signals, we tested the individual and combined effects of SNPs representing 36 type 2 diabetes loci. After combining data from discovery and replication datasets, we identified two signals not previously reported in Europeans. A variant (rs8090011) in the LAMA1 gene was associated with type 2 diabetes in lean cases (P = 8.4×10−9, OR = 1.13 [95% CI 1.09–1.18]), and this association was stronger than that in obese cases (P = 0.04, OR = 1.03 [95% CI 1.00–1.06]). A variant in HMG20A—previously identified in South Asians but not Europeans—was associated with type 2 diabetes in obese cases (P = 1.3×10−8, OR = 1.11 [95% CI 1.07–1.15]), although this association was not significantly stronger than that in lean cases (P = 0.02, OR = 1.09 [95% CI 1.02–1.17]). For 36 known type 2 diabetes loci, 29 had a larger odds ratio in the lean compared to obese (binomial P = 0.0002). In the lean analysis, we observed a weighted per-risk allele OR = 1.13 [95% CI 1.10–1.17], P = 3.2×10−14. This was larger than the same model fitted in the obese analysis where the OR = 1.06 [95% CI 1.05–1.08], P = 2.2×10−16. This study provides evidence that stratification of type 2 diabetes cases by BMI may help identify additional risk variants and that lean cases may have a stronger genetic predisposition to type 2 diabetes.
Author Summary
Individuals with Type 2 diabetes (T2D) can present with variable clinical characteristics. It is well known that obesity is a major risk factor for type 2 diabetes, yet patients can vary considerably—there are many lean diabetes patients and many overweight people without diabetes. We hypothesized that the genetic predisposition to the disease may be different in lean (BMI<25 Kg/m2) compared to obese cases (BMI≥30 Kg/m2). Specifically, as lean T2D patients had lower risk than obese patients, they must have been more genetically susceptible. Using genetic data from multiple genome-wide association studies, we tested genetic markers across the genome in 2,112 lean type 2 diabetes cases (BMI<25 kg/m2), 4,123 obese cases (BMI≥30 kg/m2), and 54,412 healthy controls. We confirmed our results in an additional 2,881 lean cases, 8,702 obese cases, and 18,957 healthy controls. Using these data we found differences in genetic enrichment between lean and obese cases, supporting our original hypothesis. We also searched for genetic variants that may be risk factors only in lean or obese patients and found two novel gene regions not previously reported in European individuals. These findings may influence future study design for type 2 diabetes and provide further insight into the biology of the disease.
doi:10.1371/journal.pgen.1002741
PMCID: PMC3364960  PMID: 22693455
4.  Identification and Functional Characterization of G6PC2 Coding Variants Influencing Glycemic Traits Define an Effector Transcript at the G6PC2-ABCB11 Locus 
Mahajan, Anubha | Sim, Xueling | Ng, Hui Jin | Manning, Alisa | Rivas, Manuel A. | Highland, Heather M. | Locke, Adam E. | Grarup, Niels | Im, Hae Kyung | Cingolani, Pablo | Flannick, Jason | Fontanillas, Pierre | Fuchsberger, Christian | Gaulton, Kyle J. | Teslovich, Tanya M. | Rayner, N. William | Robertson, Neil R. | Beer, Nicola L. | Rundle, Jana K. | Bork-Jensen, Jette | Ladenvall, Claes | Blancher, Christine | Buck, David | Buck, Gemma | Burtt, Noël P. | Gabriel, Stacey | Gjesing, Anette P. | Groves, Christopher J. | Hollensted, Mette | Huyghe, Jeroen R. | Jackson, Anne U. | Jun, Goo | Justesen, Johanne Marie | Mangino, Massimo | Murphy, Jacquelyn | Neville, Matt | Onofrio, Robert | Small, Kerrin S. | Stringham, Heather M. | Syvänen, Ann-Christine | Trakalo, Joseph | Abecasis, Goncalo | Bell, Graeme I. | Blangero, John | Cox, Nancy J. | Duggirala, Ravindranath | Hanis, Craig L. | Seielstad, Mark | Wilson, James G. | Christensen, Cramer | Brandslund, Ivan | Rauramaa, Rainer | Surdulescu, Gabriela L. | Doney, Alex S. F. | Lannfelt, Lars | Linneberg, Allan | Isomaa, Bo | Tuomi, Tiinamaija | Jørgensen, Marit E. | Jørgensen, Torben | Kuusisto, Johanna | Uusitupa, Matti | Salomaa, Veikko | Spector, Timothy D. | Morris, Andrew D. | Palmer, Colin N. A. | Collins, Francis S. | Mohlke, Karen L. | Bergman, Richard N. | Ingelsson, Erik | Lind, Lars | Tuomilehto, Jaakko | Hansen, Torben | Watanabe, Richard M. | Prokopenko, Inga | Dupuis, Josee | Karpe, Fredrik | Groop, Leif | Laakso, Markku | Pedersen, Oluf | Florez, Jose C. | Morris, Andrew P. | Altshuler, David | Meigs, James B. | Boehnke, Michael | McCarthy, Mark I. | Lindgren, Cecilia M. | Gloyn, Anna L.
PLoS Genetics  2015;11(1):e1004876.
Genome wide association studies (GWAS) for fasting glucose (FG) and insulin (FI) have identified common variant signals which explain 4.8% and 1.2% of trait variance, respectively. It is hypothesized that low-frequency and rare variants could contribute substantially to unexplained genetic variance. To test this, we analyzed exome-array data from up to 33,231 non-diabetic individuals of European ancestry. We found exome-wide significant (P<5×10-7) evidence for two loci not previously highlighted by common variant GWAS: GLP1R (p.Ala316Thr, minor allele frequency (MAF)=1.5%) influencing FG levels, and URB2 (p.Glu594Val, MAF = 0.1%) influencing FI levels. Coding variant associations can highlight potential effector genes at (non-coding) GWAS signals. At the G6PC2/ABCB11 locus, we identified multiple coding variants in G6PC2 (p.Val219Leu, p.His177Tyr, and p.Tyr207Ser) influencing FG levels, conditionally independent of each other and the non-coding GWAS signal. In vitro assays demonstrate that these associated coding alleles result in reduced protein abundance via proteasomal degradation, establishing G6PC2 as an effector gene at this locus. Reconciliation of single-variant associations and functional effects was only possible when haplotype phase was considered. In contrast to earlier reports suggesting that, paradoxically, glucose-raising alleles at this locus are protective against type 2 diabetes (T2D), the p.Val219Leu G6PC2 variant displayed a modest but directionally consistent association with T2D risk. Coding variant associations for glycemic traits in GWAS signals highlight PCSK1, RREB1, and ZHX3 as likely effector transcripts. These coding variant association signals do not have a major impact on the trait variance explained, but they do provide valuable biological insights.
Author Summary
Understanding how FI and FG levels are regulated is important because their derangement is a feature of T2D. Despite recent success from GWAS in identifying regions of the genome influencing glycemic traits, collectively these loci explain only a small proportion of trait variance. Unlocking the biological mechanisms driving these associations has been challenging because the vast majority of variants map to non-coding sequence, and the genes through which they exert their impact are largely unknown. In the current study, we sought to increase our understanding of the physiological pathways influencing both traits using exome-array genotyping in up to 33,231 non-diabetic individuals to identify coding variants and consequently genes associated with either FG or FI levels. We identified novel association signals for both traits including the receptor for GLP-1 agonists which are a widely used therapy for T2D. Furthermore, we identified coding variants at several GWAS loci which point to the genes underlying these association signals. Importantly, we found that multiple coding variants in G6PC2 result in a loss of protein function and lower fasting glucose levels.
doi:10.1371/journal.pgen.1004876
PMCID: PMC4307976  PMID: 25625282
5.  Glycemic Exposure and Blood Pressure Influencing Progression and Remission of Diabetic Retinopathy 
Diabetes Care  2013;36(12):3979-3984.
OBJECTIVE
This study sought to investigate the progression and regression of diabetic retinopathy (DR) and the effects of population risk factors on the rates of transition across retinopathy stages.
