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1.  Genetic Modulation of Lipid Profiles following Lifestyle Modification or Metformin Treatment: The Diabetes Prevention Program 
PLoS Genetics  2012;8(8):e1002895.
Weight-loss interventions generally improve lipid profiles and reduce cardiovascular disease risk, but effects are variable and may depend on genetic factors. We performed a genetic association analysis of data from 2,993 participants in the Diabetes Prevention Program to test the hypotheses that a genetic risk score (GRS) based on deleterious alleles at 32 lipid-associated single-nucleotide polymorphisms modifies the effects of lifestyle and/or metformin interventions on lipid levels and nuclear magnetic resonance (NMR) lipoprotein subfraction size and number. Twenty-three loci previously associated with fasting LDL-C, HDL-C, or triglycerides replicated (P = 0.04–1×10−17). Except for total HDL particles (r = −0.03, P = 0.26), all components of the lipid profile correlated with the GRS (partial |r| = 0.07–0.17, P = 5×10−5–1×10−19). The GRS was associated with higher baseline-adjusted 1-year LDL cholesterol levels (β = +0.87, SEE±0.22 mg/dl/allele, P = 8×10−5, Pinteraction = 0.02) in the lifestyle intervention group, but not in the placebo (β = +0.20, SEE±0.22 mg/dl/allele, P = 0.35) or metformin (β = −0.03, SEE±0.22 mg/dl/allele, P = 0.90; Pinteraction = 0.64) groups. Similarly, a higher GRS predicted a greater number of baseline-adjusted small LDL particles at 1 year in the lifestyle intervention arm (β = +0.30, SEE±0.012 ln nmol/L/allele, P = 0.01, Pinteraction = 0.01) but not in the placebo (β = −0.002, SEE±0.008 ln nmol/L/allele, P = 0.74) or metformin (β = +0.013, SEE±0.008 nmol/L/allele, P = 0.12; Pinteraction = 0.24) groups. Our findings suggest that a high genetic burden confers an adverse lipid profile and predicts attenuated response in LDL-C levels and small LDL particle number to dietary and physical activity interventions aimed at weight loss.
Author Summary
The study included 2,993 participants from the Diabetes Prevention Program, a randomized clinical trial of intensive lifestyle intervention, metformin treatment, and placebo control. We examined associations between 32 gene variants that have been reproducibly associated with dyslipidemia and concentrations of lipids and NMR lipoprotein particle sizes and numbers. We also examined whether genetic background influences a person's response to cardioprotective interventions on lipid levels. Our analysis, which focused on determining whether common genetic variants impact the effects of cardioprotective interventions on lipid and lipoprotein particle size, shows that in persons with a high genetic risk score the benefit of intensive lifestyle intervention on LDL and small LDL particle levels is substantially diminished; this information may be informative for the targeted prevention of dyslipidemia, as it suggests that genetics might help identify persons in whom lifestyle intervention is likely to be an effective treatment for elevated lipids and lipoproteins. The NMR subfraction analyses provide novel insight into the biology of dyslipidemia by illustrating how numerous genetic variants that have previously been associated with lipid levels also modulate NMR lipoprotein particle sizes and number. This information may be informative for the targeted prevention of cardiovascular disease.
doi:10.1371/journal.pgen.1002895
PMCID: PMC3431328  PMID: 22951888
2.  Common Variants in 40 Genes Assessed for Diabetes Incidence and Response to Metformin and Lifestyle Intervention in the Diabetes Prevention Program 
Diabetes  2010;59(10):2672-2681.
OBJECTIVE
Genome-wide association studies have begun to elucidate the genetic architecture of type 2 diabetes. We examined whether single nucleotide polymorphisms (SNPs) identified through targeted complementary approaches affect diabetes incidence in the at-risk population of the Diabetes Prevention Program (DPP) and whether they influence a response to preventive interventions.
RESEARCH DESIGN AND METHODS
We selected SNPs identified by prior genome-wide association studies for type 2 diabetes and related traits, or capturing common variation in 40 candidate genes previously associated with type 2 diabetes, implicated in monogenic diabetes, encoding type 2 diabetes drug targets or drug-metabolizing/transporting enzymes, or involved in relevant physiological processes. We analyzed 1,590 SNPs for association with incident diabetes and their interaction with response to metformin or lifestyle interventions in 2,994 DPP participants. We controlled for multiple hypothesis testing by assessing false discovery rates.
RESULTS
We replicated the association of variants in the metformin transporter gene SLC47A1 with metformin response and detected nominal interactions in the AMP kinase (AMPK) gene STK11, the AMPK subunit genes PRKAA1 and PRKAA2, and a missense SNP in SLC22A1, which encodes another metformin transporter. The most significant association with diabetes incidence occurred in the AMPK subunit gene PRKAG2 (hazard ratio 1.24, 95% CI 1.09–1.40, P = 7 × 10−4). Overall, there were nominal associations with diabetes incidence at 85 SNPs and nominal interactions with the metformin and lifestyle interventions at 91 and 69 mostly nonoverlapping SNPs, respectively. The lowest P values were consistent with experiment-wide 33% false discovery rates.
CONCLUSIONS
We have identified potential genetic determinants of metformin response. These results merit confirmation in independent samples.
doi:10.2337/db10-0543
PMCID: PMC3279522  PMID: 20682687
3.  Genetic studies of body mass index yield new insights for obesity biology 
Locke, Adam E. | Kahali, Bratati | Berndt, Sonja I. | Justice, Anne E. | Pers, Tune H. | Day, Felix R. | Powell, Corey | Vedantam, Sailaja | Buchkovich, Martin L. | Yang, Jian | Croteau-Chonka, Damien C. | Esko, Tonu | Fall, Tove | Ferreira, Teresa | Gustafsson, Stefan | Kutalik, Zoltán | Luan, Jian’an | Mägi, Reedik | Randall, Joshua C. | Winkler, Thomas W. | Wood, Andrew R. | Workalemahu, Tsegaselassie | Faul, Jessica D. | Smith, Jennifer A. | Zhao, Jing Hua | Zhao, Wei | Chen, Jin | Fehrmann, Rudolf | Hedman, Åsa K. | Karjalainen, Juha | Schmidt, Ellen M. | Absher, Devin | Amin, Najaf | Anderson, Denise | Beekman, Marian | Bolton, Jennifer L. | Bragg-Gresham, Jennifer L. | Buyske, Steven | Demirkan, Ayse | Deng, Guohong | Ehret, Georg B. | Feenstra, Bjarke | Feitosa, Mary F. | Fischer, Krista | Goel, Anuj | Gong, Jian | Jackson, Anne U. | Kanoni, Stavroula | Kleber, Marcus E. | Kristiansson, Kati | Lim, Unhee | Lotay, Vaneet | Mangino, Massimo | Leach, Irene Mateo | Medina-Gomez, Carolina | Medland, Sarah E. | Nalls, Michael A. | Palmer, Cameron D. | Pasko, Dorota | Pechlivanis, Sonali | Peters, Marjolein J. | Prokopenko, Inga | Shungin, Dmitry | Stančáková, Alena | Strawbridge, Rona J. | Sung, Yun Ju | Tanaka, Toshiko | Teumer, Alexander | Trompet, Stella | van der Laan, Sander W. | van Setten, Jessica | Van Vliet-Ostaptchouk, Jana V. | Wang, Zhaoming | Yengo, Loïc | Zhang, Weihua | Isaacs, Aaron | Albrecht, Eva | Ärnlöv, Johan | Arscott, Gillian M. | Attwood, Antony P. | Bandinelli, Stefania | Barrett, Amy | Bas, Isabelita N. | Bellis, Claire | Bennett, Amanda J. | Berne, Christian | Blagieva, Roza | Blüher, Matthias | Böhringer, Stefan | Bonnycastle, Lori L. | Böttcher, Yvonne | Boyd, Heather A. | Bruinenberg, Marcel | Caspersen, Ida H. | Chen, Yii-Der Ida | Clarke, Robert | Daw, E. Warwick | de Craen, Anton J. M. | Delgado, Graciela | Dimitriou, Maria | Doney, Alex S. F. | Eklund, Niina | Estrada, Karol | Eury, Elodie | Folkersen, Lasse | Fraser, Ross M. | Garcia, Melissa E. | Geller, Frank | Giedraitis, Vilmantas | Gigante, Bruna | Go, Alan S. | Golay, Alain | Goodall, Alison H. | Gordon, Scott D. | Gorski, Mathias | Grabe, Hans-Jörgen | Grallert, Harald | Grammer, Tanja B. | Gräßler, Jürgen | Grönberg, Henrik | Groves, Christopher J. | Gusto, Gaëlle | Haessler, Jeffrey | Hall, Per | Haller, Toomas | Hallmans, Goran | Hartman, Catharina A. | Hassinen, Maija | Hayward, Caroline | Heard-Costa, Nancy L. | Helmer, Quinta | Hengstenberg, Christian | Holmen, Oddgeir | Hottenga, Jouke-Jan | James, Alan L. | Jeff, Janina M. | Johansson, Åsa | Jolley, Jennifer | Juliusdottir, Thorhildur | Kinnunen, Leena | Koenig, Wolfgang | Koskenvuo, Markku | Kratzer, Wolfgang | Laitinen, Jaana | Lamina, Claudia | Leander, Karin | Lee, Nanette R. | Lichtner, Peter | Lind, Lars | Lindström, Jaana | Lo, Ken Sin | Lobbens, Stéphane | Lorbeer, Roberto | Lu, Yingchang | Mach, François | Magnusson, Patrik K. E. | Mahajan, Anubha | McArdle, Wendy L. | McLachlan, Stela | Menni, Cristina | Merger, Sigrun | Mihailov, Evelin | Milani, Lili | Moayyeri, Alireza | Monda, Keri L. | Morken, Mario A. | Mulas, Antonella | Müller, Gabriele | Müller-Nurasyid, Martina | Musk, Arthur W. | Nagaraja, Ramaiah | Nöthen, Markus M. | Nolte, Ilja M. | Pilz, Stefan | Rayner, Nigel W. | Renstrom, Frida | Rettig, Rainer | Ried, Janina S. | Ripke, Stephan | Robertson, Neil R. | Rose, Lynda M. | Sanna, Serena | Scharnagl, Hubert | Scholtens, Salome | Schumacher, Fredrick R. | Scott, William R. | Seufferlein, Thomas | Shi, Jianxin | Smith, Albert Vernon | Smolonska, Joanna | Stanton, Alice V. | Steinthorsdottir, Valgerdur | Stirrups, Kathleen | Stringham, Heather M. | Sundström, Johan | Swertz, Morris A. | Swift, Amy J. | Syvänen, Ann-Christine | Tan, Sian-Tsung | Tayo, Bamidele O. | Thorand, Barbara | Thorleifsson, Gudmar | Tyrer, Jonathan P. | Uh, Hae-Won | Vandenput, Liesbeth | Verhulst, Frank C. | Vermeulen, Sita H. | Verweij, Niek | Vonk, Judith M. | Waite, Lindsay L. | Warren, Helen R. | Waterworth, Dawn | Weedon, Michael N. | Wilkens, Lynne R. | Willenborg, Christina | Wilsgaard, Tom | Wojczynski, Mary K. | Wong, Andrew | Wright, Alan F. | Zhang, Qunyuan | Brennan, Eoin P. | Choi, Murim | Dastani, Zari | Drong, Alexander W. | Eriksson, Per | Franco-Cereceda, Anders | Gådin, Jesper R. | Gharavi, Ali G. | Goddard, Michael E. | Handsaker, Robert E. | Huang, Jinyan | Karpe, Fredrik | Kathiresan, Sekar | Keildson, Sarah | Kiryluk, Krzysztof | Kubo, Michiaki | Lee, Jong-Young | Liang, Liming | Lifton, Richard P. | Ma, Baoshan | McCarroll, Steven A. | McKnight, Amy J. | Min, Josine L. | Moffatt, Miriam F. | Montgomery, Grant W. | Murabito, Joanne M. | Nicholson, George | Nyholt, Dale R. | Okada, Yukinori | Perry, John R. B. | Dorajoo, Rajkumar | Reinmaa, Eva | Salem, Rany M. | Sandholm, Niina | Scott, Robert A. | Stolk, Lisette | Takahashi, Atsushi | Tanaka, Toshihiro | van ’t Hooft, Ferdinand M. | Vinkhuyzen, Anna A. E. | Westra, Harm-Jan | Zheng, Wei | Zondervan, Krina T. | Heath, Andrew C. | Arveiler, Dominique | Bakker, Stephan J. L. | Beilby, John | Bergman, Richard N. | Blangero, John | Bovet, Pascal | Campbell, Harry | Caulfield, Mark J. | Cesana, Giancarlo | Chakravarti, Aravinda | Chasman, Daniel I. | Chines, Peter S. | Collins, Francis S. | Crawford, Dana C. | Cupples, L. Adrienne | Cusi, Daniele | Danesh, John | de Faire, Ulf | den Ruijter, Hester M. | Dominiczak, Anna F. | Erbel, Raimund | Erdmann, Jeanette | Eriksson, Johan G. | Farrall, Martin | Felix, Stephan B. | Ferrannini, Ele | Ferrières, Jean | Ford, Ian | Forouhi, Nita G. | Forrester, Terrence | Franco, Oscar H. | Gansevoort, Ron T. | Gejman, Pablo V. | Gieger, Christian | Gottesman, Omri | Gudnason, Vilmundur | Gyllensten, Ulf | Hall, Alistair S. | Harris, Tamara B. | Hattersley, Andrew T. | Hicks, Andrew A. | Hindorff, Lucia A. | Hingorani, Aroon D. | Hofman, Albert | Homuth, Georg | Hovingh, G. Kees | Humphries, Steve E. | Hunt, Steven C. | Hyppönen, Elina | Illig, Thomas | Jacobs, Kevin B. | Jarvelin, Marjo-Riitta | Jöckel, Karl-Heinz | Johansen, Berit | Jousilahti, Pekka | Jukema, J. Wouter | Jula, Antti M. | Kaprio, Jaakko | Kastelein, John J. P. | Keinanen-Kiukaanniemi, Sirkka M. | Kiemeney, Lambertus A. | Knekt, Paul | Kooner, Jaspal S. | Kooperberg, Charles | Kovacs, Peter | Kraja, Aldi T. | Kumari, Meena | Kuusisto, Johanna | Lakka, Timo A. | Langenberg, Claudia | Marchand, Loic Le | Lehtimäki, Terho | Lyssenko, Valeriya | Männistö, Satu | Marette, André | Matise, Tara C. | McKenzie, Colin A. | McKnight, Barbara | Moll, Frans L. | Morris, Andrew D. | Morris, Andrew P. | Murray, Jeffrey C. | Nelis, Mari | Ohlsson, Claes | Oldehinkel, Albertine J. | Ong, Ken K. | Madden, Pamela A. F. | Pasterkamp, Gerard | Peden, John F. | Peters, Annette | Postma, Dirkje S. | Pramstaller, Peter P. | Price, Jackie F. | Qi, Lu | Raitakari, Olli T. | Rankinen, Tuomo | Rao, D. C. | Rice, Treva K. | Ridker, Paul M. | Rioux, John D. | Ritchie, Marylyn D. | Rudan, Igor | Salomaa, Veikko | Samani, Nilesh J. | Saramies, Jouko | Sarzynski, Mark A. | Schunkert, Heribert | Schwarz, Peter E. H. | Sever, Peter | Shuldiner, Alan R. | Sinisalo, Juha | Stolk, Ronald P. | Strauch, Konstantin | Tönjes, Anke | Trégouët, David-Alexandre | Tremblay, Angelo | Tremoli, Elena | Virtamo, Jarmo | Vohl, Marie-Claude | Völker, Uwe | Waeber, Gérard | Willemsen, Gonneke | Witteman, Jacqueline C. | Zillikens, M. Carola | Adair, Linda S. | Amouyel, Philippe | Asselbergs, Folkert W. | Assimes, Themistocles L. | Bochud, Murielle | Boehm, Bernhard O. | Boerwinkle, Eric | Bornstein, Stefan R. | Bottinger, Erwin P. | Bouchard, Claude | Cauchi, Stéphane | Chambers, John C. | Chanock, Stephen J. | Cooper, Richard S. | de Bakker, Paul I. W. | Dedoussis, George | Ferrucci, Luigi | Franks, Paul W. | Froguel, Philippe | Groop, Leif C. | Haiman, Christopher A. | Hamsten, Anders | Hui, Jennie | Hunter, David J. | Hveem, Kristian | Kaplan, Robert C. | Kivimaki, Mika | Kuh, Diana | Laakso, Markku | Liu, Yongmei | Martin, Nicholas G. | März, Winfried | Melbye, Mads | Metspalu, Andres | Moebus, Susanne | Munroe, Patricia B. | Njølstad, Inger | Oostra, Ben A. | Palmer, Colin N. A. | Pedersen, Nancy L. | Perola, Markus | Pérusse, Louis | Peters, Ulrike | Power, Chris | Quertermous, Thomas | Rauramaa, Rainer | Rivadeneira, Fernando | Saaristo, Timo E. | Saleheen, Danish | Sattar, Naveed | Schadt, Eric E. | Schlessinger, David | Slagboom, P. Eline | Snieder, Harold | Spector, Tim D. | Thorsteinsdottir, Unnur | Stumvoll, Michael | Tuomilehto, Jaakko | Uitterlinden, André G. | Uusitupa, Matti | van der Harst, Pim | Walker, Mark | Wallaschofski, Henri | Wareham, Nicholas J. | Watkins, Hugh | Weir, David R. | Wichmann, H-Erich | Wilson, James F. | Zanen, Pieter | Borecki, Ingrid B. | Deloukas, Panos | Fox, Caroline S. | Heid, Iris M. | O’Connell, Jeffrey R. | Strachan, David P. | Stefansson, Kari | van Duijn, Cornelia M. | Abecasis, Gonçalo R. | Franke, Lude | Frayling, Timothy M. | McCarthy, Mark I. | Visscher, Peter M. | Scherag, André | Willer, Cristen J. | Boehnke, Michael | Mohlke, Karen L. | Lindgren, Cecilia M. | Beckmann, Jacques S. | Barroso, Inês | North, Kari E. | Ingelsson, Erik | Hirschhorn, Joel N. | Loos, Ruth J. F. | Speliotes, Elizabeth K.
