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  • 51.
    Estampador, Angela C.
    et al.
    Genetic and Molecular Epidemiology Unit, Department of Clinical Sciences, Skåne University Hospital, Lund University, Malmö, Sweden .
    Pomeroy, Jeremy
    Genetic and Molecular Epidemiology Unit, Department of Clinical Sciences, Skåne University Hospital, Lund University, Malmö, Sweden ; Phoenix Epidemiology and Clinical Research Branch, National Institutes of Health, Phoenix, AZ .
    Renström, Frida
    Umeå universitet, Medicinska fakulteten, Institutionen för folkhälsa och klinisk medicin, Medicin. Genetic and Molecular Epidemiology Unit, Department of Clinical Sciences, Skåne University Hospital, Lund University, Malmö, Sweden .
    Nelson, Scott M.
    Reproductive and Maternal Medicine, Faculty of Medicine, University of Glasgow, Glasgow, U.K..
    Mogren, Ingrid
    Umeå universitet, Medicinska fakulteten, Institutionen för klinisk vetenskap, Obstetrik och gynekologi.
    Persson, Margareta
    Umeå universitet, Medicinska fakulteten, Institutionen för klinisk vetenskap, Obstetrik och gynekologi. Dalarna University, School of Health and Social Studies, Falun, Sweden.
    Sattar, Naveed
    British Heart Foundation Cardiovascular Research Center, University of Glasgow, Glasgow, U.K..
    Domellöf, Magnus
    Umeå universitet, Medicinska fakulteten, Institutionen för klinisk vetenskap, Pediatrik.
    Franks, Paul W.
    Umeå universitet, Medicinska fakulteten, Institutionen för folkhälsa och klinisk medicin, Medicin. Genetic and Molecular Epidemiology Unit, Department of Clinical Sciences, Skåne University Hospital, Lund University, Malmö, Sweden ; Department of Nutrition, Harvard School of Public Health, Boston, MA.
    Infant body composition and adipokine concentrations in relation to maternal gestational weight gain2014Ingår i: Diabetes Care, ISSN 0149-5992, E-ISSN 1935-5548, Vol. 37, nr 5, s. 1432-1438Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    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.

  • 52. Fawcett, Katherine A
    et al.
    Wheeler, Eleanor
    Morris, Andrew P
    Ricketts, Sally L
    Hallmans, Göran
    Umeå universitet, Medicinska fakulteten, Institutionen för folkhälsa och klinisk medicin, Näringsforskning.
    Rolandsson, Olov
    Umeå universitet, Medicinska fakulteten, Institutionen för folkhälsa och klinisk medicin, Allmänmedicin.
    Daly, Allan
    Wasson, Jon
    Permutt, Alan
    Hattersley, Andrew T
    Glaser, Benjamin
    Franks, Paul W
    Umeå universitet, Medicinska fakulteten, Institutionen för folkhälsa och klinisk medicin, Medicin.
    McCarthy, Mark I
    Wareham, Nicholas J
    Sandhu, Manjinder S
    Barroso, Inês
    Detailed investigation of the role of common and low-frequency WFS1 variants in type 2 diabetes risk2010Ingår i: Diabetes, ISSN 0012-1797, E-ISSN 1939-327X, Vol. 59, nr 3, s. 741-746Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    We identified six highly correlated SNPs that show strong and comparable associations with risk of type 2 diabetes, but further refinement of these associations will require large sample sizes (>100,000) or studies in ethnically diverse populations. Low frequency variants in WFS1 are unlikely to have a large impact on type 2 diabetes risk in white U.K. populations, highlighting the complexities of undertaking association studies with low-frequency variants identified by resequencing.

  • 53. Fitipaldi, Hugo
    et al.
    McCarthy, Mark I.
    Florez, Jose C.
    Franks, Paul W.
    Umeå universitet, Medicinska fakulteten, Institutionen för folkhälsa och klinisk medicin, Medicin. Genetic and Molecular Epidemiology Unit, Department of Clinical Sciences Malmö, Lund University Diabetes Centre, Skåne University Hospital, Malmö, Sweden; Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, U.K; Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA.
    A Global Overview of Precision Medicine in Type 2 Diabetes2018Ingår i: Diabetes, ISSN 0012-1797, E-ISSN 1939-327X, Vol. 67, nr 10, s. 1911-1922Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The detailed characterization of human biology and behaviors is now possible at scale owing to innovations in biomarkers, bioimaging, and wearable technologies; "big data" from electronic medical records, health insurance databases, and other platforms becoming increasingly accessible; and rapidly evolving computational power and bioinformatics methods. Collectively, these advances are creating unprecedented opportunities to better understand diabetes and many other complex traits. Identifying hidden structures within these complex data sets and linking these structures to outcome data may yield unique insights into the risk factors and natural history of diabetes, which in turn may help optimize the prevention and management of the disease. This emerging area is broadly termed "precision medicine." In this Perspective, we give an overview of the evidence and barriers to the development and implementation of precision medicine in type 2 diabetes. We also discuss recently presented paradigms through which complex data might enhance our understanding of diabetes and ultimately our ability to tackle the disease more effectively than ever before.