RESEARCH DESIGN AND METHODS
The study cohort consisted of 44,871 observed DR events between the calendar years 1990 and 2011 for 4,758 diabetic patients who were diagnosed at 35 years of age or older. The first retinal observation was recorded within a year from diagnosis, and the result was recorded as free of retinopathy. A multistate Markov model was applied for analyzing the development of DR and its relation to the patterns of changes in risk factors.
RESULTS
We observed a consistent risk effect of HbA1c on the progression (no retinopathy to mild background DR [BDR] hazard ratio per SD of HbA1c [HR] 1.42 [95% CI 1.32–1.52], mild BDR to observable BDR HR 1.32 [95% CI 1.08–1.60], and observable BDR to severe nonproliferative/proliferative DR HR 2.23 [95% CI 1.16–4.29]). Similarly, systolic blood pressure (SBP) and diastolic blood pressure increased the risk for the transition from the asymptomatic phase to mild BDR (HR 1.20 [95% CI 1.11–1.30]) and the mild BDR to observable BDR (HR 1.87 [95% CI 1.46–2.40]), respectively. Regression from mild BDR to no DR was associated with lower SBP (HR 0.79 [95% CI 0.64–0.97]) and lower HbA1c (HR 0.76 [95% CI 0.64–0.89]).
CONCLUSIONS
Progression and regression of DR were strongly associated with blood pressure and glycemic exposure.
doi:10.2337/dc12-2392
PMCID: PMC3836116  PMID: 24170761
6.  Mendelian Randomization Studies Do Not Support a Causal Role for Reduced Circulating Adiponectin Levels in Insulin Resistance and Type 2 Diabetes 
Yaghootkar, Hanieh | Lamina, Claudia | Scott, Robert A. | Dastani, Zari | Hivert, Marie-France | Warren, Liling L. | Stancáková, Alena | Buxbaum, Sarah G. | Lyytikäinen, Leo-Pekka | Henneman, Peter | Wu, Ying | Cheung, Chloe Y.Y. | Pankow, James S. | Jackson, Anne U. | Gustafsson, Stefan | Zhao, Jing Hua | Ballantyne, Christie M. | Xie, Weijia | Bergman, Richard N. | Boehnke, Michael | el Bouazzaoui, Fatiha | Collins, Francis S. | Dunn, Sandra H. | Dupuis, Josee | Forouhi, Nita G. | Gillson, Christopher | Hattersley, Andrew T. | Hong, Jaeyoung | Kähönen, Mika | Kuusisto, Johanna | Kedenko, Lyudmyla | Kronenberg, Florian | Doria, Alessandro | Assimes, Themistocles L. | Ferrannini, Ele | Hansen, Torben | Hao, Ke | Häring, Hans | Knowles, Joshua W. | Lindgren, Cecilia M. | Nolan, John J. | Paananen, Jussi | Pedersen, Oluf | Quertermous, Thomas | Smith, Ulf | Lehtimäki, Terho | Liu, Ching-Ti | Loos, Ruth J.F. | McCarthy, Mark I. | Morris, Andrew D. | Vasan, Ramachandran S. | Spector, Tim D. | Teslovich, Tanya M. | Tuomilehto, Jaakko | van Dijk, Ko Willems | Viikari, Jorma S. | Zhu, Na | Langenberg, Claudia | Ingelsson, Erik | Semple, Robert K. | Sinaiko, Alan R. | Palmer, Colin N.A. | Walker, Mark | Lam, Karen S.L. | Paulweber, Bernhard | Mohlke, Karen L. | van Duijn, Cornelia | Raitakari, Olli T. | Bidulescu, Aurelian | Wareham, Nick J. | Laakso, Markku | Waterworth, Dawn M. | Lawlor, Debbie A. | Meigs, James B. | Richards, J. Brent | Frayling, Timothy M.
Diabetes  2013;62(10):3589-3598.
Adiponectin is strongly inversely associated with insulin resistance and type 2 diabetes, but its causal role remains controversial. We used a Mendelian randomization approach to test the hypothesis that adiponectin causally influences insulin resistance and type 2 diabetes. We used genetic variants at the ADIPOQ gene as instruments to calculate a regression slope between adiponectin levels and metabolic traits (up to 31,000 individuals) and a combination of instrumental variables and summary statistics–based genetic risk scores to test the associations with gold-standard measures of insulin sensitivity (2,969 individuals) and type 2 diabetes (15,960 case subjects and 64,731 control subjects). In conventional regression analyses, a 1-SD decrease in adiponectin levels was correlated with a 0.31-SD (95% CI 0.26–0.35) increase in fasting insulin, a 0.34-SD (0.30–0.38) decrease in insulin sensitivity, and a type 2 diabetes odds ratio (OR) of 1.75 (1.47–2.13). The instrumental variable analysis revealed no evidence of a causal association between genetically lower circulating adiponectin and higher fasting insulin (0.02 SD; 95% CI −0.07 to 0.11; N = 29,771), nominal evidence of a causal relationship with lower insulin sensitivity (−0.20 SD; 95% CI −0.38 to −0.02; N = 1,860), and no evidence of a relationship with type 2 diabetes (OR 0.94; 95% CI 0.75–1.19; N = 2,777 case subjects and 13,011 control subjects). Using the ADIPOQ summary statistics genetic risk scores, we found no evidence of an association between adiponectin-lowering alleles and insulin sensitivity (effect per weighted adiponectin-lowering allele: −0.03 SD; 95% CI −0.07 to 0.01; N = 2,969) or type 2 diabetes (OR per weighted adiponectin-lowering allele: 0.99; 95% CI 0.95–1.04; 15,960 case subjects vs. 64,731 control subjects). These results do not provide any consistent evidence that interventions aimed at increasing adiponectin levels will improve insulin sensitivity or risk of type 2 diabetes.
doi:10.2337/db13-0128
PMCID: PMC3781444  PMID: 23835345
7.  Robust association of the LPA locus with LDLc lowering response to statin treatment in a meta-analysis of 30,467 individuals from both randomised control trials and observational studies and association with coronary artery disease outcome during statin treatment 
Pharmacogenetics and genomics  2013;23(10):518-525.
Objectives
The LPA SNP rs10455872 has been associated with low density lipoprotein cholesterol (LDLc) lowering response to statins in several randomised control trials (RCTs) and is a known coronary artery disease (CAD) marker. However it is unclear what residual risk of CAD this marker may have during statin treatment.
Methods
Using electronic medical records linked to the GoDARTS genotyped population we identified over 8000 patients on statins in Tayside, Scotland.
Results
We replicated the findings of the RCTs, with the G allele of rs10455872 being associated with a 0.10mmol/l per allele poorer reduction in LDLc in response to statin treatment, and conducted a meta-analysis with previously published RCTs (P=1.46×10−29, n=30,467). We demonstrate an association between rs10455872 and CAD in statin treated individuals and have replicated this finding in the Utrecht Cardiovascular Pharmacogenetics Study (combined odds ratio 1·41, 95% CI 1.17-1.68, P=4.5×10−5, n=8822) suggesting that statin treatment does not abrogate this well-established genetic risk for CAD. Furthermore in a Cox proportional hazards model with LDLc measured time dependently we demonstrated the relationship between CAD and rs10455872 was independent of LDLc during statin treatment.
Conclusion
Individuals with the G allele of rs10455872, which represents approximately 1 in 7 patients, have a higher risk of CAD than the majority of the population even after treatment with statins, and therefore represent a vulnerable group requiring an alternative medication in addition to statin treatment.
doi:10.1097/FPC.0b013e3283642fd6
PMCID: PMC4110714  PMID: 23903772
Lipoproteins; drug response; pharmacogenetics; HMG CoA Reductase inhibitors; coronary artery disease
8.  Targeted genetic testing for familial hypercholesterolaemia using next generation sequencing: a population-based study 
BMC Medical Genetics  2014;15:70.
Background
Familial hypercholesterolaemia (FH) is a common Mendelian condition which, untreated, results in premature coronary heart disease. An estimated 88% of FH cases are undiagnosed in the UK. We previously validated a method for FH mutation detection in a lipid clinic population using next generation sequencing (NGS), but this did not address the challenge of identifying index cases in primary care where most undiagnosed patients receive healthcare. Here, we evaluate the targeted use of NGS as a potential route to diagnosis of FH in a primary care population subset selected for hypercholesterolaemia.