Nature  2015;518(7538):197-206.
Obesity is heritable and predisposes to many diseases. To understand the genetic basis of obesity better, here we conduct a genome-wide association study and Metabochip meta-analysis of body mass index (BMI), a measure commonly used to define obesity and assess adiposity, in up to 339,224 individuals. This analysis identifies 97 BMI-associated loci (P < 5 × 10−8), 56 of which are novel. Five loci demonstrate clear evidence of several independent association signals, and many loci have significant effects on other metabolic phenotypes. The 97 loci account for ~2.7% of BMI variation, and genome-wide estimates suggest that common variation accounts for >20% of BMI variation. Pathway analyses provide strong support for a role of the central nervous system in obesity susceptibility and implicate new genes and pathways, including those related to synaptic function, glutamate signalling, insulin secretion/action, energy metabolism, lipid biology and adipogenesis.
doi:10.1038/nature14177
PMCID: PMC4382211  PMID: 25673413
4.  Infant Body Composition and Adipokine Concentrations in Relation to Maternal Gestational Weight Gain 
Diabetes Care  2014;37(5):1432-1438.
OBJECTIVE
To investigate associations of maternal gestational weight gain and body composition and their impact on offspring body composition and adipocytokine, glucose, and insulin concentrations at age 4 months.
RESEARCH DESIGN AND METHODS
This was a prospective study including 31 mother-infant pairs (N = 62). Maternal body composition was assessed using doubly labeled water. Infant body composition was assessed at 4 months using air displacement plethysmography, and venous blood was assayed for glucose, insulin, adiponectin, interleukin-6 (IL-6), and leptin concentrations.
RESULTS
Rate of gestational weight gain in midpregnancy was significantly associated with infant fat mass (r = 0.41, P = 0.03); rate of gestational weight in late pregnancy was significantly associated with infant fat-free mass (r = 0.37, P = 0.04). Infant birth weight was also strongly correlated with infant fat-free mass at 4 months (r = 0.63, P = 0.0002). Maternal BMI and maternal fat mass were strongly inversely associated with infant IL-6 concentrations (r = −0.60, P = 0.002 and r = −0.52, P = 0.01, respectively). Infant fat-free mass was inversely related to infant adiponectin concentrations (r = −0.48, P = 0.008) and positively correlated with infant blood glucose adjusted for insulin concentrations (r = 0.42, P = 0.04). No significant associations for leptin were observed.
CONCLUSIONS
Timing of maternal weight gain differentially impacts body composition of the 4-month-old infant, which in turn appears to affect the infant’s glucose and adipokine concentrations.
doi:10.2337/dc13-2265
PMCID: PMC3994936  PMID: 24623025
5.  Common variation at PPARGC1A/B and change in body composition and metabolic traits following preventive interventions: the Diabetes Prevention Program 
Diabetologia  2013;57(3):485-490.
Aims/hypothesis
PPARGC1A and PPARGCB encode transcriptional coactivators that regulate numerous metabolic processes. We tested associations and treatment (i.e. metformin or lifestyle modification) interactions with metabolic traits in the Diabetes Prevention Program, a randomised controlled trial in persons at high risk of type 2 diabetes.
Methods
We used Tagger software to select 75 PPARGCA1 and 94 PPARGC1B tag single-nucleotide polymorphisms (SNPs) for analysis. These SNPs were tested for associations with relevant cardiometabolic quantitative traits using generalised linear models. Aggregate genetic effects were tested using the sequence kernel association test.
Results
In aggregate, PPARGC1A variation was strongly associated with baseline triacylglycerol concentrations (p=2.9×10−30), BMI (p=2.0×10−5) and visceral adiposity (p=1.9×10−4), as well as with changes in triacylglycerol concentrations (p=1.7×10−5) and BMI (p=9.9×10−5) from baseline to 1 year. PPARGC1B variation was only 3 associated with baseline subcutaneous adiposity (p=0.01). In individual SNP analyses, Gly482Ser (rs8192678, PPARGC1A) was associated with accumulation of subcutaneous adiposity and worsening insulin resistance at 1 year (both p<0.05), while rs2970852 (PPARGC1A) modified the effects of metformin on triacylglycerol levels (pinteraction=0.04).
Conclusions/interpretation
These findings provide several novel and other confirmatory insights into the role of PPARGC1A variation with respect to diabetesrelated metabolic traits.
Trial registration
ClinicalTrials.gov NCT00004992
doi:10.1007/s00125-013-3133-4
PMCID: PMC4154629  PMID: 24317794
Cholesterol; Dyslipidaemia; Gene × environment interaction; Gene × lifestyle interaction; Genetics; Lifestyle intervention; Metformin; Pharmacogenetics; PPARGC1A; PPARGC1B; Randomised controlled trial; Triacylglycerol
6.  Genetic and epigenetic catalysts in early-life programming of adult cardiometabolic disorders 
Evidence has emerged across the past few decades that the lifetime risk of developing morbidities like type 2 diabetes, obesity, and cardiovascular disease may be influenced by exposures that occur in utero and in childhood. Developmental abnormalities are known to occur at various stages in fetal growth. Epidemiological and mechanistic studies have sought to delineate developmental processes and plausible risk factors influencing pregnancy outcomes and later health. Whether these observations reflect causal processes or are confounded by genetic and social factors remains unclear, although animal (and some human) studies suggest that epigenetic programming events may be involved. Regardless of the causal basis to observations of early-life risk factors and later disease risk, the fact that such associations exist and that they are of a fairly large magnitude justifies further research around this topic. Furthermore, additional information is needed to substantiate public health guidelines on lifestyle behaviors during pregnancy to improve infant health outcomes. Indeed, lifestyle intervention clinical trials in pregnancy are now coming online, where materials and data are being collected that should facilitate understanding of the causal nature of intrauterine exposures related with gestational weight gain, such as elevated maternal blood glucose concentrations. In this review, we provide an overview of these concepts.