  • 54. Flannick, Jason
    et al.
    Fuchsberger, Christian
    Mahajan, Anubha
    Teslovich, Tanya M.
    Agarwala, Vineeta
    Gaulton, Kyle J.
    Caulkins, Lizz
    Koesterer, Ryan
    Ma, Clement
    Moutsianas, Loukas
    McCarthy, Davis J.
    Rivas, Manuel A.
    Perry, John R. B.
    Sim, Xueling
    Blackwell, Thomas W.
    Robertson, Neil R.
    Rayner, N. William
    Cingolani, Pablo
    Locke, Adam E.
    Tajes, Juan Fernandez
    Highland, Heather M.
    Dupuis, Josee
    Chines, Peter S.
    Lindgren, Cecilia M.
    Hartl, Christopher
    Jackson, Anne U.
    Chen, Han
    Huyghe, Jeroen R.
    De Bunt, Martijn Van
    Pearson, Richard D.
    Kumar, Ashish
    Muller-Nurasyid, Martina
    Grarup, Niels
    Stringham, Heather M.
    Gamazon, Eric R.
    Lee, Jaehoon
    Chen, Yuhui
    Scott, Robert A.
    Below, Jennifer E.
    Chen, Peng
    Huang, Jinyan
    Go, Min Jin
    Stitzel, Michael L.
    Pasko, Dorota
    Parker, Stephen C. J.
    Varga, Tibor V.
    Green, Todd
    Beer, Nicola L.
    Day-Williams, Aaron G.
    Ferreira, Teresa
    Fingerlin, Tasha
    Horikoshi, Momoko
    Hu, Cheng
    Huh, Iksoo
    Ikram, Mohammad Kamran
    Kim, Bong-Jo
    Kim, Yongkang
    Kim, Young Jin
    Kwon, Min-Seok
    Lee, Juyoung
    Lee, Selyeong
    Lin, Keng-Han
    Maxwell, Taylor J.
    Nagai, Yoshihiko
    Wang, Xu
    Welch, Ryan P.
    Yoon, Joon
    Zhang, Weihua
    Barzilai, Nir
    Voight, Benjamin F.
    Han, Bok-Ghee
    Jenkinson, Christopher P.
    Kuulasmaa, Teemu
    Kuusisto, Johanna
    Manning, Alisa
    Ng, Maggie C. Y.
    Palmer, Nicholette D.
    Balkau, Beverley
    Stancakova, Alena
    Abboud, Hanna E.
    Boeing, Heiner
    Giedraitis, Vilmantas
    Prabhakaran, Dorairaj
    Gottesman, Omri
    Scott, James
    Carey, Jason
    Kwan, Phoenix
    Grant, George
    Smith, Joshua D.
    Neale, Benjamin M.
    Purcell, Shaun
    Butterworth, Adam S.
    Howson, Joanna M. M.
    Lee, Heung Man
    Lu, Yingchang
    Kwak, Soo-Heon
    Zhao, Wei
    Danesh, John
    Lam, Vincent K. L.
    Park, Kyong Soo
    Saleheen, Danish
    So, Wing Yee
    Tam, Claudia H. T.
    Afzal, Uzma
    Aguilar, David
    Arya, Rector
    Aung, Tin
    Chan, Edmund
    Navarro, Carmen
    Cheng, Ching-Yu
    Palli, Domenico
    Correa, Adolfo
    Curran, Joanne E.
    Rybin, Dennis
    Farook, Vidya S.
    Fowler, Sharon P.
    Freedman, Barry I.
    Griswold, Michael
    Hale, Daniel Esten
    Hicks, Pamela J.
    Khor, Chiea-Chuen
    Kumar, Satish
    Lehne, Benjamin
    Thuillier, Dorothee
    Lim, Wei Yen
    Liu, Jianjun
    Loh, Marie
    Musani, Solomon K.
    Puppala, Sobha
    Scott, William R.
    Yengo, Loic
    Tan, Sian-Tsung
    Taylor, Herman A.
    Thameem, Farook
    Wilson, Gregory
    Wong, Tien Yin
    Njolstad, Pal Rasmus
    Levy, Jonathan C.
    Mangino, Massimo
    Bonnycastle, Lori L.
    Schwarzmayr, Thomas
    Fadista, Joao
    Surdulescu, Gabriela L.
    Herder, Christian
    Groves, Christopher J.
    Wieland, Thomas
    Bork-Jensen, Jette
    Brandslund, Ivan
    Christensen, Cramer
    Koistinen, Heikki A.
    Doney, Alex S. F.
    Kinnunen, Leena
    Esko, Tonu
    Farmer, Andrew J.
    Hakaste, Liisa
    Hodgkiss, Dylan
    Kravic, Jasmina
    Lyssenko, Valeriya
    Hollensted, Mette
    Jorgensen, Marit E.
    Jorgensen, Torben
    Ladenvall, Claes
    Justesen, Johanne Marie
    Karajamaki, Annemari
    Kriebel, Jennifer
    Rathmann, Wolfgang
    Lannfelt, Lars
    Lauritzen, Torsten
    Narisu, Narisu
    Linneberg, Allan
    Melander, Olle
    Milani, Lili
    Neville, Matt
    Orho-Melander, Marju
    Qi, Lu
    Qi, Qibin
    Roden, Michael
    Rolandsson, Olov
    Swift, Amy
    Rosengren, Anders H.
    Stirrups, Kathleen
    Wood, Andrew R.
    Mihailov, Evelin
    Blancher, Christine
    Carneiro, Mauricio O.
    Maguire, Jared
    Poplin, Ryan
    Shakir, Khalid
    Fennell, Timothy
    DePristo, Mark
    De Angelis, Martin Hrabe
    Deloukas, Panos
    Gjesing, Anette P.
    Jun, Goo
    Nilsson, Peter M.
    Murphy, Jacquelyn
    Onofrio, Robert
    Thorand, Barbara
    Hansen, Torben
    Meisinger, Christa
    Hu, Frank B.
    Isomaa, Bo
    Karpe, Fredrik
    Liang, Liming
    Peters, Annette
    Huth, Cornelia
    O'Rahilly, Stephen P.
    Palmer, Colin N. A.
    Pedersen, Oluf
    Rauramaa, Rainer
    Tuomilehto, Jaakko
    Salomaa, Veikko
    Watanabe, Richard M.
    Syvanen, Ann-Christine
    Bergman, Richard N.
    Bharadwaj, Dwaipayan
    Bottinger, Erwin P.
    Cho, Yoon Shin
    Chandak, Giriraj R.
    Chan, Juliana Cn
    Chia, Kee Seng
    Daly, Mark J.
    Ebrahim, Shah B.
    Langenberg, Claudia
    Elliott, Paul
    Jablonski, Kathleen A.
    Lehman, Donna M.
    Jia, Weiping
    Ma, Ronald Cw
    Pollin, Toni I.
    Sandhu, Manjinder
    Tandon, Nikhil
    Froguel, Philippe
    Barroso, Ines
    Teo, Yik Ying
    Zeggini, Eleftheria
    Loos, Ruth J. F.
    Small, Kerrin S.
    Ried, Janina S.
    DeFronzo, Ralph A.
    Grallert, Harald
    Glaser, Benjamin
    Metspalu, Andres
    Wareham, Nicholas J.
    Walker, Mark
    Banks, Eric
    Gieger, Christian
    Ingelsson, Erik
    Im, Hae Kyung
    Illig, Thomas
    Franks, Paul W.
    Umeå universitet, Medicinska fakulteten, Institutionen för folkhälsa och klinisk medicin, Medicin.
    Buck, Gemma
    Trakalo, Joseph
    Buck, David
    Prokopenko, Inga
    Magi, Reedik
    Lind, Lars
    Farjoun, Yossi
    Owen, Katharine R.
    Gloyn, Anna L.
    Strauch, Konstantin
    Tuomi, Tiinamaija
    Kooner, Jaspal Singh
    Lee, Jong-Young
    Park, Taesung
    Donnelly, Peter
    Morris, Andrew D.
    Hattersley, Andrew T.
    Bowden, Donald W.
    Collins, Francis S.
    Atzmon, Gil
    Chambers, John C.
    Spector, Timothy D.
    Laakso, Markku
    Strom, Tim M.
    Bell, Graeme I.
    Blangero, John
    Duggirala, Ravindranath
    Tai, EShyong
    McVean, Gilean
    Hanis, Craig L.
    Wilson, James G.
    Seielstad, Mark
    Frayling, Timothy M.
    Meigs, James B.
    Cox, Nancy J.
    Sladek, Rob
    Lander, Eric S.
    Gabriel, Stacey
    Mohlke, Karen L.
    Meitinger, Thomas
    Groop, Leif
    Abecasis, Goncalo
    Scott, Laura J.
    Morris, Andrew P.
    Kang, Hyun Min
    Altshuler, David
    Burtt, Noel P.
    Florez, Jose C.
    Boehnke, Michael
    McCarthy, Mark I.
    Sequence data and association statistics from 12,940 type 2 diabetes cases and controls2017Ingår i: Scientific Data, E-ISSN 2052-4463, Vol. 4, artikel-id 170179Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    To investigate the genetic basis of type 2 diabetes (T2D) to high resolution, the GoT2D and T2D-GENES consortia catalogued variation from whole-genome sequencing of 2,657 European individuals and exome sequencing of 12,940 individuals of multiple ancestries. Over 27M SNPs, indels, and structural variants were identified, including 99% of low-frequency (minor allele frequency [MAF] 0.1-5%) non-coding variants in the whole-genome sequenced individuals and 99.7% of low-frequency coding variants in the whole-exome sequenced individuals. Each variant was tested for association with T2D in the sequenced individuals, and, to increase power, most were tested in larger numbers of individuals (> 80% of low-frequency coding variants in similar to 82 K Europeans via the exome chip, and similar to 90% of low-frequency non-coding variants in similar to 44 K Europeans via genotype imputation). The variants, genotypes, and association statistics from these analyses provide the largest reference to date of human genetic information relevant to T2D, for use in activities such as T2D-focused genotype imputation, functional characterization of variants or genes, and other novel analyses to detect associations between sequence variation and T2D.