Methods
We used microfluidics-based PCR amplification coupled with NGS and multiplex ligation-dependent probe amplification (MLPA) to detect mutations in LDLR, APOB and PCSK9 in three phenotypic groups within the Generation Scotland: Scottish Family Health Study including 193 individuals with high total cholesterol, 232 with moderately high total cholesterol despite cholesterol-lowering therapy, and 192 normocholesterolaemic controls.
Results
Pathogenic mutations were found in 2.1% of hypercholesterolaemic individuals, in 2.2% of subjects on cholesterol-lowering therapy and in 42% of their available first-degree relatives. In addition, variants of uncertain clinical significance (VUCS) were detected in 1.4% of the hypercholesterolaemic and cholesterol-lowering therapy groups. No pathogenic variants or VUCS were detected in controls.
Conclusions
We demonstrated that population-based genetic testing using these protocols is able to deliver definitive molecular diagnoses of FH in individuals with high cholesterol or on cholesterol-lowering therapy. The lower cost and labour associated with NGS-based testing may increase the attractiveness of a population-based approach to FH detection compared to genetic testing with conventional sequencing. This could provide one route to increasing the present low percentage of FH cases with a genetic diagnosis.
doi:10.1186/1471-2350-15-70
PMCID: PMC4083361  PMID: 24956927
Familial hypercholesterolaemia; Total cholesterol; LDLR; Molecular diagnostic testing; Next-generation sequencing; Primary care; Generation Scotland
9.  Common Carotid Intima Media Thickness and Ankle-Brachial Pressure Index Correlate with Local but Not Global Atheroma Burden: A Cross Sectional Study Using Whole Body Magnetic Resonance Angiography 
PLoS ONE  2014;9(6):e99190.
Background
Common carotid intima media thickness (CIMT) and ankle brachial pressure index (ABPI) are used as surrogate marker of atherosclerosis, and have been shown to correlate with arterial stiffness, however their correlation with global atherosclerotic burden has not been previously assessed. We compare CIMT and ABPI with atheroma burden as measured by whole body magnetic resonance angiography (WB-MRA).
Methods
50 patients with symptomatic peripheral arterial disease were recruited. CIMT was measured using ultrasound while rest and exercise ABPI were performed. WB-MRA was performed in a 1.5T MRI scanner using 4 volume acquisitions with a divided dose of intravenous gadolinium gadoterate meglumine (Dotarem, Guerbet, FR). The WB-MRA data was divided into 31 anatomical arterial segments with each scored according to degree of luminal narrowing: 0 = normal, 1 = <50%, 2 = 50–70%, 3 = 70–99%, 4 = vessel occlusion. The segment scores were summed and from this a standardized atheroma score was calculated.
Results
The atherosclerotic burden was high with a standardised atheroma score of 39.5±11. Common CIMT showed a positive correlation with the whole body atheroma score (β 0.32, p = 0.045), however this was due to its strong correlation with the neck and thoracic segments (β 0.42 p = 0.01) with no correlation with the rest of the body. ABPI correlated with the whole body atheroma score (β −0.39, p = 0.012), which was due to a strong correlation with the ilio-femoral vessels with no correlation with the thoracic or neck vessels. On multiple linear regression, no correlation between CIMT and global atheroma burden was present (β 0.13 p = 0.45), while the correlation between ABPI and atheroma burden persisted (β −0.45 p = 0.005).
Conclusion
ABPI but not CIMT correlates with global atheroma burden as measured by whole body contrast enhanced magnetic resonance angiography in a population with symptomatic peripheral arterial disease. However this is primarily due to a strong correlation with ilio-femoral atheroma burden.
doi:10.1371/journal.pone.0099190
PMCID: PMC4059661  PMID: 24933122
10.  Clinical and genetic determinants of progression of type 2 diabetes: A DIRECT Study 
Diabetes care  2013;37(3):718-724.
Objective
The rate at which diabetes progresses following diagnosis of type 2 diabetes is highly variable between individuals.
Research Design and Methods
We studied 5250 patients with type 2 diabetes using comprehensive electronic medical records on all patients in Tayside, Scotland from 1992 onwards. We investigated the association of clinical, biochemical and genetic factors with the risk of progression of type 2 diabetes from diagnosis to requirement for insulin treatment (defined as insulin treatment or HbA1c ≥8.5%/69 mmol/mol treated with two or more non-insulin diabetes therapies).
Results
Risk of progression was associated with both low and high BMI. In an analysis stratified by BMI and HbA1c at diagnosis, faster progression was independently associated with younger age at diagnosis, higher log triacylglyceride concentrations (Hazard Ratio (HR) 1.28 per mmol/L (95% CI 1.15-1.42)) and lower HDL concentrations (HR 0.70 per mmol/L (95% CI 0.55-0.87)). A high genetic risk score derived from 61 diabetes risk variants was associated with a younger age of diagnosis, a younger age at starting insulin, but was not associated with the progression rate from diabetes to requirement for insulin treatment.
Conclusions
Increased triacylglyceride and low HDL are independently associated with increased rate of progression of diabetes. The genetic factors that predispose to diabetes are different from those that cause rapid progression of diabetes suggesting a difference in biological process that needs further investigation.
doi:10.2337/dc13-1995
PMCID: PMC4038744  PMID: 24186880
11.  Heritability of variation in glycaemic response to metformin: a genome-wide complex trait analysis 
Summary
Background
Metformin is a first-line oral agent used in the treatment of type 2 diabetes, but glycaemic response to this drug is highly variable. Understanding the genetic contribution to metformin response might increase the possibility of personalising metformin treatment. We aimed to establish the heritability of glycaemic response to metformin using the genome-wide complex trait analysis (GCTA) method.
Methods
In this GCTA study, we obtained data about HbA1c concentrations before and during metformin treatment from patients in the Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS) study, which includes a cohort of patients with type 2 diabetes and is linked to comprehensive clinical databases and genome-wide association study data. We applied the GCTA method to estimate heritability for four definitions of glycaemic response to metformin: absolute reduction in HbA1c; proportional reduction in HbA1c; adjusted reduction in HbA1c; and whether or not the target on-treatment HbA1c of less than 7% (53 mmol/mol) was achieved, with adjustment for baseline HbA1c and known clinical covariates. Chromosome-wise heritability estimation was used to obtain further information about the genetic architecture.
Findings
5386 individuals were included in the final dataset, of whom 2085 had enough clinical data to define glycaemic response to metformin. The heritability of glycaemic response to metformin varied by response phenotype, with a heritability of 34% (95% CI 1–68; p=0·022) for the absolute reduction in HbA1c, adjusted for pretreatment HbA1c. Chromosome-wise heritability estimates suggest that the genetic contribution is probably from individual variants scattered across the genome, which each have a small to moderate effect, rather than from a few loci that each have a large effect.
Interpretation
Glycaemic response to metformin is heritable, thus glycaemic response to metformin is, in part, intrinsic to individual biological variation. Further genetic analysis might enable us to make better predictions for stratified medicine and to unravel new mechanisms of metformin action.
Funding
Wellcome Trust.
doi:10.1016/S2213-8587(14)70050-6
PMCID: PMC4038749  PMID: 24731673
12.  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
13.  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
14.  Risk of Cardiovascular Disease and Total Mortality in Adults with Type 1 Diabetes: Scottish Registry Linkage Study 
PLoS Medicine  2012;9(10):e1001321.
Helen Colhoun and colleagues report findings from a Scottish registry linkage study regarding contemporary risks for cardiovascular events and all-cause mortality among individuals diagnosed with type 1 diabetes.
Background
Randomized controlled trials have shown the importance of tight glucose control in type 1 diabetes (T1DM), but few recent studies have evaluated the risk of cardiovascular disease (CVD) and all-cause mortality among adults with T1DM. We evaluated these risks in adults with T1DM compared with the non-diabetic population in a nationwide study from Scotland and examined control of CVD risk factors in those with T1DM.