Video abstract
doi:10.2147/DMSO.S51433
PMCID: PMC4257022  PMID: 25489250
early-life; epigenetic; programming; pregnancy; cardiometabolic; obesity; cardiovascular disease; type 2 diabetes
7.  The Complex Interplay of Genetic and Lifestyle Risk Factors in Type 2 Diabetes: An Overview 
Scientifica  2012;2012:482186.
Type 2 diabetes (T2D) is one of the scourges of modern times, with many millions of people affected by the disease. Diabetes occurs most frequently in those who are overweight or obese. However, not all overweight and obese persons develop diabetes, and there are those who develop the disease who are lean and physically active. Certain ethnicities, especially indigenous populations, are at considerably higher risk of obesity and diabetes than those of white European ancestry. The patterns and distributions of diabetes have led some to speculate that the disease is caused by interactions between genetic and obesogenic lifestyle factors. Whilst to many this is a plausible explanation, remarkably little reliable evidence exists to support it. In this review, an overview of published literature relating to genetic and lifestyle risk factors for T2D is provided. The review also describes the concepts and rationale that have motivated the view that gene-lifestyle interactions cause diabetes and overviews the empirical evidence published to date to support this hypothesis.
doi:10.6064/2012/482186
PMCID: PMC3820646  PMID: 24278702
8.  Variation at the Melanocortin 4 Receptor gene and response to weight-loss interventions in the Diabetes Prevention Program 
Obesity (Silver Spring, Md.)  2013;21(9):E520-E526.
Objective
To assess associations and genotype × treatment interactions for melanocortin 4 receptor (MC4R) locus variants and obesity-related traits.
Design and Methods
Diabetes Prevention Program (DPP) participants (N=3,819, of whom 3,356 were genotyped for baseline and 3,234 for longitudinal analyses) were randomized into intensive lifestyle modification (diet, exercise, weight loss), metformin or placebo control. Adiposity was assessed in a subgroup (n=909) using computed tomography. All analyses were adjusted for age, sex, ethnicity and treatment.
Results
The rs1943218 minor allele was nominally associated with short-term (6 month; P=0.032) and long-term (2 year; P=0.038) weight change. Eight SNPs modified response to treatment on short-term (rs17066856, rs9966412, rs17066859, rs8091237, rs17066866, rs7240064) or long-term (rs12970134, rs17066866) reduction in body weight, or diabetes incidence (rs17066829) (all Pinteraction <0.05).
Conclusion
This is the first study to comprehensively assess the role of MC4R variants and weight regulation in a weight loss intervention trial. One MC4R variant was directly associated with obesity-related traits or diabetes; numerous other variants appear to influence body weight and diabetes risk by modifying the protective effects of the DPP interventions.
doi:10.1002/oby.20459
PMCID: PMC4023472  PMID: 23512951
MC4R; Randomized Controlled Trial; Lifestyle; Metformin; Gene-Environment Interaction; Genetic; Obesity; Diabetes Prevention Program; Adiposity
9.  Gene-Environment and Gene-Treatment Interactions in Type 2 Diabetes 
Diabetes Care  2013;36(5):1413-1421.
doi:10.2337/dc12-2211
PMCID: PMC3631878  PMID: 23613601
10.  Low-frequency and rare exome chip variants associate with fasting glucose and type 2 diabetes susceptibility 
Wessel, Jennifer | Chu, Audrey Y. | Willems, Sara M. | Wang, Shuai | Yaghootkar, Hanieh | Brody, Jennifer A. | Dauriz, Marco | Hivert, Marie-France | Raghavan, Sridharan | Lipovich, Leonard | Hidalgo, Bertha | Fox, Keolu | Huffman, Jennifer E. | An, Ping | Lu, Yingchang | Rasmussen-Torvik, Laura J. | Grarup, Niels | Ehm, Margaret G. | Li, Li | Baldridge, Abigail S. | Stančáková, Alena | Abrol, Ravinder | Besse, Céline | Boland, Anne | Bork-Jensen, Jette | Fornage, Myriam | Freitag, Daniel F. | Garcia, Melissa E. | Guo, Xiuqing | Hara, Kazuo | Isaacs, Aaron | Jakobsdottir, Johanna | Lange, Leslie A. | Layton, Jill C. | Li, Man | Zhao, Jing Hua | Meidtner, Karina | Morrison, Alanna C. | Nalls, Mike A. | Peters, Marjolein J. | Sabater-Lleal, Maria | Schurmann, Claudia | Silveira, Angela | Smith, Albert V. | Southam, Lorraine | Stoiber, Marcus H. | Strawbridge, Rona J. | Taylor, Kent D. | Varga, Tibor V. | Allin, Kristine H. | Amin, Najaf | Aponte, Jennifer L. | Aung, Tin | Barbieri, Caterina | Bihlmeyer, Nathan A. | Boehnke, Michael | Bombieri, Cristina | Bowden, Donald W. | Burns, Sean M. | Chen, Yuning | Chen, Yii-Der I. | Cheng, Ching-Yu | Correa, Adolfo | Czajkowski, Jacek | Dehghan, Abbas | Ehret, Georg B. | Eiriksdottir, Gudny | Escher, Stefan A. | Farmaki, Aliki-Eleni | Frånberg, Mattias | Gambaro, Giovanni | Giulianini, Franco | III, William A. Goddard | Goel, Anuj | Gottesman, Omri | Grove, Megan L. | Gustafsson, Stefan | Hai, Yang | Hallmans, Göran | Heo, Jiyoung | Hoffmann, Per | Ikram, Mohammad K. | Jensen, Richard A. | Jørgensen, Marit E. | Jørgensen, Torben | Karaleftheri, Maria | Khor, Chiea C. | Kirkpatrick, Andrea | Kraja, Aldi T. | Kuusisto, Johanna | Lange, Ethan M. | Lee, I.T. | Lee, Wen-Jane | Leong, Aaron | Liao, Jiemin | Liu, Chunyu | Liu, Yongmei | Lindgren, Cecilia M. | Linneberg, Allan | Malerba, Giovanni | Mamakou, Vasiliki | Marouli, Eirini | Maruthur, Nisa M. | Matchan, Angela | McKean, Roberta | McLeod, Olga | Metcalf, Ginger A. | Mohlke, Karen L. | Muzny, Donna M. | Ntalla, Ioanna | Palmer, Nicholette D. | Pasko, Dorota | Peter, Andreas | Rayner, Nigel W. | Renström, Frida | Rice, Ken | Sala, Cinzia F. | Sennblad, Bengt | Serafetinidis, Ioannis | Smith, Jennifer A. | Soranzo, Nicole | Speliotes, Elizabeth K. | Stahl, Eli A. | Stirrups, Kathleen | Tentolouris, Nikos | Thanopoulou, Anastasia | Torres, Mina | Traglia, Michela | Tsafantakis, Emmanouil | Javad, Sundas | Yanek, Lisa R. | Zengini, Eleni | Becker, Diane M. | Bis, Joshua C. | Brown, James B. | Cupples, L. Adrienne | Hansen, Torben | Ingelsson, Erik | Karter, Andrew J. | Lorenzo, Carlos | Mathias, Rasika A. | Norris, Jill M. | Peloso, Gina M. | Sheu, Wayne H.-H. | Toniolo, Daniela | Vaidya, Dhananjay | Varma, Rohit | Wagenknecht, Lynne E. | Boeing, Heiner | Bottinger, Erwin P. | Dedoussis, George | Deloukas, Panos | Ferrannini, Ele | Franco, Oscar H. | Franks, Paul W. | Gibbs, Richard A. | Gudnason, Vilmundur | Hamsten, Anders | Harris, Tamara B. | Hattersley, Andrew T. | Hayward, Caroline | Hofman, Albert | Jansson, Jan-Håkan | Langenberg, Claudia | Launer, Lenore J. | Levy, Daniel | Oostra, Ben A. | O'Donnell, Christopher J. | O'Rahilly, Stephen | Padmanabhan, Sandosh | Pankow, James S. | Polasek, Ozren | Province, Michael A. | Rich, Stephen S. | Ridker, Paul M | Rudan, Igor | Schulze, Matthias B. | Smith, Blair H. | Uitterlinden, André G. | Walker, Mark | Watkins, Hugh | Wong, Tien Y. | Zeggini, Eleftheria | Scotland, Generation | Laakso, Markku | Borecki, Ingrid B. | Chasman, Daniel I. | Pedersen, Oluf | Psaty, Bruce M. | Tai, E. Shyong | van Duijn, Cornelia M. | Wareham, Nicholas J. | Waterworth, Dawn M. | Boerwinkle, Eric | Kao, WH Linda | Florez, Jose C. | Loos, Ruth J.F. | Wilson, James G. | Frayling, Timothy M. | Siscovick, David S. | Dupuis, Josée | Rotter, Jerome I. | Meigs, James B. | Scott, Robert A. | Goodarzi, Mark O.