  • 55. Florez, J
    et al.
    Jablonski, K
    McAteer, J
    Sandhu, M
    Wareham, N
    Barroso, I
    Franks, Paul
    Umeå universitet, Medicinska fakulteten, Institutionen för folkhälsa och klinisk medicin.
    Altshuler, D
    Knowler, W
    Testing of diabetes-associated WFS1 polymorphisms in the Diabetes Prevention Program2007Ingår i: Diabetologia, ISSN 0012-186X, E-ISSN 1432-0428, Vol. 51, nr 3, s. 451-457Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Aims/hypothesis: Wolfram syndrome (diabetes insipidus, diabetes mellitus, optic atrophy and deafness) is caused by mutations in the WFS1 gene. Recently, single nucleotide polymorphisms (SNPs) in WFS1 have been reproducibly associated with type 2 diabetes. We therefore examined the effects of these variants on diabetes incidence and response to interventions in the Diabetes Prevention Program (DPP), in which a lifestyle intervention or metformin treatment was compared with placebo.

    Methods: We genotyped the WFS1 SNPs rs10010131, rs752854 and rs734312 (H611R) in 3,548 DPP participants and performed Cox regression analysis using genotype, intervention and their interactions as predictors of diabetes incidence. We also evaluated the effect of these SNPs on insulin resistance and beta cell function at 1 year.

    Results: Although none of the three SNPs was associated with diabetes incidence in the overall cohort, white homozygotes for the previously reported protective alleles appeared less likely to develop diabetes in the lifestyle arm. Examination of the publicly available Diabetes Genetics Initiative genome-wide association dataset revealed that rs10012946, which is in strong linkage disequilibrium with the three WFS1 SNPs (r 2 = 0.88–1.0), was associated with type 2 diabetes (allelic odds ratio 0.85, 95% CI 0.75–0.97, p = 0.026). In the DPP, we noted a trend towards increased insulin secretion in carriers of the protective variants, although for most SNPs this was seen as compensatory for the diminished insulin sensitivity.

    Conclusions/interpretation: The previously reported protective effect of select WFS1 alleles may be magnified by a lifestyle intervention. These variants appear to confer an improvement in beta cell function.