Methods and Findings
The Scottish Care Information-Diabetes Collaboration database was used to identify all people registered with T1DM and aged ≥20 years in 2005–2007 and to provide risk factor data. Major CVD events and deaths were obtained from the national hospital admissions database and death register. The age-adjusted incidence rate ratio (IRR) for CVD and mortality in T1DM (n = 21,789) versus the non-diabetic population (3.96 million) was estimated using Poisson regression. The age-adjusted IRR for first CVD event associated with T1DM versus the non-diabetic population was higher in women (3.0: 95% CI 2.4–3.8, p<0.001) than men (2.3: 2.0–2.7, p<0.001) while the IRR for all-cause mortality associated with T1DM was comparable at 2.6 (2.2–3.0, p<0.001) in men and 2.7 (2.2–3.4, p<0.001) in women. Between 2005–2007, among individuals with T1DM, 34 of 123 deaths among 10,173 who were <40 years and 37 of 907 deaths among 12,739 who were ≥40 years had an underlying cause of death of coma or diabetic ketoacidosis. Among individuals 60–69 years, approximately three extra deaths per 100 per year occurred among men with T1DM (28.51/1,000 person years at risk), and two per 100 per year for women (17.99/1,000 person years at risk). 28% of those with T1DM were current smokers, 13% achieved target HbA1c of <7% and 37% had very poor (≥9%) glycaemic control. Among those aged ≥40, 37% had blood pressures above even conservative targets (≥140/90 mmHg) and 39% of those ≥40 years were not on a statin. Although many of these risk factors were comparable to those previously reported in other developed countries, CVD and mortality rates may not be generalizable to other countries. Limitations included lack of information on the specific insulin therapy used.
Conclusions
Although the relative risks for CVD and total mortality associated with T1DM in this population have declined relative to earlier studies, T1DM continues to be associated with higher CVD and death rates than the non-diabetic population. Risk factor management should be improved to further reduce risk but better treatment approaches for achieving good glycaemic control are badly needed.
Please see later in the article for the Editors' Summary
Editors' Summary
Background. People with diabetes are more likely to have cardiovascular disease such as heart attacks and strokes. They also have a higher risk of dying prematurely from any cause. Controlling blood sugar (glucose), blood pressure, and cholesterol can help reduce these risks. Some people with type 1 diabetes can achieve tight blood glucose control through a strict regimen that includes a carefully calculated diet, frequent physical activity, regular blood glucose testing several times a day, and multiple daily doses of insulin. Other drugs can reduce blood pressure and cholesterol levels. Keeping one's weight in the normal range and not smoking are important ways in which all people, including those with type 1 diabetes can reduce their risks of heart disease and premature death.
Why Was This Study Done? Researchers and doctors have known for almost two decades what patients with type 1 diabetes can do to minimize the complications from the disease and thereby reduce their risks for cardiovascular disease and early death. So for some time now, patients should have been treated and counseled accordingly. This study was done to evaluate the current risks for have cardiovascular disease and premature death amongst people living with type 1 diabetes in a high-income country (Scotland).
What Did the Researchers Do and Find? From a national register of all people with type 1 diabetes in Scotland, the researchers selected those who were older than 20 years and alive at any time from January 2005 to May 2008. This included about 19,000 people who had been diagnosed with type 1 diabetes before 2005. Another 2,600 were diagnosed between 2005 and 2008. They also obtained data on heart attacks and strokes in these patients from hospital records and on deaths from the natural death register. To obtain a good picture of the current relative risks, they compared the patients with type 1 diabetes with the non-diabetic general Scottish population with regard to the risk of heart attacks/strokes and death from all causes. They also collected information on how well the people with diabetes controlled their blood glucose, on their weight, and whether they smoked.
They found that the current risks compared with the general Scottish population are quite a bit lower than those of people with type 1 diabetes in earlier decades. However, people with type 1 diabetes in Scotland still have much higher (more than twice) the risk of heart attacks, strokes, or premature death than the general population. Moreover, the researchers found a high number of deaths in younger people with diabetes from coma—caused by either too much blood sugar (hyperglycemia) or too little (hypoglycemia). Severe hyperglycemia and hypoglycemia happen when blood glucose control is poor. When the scientists looked at test results for HbA1c levels (a test that is done once or twice a year to see how well patients controlled their blood sugar over the previous 3 months) for all patients, they found that the majority of them did not come close to controlling their blood glucose within the recommended range.
When the researchers compared body mass index (a measure of weight that takes height into account) and smoking between the people with type 1 diabetes and the general population, they found similar proportions of smokers and overweight or obese people.
What Do these Findings Mean? The results represent a snapshot of the recent situation regarding complications from type 1 diabetes in the Scottish population. The results suggest that within this population, strategies over the past two decades to reduce complications from type 1 diabetes that cause cardiovascular disease and death are working, in principle. However, there is much need for further improvement. This includes the urgent need to understand why so few people with type 1 diabetes achieve good control of their blood sugar, and what can be done to improve this situation. It is also important to put more effort into keeping people with diabetes from taking up smoking or getting them to quit, as well as preventing them from getting overweight or promoting weight reduction, because this could further reduce the risks of cardiovascular disease and premature death.
Additional Information
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001321
National Diabetes Information Clearinghouse, a service of the US National Institute of Diabetes and Digestive and Kidney Diseases, has information on heart disease and diabetes, on general complications of diabetes, and on the HbA1c test (on this site and some others called A1C test) that measures control of blood sugar over the past 3 months
Diabetes.co.uk provides general information on type 1 diabetes, its complications, and what people with the disease can do to reduce their risks
The Canadian Diabetes Association offers a cardiovascular risk self-assessment tool and other relevant information
The American Diabetes Association has information on the benefits and challenges of tight blood sugar control and how it is tested
The Juvenile Diabetes Research Foundation funds research to prevent, cure, and treat type 1 diabetes
Diabetes UK provides extensive information on diabetes for patients, carers, and clinicians
doi:10.1371/journal.pmed.1001321
PMCID: PMC3462745  PMID: 23055834
15.  Reduced Incidence of Lower-Extremity Amputations in People With Diabetes in Scotland 
Diabetes Care  2012;35(12):2588-2590.
OBJECTIVE
To establish the incidence of nontraumatic lower-extremity amputation (LEA) in people with diabetes in Scotland.
RESEARCH DESIGN AND METHODS
This cohort study linked national morbidity records and diabetes datasets to establish the number of people with diabetes who underwent nontraumatic major and minor LEA in Scotland from 2004 to 2008.
RESULTS
Two thousand three hundred eighty-two individuals with diabetes underwent a nontraumatic LEA between 2004 and 2008; 57.1% (n = 1,359) underwent major LEAs. The incidence of any LEA among persons with diabetes fell over the 5-year study period by 29.8% (3.04 per 1,000 in 2004 to 2.13 per 1,000 in 2008, P < 0.001). Major LEA rates decreased by 40.7% from 1.87 per 1,000 in 2004 to 1.11 per 1,000 in 2008 (P < 0.001).
CONCLUSIONS
There has been a significant reduction in the incidence of LEA in persons with diabetes in Scotland between 2004 and 2008, principally explained by a reduction in major amputation.
doi:10.2337/dc12-0511
PMCID: PMC3507601  PMID: 23011727
16.  Multiple type 2 diabetes susceptibility genes following genome-wide association scan in UK samples 
Science (New York, N.Y.)  2007;316(5829):1336-1341.
The molecular mechanisms involved in the development of type 2 diabetes are poorly understood. Starting from genome-wide genotype data for 1,924 diabetic cases and 2,938 population controls generated by the Wellcome Trust Case Control Consortium, we set out to detect replicated diabetes association signals through analysis of 3,757 additional cases and 5,346 controls, and by integration of our findings with equivalent data from other international consortia. We detected diabetes susceptibility loci in and around the genes CDKAL1, CDKN2A/CDKN2B and IGF2BP2 and confirmed the recently described associations at HHEX/IDE and SLC30A8. Our findings provide insights into the genetic architecture of type 2 diabetes, emphasizing the contribution of multiple variants of modest effect. The regions identified underscore the importance of pathways influencing pancreatic beta cell development and function in the etiology of type 2 diabetes.
doi:10.1126/science.1142364
PMCID: PMC3772310  PMID: 17463249
17.  Genetic variants predicting left ventricular hypertrophy in a diabetic population: a Go-DARTS study including meta-analysis 
Background
Left ventricular hypertrophy has multiple aetiologies including diabetes and genetic factors. We aimed to identify genetic variants predicting left ventricular hypertrophy in diabetic individuals.