Nature communications  2015;6:5897.
Fasting glucose and insulin are intermediate traits for type 2 diabetes. Here we explore the role of coding variation on these traits by analysis of variants on the HumanExome BeadChip in 60,564 non-diabetic individuals and in 16,491 T2D cases and 81,877 controls. We identify a novel association of a low-frequency nonsynonymous SNV in GLP1R (A316T; rs10305492; MAF=1.4%) with lower FG (β=-0.09±0.01 mmol L−1, p=3.4×10−12), T2D risk (OR[95%CI]=0.86[0.76-0.96], p=0.010), early insulin secretion (β=-0.07±0.035 pmolinsulin mmolglucose−1, p=0.048), but higher 2-h glucose (β=0.16±0.05 mmol L−1, p=4.3×10−4). We identify a gene-based association with FG at G6PC2 (pSKAT=6.8×10−6) driven by four rare protein-coding SNVs (H177Y, Y207S, R283X and S324P). We identify rs651007 (MAF=20%) in the first intron of ABO at the putative promoter of an antisense lncRNA, associating with higher FG (β=0.02±0.004 mmol L−1, p=1.3×10−8). Our approach identifies novel coding variant associations and extends the allelic spectrum of variation underlying diabetes-related quantitative traits and T2D susceptibility.
doi:10.1038/ncomms6897
PMCID: PMC4311266  PMID: 25631608
11.  Maternal Physical Activity and Insulin Action in Pregnancy and Their Relationships With Infant Body Composition 
Diabetes Care  2013;36(2):267-269.
OBJECTIVE
We sought to assess the association between maternal gestational physical activity and insulin action and body composition in early infancy.
RESEARCH DESIGN AND METHODS
At 28–32 weeks' gestation, pregnant women participating in an observational study in Sweden underwent assessments of height, weight, and body composition, an oral glucose tolerance test, and 10 days of objective physical activity assessment. Thirty mothers and infants returned at 11–19 weeks postpartum. Infants underwent assessments of weight, length, and body composition.
RESULTS
Early insulin response was correlated with total physical activity (r = −0.47; P = 0.007). Early insulin response (r = −0.36; P = 0.045) and total physical activity (r = 0.52; P = 0.037) were also correlated with infant fat-free mass. No maternal variable was significantly correlated with infant adiposity.
CONCLUSIONS
The relationships between maternal physical activity, insulin response, and infant fat-free mass suggest that physical activity during pregnancy may affect metabolic outcomes in the mother and her offspring.
doi:10.2337/dc12-0885
PMCID: PMC3554315  PMID: 22966095
12.  Telomere length in blood and skeletal muscle in relation to measures of glycaemia and insulinaemia 
Summary
Aims
Skeletal muscle is a major metabolic organ and plays important roles in glucose metabolism, insulin sensitivity, and insulin action. Muscle telomere length reflects the myocyte's exposure to harmful environmental factors. Leukocyte telomere length is considered a marker of muscle telomere length and is used in epidemiologic studies to assess associations with ageing-related diseases where muscle physiology is important. However, the extent to which leucocyte telomere length and muscle telomere length are correlated is unknown, as are their relative correlations with glucose and insulin concentrations. The purpose of this study was to determine the extent of these relationships.
Methods
Leucocyte telomere length and muscle telomere length were measured by quantitative real-time PCR in participants from the Malmö Exercise Intervention (MEI; n=27) and the PPP-Botnia studies (n=31). Participants in both studies were free from type 2 diabetes. We assessed the association between leucocyte telomere length, muscle telomere length and metabolic traits using Spearmen correlations and multivariate linear regression. Bland-Altman analysis was used to assess agreement between leucocyte telomere length and muscle telomere length.
Results
In age-, study-, diabetes family history- and sex-adjusted models, leucocyte telomere length and muscle telomere length were positively correlated (r=0.39, 95% CI: 0.15, 0.59). Leucocyte telomere length was inversely associated with 2hr glucose concentrations (r= -0.58, 95% CI: -1.0, -0.16), but there was no correlation between muscle telomere length and 2 hr glucose concentrations (r=0.05, 95% CI: -0.35, 0.46) or between leucocyte telomere length or muscle telomere length with other metabolic traits.
Conclusions
In summary, the current study supports the use of leucocyte telomere length as a proxy for muscle telomere length in epidemiological studies of type 2 diabetes aetiology.
doi:10.1111/j.1464-5491.2012.03737.x
PMCID: PMC3698879  PMID: 22747879
Leukocyte telomere length; muscle telomere length; cardiometabolic; type 2 diabetes; skeletal muscle physiology
13.  Gene × dietary pattern interactions in obesity: analysis of up to 68 317 adults of European ancestry 
Human Molecular Genetics  2015;24(16):4728-4738.
Obesity is highly heritable. Genetic variants showing robust associations with obesity traits have been identified through genome-wide association studies. We investigated whether a composite score representing healthy diet modifies associations of these variants with obesity traits. Totally, 32 body mass index (BMI)- and 14 waist–hip ratio (WHR)-associated single nucleotide polymorphisms were genotyped, and genetic risk scores (GRS) were calculated in 18 cohorts of European ancestry (n = 68 317). Diet score was calculated based on self-reported intakes of whole grains, fish, fruits, vegetables, nuts/seeds (favorable) and red/processed meats, sweets, sugar-sweetened beverages and fried potatoes (unfavorable). Multivariable adjusted, linear regression within each cohort followed by inverse variance-weighted, fixed-effects meta-analysis was used to characterize: (a) associations of each GRS with BMI and BMI-adjusted WHR and (b) diet score modification of genetic associations with BMI and BMI-adjusted WHR. Nominally significant interactions (P = 0.006–0.04) were observed between the diet score and WHR-GRS (but not BMI-GRS), two WHR loci (GRB14 rs10195252; LYPLAL1 rs4846567) and two BMI loci (LRRN6C rs10968576; MTIF3 rs4771122), for the respective BMI-adjusted WHR or BMI outcomes. Although the magnitudes of these select interactions were small, our data indicated that associations between genetic predisposition and obesity traits were stronger with a healthier diet. Our findings generate interesting hypotheses; however, experimental and functional studies are needed to determine their clinical relevance.
doi:10.1093/hmg/ddv186
PMCID: PMC4512626  PMID: 25994509
14.  Genetic Risk of Progression to Type 2 Diabetes and Response to Intensive Lifestyle or Metformin in Prediabetic Women With and Without a History of Gestational Diabetes Mellitus 
Diabetes Care  2014;37(4):909-911.
OBJECTIVE
The Diabetes Prevention Program (DPP) trial investigated rates of progression to diabetes among adults with prediabetes randomized to treatment with placebo, metformin, or intensive lifestyle intervention. Among women in the DPP, diabetes risk reduction with metformin was greater in women with prior gestational diabetes mellitus (GDM) compared with women without GDM but with one or more previous live births.