  • 56. Florez, Jose C
    et al.
    Jablonski, Kathleen A
    Kahn, Steven E
    Franks, Paul
    Umeå universitet, Medicinsk fakultet, Folkhälsa och klinisk medicin, Medicin.
    Dabelea, Dana
    Hamman, Richard F
    Knowler, William C
    Nathan, David M
    Altshuler, David
    Type 2 diabetes-associated missense polymorphisms KCNJ11 E23K and ABCC8 A1369S influence progression to diabetes and response to interventions in the Diabetes Prevention Program.2007Ingår i: Diabetes, ISSN 0012-1797, Vol. 56, nr 2, s. 531-6Artikel i tidskrift (Refereegranskat)
  • 57. Fontaine-Bisson, B
    et al.
    Renström, Frida
    Umeå universitet, Medicinska fakulteten, Institutionen för folkhälsa och klinisk medicin, Medicin.
    Rolandsson, Olov
    Umeå universitet, Medicinska fakulteten, Institutionen för folkhälsa och klinisk medicin, Allmänmedicin.
    Payne, F
    Hallmans, Göran
    Umeå universitet, Medicinska fakulteten, Institutionen för folkhälsa och klinisk medicin, Näringsforskning.
    Barroso, I
    Franks, Paul W
    Umeå universitet, Medicinska fakulteten, Institutionen för folkhälsa och klinisk medicin, Medicin.
    Evaluating the discriminative power of multi-trait genetic risk scores for type 2 diabetes in a northern Swedish population.2010Ingår i: Diabetologia, ISSN 0012-186X, E-ISSN 1432-0428, Vol. 53, nr 10, s. 2155-2162Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    AIMS/HYPOTHESIS: We determined whether single nucleotide polymorphisms (SNPs) previously associated with diabetogenic traits improve the discriminative power of a type 2 diabetes genetic risk score. METHODS: Participants (n = 2,751) were genotyped for 73 SNPs previously associated with type 2 diabetes, fasting glucose/insulin concentrations, obesity or lipid levels, from which five genetic risk scores (one for each of the four traits and one combining all SNPs) were computed. Type 2 diabetes patients and non-diabetic controls (n = 1,327/1,424) were identified using medical records in addition to an independent oral glucose tolerance test. RESULTS: Model 1, including only SNPs associated with type 2 diabetes, had a discriminative power of 0.591 (p < 1.00 x 10(-20) vs null model) as estimated by the area under the receiver operator characteristic curve (ROC AUC). Model 2, including only fasting glucose/insulin SNPs, had a significantly higher discriminative power than the null model (ROC AUC 0.543; p = 9.38 x 10(-6) vs null model), but lower discriminative power than model 1 (p = 5.92 x 10(-5)). Model 3, with only lipid-associated SNPs, had significantly higher discriminative power than the null model (ROC AUC 0.565; p = 1.44 x 10(-9)) and was not statistically different from model 1 (p = 0.083). The ROC AUC of model 4, which included only obesity SNPs, was 0.557 (p = 2.30 x 10(-7) vs null model) and smaller than model 1 (p = 0.025). Finally, the model including all SNPs yielded a significant improvement in discriminative power compared with the null model (p < 1.0 x 10(-20)) and model 1 (p = 1.32 x 10(-5)); its ROC AUC was 0.626. CONCLUSIONS/INTERPRETATION: Adding SNPs previously associated with fasting glucose, insulin, lipids or obesity to a genetic risk score for type 2 diabetes significantly increases the power to discriminate between people with and without clinically manifest type 2 diabetes compared with a model including only conventional type 2 diabetes loci.

  • 58. Forouhi, Nita G.
    et al.
    Imamura, Fumiaki
    Sharp, Stephen J.
    Koulman, Albert
    Schulze, Matthias B.
    Zheng, Jusheng
    Ye, Zheng
    Sluijs, Ivonne
    Guevara, Marcela
    Maria Huerta, Jose
    Kroeger, Janine
    Wang, Laura Yun
    Summerhill, Keith
    Griffin, Julian L.
    Feskens, Edith J. M.
    Affret, Aurelie
    Amiano, Pilar
    Boeing, Heiner
    Dow, Courtney
    Fagherazzi, Guy
    Franks, Paul W.
    Umeå universitet, Medicinska fakulteten, Institutionen för folkhälsa och klinisk medicin, Medicin. Lund University, Malmö, Sweden.
    Gonzalez, Carlos
    Kaaks, Rudolf
    Key, Timothy J.
    Khaw, Kay Tee
    Kuehn, Tilman
    Mortensen, Lotte Maxild
    Nilsson, Peter M.
    Overvad, Kim
    Pala, Valeria
    Palli, Domenico
    Panico, Salvatore
    Ramon Quiros, J.
    Rodriguez-Barranco, Miguel
    Rolandsson, Olov
    Umeå universitet, Medicinska fakulteten, Institutionen för folkhälsa och klinisk medicin, Allmänmedicin.
    Sacerdote, Carlotta
    Scalbert, Augustin
    Slimani, Nadia
    Spijkerman, Annemieke M. W.
    Tjonneland, Anne
    Tormo, Maria-Jose
    Tumino, Rosario
    van der A, Daphne L.
    van der Schouw, Yvonne T.
    Langenberg, Claudia
    Riboli, Elio
    Wareham, Nicholas J.
    Association of Plasma Phospholipid n-3 and n-6 Polyunsaturated Fatty Acids with Type 2 Diabetes: The EPIC-InterAct Case-Cohort Study2016Ingår i: PLoS Medicine, ISSN 1549-1277, E-ISSN 1549-1676, Vol. 13, nr 7, artikel-id e1002094Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Background Whether and how n-3 and n-6 polyunsaturated fatty acids (PUFAs) are related to type 2 diabetes (T2D) is debated. Objectively measured plasma PUFAs can help to clarify these associations.

    Methods and Findings Plasma phospholipid PUFAs were measured by gas chromatography among 12,132 incident T2D cases and 15,919 subcohort participants in the European Prospective Investigation into Cancer and Nutrition (EPIC)-InterAct study across eight European countries. Country-specific hazard ratios (HRs) were estimated using Prentice-weighted Cox regression and pooled by random-effects meta-analysis. We also systematically reviewed published prospective studies on circulating PUFAs and T2D risk and pooled the quantitative evidence for comparison with results from EPIC-InterAct. In EPIC-InterAct, among long-chain n-3 PUFAs, a-linolenic acid (ALA) was inversely associated with T2D (HR per standard deviation [SD] 0.93; 95% CI 0.88-0.98), but eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) were not significantly associated. Among n-6 PUFAs, linoleic acid (LA) (0.80; 95% CI 0.77-0.83) and eicosadienoic acid (EDA) (0.89; 95% CI 0.85-0.94) were inversely related, and arachidonic acid (AA) was not significantly associated, while significant positive associations were observed with.-linolenic acid (GLA), dihomo-GLA, docosatetraenoic acid (DTA), and docosapentaenoic acid (n6-DPA), with HRs between 1.13 to 1.46 per SD. These findings from EPIC-InterAct were broadly similar to comparative findings from summary estimates from up to nine studies including between 71 to 2,499 T2D cases. Limitations included potential residual confounding and the inability to distinguish between dietary and metabolic influences on plasma phospholipid PUFAs.