Methods
Demographic, echocardiographic, prescribing, morbidity, mortality and genotyping databases connected with the Genetics of Diabetes Audit and Research in Tayside, Scotland project were accurately linked using a patient-specific identifier. Left ventricular hypertrophy cases were identified using echocardiographic data.
Genotyping data from 973 cases and 1443 non-left ventricular hypertrophy controls were analysed, investigating whether single nucleotide polymorphisms associated with left ventricular hypertrophy in previous Genome Wide Association Studies predicted left ventricular hypertrophy in our population of individuals with type 2 diabetes. Meta-analysis assessed overall significance of these single nucleotide polymorphisms, which were also used to create gene scores. Logistic regression assessed whether these scores predicted left ventricular hypertrophy.
Results
Two single nucleotide polymorphisms previously associated with left ventricular hypertrophy were significant: rs17132261: OR 2.03, 95% CI 1.10-3.73, p-value 0.02 and rs2292462: OR 0.82, 95% CI 0.73-0.93 and p-value 2.26x10-3. Meta-analysis confirmed rs17132261 and rs2292462 were associated with left ventricular hypertrophy (p=1.03x10-8 and p=5.86x10-10 respectively) and one single nucleotide polymorphisms in IGF1R (rs4966014) became genome wide significant upon meta-analysis although was not significant in our study. Gene scoring based on published single nucleotide polymorphisms also predicted left ventricular hypertrophy in our study.
Rs17132261, within SLC25A46, encodes a mitochondrial phosphate transporter, implying abnormal myocardial energetics contribute to left ventricular hypertrophy development. Rs2292462 lies within the obesity-implicated neuromedin B gene. Rs4966014 lies within the IGF1R1 gene. IGF1 signalling is an established factor in cardiac hypertrophy.
Conclusions
We created a resource to study genetics of left ventricular hypertrophy in diabetes and validated our left ventricular hypertrophy phenotype in replicating single nucleotide polymorphisms identified by previous genome wide association studies investigating left ventricular hypertrophy.
doi:10.1186/1475-2840-12-109
PMCID: PMC3729417  PMID: 23879873
Left ventricular hypertrophy; Genetics; Type 2 diabetes mellitus
18.  The Role of Adiposity in Cardiometabolic Traits: A Mendelian Randomization Analysis 
Fall, Tove | Hägg, Sara | Mägi, Reedik | Ploner, Alexander | Fischer, Krista | Horikoshi, Momoko | Sarin, Antti-Pekka | Thorleifsson, Gudmar | Ladenvall, Claes | Kals, Mart | Kuningas, Maris | Draisma, Harmen H. M. | Ried, Janina S. | van Zuydam, Natalie R. | Huikari, Ville | Mangino, Massimo | Sonestedt, Emily | Benyamin, Beben | Nelson, Christopher P. | Rivera, Natalia V. | Kristiansson, Kati | Shen, Huei-yi | Havulinna, Aki S. | Dehghan, Abbas | Donnelly, Louise A. | Kaakinen, Marika | Nuotio, Marja-Liisa | Robertson, Neil | de Bruijn, Renée F. A. G. | Ikram, M. Arfan | Amin, Najaf | Balmforth, Anthony J. | Braund, Peter S. | Doney, Alexander S. F. | Döring, Angela | Elliott, Paul | Esko, Tõnu | Franco, Oscar H. | Gretarsdottir, Solveig | Hartikainen, Anna-Liisa | Heikkilä, Kauko | Herzig, Karl-Heinz | Holm, Hilma | Hottenga, Jouke Jan | Hyppönen, Elina | Illig, Thomas | Isaacs, Aaron | Isomaa, Bo | Karssen, Lennart C. | Kettunen, Johannes | Koenig, Wolfgang | Kuulasmaa, Kari | Laatikainen, Tiina | Laitinen, Jaana | Lindgren, Cecilia | Lyssenko, Valeriya | Läärä, Esa | Rayner, Nigel W. | Männistö, Satu | Pouta, Anneli | Rathmann, Wolfgang | Rivadeneira, Fernando | Ruokonen, Aimo | Savolainen, Markku J. | Sijbrands, Eric J. G. | Small, Kerrin S. | Smit, Jan H. | Steinthorsdottir, Valgerdur | Syvänen, Ann-Christine | Taanila, Anja | Tobin, Martin D. | Uitterlinden, Andre G. | Willems, Sara M. | Willemsen, Gonneke | Witteman, Jacqueline | Perola, Markus | Evans, Alun | Ferrières, Jean | Virtamo, Jarmo | Kee, Frank | Tregouet, David-Alexandre | Arveiler, Dominique | Amouyel, Philippe | Ferrario, Marco M. | Brambilla, Paolo | Hall, Alistair S. | Heath, Andrew C. | Madden, Pamela A. F. | Martin, Nicholas G. | Montgomery, Grant W. | Whitfield, John B. | Jula, Antti | Knekt, Paul | Oostra, Ben | van Duijn, Cornelia M. | Penninx, Brenda W. J. H. | Davey Smith, George | Kaprio, Jaakko | Samani, Nilesh J. | Gieger, Christian | Peters, Annette | Wichmann, H.-Erich | Boomsma, Dorret I. | de Geus, Eco J. C. | Tuomi, TiinaMaija | Power, Chris | Hammond, Christopher J. | Spector, Tim D. | Lind, Lars | Orho-Melander, Marju | Palmer, Colin Neil Alexander | Morris, Andrew D. | Groop, Leif | Järvelin, Marjo-Riitta | Salomaa, Veikko | Vartiainen, Erkki | Hofman, Albert | Ripatti, Samuli | Metspalu, Andres | Thorsteinsdottir, Unnur | Stefansson, Kari | Pedersen, Nancy L. | McCarthy, Mark I. | Ingelsson, Erik | Prokopenko, Inga
PLoS Medicine  2013;10(6):e1001474.
In this study, Prokopenko and colleagues provide novel evidence for causal relationship between adiposity and heart failure and increased liver enzymes using a Mendelian randomization study design.
Please see later in the article for the Editors' Summary
Background
The association between adiposity and cardiometabolic traits is well known from epidemiological studies. Whilst the causal relationship is clear for some of these traits, for others it is not. We aimed to determine whether adiposity is causally related to various cardiometabolic traits using the Mendelian randomization approach.
Methods and Findings
We used the adiposity-associated variant rs9939609 at the FTO locus as an instrumental variable (IV) for body mass index (BMI) in a Mendelian randomization design. Thirty-six population-based studies of individuals of European descent contributed to the analyses.
Age- and sex-adjusted regression models were fitted to test for association between (i) rs9939609 and BMI (n = 198,502), (ii) rs9939609 and 24 traits, and (iii) BMI and 24 traits. The causal effect of BMI on the outcome measures was quantified by IV estimators. The estimators were compared to the BMI–trait associations derived from the same individuals. In the IV analysis, we demonstrated novel evidence for a causal relationship between adiposity and incident heart failure (hazard ratio, 1.19 per BMI-unit increase; 95% CI, 1.03–1.39) and replicated earlier reports of a causal association with type 2 diabetes, metabolic syndrome, dyslipidemia, and hypertension (odds ratio for IV estimator, 1.1–1.4; all p<0.05). For quantitative traits, our results provide novel evidence for a causal effect of adiposity on the liver enzymes alanine aminotransferase and gamma-glutamyl transferase and confirm previous reports of a causal effect of adiposity on systolic and diastolic blood pressure, fasting insulin, 2-h post-load glucose from the oral glucose tolerance test, C-reactive protein, triglycerides, and high-density lipoprotein cholesterol levels (all p<0.05). The estimated causal effects were in agreement with traditional observational measures in all instances except for type 2 diabetes, where the causal estimate was larger than the observational estimate (p = 0.001).