RESEARCH DESIGN AND METHODS
We asked if genetic variability could account for these differences by comparing β-cell function and genetic risk scores (GRS), calculated from 34 diabetes-associated loci, between women with and without histories of GDM.
RESULTS
β-Cell function was reduced in women with GDM. The GRS was positively associated with a history of GDM; however, the GRS did not predict progression to diabetes or modulate response to intervention.
CONCLUSIONS
These data suggest that a diabetes-associated GRS is associated with development of GDM and may characterize women at risk for development of diabetes due to β-cell dysfunction.
doi:10.2337/dc13-0700
PMCID: PMC3964494  PMID: 24271189
15.  Genetic Predictors of Weight Loss and Weight Regain After Intensive Lifestyle Modification, Metformin Treatment, or Standard Care in the Diabetes Prevention Program 
Diabetes Care  2012;35(2):363-366.
OBJECTIVE
We tested genetic associations with weight loss and weight regain in the Diabetes Prevention Program, a randomized controlled trial of weight loss–inducing interventions (lifestyle and metformin) versus placebo.
RESEARCH DESIGN AND METHODS
Sixteen obesity-predisposing single nucleotide polymorphisms (SNPs) were tested for association with short-term (baseline to 6 months) and long-term (baseline to 2 years) weight loss and weight regain (6 months to study end).
RESULTS
Irrespective of treatment, the Ala12 allele at PPARG associated with short- and long-term weight loss (−0.63 and −0.93 kg/allele, P ≤ 0.005, respectively). Gene–treatment interactions were observed for short-term (LYPLAL1 rs2605100, Plifestyle*SNP = 0.032; GNPDA2 rs10938397, Plifestyle*SNP = 0.016; MTCH2 rs10838738, Plifestyle*SNP = 0.022) and long-term (NEGR1 rs2815752, Pmetformin*SNP = 0.028; FTO rs9939609, Plifestyle*SNP = 0.044) weight loss. Three of 16 SNPs were associated with weight regain (NEGR1 rs2815752, BDNF rs6265, PPARG rs1801282), irrespective of treatment. TMEM18 rs6548238 and KTCD15 rs29941 showed treatment-specific effects (Plifestyle*SNP < 0.05).
CONCLUSIONS
Genetic information may help identify people who require additional support to maintain reduced weight after clinical intervention.
doi:10.2337/dc11-1328
PMCID: PMC3263869  PMID: 22179955
16.  Low-frequency and rare exome chip variants associate with fasting glucose and type 2 diabetes susceptibility 
Wessel, Jennifer | Chu, Audrey Y | Willems, Sara M | Wang, Shuai | Yaghootkar, Hanieh | Brody, Jennifer A | Dauriz, Marco | Hivert, Marie-France | Raghavan, Sridharan | Lipovich, Leonard | Hidalgo, Bertha | Fox, Keolu | Huffman, Jennifer E | An, Ping | Lu, Yingchang | Rasmussen-Torvik, Laura J | Grarup, Niels | Ehm, Margaret G | Li, Li | Baldridge, Abigail S | Stančáková, Alena | Abrol, Ravinder | Besse, Céline | Boland, Anne | Bork-Jensen, Jette | Fornage, Myriam | Freitag, Daniel F | Garcia, Melissa E | Guo, Xiuqing | Hara, Kazuo | Isaacs, Aaron | Jakobsdottir, Johanna | Lange, Leslie A | Layton, Jill C | Li, Man | Hua Zhao, Jing | Meidtner, Karina | Morrison, Alanna C | Nalls, Mike A | Peters, Marjolein J | Sabater-Lleal, Maria | Schurmann, Claudia | Silveira, Angela | Smith, Albert V | Southam, Lorraine | Stoiber, Marcus H | Strawbridge, Rona J | Taylor, Kent D | Varga, Tibor V | Allin, Kristine H | Amin, Najaf | Aponte, Jennifer L | Aung, Tin | Barbieri, Caterina | Bihlmeyer, Nathan A | Boehnke, Michael | Bombieri, Cristina | Bowden, Donald W | Burns, Sean M | Chen, Yuning | Chen, Yii-DerI | Cheng, Ching-Yu | Correa, Adolfo | Czajkowski, Jacek | Dehghan, Abbas | Ehret, Georg B | Eiriksdottir, Gudny | Escher, Stefan A | Farmaki, Aliki-Eleni | Frånberg, Mattias | Gambaro, Giovanni | Giulianini, Franco | Goddard, William A | Goel, Anuj | Gottesman, Omri | Grove, Megan L | Gustafsson, Stefan | Hai, Yang | Hallmans, Göran | Heo, Jiyoung | Hoffmann, Per | Ikram, Mohammad K | Jensen, Richard A | Jørgensen, Marit E | Jørgensen, Torben | Karaleftheri, Maria | Khor, Chiea C | Kirkpatrick, Andrea | Kraja, Aldi T | Kuusisto, Johanna | Lange, Ethan M | Lee, I T | Lee, Wen-Jane | Leong, Aaron | Liao, Jiemin | Liu, Chunyu | Liu, Yongmei | Lindgren, Cecilia M | Linneberg, Allan | Malerba, Giovanni | Mamakou, Vasiliki | Marouli, Eirini | Maruthur, Nisa M | Matchan, Angela | McKean-Cowdin, Roberta | McLeod, Olga | Metcalf, Ginger A | Mohlke, Karen L | Muzny, Donna M | Ntalla, Ioanna | Palmer, Nicholette D | Pasko, Dorota | Peter, Andreas | Rayner, Nigel W | Renström, Frida | Rice, Ken | Sala, Cinzia F | Sennblad, Bengt | Serafetinidis, Ioannis | Smith, Jennifer A | Soranzo, Nicole | Speliotes, Elizabeth K | Stahl, Eli A | Stirrups, Kathleen | Tentolouris, Nikos | Thanopoulou, Anastasia | Torres, Mina | Traglia, Michela | Tsafantakis, Emmanouil | Javad, Sundas | Yanek, Lisa R | Zengini, Eleni | Becker, Diane M | Bis, Joshua C | Brown, James B | Adrienne Cupples, L | Hansen, Torben | Ingelsson, Erik | Karter, Andrew J | Lorenzo, Carlos | Mathias, Rasika A | Norris, Jill M | Peloso, Gina M | Sheu, Wayne H.-H. | Toniolo, Daniela | Vaidya, Dhananjay | Varma, Rohit | Wagenknecht, Lynne E | Boeing, Heiner | Bottinger, Erwin P | Dedoussis, George | Deloukas, Panos | Ferrannini, Ele | Franco, Oscar H | Franks, Paul W | Gibbs, Richard A | Gudnason, Vilmundur | Hamsten, Anders | Harris, Tamara B | Hattersley, Andrew T | Hayward, Caroline | Hofman, Albert | Jansson, Jan-Håkan | Langenberg, Claudia | Launer, Lenore J | Levy, Daniel | Oostra, Ben A | O'Donnell, Christopher J | O'Rahilly, Stephen | Padmanabhan, Sandosh | Pankow, James S | Polasek, Ozren | Province, Michael A | Rich, Stephen S | Ridker, Paul M | Rudan, Igor | Schulze, Matthias B | Smith, Blair H | Uitterlinden, André G | Walker, Mark | Watkins, Hugh | Wong, Tien Y | Zeggini, Eleftheria | Laakso, Markku | Borecki, Ingrid B | Chasman, Daniel I | Pedersen, Oluf | Psaty, Bruce M | Shyong Tai, E | van Duijn, Cornelia M | Wareham, Nicholas J | Waterworth, Dawn M | Boerwinkle, Eric | Linda Kao, W H | Florez, Jose C | Loos, Ruth J.F. | Wilson, James G | Frayling, Timothy M | Siscovick, David S | Dupuis, Josée | Rotter, Jerome I | Meigs, James B | Scott, Robert A | Goodarzi, Mark O
Nature Communications  2015;6:5897.