    Conclusions These large-scale findings suggest an important inverse association of circulating plant-origin n-3 PUFA (ALA) but no convincing association of marine-derived n3 PUFAs (EPA and DHA) with T2D. Moreover, they highlight that the most abundant n6-PUFA (LA) is inversely associated with T2D. The detection of associations with previously less well-investigated PUFAs points to the importance of considering individual fatty acids rather than focusing on fatty acid class.

  • 59. Forouhi, Nita G.
    et al.
    Koulman, Albert
    Sharp, Stephen J.
    Imamura, Fumiaki
    Kroger, Janine
    Schulze, Matthias B.
    Crowe, Francesca L.
    Huerta, Jose Maria
    Guevara, Marcela
    Beulens, Joline W. J.
    van Woudenbergh, Geertruida J.
    Wang, Laura
    Summerhill, Keith
    Griffin, Julian L.
    Feskens, Edith J. M.
    Amiano, Pilar
    Boeing, Heiner
    Clavel-Chapelon, Francoise
    Dartois, Laureen
    Fagherazzi, Guy
    Franks, Paul W.
    Umeå universitet, Medicinska fakulteten, Institutionen för folkhälsa och klinisk medicin, Medicin. Lund University, Malmö, Sweden.
    Gonzalez, Carlos
    Jakobsen, Marianne Uhre
    Kaaks, Rudolf
    Key, Timothy J.
    Khaw, Kay-Tee
    Kuhn, Tilman
    Mattiello, Amalia
    Nilsson, Peter M.
    Overvad, Kim
    Pala, Valeria
    Palli, Domenico
    Quiros, J. Ramon
    Rolandsson, Olov
    Umeå universitet, Medicinska fakulteten, Institutionen för folkhälsa och klinisk medicin, Allmänmedicin.
    Roswall, Nina
    Sacerdote, Carlotta
    Sanchez, Mara-Jose
    Slimani, Nadia
    Spijkerman, Annemieke M. W.
    Tjonneland, Anne
    Tormo, Maria-Jose
    Tumino, Rosario
    van der A, Daphne L.
    van der Schouw, Yvonne T.
    Langenberg, Claudia
    Riboli, Elio
    Wareham, Nicholas J.
    Differences in the prospective association between individual plasma phospholipid saturated fatty acids and incident type 2 diabetes: the EPIC-InterAct case-cohort study2014Ingår i: LANCET DIABETES & ENDOCRINOLOGY, ISSN 2213-8587, Vol. 2, nr 10, s. 810-818Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Background Conflicting evidence exists regarding the association between saturated fatty acids (SFAs) and type 2 diabetes. In this longitudinal case-cohort study, we aimed to investigate the prospective associations between objectively measured individual plasma phospholipid SFAs and incident type 2 diabetes in EPIC-InterAct participants. Methods The EPIC-InterAct case-cohort study includes 12 403 people with incident type 2 diabetes and a representative subcohort of 16 154 individuals who were selected from a cohort of 340 234 European participants with 3 . 99 million person-years of follow-up (the EPIC study). Incident type 2 diabetes was ascertained until Dec 31, 2007, by a review of several sources of evidence. Gas chromatography was used to measure the distribution of fatty acids in plasma phospholipids (mol%); samples from people with type 2 diabetes and subcohort participants were processed in a random order by centre, and laboratory staff were masked to participant characteristics. We estimated country-specific hazard ratios (HRs) for associations per SD of each SFA with incident type 2 diabetes using Prentice-weighted Cox regression, which is weighted for case-cohort sampling, and pooled our findings using random-effects meta-analysis. Findings SFAs accounted for 46% of total plasma phospholipid fatty acids. In adjusted analyses, different individual SFAs were associated with incident type 2 diabetes in opposing directions. Even-chain SFAs that were measured (14: 0 [myristic acid], 16: 0 [palmitic acid], and 18: 0 [stearic acid]) were positively associated with incident type 2 diabetes (HR [95% CI] per SD difference: myristic acid 1.15 [95% CI 1.09-1.22], palmitic acid 1.26 [1.15-1.37], and stearic acid 1.06 [1.00-1.13]). By contrast, measured odd-chain SFAs (15: 0 [pentadecanoic acid] and 17: 0 [heptadecanoic acid]) were inversely associated with incident type 2 diabetes (HR [95% CI] per 1 SD difference: 0.79 [0.73-0.85] for pentadecanoic acid and 0.67 [0.63-0.71] for heptadecanoic acid), as were measured longer-chain SFAs (20: 0 [arachidic acid], 22:0 [behenic acid], 23:0 [tricosanoic acid], and 24:0 [lignoceric acid]), with HRs ranging from 0.72 to 0.81 (95% CIs ranging between 0.61 and 0.92). Our findings were robust to a range of sensitivity analyses. Interpretation Different individual plasma phospholipid SFAs were associated with incident type 2 diabetes in opposite directions, which suggests that SFAs are not homogeneous in their effects. Our findings emphasise the importance of the recognition of subtypes of these fatty acids. An improved understanding of differences in sources of individual SFAs from dietary intake versus endogenous metabolism is needed.