Conclusions
We provide novel evidence for a causal relationship between adiposity and heart failure as well as between adiposity and increased liver enzymes.
Please see later in the article for the Editors' Summary
Editors' Summary
Cardiovascular disease (CVD)—disease that affects the heart and/or the blood vessels—is a major cause of illness and death worldwide. In the US, for example, coronary heart disease—a CVD in which narrowing of the heart's blood vessels by fatty deposits slows the blood supply to the heart and may eventually cause a heart attack—is the leading cause of death, and stroke—a CVD in which the brain's blood supply is interrupted—is the fourth leading cause of death. Globally, both the incidence of CVD (the number of new cases in a population every year) and its prevalence (the proportion of the population with CVD) are increasing, particularly in low- and middle-income countries. This increasing burden of CVD is occurring in parallel with a global increase in the incidence and prevalence of obesity—having an unhealthy amount of body fat (adiposity)—and of metabolic diseases—conditions such as diabetes in which metabolism (the processes that the body uses to make energy from food) is disrupted, with resulting high blood sugar and damage to the blood vessels.
Why Was This Study Done?
Epidemiological studies—investigations that record the patterns and causes of disease in populations—have reported an association between adiposity (indicated by an increased body mass index [BMI], which is calculated by dividing body weight in kilograms by height in meters squared) and cardiometabolic traits such as coronary heart disease, stroke, heart failure (a condition in which the heart is incapable of pumping sufficient amounts of blood around the body), diabetes, high blood pressure (hypertension), and high blood cholesterol (dyslipidemia). However, observational studies cannot prove that adiposity causes any particular cardiometabolic trait because overweight individuals may share other characteristics (confounding factors) that are the real causes of both obesity and the cardiometabolic disease. Moreover, it is possible that having CVD or a metabolic disease causes obesity (reverse causation). For example, individuals with heart failure cannot do much exercise, so heart failure may cause obesity rather than vice versa. Here, the researchers use “Mendelian randomization” to examine whether adiposity is causally related to various cardiometabolic traits. Because gene variants are inherited randomly, they are not prone to confounding and are free from reverse causation. It is known that a genetic variant (rs9939609) within the genome region that encodes the fat-mass- and obesity-associated gene (FTO) is associated with increased BMI. Thus, an investigation of the associations between rs9939609 and cardiometabolic traits can indicate whether obesity is causally related to these traits.
What Did the Researchers Do and Find?
The researchers analyzed the association between rs9939609 (the “instrumental variable,” or IV) and BMI, between rs9939609 and 24 cardiometabolic traits, and between BMI and the same traits using genetic and health data collected in 36 population-based studies of nearly 200,000 individuals of European descent. They then quantified the strength of the causal association between BMI and the cardiometabolic traits by calculating “IV estimators.” Higher BMI showed a causal relationship with heart failure, metabolic syndrome (a combination of medical disorders that increases the risk of developing CVD), type 2 diabetes, dyslipidemia, hypertension, increased blood levels of liver enzymes (an indicator of liver damage; some metabolic disorders involve liver damage), and several other cardiometabolic traits. All the IV estimators were similar to the BMI–cardiovascular trait associations (observational estimates) derived from the same individuals, with the exception of diabetes, where the causal estimate was higher than the observational estimate, probably because the observational estimate is based on a single BMI measurement, whereas the causal estimate considers lifetime changes in BMI.
What Do These Findings Mean?
Like all Mendelian randomization studies, the reliability of the causal associations reported here depends on several assumptions made by the researchers. Nevertheless, these findings provide support for many previously suspected and biologically plausible causal relationships, such as that between adiposity and hypertension. They also provide new insights into the causal effect of obesity on liver enzyme levels and on heart failure. In the latter case, these findings suggest that a one-unit increase in BMI might increase the incidence of heart failure by 17%. In the US, this corresponds to 113,000 additional cases of heart failure for every unit increase in BMI at the population level. Although additional studies are needed to confirm and extend these findings, these results suggest that global efforts to reduce the burden of obesity will likely also reduce the occurrence of CVD and metabolic disorders.
Additional Information
Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001474.
The American Heart Association provides information on all aspects of cardiovascular disease and tips on keeping the heart healthy, including weight management (in several languages); its website includes personal stories about stroke and heart attacks
The US Centers for Disease Control and Prevention has information on heart disease, stroke, and all aspects of overweight and obesity (in English and Spanish)
The UK National Health Service Choices website provides information about cardiovascular disease and obesity, including a personal story about losing weight
The World Health Organization provides information on obesity (in several languages)
The International Obesity Taskforce provides information about the global obesity epidemic
Wikipedia has a page on Mendelian randomization (note: Wikipedia is a free online encyclopedia that anyone can edit; available in several languages)
MedlinePlus provides links to other sources of information on heart disease, on vascular disease, on obesity, and on metabolic disorders (in English and Spanish)
The International Association for the Study of Obesity provides maps and information about obesity worldwide
The International Diabetes Federation has a web page that describes types, complications, and risk factors of diabetes
doi:10.1371/journal.pmed.1001474
PMCID: PMC3692470  PMID: 23824655
19.  Genetic Loci for Retinal Arteriolar Microcirculation 
PLoS ONE  2013;8(6):e65804.
Narrow arterioles in the retina have been shown to predict hypertension as well as other vascular diseases, likely through an increase in the peripheral resistance of the microcirculatory flow. In this study, we performed a genome-wide association study in 18,722 unrelated individuals of European ancestry from the Cohorts for Heart and Aging Research in Genomic Epidemiology consortium and the Blue Mountain Eye Study, to identify genetic determinants associated with variations in retinal arteriolar caliber. Retinal vascular calibers were measured on digitized retinal photographs using a standardized protocol. One variant (rs2194025 on chromosome 5q14 near the myocyte enhancer factor 2C MEF2C gene) was associated with retinal arteriolar caliber in the meta-analysis of the discovery cohorts at genome-wide significance of P-value <5×10−8. This variant was replicated in an additional 3,939 individuals of European ancestry from the Australian Twins Study and Multi-Ethnic Study of Atherosclerosis (rs2194025, P-value = 2.11×10−12 in combined meta-analysis of discovery and replication cohorts). In independent studies of modest sample sizes, no significant association was found between this variant and clinical outcomes including coronary artery disease, stroke, myocardial infarction or hypertension. In conclusion, we found one novel loci which underlie genetic variation in microvasculature which may be relevant to vascular disease. The relevance of these findings to clinical outcomes remains to be determined.
doi:10.1371/journal.pone.0065804
PMCID: PMC3680438  PMID: 23776548
20.  Deep Resequencing Unveils Genetic Architecture of ADIPOQ and Identifies a Novel Low-Frequency Variant Strongly Associated With Adiponectin Variation 
Diabetes  2012;61(5):1297-1301.