Fasting glucose and insulin are intermediate traits for type 2 diabetes. Here we explore the role of coding variation on these traits by analysis of variants on the HumanExome BeadChip in 60,564 non-diabetic individuals and in 16,491 T2D cases and 81,877 controls. We identify a novel association of a low-frequency nonsynonymous SNV in GLP1R (A316T; rs10305492; MAF=1.4%) with lower FG (β=−0.09±0.01 mmol l−1, P=3.4 × 10−12), T2D risk (OR[95%CI]=0.86[0.76–0.96], P=0.010), early insulin secretion (β=−0.07±0.035 pmolinsulin mmolglucose−1, P=0.048), but higher 2-h glucose (β=0.16±0.05 mmol l−1, P=4.3 × 10−4). We identify a gene-based association with FG at G6PC2 (pSKAT=6.8 × 10−6) driven by four rare protein-coding SNVs (H177Y, Y207S, R283X and S324P). We identify rs651007 (MAF=20%) in the first intron of ABO at the putative promoter of an antisense lncRNA, associating with higher FG (β=0.02±0.004 mmol l−1, P=1.3 × 10−8). Our approach identifies novel coding variant associations and extends the allelic spectrum of variation underlying diabetes-related quantitative traits and T2D susceptibility.
Both rare and common variants contribute to the aetiology of complex traits such as type 2 diabetes (T2D). Here, the authors examine the effect of coding variation on glycaemic traits and T2D, and identify low-frequency variation in GLP1R significantly associated with these traits.
doi:10.1038/ncomms6897
PMCID: PMC4311266  PMID: 25631608
17.  Expression of Phosphofructokinase in Skeletal Muscle Is Influenced by Genetic Variation and Associated With Insulin Sensitivity 
Diabetes  2014;63(3):1154-1165.
Using an integrative approach in which genetic variation, gene expression, and clinical phenotypes are assessed in relevant tissues may help functionally characterize the contribution of genetics to disease susceptibility. We sought to identify genetic variation influencing skeletal muscle gene expression (expression quantitative trait loci [eQTLs]) as well as expression associated with measures of insulin sensitivity. We investigated associations of 3,799,401 genetic variants in expression of >7,000 genes from three cohorts (n = 104). We identified 287 genes with cis-acting eQTLs (false discovery rate [FDR] <5%; P < 1.96 × 10−5) and 49 expression–insulin sensitivity phenotype associations (i.e., fasting insulin, homeostasis model assessment–insulin resistance, and BMI) (FDR <5%; P = 1.34 × 10−4). One of these associations, fasting insulin/phosphofructokinase (PFKM), overlaps with an eQTL. Furthermore, the expression of PFKM, a rate-limiting enzyme in glycolysis, was nominally associated with glucose uptake in skeletal muscle (P = 0.026; n = 42) and overexpressed (Bonferroni-corrected P = 0.03) in skeletal muscle of patients with T2D (n = 102) compared with normoglycemic controls (n = 87). The PFKM eQTL (rs4547172; P = 7.69 × 10−6) was nominally associated with glucose uptake, glucose oxidation rate, intramuscular triglyceride content, and metabolic flexibility (P = 0.016–0.048; n = 178). We explored eQTL results using published data from genome-wide association studies (DIAGRAM and MAGIC), and a proxy for the PFKM eQTL (rs11168327; r2 = 0.75) was nominally associated with T2D (DIAGRAM P = 2.7 × 10−3). Taken together, our analysis highlights PFKM as a potential regulator of skeletal muscle insulin sensitivity.
doi:10.2337/db13-1301
PMCID: PMC3931395  PMID: 24306210
18.  Total Zinc Intake May Modify the Glucose-Raising Effect of a Zinc Transporter (SLC30A8) Variant 
Diabetes  2011;60(9):2407-2416.
OBJECTIVE
Many genetic variants have been associated with glucose homeostasis and type 2 diabetes in genome-wide association studies. Zinc is an essential micronutrient that is important for β-cell function and glucose homeostasis. We tested the hypothesis that zinc intake could influence the glucose-raising effect of specific variants.
RESEARCH DESIGN AND METHODS
We conducted a 14-cohort meta-analysis to assess the interaction of 20 genetic variants known to be related to glycemic traits and zinc metabolism with dietary zinc intake (food sources) and a 5-cohort meta-analysis to assess the interaction with total zinc intake (food sources and supplements) on fasting glucose levels among individuals of European ancestry without diabetes.
RESULTS
We observed a significant association of total zinc intake with lower fasting glucose levels (β-coefficient ± SE per 1 mg/day of zinc intake: −0.0012 ± 0.0003 mmol/L, summary P value = 0.0003), while the association of dietary zinc intake was not significant. We identified a nominally significant interaction between total zinc intake and the SLC30A8 rs11558471 variant on fasting glucose levels (β-coefficient ± SE per A allele for 1 mg/day of greater total zinc intake: −0.0017 ± 0.0006 mmol/L, summary interaction P value = 0.005); this result suggests a stronger inverse association between total zinc intake and fasting glucose in individuals carrying the glucose-raising A allele compared with individuals who do not carry it. None of the other interaction tests were statistically significant.
CONCLUSIONS
Our results suggest that higher total zinc intake may attenuate the glucose-raising effect of the rs11558471 SLC30A8 (zinc transporter) variant. Our findings also support evidence for the association of higher total zinc intake with lower fasting glucose levels.
doi:10.2337/db11-0176
PMCID: PMC3161318  PMID: 21810599
19.  Association between parental history of diabetes and type 2 diabetes genetic risk scores in the PPP-Botnia and Framingham Offspring Studies 
Objective
Parental history of diabetes and specific gene variants are risk factors for type 2 diabetes, but the extent to which these factors are associated is unknown.
Methods
We examined the association between parental history of diabetes and a type 2 diabetes genetic risk score (GRS) in two cohort studies from Finland (population-based PPP-Botnia Study) and the US (family-based Framingham Offspring Study).
Results
Mean (95% CI) GRS increased from 16.8 (16.8–16.9) to 16.9 (16.8–17.1) to 17.1 (16.8–17.4) among PPP-Botnia participants with 0, 1, and 2 parents with diabetes, respectively (ptrend=0.03). The trend was similar among Framingham Offspring but was not statistically significant (p=0.07). The meta-analyzed p value for trend from the two studies was 0.005.
Conclusions
The very modest associations reported above suggest that the increased risk of diabetes in offspring of parents with diabetes is largely the result of shared environmental/lifestyle factors and/or hitherto unknown genetic factors.
doi:10.1016/j.diabres.2011.04.013
PMCID: PMC3156338  PMID: 21570145
Type 2 diabetes mellitus; genetic risk score; family history
20.  Genetic Predisposition to Long-Term Nondiabetic Deteriorations in Glucose Homeostasis 
Diabetes  2010;60(1):345-354.
OBJECTIVE
To assess whether recently discovered genetic loci associated with hyperglycemia also predict long-term changes in glycemic traits.
RESEARCH DESIGN AND METHODS
Sixteen fasting glucose-raising loci were genotyped in middle-aged adults from the Gene x Lifestyle interactions And Complex traits Involved in Elevated disease Risk (GLACIER) Study, a population-based prospective cohort study from northern Sweden. Genotypes were tested for association with baseline fasting and 2-h postchallenge glycemia (N = 16,330), and for changes in these glycemic traits during a 10-year follow-up period (N = 4,059).
RESULTS
Cross-sectional directionally consistent replication with fasting glucose concentrations was achieved for 12 of 16 variants; 10 variants were also associated with impaired fasting glucose (IFG) and 7 were independently associated with 2-h postchallenge glucose concentrations. In prospective analyses, the effect alleles at four loci (GCK rs4607517, ADRA2A rs10885122, DGKB-TMEM195 rs2191349, and G6PC2 rs560887) were nominally associated with worsening fasting glucose concentrations during 10-years of follow-up. MTNR1B rs10830963, which was predictive of elevated fasting glucose concentrations in cross-sectional analyses, was associated with a protective effect on postchallenge glucose concentrations during follow-up; however, this was only when baseline fasting and 2-h glucoses were adjusted for. An additive effect of multiple risk alleles on glycemic traits was observed: a weighted genetic risk score (80th vs. 20th centiles) was associated with a 0.16 mmol/l (P = 2.4 × 10−6) greater elevation in fasting glucose and a 64% (95% CI: 33–201%) higher risk of developing IFG during 10 years of follow-up.