  • 60.
    Franks, P. W.
    Umeå universitet, Medicinska fakulteten, Institutionen för folkhälsa och klinisk medicin, Medicin.
    Genetic risk scores ascertained in early adulthood and the prediction of type 2 diabetes later in life2012Ingår i: Diabetologia, ISSN 0012-186X, E-ISSN 1432-0428, Vol. 55, nr 10, s. 2555-2558Artikel i tidskrift (Övrigt vetenskapligt)
    Abstract [en]

    It is hoped that information garnered from studies on population genetics will one day be translated into a form in which it meaningfully improves the prediction, prevention or treatment of type 2 diabetes. Type 2 diabetes genetics researchers have made extraordinary progress in identifying common genetic variants that are associated with type 2 diabetes, which has shed light on the biological pathways in which molecular defects that cause the disease likely reside. However, the expectation that genetic discoveries will aid the prevention or treatment of type 2 diabetes has not, so far, been fulfilled. In a paper published in this edition of the journal, Vassy and colleagues (DOI: 10.1007/s00125-012-2637-7) test the hypothesis that the predictive accuracy of established genetic risk markers for type 2 diabetes varies by age, with the predictive accuracy being greatest in younger cohorts. The authors found no substantive support for this hypothesis. However, a number of interesting questions are raised by their study concerning why risk alleles for a given genotype may differ in younger and older cohorts and why prospective cohort studies may yield results that are inconsistent with those derived from cross-sectional studies; this commentary discusses these points.

  • 61.
    Franks, P. W.
    et al.
    Umeå universitet, Medicinska fakulteten, Institutionen för folkhälsa och klinisk medicin, Medicin. Genetic & Molecular Epidemiology Unit, Department of Clinical Sciences, Lund University Diabetes Center, Skåne University Hospital, Malmö; Department of Nutrition, Harvard TH Chan School of Public Health, Boston, MA, USA.
    Atabaki-Pasdar, N.
    Causal inference in obesity research2017Ingår i: Journal of Internal Medicine, ISSN 0954-6820, E-ISSN 1365-2796, Vol. 281, nr 3, s. 222-232Artikel, forskningsöversikt (Refereegranskat)
    Abstract [en]

    Obesity is a risk factor for a plethora of severe morbidities and premature death. Most supporting evidence comes from observational studies that are prone to chance, bias and confounding. Even data on the protective effects of weight loss from randomized controlled trials will be susceptible to confounding and bias if treatment assignment cannot be masked, which is usually the case with lifestyle and surgical interventions. Thus, whilst obesity is widely considered the major modifiable risk factor for many chronic diseases, its causes and consequences are often difficult to determine. Addressing this is important, as the prevention and treatment of any disease requires that interventions focus on causal risk factors. Disease prediction, although not dependent on knowing the causes, is nevertheless enhanced by such knowledge. Here, we provide an overview of some of the barriers to causal inference in obesity research and discuss analytical approaches, such as Mendelian randomization, that can help to overcome these obstacles. In a systematic review of the literature in this field, we found: (i) probable causal relationships between adiposity and bone health/disease, cancers (colorectal, lung and kidney cancers), cardiometabolic traits (blood pressure, fasting insulin, inflammatory markers and lipids), uric acid concentrations, coronary heart disease and venous thrombosis (in the presence of pulmonary embolism), (ii) possible causal relationships between adiposity and gray matter volume, depression and common mental disorders, oesophageal cancer, macroalbuminuria, end-stage renal disease, diabetic kidney disease, nuclear cataract and gall stone disease, and (iii) no evidence for causal relationships between adiposity and Alzheimer's disease, pancreatic cancer, venous thrombosis (in the absence of pulmonary embolism), liver function and periodontitis.

  • 62.
    Franks, Paul
    Umeå universitet, Medicinska fakulteten, Institutionen för folkhälsa och klinisk medicin, Medicin.
    Identifying genes for primary hypertension: methodological limitations and gene-environment interactions2009Ingår i: Journal of Human Hypertension, ISSN 0950-9240, E-ISSN 1476-5527, Vol. 23, nr 4, s. 227-237Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Hypertension segregates within families, indicating that genetic factors explain some of the variance in the risk of developing the disease; however, even with major advances in genotyping technologies facilitating the discovery of multiple genetic risk markers for cardiovascular and metabolic diseases, little progress has been made in defining the genetic defects that cause elevations in blood pressure. Several plausible explanations exist for this apparent paradox, one of which is that the risk conveyed by genes involved in the development of hypertension is context dependent. This notion is supported by a growing number of published animal and human studies, although none has yet provided unequivocal evidence that genetic and environmental factors interact to influence the risk of primary hypertension in humans. In this review, an assumption is made that common genetic variation contributes meaningfully to the development of primary hypertension. The review focuses on (i) several methodological limitations of genetic association studies and (ii) the roles that gene-environment interactions might play in the development of primary hypertension. The proceeding sections of the review examine the design features necessary for future studies to adequately test the hypothesis that genes for primary hypertension act in a context-dependent manner. Finally, an outline of how knowledge of gene-environment interactions might be used to optimize the prevention or treatment of primary hypertension is provided.Journal of Human Hypertension advance online publication, 13 November 2008; doi:10.1038/jhh.2008.134.

  • 63.
    Franks, Paul
    Umeå universitet, Medicinsk fakultet, Folkhälsa och klinisk medicin, Medicin.
    Muscling in on the genetics of quantitative disease traits.2007Ingår i: J Appl Physiol, ISSN 8750-7587, Vol. 103, nr 4, s. 1111-2Artikel i tidskrift (Refereegranskat)
  • 64.
    Franks, Paul
    Umeå universitet, Medicinsk fakultet, Folkhälsa och klinisk medicin, Medicin.
    Obesity, inflammatory markers and cardiovascular disease: distinguishing causality from confounding.2006Ingår i: J Hum Hypertens, ISSN 0950-9240, Vol. 20, nr 11, s. 837-40Artikel i tidskrift (Refereegranskat)
  • 65.
    Franks, Paul
    et al.
    Umeå universitet, Medicinska fakulteten, Institutionen för folkhälsa och klinisk medicin, Medicin.
    Christophi, Costas A.
    Jablonski, Kathleen A.
    Billings, Liana K.
    Delahanty, Linda M.
    Horton, Edward S.
    Knowler, William C.
    Florez, Jose C.
    Common variation at PPARGC1A/B and change in body composition and metabolic traits following preventive interventions: the Diabetes Prevention Program2014Ingår i: Diabetologia, ISSN 0012-186X, E-ISSN 1432-0428, Vol. 57, nr 3, s. 485-490Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    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. 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. In aggregate, PPARGC1A variation was strongly associated with baseline triacylglycerol concentrations (p = 2.9 x 10(-30)), BMI (p = 2.0 x 10(-5)) and visceral adiposity (p = 1.9 x 10(-4)), as well as with changes in triacylglycerol concentrations (p = 1.7 x 10(-5)) and BMI (p = 9.9 x 10(-5)) from baseline to 1 year. PPARGC1B variation was only 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 (p (interaction) = 0.04). These findings provide several novel and other confirmatory insights into the role of PPARGC1A variation with respect to diabetes-related metabolic traits. Trial registration ClinicalTrials.gov NCT00004992.