Increased adiponectin levels have been shown to be associated with a lower risk of type 2 diabetes. To understand the relations between genetic variation at the adiponectin-encoding gene, ADIPOQ, and adiponectin levels, and subsequently its role in disease, we conducted a deep resequencing experiment of ADIPOQ in 14,002 subjects, including 12,514 Europeans, 594 African Americans, and 567 Indian Asians. We identified 296 single nucleotide polymorphisms (SNPs), including 30 amino acid changes, and carried out association analyses in a subset of 3,665 subjects from two independent studies. We confirmed multiple genome-wide association study findings and identified a novel association between a low-frequency SNP (rs17366653) and adiponectin levels (P = 2.2E–17). We show that seven SNPs exert independent effects on adiponectin levels. Together, they explained 6% of adiponectin variation in our samples. We subsequently assessed association between these SNPs and type 2 diabetes in the Genetics of Diabetes Audit and Research in Tayside Scotland (GO-DARTS) study, comprised of 5,145 case and 6,374 control subjects. No evidence of association with type 2 diabetes was found, but we were also unable to exclude the possibility of substantial effects (e.g., odds ratio 95% CI for rs7366653 [0.91–1.58]). Further investigation by large-scale and well-powered Mendelian randomization studies is warranted.
doi:10.2337/db11-0985
PMCID: PMC3331741  PMID: 22403302
21.  Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways 
Scott, Robert A | Lagou, Vasiliki | Welch, Ryan P | Wheeler, Eleanor | Montasser, May E | Luan, Jian’an | Mägi, Reedik | Strawbridge, Rona J | Rehnberg, Emil | Gustafsson, Stefan | Kanoni, Stavroula | Rasmussen-Torvik, Laura J | Yengo, Loïc | Lecoeur, Cecile | Shungin, Dmitry | Sanna, Serena | Sidore, Carlo | Johnson, Paul C D | Jukema, J Wouter | Johnson, Toby | Mahajan, Anubha | Verweij, Niek | Thorleifsson, Gudmar | Hottenga, Jouke-Jan | Shah, Sonia | Smith, Albert V | Sennblad, Bengt | Gieger, Christian | Salo, Perttu | Perola, Markus | Timpson, Nicholas J | Evans, David M | Pourcain, Beate St | Wu, Ying | Andrews, Jeanette S | Hui, Jennie | Bielak, Lawrence F | Zhao, Wei | Horikoshi, Momoko | Navarro, Pau | Isaacs, Aaron | O’Connell, Jeffrey R | Stirrups, Kathleen | Vitart, Veronique | Hayward, Caroline | Esko, Tönu | Mihailov, Evelin | Fraser, Ross M | Fall, Tove | Voight, Benjamin F | Raychaudhuri, Soumya | Chen, Han | Lindgren, Cecilia M | Morris, Andrew P | Rayner, Nigel W | Robertson, Neil | Rybin, Denis | Liu, Ching-Ti | Beckmann, Jacques S | Willems, Sara M | Chines, Peter S | Jackson, Anne U | Kang, Hyun Min | Stringham, Heather M | Song, Kijoung | Tanaka, Toshiko | Peden, John F | Goel, Anuj | Hicks, Andrew A | An, Ping | Müller-Nurasyid, Martina | Franco-Cereceda, Anders | Folkersen, Lasse | Marullo, Letizia | Jansen, Hanneke | Oldehinkel, Albertine J | Bruinenberg, Marcel | Pankow, James S | North, Kari E | Forouhi, Nita G | Loos, Ruth J F | Edkins, Sarah | Varga, Tibor V | Hallmans, Göran | Oksa, Heikki | Antonella, Mulas | Nagaraja, Ramaiah | Trompet, Stella | Ford, Ian | Bakker, Stephan J L | Kong, Augustine | Kumari, Meena | Gigante, Bruna | Herder, Christian | Munroe, Patricia B | Caulfield, Mark | Antti, Jula | Mangino, Massimo | Small, Kerrin | Miljkovic, Iva | Liu, Yongmei | Atalay, Mustafa | Kiess, Wieland | James, Alan L | Rivadeneira, Fernando | Uitterlinden, Andre G | Palmer, Colin N A | Doney, Alex S F | Willemsen, Gonneke | Smit, Johannes H | Campbell, Susan | Polasek, Ozren | Bonnycastle, Lori L | Hercberg, Serge | Dimitriou, Maria | Bolton, Jennifer L | Fowkes, Gerard R | Kovacs, Peter | Lindström, Jaana | Zemunik, Tatijana | Bandinelli, Stefania | Wild, Sarah H | Basart, Hanneke V | Rathmann, Wolfgang | Grallert, Harald | Maerz, Winfried | Kleber, Marcus E | Boehm, Bernhard O | Peters, Annette | Pramstaller, Peter P | Province, Michael A | Borecki, Ingrid B | Hastie, Nicholas D | Rudan, Igor | Campbell, Harry | Watkins, Hugh | Farrall, Martin | Stumvoll, Michael | Ferrucci, Luigi | Waterworth, Dawn M | Bergman, Richard N | Collins, Francis S | Tuomilehto, Jaakko | Watanabe, Richard M | de Geus, Eco J C | Penninx, Brenda W | Hofman, Albert | Oostra, Ben A | Psaty, Bruce M | Vollenweider, Peter | Wilson, James F | Wright, Alan F | Hovingh, G Kees | Metspalu, Andres | Uusitupa, Matti | Magnusson, Patrik K E | Kyvik, Kirsten O | Kaprio, Jaakko | Price, Jackie F | Dedoussis, George V | Deloukas, Panos | Meneton, Pierre | Lind, Lars | Boehnke, Michael | Shuldiner, Alan R | van Duijn, Cornelia M | Morris, Andrew D | Toenjes, Anke | Peyser, Patricia A | Beilby, John P | Körner, Antje | Kuusisto, Johanna | Laakso, Markku | Bornstein, Stefan R | Schwarz, Peter E H | Lakka, Timo A | Rauramaa, Rainer | Adair, Linda S | Smith, George Davey | Spector, Tim D | Illig, Thomas | de Faire, Ulf | Hamsten, Anders | Gudnason, Vilmundur | Kivimaki, Mika | Hingorani, Aroon | Keinanen-Kiukaanniemi, Sirkka M | Saaristo, Timo E | Boomsma, Dorret I | Stefansson, Kari | van der Harst, Pim | Dupuis, Josée | Pedersen, Nancy L | Sattar, Naveed | Harris, Tamara B | Cucca, Francesco | Ripatti, Samuli | Salomaa, Veikko | Mohlke, Karen L | Balkau, Beverley | Froguel, Philippe | Pouta, Anneli | Jarvelin, Marjo-Riitta | Wareham, Nicholas J | Bouatia-Naji, Nabila | McCarthy, Mark I | Franks, Paul W | Meigs, James B | Teslovich, Tanya M | Florez, Jose C | Langenberg, Claudia | Ingelsson, Erik | Prokopenko, Inga | Barroso, Inês
Nature genetics  2012;44(9):991-1005.
Through genome-wide association meta-analyses of up to 133,010 individuals of European ancestry without diabetes, including individuals newly genotyped using the Metabochip, we have raised the number of confirmed loci influencing glycemic traits to 53, of which 33 also increase type 2 diabetes risk (q < 0.05). Loci influencing fasting insulin showed association with lipid levels and fat distribution, suggesting impact on insulin resistance. Gene-based analyses identified further biologically plausible loci, suggesting that additional loci beyond those reaching genome-wide significance are likely to represent real associations. This conclusion is supported by an excess of directionally consistent and nominally significant signals between discovery and follow-up studies. Functional follow-up of these newly discovered loci will further improve our understanding of glycemic control.
doi:10.1038/ng.2385
PMCID: PMC3433394  PMID: 22885924
22.  Obesity-Induced Insulin Resistance in Human Skeletal Muscle Is Characterised by Defective Activation of p42/p44 MAP Kinase 
PLoS ONE  2013;8(2):e56928.
Insulin resistance (IR), an impaired cellular, tissue and whole body response to insulin, is a major pathophysiological defect of type 2 diabetes mellitus. Although IR is closely associated with obesity, the identity of the molecular defect(s) underlying obesity-induced IR in skeletal muscle remains controversial; reduced post-receptor signalling of the insulin receptor substrate 1 (IRS1) adaptor protein and downstream effectors such as protein kinase B (PKB) have previously been implicated. We examined expression and/or activation of a number of components of the insulin-signalling cascade in skeletal muscle of 22 healthy young men (with body mass index (BMI) range, 20–37 kg/m2). Whole body insulin sensitivity (M value) and body composition was determined by the hyperinsulinaemic (40 mU. min−1.m−2.), euglycaemic clamp and by dual energy X-ray absorptiometry (DEXA) respectively. Skeletal muscle (vastus lateralis) biopsies were taken before and after one hour of hyperinsulinaemia and the muscle insulin signalling proteins examined by western blot and immunoprecipitation assay. There was a strong inverse relationship between M-value and BMI. The most striking abnormality was significantly reduced insulin-induced activation of p42/44 MAP kinase, measured by specific assay, in the volunteers with poor insulin sensitivity. However, there was no relationship between individuals' BMI or M-value and protein expression/phosphorylation of IRS1, PKB, or p42/44 MAP kinase protein, under basal or hyperinsulinaemic conditions. In the few individuals with poor insulin sensitivity but preserved p42/44 MAP kinase activation, other signalling defects were evident. These findings implicate defective p42/44 MAP kinase signalling as a potential contributor to obesity-related IR in a non-diabetic population, although clearly multiple signalling defects underlie obesity associated IR.