CONCLUSIONS
Our findings imply that genetic profiling might facilitate the early detection of persons who are genetically susceptible to deteriorating glucose control; studies of incident type 2 diabetes and discrete cardiovascular end points will help establish whether the magnitude of these changes is clinically relevant.
doi:10.2337/db10-0933
PMCID: PMC3012192  PMID: 20870969
21.  Diabetes Family History: A Metabolic Storm You Should Not Sit Out 
Diabetes  2010;59(11):2732-2734.
doi:10.2337/db10-0768
PMCID: PMC2963529  PMID: 20980473
22.  Invited Commentary: Gene × Lifestyle Interactions and Complex Disease Traits—Inferring Cause and Effect From Observational Data, Sine Qua Non 
American Journal of Epidemiology  2010;172(9):992-997.
Observational epidemiology has made outstanding contributions to the discovery and elucidation of relations between lifestyle factors and common complex diseases such as type 2 diabetes. Recent major advances in the understanding of the human genetics of this disease have inspired studies that seek to determine whether the risk conveyed by bona fide risk loci might be modified by lifestyle factors such as diet composition and physical activity levels. A major challenge is to determine which of the reported findings are likely to represent causal interactions and which might be explained by other factors. The authors of this commentary use the Bradford-Hill criteria, a set of tried-and-tested guidelines for causal inference, to evaluate the findings of a recent study on interaction between variation at the cyclin-dependent kinase 5 regulatory subunit-associated protein 1-like 1 (CDKAL1) locus and total energy intake with respect to prevalent metabolic syndrome and hemoglobin A1c levels in a cohort of 313 Japanese men. The current authors conclude that the study, while useful for hypothesis generation, does not provide overwhelming evidence of causal interactions. They overview ways in which future studies of gene × lifestyle interactions might overcome the limitations that motivated this conclusion.
doi:10.1093/aje/kwq280
PMCID: PMC2984255  PMID: 20847104
CDKAL1 protein, human; energy intake; hemoglobin A1c protein, human; Japan; metabolic syndrome X
23.  The Association Between Dietary Flavonoid and Lignan Intakes and Incident Type 2 Diabetes in European Populations 
Diabetes Care  2013;36(12):3961-3970.
OBJECTIVE
To study the association between dietary flavonoid and lignan intakes, and the risk of development of type 2 diabetes among European populations.
RESEARCH DESIGN AND METHODS
The European Prospective Investigation into Cancer and Nutrition-InterAct case-cohort study included 12,403 incident type 2 diabetes cases and a stratified subcohort of 16,154 participants from among 340,234 participants with 3.99 million person-years of follow-up in eight European countries. At baseline, country-specific validated dietary questionnaires were used. A flavonoid and lignan food composition database was developed from the Phenol-Explorer, the U.K. Food Standards Agency, and the U.S. Department of Agriculture databases. Hazard ratios (HRs) from country-specific Prentice-weighted Cox regression models were pooled using random-effects meta-analysis.
RESULTS
In multivariable models, a trend for an inverse association between total flavonoid intake and type 2 diabetes was observed (HR for the highest vs. the lowest quintile, 0.90 [95% CI 0.77–1.04]; P valuetrend = 0.040), but not with lignans (HR 0.88 [95% CI 0.72–1.07]; P valuetrend = 0.119). Among flavonoid subclasses, flavonols (HR 0.81 [95% CI 0.69–0.95]; P valuetrend = 0.020) and flavanols (HR 0.82 [95% CI 0.68–0.99]; P valuetrend = 0.012), including flavan-3-ol monomers (HR 0.73 [95% CI 0.57–0.93]; P valuetrend = 0.029), were associated with a significantly reduced hazard of diabetes.
CONCLUSIONS
Prospective findings in this large European cohort demonstrate inverse associations between flavonoids, particularly flavanols and flavonols, and incident type 2 diabetes. This suggests a potential protective role of eating a diet rich in flavonoids, a dietary pattern based on plant-based foods, in the prevention of type 2 diabetes.
doi:10.2337/dc13-0877
PMCID: PMC3836159  PMID: 24130345
24.  Common genetic variants highlight the role of insulin resistance and body fat distribution in type 2 diabetes, independently of obesity 
Diabetes  2014;63(12):4378-4387.
We aimed to validate genetic variants as instruments for insulin resistance and secretion, to characterise their association with intermediate phenotypes, and to investigate their role in T2D risk among normal-weight, overweight and obese individuals.We investigated the association of genetic scores with euglycaemic-hyperinsulinaemic clamp- and OGTT-based measures of insulin resistance and secretion, and a range of metabolic measures in up to 18,565 individuals. We also studied their association with T2D risk among normal-weight, overweight and obese individuals in up to 8,124 incident T2D cases. The insulin resistance score was associated with lower insulin sensitivity measured by M/I value (β in SDs-per-allele [95%CI]:−0.03[−0.04,−0.01];p=0.004). This score was associated with lower BMI (−0.01[−0.01,−0.0;p=0.02) and gluteofemoral fat-mass (−0.03[−0.05,−0.02;p=1.4×10−6), and with higher ALT (0.02[0.01,0.03];p=0.002) and gamma-GT (0.02[0.01,0.03];p=0.001). While the secretion score had a stronger association with T2D in leaner individuals (pinteraction=0.001), we saw no difference in the association of the insulin resistance score with T2D among BMI- or waist-strata(pinteraction>0.31). While insulin resistance is often considered secondary to obesity, the association of the insulin resistance score with lower BMI and adiposity and with incident T2D even among individuals of normal weight highlights the role of insulin resistance and ectopic fat distribution in T2D, independently of body size.
doi:10.2337/db14-0319
PMCID: PMC4241116  PMID: 24947364
Genetics; type 2 diabetes; insulin resistance; insulin secretion; adipose expandability
25.  Dietary vitamin D intake and risk of type 2 diabetes in the European Prospective Investigation into Cancer and Nutrition – the EPIC-InterAct study 
Background
Prospective cohort studies have indicated that serum vitamin D levels are inversely related to risk of type 2 diabetes. However, such studies cannot determine the source of vitamin D. Therefore, we examined the association of dietary vitamin D intake with incident type 2 diabetes within the European Prospective Investigation into Cancer and Nutrition (EPIC)-InterAct study in a heterogeneous European population including 8 countries with large geographical variation.
Methods
Using a case-cohort design, 11,245 incident cases of type 2 diabetes and a representative subcohort (N=15,798) were included in the analyses. Hazard ratios (HR) and 95% confidence intervals (CIs) for type 2 diabetes were calculated using a Prentice-weighted Cox regression adjusted for potential confounders. 24-h diet recall data from a subsample (N=2347) were used to calibrate habitual intake data derived from dietary questionnaires.
Results
Median follow-up time was 10.8 years. Dietary vitamin D intake was not significantly associated with the risk of type 2 diabetes. HR and 95 % CIs for the highest compared to the lowest quintile of uncalibrated vitamin D intake was 1.09 (0.97-1.22), (ptrend=0.17). No associations were observed in a sex-specific analysis. The overall pooled effect [HR (95% CI)] using the continuous calibrated variable was 1.00 (0.97-1.03) per increase of 1 μg/day dietary vitamin D.
Conclusion
This observational study does not support an association between higher dietary vitamin D intake and type-2 diabetes incidence. This result has to be interpreted in light of the limited contribution of dietary vitamin D on the overall vitamin D status of a person.
doi:10.1038/ejcn.2013.235
PMCID: PMC4234029  PMID: 24253760
vitamin D; type-2 diabetes; dietary intake; observational study; EPIC

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