  • 66.
    Franks, Paul
    et al.
    Umeå universitet, Medicinsk fakultet, Folkhälsa och klinisk medicin, Medicin.
    Ekelund, U
    Brage, S
    Luan, J
    Schafer, A J
    O'Rahilly, S
    Barroso, I
    Wareham, N J
    PPARGC1A coding variation may initiate impaired NEFA clearance during glucose challenge.2007Ingår i: Diabetologia, ISSN 0012-186X, Vol. 50, nr 3, s. 569-73Artikel i tidskrift (Refereegranskat)
  • 67.
    Franks, Paul
    et al.
    Umeå universitet, Medicinsk fakultet, Folkhälsa och klinisk medicin, Medicin.
    Hanson, Robert L
    Knowler, William C
    Moffett, Carol
    Enos, Gleebah
    Infante, Aniello M
    Krakoff, Jonathan
    Looker, Helen C
    Childhood predictors of young onset type 2 diabetes mellitus.2007Ingår i: Diabetes, ISSN 0012-1797Artikel i tidskrift (Refereegranskat)
  • 68.
    Franks, Paul
    et al.
    Umeå universitet, Medicinsk fakultet, Folkhälsa och klinisk medicin, Medicin.
    Huang, TT
    Ball, GDC
    Lifestyle intervention for type 2 diabetes risk reduction: using the Diabetes Prevention Program to inform new directions in pediatric research.2007Ingår i: Canadian Journal of Diabetes, ISSN 14992671, Vol. 31, nr 3, s. 242-51Artikel i tidskrift (Refereegranskat)
  • 69.
    Franks, Paul
    et al.
    Umeå universitet, Medicinsk fakultet, Folkhälsa och klinisk medicin, Medicin.
    Jablonski, K
    Delahanty, L
    Hanson, R
    Kahn, S
    Altshuler, D
    Knowler, W
    Florez, J
    The Pro12Ala variant at the peroxisome proliferator-activated receptor gamma gene and change in obesity-related traits in the Diabetes Prevention Program.2007Ingår i: Diabetologia, ISSN 0012-186XArtikel i tidskrift (Refereegranskat)
  • 70.
    Franks, Paul
    et al.
    Umeå universitet, Medicinsk fakultet, Folkhälsa och klinisk medicin, Medicin.
    Looker, Helen C
    Kobes, Sayuko
    Touger, Leslie
    Tataranni, P Antonio
    Hanson, Robert L
    Knowler, William C
    Gestational glucose tolerance and risk of type 2 diabetes in young Pima Indian offspring.2006Ingår i: Diabetes, ISSN 0012-1797, Vol. 55, nr 2, s. 460-5Artikel i tidskrift (Refereegranskat)
  • 71.
    Franks, Paul
    et al.
    Umeå universitet, Medicinsk fakultet, Folkhälsa och klinisk medicin, Medicin.
    Loos, R J F
    Brage, S
    O'Rahilly, S
    Wareham, N J
    Ekelund, U
    Physical activity energy expenditure may mediate the relationship between plasma leptin levels and worsening insulin resistance independently of adiposity.2007Ingår i: J Appl Physiol, ISSN 8750-7587, Vol. 102, nr 5, s. 1921-6Artikel i tidskrift (Refereegranskat)
  • 72.
    Franks, Paul
    et al.
    Umeå universitet, Medicinsk fakultet, Folkhälsa och klinisk medicin, Medicin.
    Loos, Ruth J F
    PGC-1alpha gene and physical activity in type 2 diabetes mellitus.2006Ingår i: Exerc Sport Sci Rev, ISSN 0091-6331, Vol. 34, nr 4, s. 171-5Artikel i tidskrift (Övrigt vetenskapligt)
  • 73.
    Franks, Paul
    et al.
    Umeå universitet, Medicinsk fakultet, Folkhälsa och klinisk medicin, Medicin.
    Mesa, Jose-Luis
    Harding, Anne Helen
    Wareham, Nicholas J
    Gene-lifestyle interaction on risk of type 2 diabetes.2007Ingår i: Nutr Metab Cardiovasc Dis, ISSN 1590-3729, Vol. 17, nr 2, s. 104-24Artikel i tidskrift (Refereegranskat)
  • 74.
    Franks, Paul
    et al.
    Umeå universitet, Medicinsk fakultet, Folkhälsa och klinisk medicin, Medicin.
    Olsson, Tommy
    Umeå universitet, Medicinsk fakultet, Folkhälsa och klinisk medicin, Medicin.
    Metabolic syndrome and early death: getting to the heart of the problem.2007Ingår i: Hypertension, ISSN 1524-4563, Vol. 49, nr 1, s. 10-2Artikel i tidskrift (Refereegranskat)
  • 75.
    Franks, Paul
    et al.
    Umeå universitet, Medicinsk fakultet, Folkhälsa och klinisk medicin, Medicin.
    Olsson, Tommy
    Umeå universitet, Medicinsk fakultet, Folkhälsa och klinisk medicin, Medicin.
    Response to Metabolic Syndrome and Early Death: Extending the Discussion on Heterogeneity.2007Ingår i: Hypertension, ISSN 1524-4563Artikel i tidskrift (Refereegranskat)
  • 76.
    Franks, Paul
    et al.
    Umeå universitet, Medicinska fakulteten, Institutionen för folkhälsa och klinisk medicin, Medicin. Medicin.
    Rolandsson, Olov
    Allmänmedicin.
    Debenham, S L
    Fawcett, K A
    Payne, F
    Dina, C
    Froguel, P
    Mohlke, K L
    Willer, C
    Olsson, Tommy
    Umeå universitet, Medicinska fakulteten, Institutionen för folkhälsa och klinisk medicin, Medicin. Medicin.
    Wareham, N J
    Hallmans, Göran
    Umeå universitet, Medicinska fakulteten, Institutionen för folkhälsa och klinisk medicin, Näringsforskning.
    Barroso, I
    Sandhu, M S
    Replication of the association between variants in WFS1 and risk of type 2 diabetes in European populations.2008Ingår i: Diabetologia, ISSN 0012-186X, Vol. 51, nr 3, s. 458-63Artikel i tidskrift (Refereegranskat)
  • 77.
    Franks, Paul W.
    Umeå universitet, Medicinska fakulteten, Institutionen för folkhälsa och klinisk medicin, Medicin. Lund University, Sweden and Harvard University, USA.
    Body Weight and Risk of Early Death2013Ingår i: Obesity, ISSN 1930-7381, E-ISSN 1930-739X, Vol. 21, nr 9, s. 1743-1743Artikel i tidskrift (Övrigt vetenskapligt)
  • 78.
    Franks, Paul W
    Umeå universitet, Medicinska fakulteten, Institutionen för folkhälsa och klinisk medicin, Medicin.
    Childhood obesity, other cardiovascular risk factors, and premature death2010Ingår i: New England Journal of Medicine, ISSN 0028-4793, E-ISSN 1533-4406, Vol. 362, nr 13, s. 1840-1842Artikel, recension (Övrigt vetenskapligt)
  • 79.
    Franks, Paul W.
    Umeå universitet, Medicinska fakulteten, Institutionen för folkhälsa och klinisk medicin, Medicin. Skane Univ Hosp, Dept Clin Sci, Malmo, Sweden ; Harvard TH Chan Sch Publ Hlth, Dept Nutr, Boston, MA USA.
    Commentary: mining gene-lifestyle interactions in UK Biobank: all that glitters isn't gold2017Ingår i: International Journal of Epidemiology, ISSN 0300-5771, E-ISSN 1464-3685, Vol. 46, nr 2, s. 576-577Artikel i tidskrift (Refereegranskat)
  • 80.
    Franks, Paul W
    Umeå universitet, Medicinska fakulteten, Institutionen för folkhälsa och klinisk medicin, Medicin.
    Diabetes family history: a metabolic storm you should not sit out2010Ingår i: Diabetes, ISSN 0012-1797, E-ISSN 1939-327X, Vol. 59, nr 11, s. 2732-2734Artikel i tidskrift (Refereegranskat)
  • 81.
    Franks, Paul W.
    Umeå universitet, Medicinska fakulteten, Institutionen för folkhälsa och klinisk medicin.
    Time to reappraise the use of Body Mass Index in genetic association studies of children?2014Ingår i: Obesity, ISSN 1930-7381, E-ISSN 1930-739X, Vol. 22, nr 10, s. 2260-2261Artikel i tidskrift (Övrigt vetenskapligt)
  • 82.
    Franks, Paul W.
    et al.
    Umeå universitet, Medicinska fakulteten, Institutionen för folkhälsa och klinisk medicin.
    Brito, Ema C.
    Interaction Between Exercise and Genetics in Type 2 Diabetes Mellitus: An Epidemiological Perspective2011Ingår i: Exercise Genomics / [ed] Linda S. Pescatello, Stephen M. Roth, TOTOWA: Humana Press, 2011, s. 73-100Kapitel i bok, del av antologi (Övrigt vetenskapligt)
    Abstract [en]