doi:10.1371/journal.pone.0056928
PMCID: PMC3585240  PMID: 23468892
23.  Variants in MTNR1B influence fasting glucose levels 
Prokopenko, Inga | Langenberg, Claudia | Florez, Jose C | Saxena, Richa | Soranzo, Nicole | Thorleifsson, Gudmar | Loos, Ruth J F | Manning, Alisa K | Jackson, Anne U | Aulchenko, Yurii | Potter, Simon C | Erdos, Michael R | Sanna, Serena | Hottenga, Jouke-Jan | Wheeler, Eleanor | Kaakinen, Marika | Lyssenko, Valeriya | Chen, Wei-Min | Ahmadi, Kourosh | Beckmann, Jacques S | Bergman, Richard N | Bochud, Murielle | Bonnycastle, Lori L | Buchanan, Thomas A | Cao, Antonio | Cervino, Alessandra | Coin, Lachlan | Collins, Francis S | Crisponi, Laura | de Geus, Eco J C | Dehghan, Abbas | Deloukas, Panos | Doney, Alex S F | Elliott, Paul | Freimer, Nelson | Gateva, Vesela | Herder, Christian | Hofman, Albert | Hughes, Thomas E | Hunt, Sarah | Illig, Thomas | Inouye, Michael | Isomaa, Bo | Johnson, Toby | Kong, Augustine | Krestyaninova, Maria | Kuusisto, Johanna | Laakso, Markku | Lim, Noha | Lindblad, Ulf | Lindgren, Cecilia M | McCann, Owen T | Mohlke, Karen L | Morris, Andrew D | Naitza, Silvia | Orrù, Marco | Palmer, Colin N A | Pouta, Anneli | Randall, Joshua | Rathmann, Wolfgang | Saramies, Jouko | Scheet, Paul | Scott, Laura J | Scuteri, Angelo | Sharp, Stephen | Sijbrands, Eric | Smit, Jan H | Song, Kijoung | Steinthorsdottir, Valgerdur | Stringham, Heather M | Tuomi, Tiinamaija | Tuomilehto, Jaakko | Uitterlinden, André G | Voight, Benjamin F | Waterworth, Dawn | Wichmann, H-Erich | Willemsen, Gonneke | Witteman, Jacqueline C M | Yuan, Xin | Zhao, Jing Hua | Zeggini, Eleftheria | Schlessinger, David | Sandhu, Manjinder | Boomsma, Dorret I | Uda, Manuela | Spector, Tim D | Penninx, Brenda WJH | Altshuler, David | Vollenweider, Peter | Jarvelin, Marjo Riitta | Lakatta, Edward | Waeber, Gerard | Fox, Caroline S | Peltonen, Leena | Groop, Leif C | Mooser, Vincent | Cupples, L Adrienne | Thorsteinsdottir, Unnur | Boehnke, Michael | Barroso, Inês | Van Duijn, Cornelia | Dupuis, Josée | Watanabe, Richard M | Stefansson, Kari | McCarthy, Mark I | Wareham, Nicholas J | Meigs, James B | Abecasis, Gonçalo R
Nature genetics  2008;41(1):77-81.
To identify previously unknown genetic loci associated with fasting glucose concentrations, we examined the leading association signals in ten genome-wide association scans involving a total of 36,610 individuals of European descent. Variants in the gene encoding melatonin receptor 1B (MTNR1B) were consistently associated with fasting glucose across all ten studies. The strongest signal was observed at rs10830963, where each G allele (frequency 0.30 in HapMap CEU) was associated with an increase of 0.07 (95% CI = 0.06-0.08) mmol/l in fasting glucose levels (P = 3.2 = × 10−50) and reduced beta-cell function as measured by homeostasis model assessment (HOMA-B, P = 1.1 × 10−15). The same allele was associated with an increased risk of type 2 diabetes (odds ratio = 1.09 (1.05-1.12), per G allele P = 3.3 × 10−7) in a meta-analysis of 13 case-control studies totaling 18,236 cases and 64,453 controls. Our analyses also confirm previous associations of fasting glucose with variants at the G6PC2 (rs560887, P = 1.1 × 10−57) and GCK (rs4607517, P = 1.0 × 10−25) loci.
doi:10.1038/ng.290
PMCID: PMC2682768  PMID: 19060907
24.  Genetic variation in Hyperpolarization-activated cyclic nucleotide-gated channels and its relationship with neuroticism, cognition and risk of depression 
Frontiers in Genetics  2012;3:116.
Hyperpolarization-activated cyclic nucleotide-gated (HCN) channels are encoded by four genes (HCN1–4) and, through activation by cyclic AMP (cAMP), represent a point of convergence for several psychosis risk genes. On the basis of positive preliminary data, we sought to test whether genetic variation in HCN1–4 conferred risk of depression or cognitive impairment in the Generation Scotland: Scottish Family Health Study. HCN1, HCN2, HCN3, and HCN4 were genotyped for 43 haplotype-tagging SNPs and tested for association with DSM-IV depression, neuroticism, and a battery of cognitive tests assessing cognitive ability, memory, verbal fluency, and psychomotor performance. No association was found between any HCN channel gene SNP and risk of depression, neuroticism, or on any cognitive measure. The current study does not support a genetic role for HCN channels in conferring risk of depression or cognitive impairment in individuals from the Scottish population.
doi:10.3389/fgene.2012.00116
PMCID: PMC3387669  PMID: 22783272
stress; depression; HCN channel; genetics; association; cognition; neuroticism
25.  Paradoxical Lower Serum Triglyceride Levels and Higher Type 2 Diabetes Mellitus Susceptibility in Obese Individuals with the PNPLA3 148M Variant 
PLoS ONE  2012;7(6):e39362.
Background
Obesity is highly associated with elevated serum triglycerides, hepatic steatosis and type 2 diabetes (T2D). The I148M (rs738409) genetic variant of patatin-like phospholipase domain-containing 3 gene (PNPLA3) is known to modulate hepatic triglyceride accumulation, leading to steatosis. No association between PNPLA3 I148M genotype and T2D in Europeans has been reported. Aim of this study is to examine the relationship between PNPLA3 I148M genotypes and serum triglycerides, insulin resistance and T2D susceptibility by testing a gene-environment interaction model with severe obesity.
Methods and Findings
PNPLA3 I148M was genotyped in a large obese cohort, the SOS study (n = 3,473) and in the Go-DARTS (n = 15,448), a T2D case-control study. Metabolic parameters were examined across the PNPLA3 I148M genotypes in participants of the SOS study at baseline and at 2- and 10-year follow up after bariatric surgery or conventional therapy. The associations with metabolic parameters were validated in the Go-DARTS study. Serum triglycerides were found to be lower in the PNPLA3 148M carriers from the SOS study at baseline and from the Go-DARTS T2D cohort. An increased risk for T2D conferred by the 148M allele was found in the SOS study (O.R. 1.09, 95% C.I. 1.01-1.39, P = 0.040) and in severely obese individuals in the Go-DARTS study (O.R. 1.37, 95% C.I. 1.13-1.66, P = 0.001). The 148M allele was no longer associated with insulin resistance or T2D after bariatric surgery in the SOS study and no association with the 148M allele was observed in the less obese (BMI<35) individuals in the Go-DARTS study (P for interaction  = 0.002). This provides evidence for the obesity interaction with I48M allele and T2D risk in a large-scale cross-sectional and a prospective interventional study.
Conclusions
Severely obese individuals carrying the PNPLA3 148M allele have lower serum triglyceride levels, are more insulin resistant and more susceptible to T2D. This study supports the hypothesis that obesity-driven hepatic lipid accumulation may contribute to T2D susceptibility.
doi:10.1371/journal.pone.0039362
PMCID: PMC3377675  PMID: 22724004

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