    Type 2 diabetes mellitus is a heterogeneous disease characterized by an inability to transport glucose from the blood into the cells. The disease has genetic and lifestyle determinants and probably results from the interaction of these risk factors. While this notion is widely accepted and endorsed, the available evidence is far from concrete. In this chapter the evidence that implicates physical inactivity and common genetic variation in type 2 diabetes risk will be described. Then, the fundamental concepts of gene × exercise interactions in type 2 diabetes will be defined by summarizing the evidence from epidemiological studies and clinical trials that have tested related hypotheses. The penultimate section of this chapter discusses the strengths and limitations of existing studies of interaction and outlines some of the common methodological hurdles inherent when testing hypotheses of gene × exercise interactions. The chapter concludes with a short section looking forward to where this field of research is heading and the possibilities for clinical translation.

  • 83.
    Franks, Paul W.
    et al.
    Umeå universitet, Medicinska fakulteten, Institutionen för folkhälsa och klinisk medicin. Lund Univ, Ctr Diabet, Genet & Mol Epidemiol Unit, Malmo, Sweden.
    Chen, Y.
    Estampador, A.
    Keller, M.
    Poveda, A.
    Dalla-Riva, J.
    Renstrom, F.
    Kurbasic, A.
    Varga, T. V.
    Gene-diet interaction analysis, fine mapping and genomic annotation of the FADS1-2-3 gene cluster reveals regulatory potential in diabetes2017Ingår i: Diabetologia, ISSN 0012-186X, E-ISSN 1432-0428, Vol. 60, s. S163-S163Artikel i tidskrift (Övrigt vetenskapligt)
  • 84.
    Franks, Paul W
    et al.
    Umeå universitet, Medicinsk fakultet, Folkhälsa och klinisk medicin, Medicin.
    Hanson, Robert L
    Knowler, William C
    Sievers, Maurice L
    Bennett, Peter H
    Looker, Helen C
    Childhood obesity, other cardiovascular risk factors, and premature death.2010Ingår i: New England Journal of Medicine, ISSN 0028-4793, E-ISSN 1533-4406, Vol. 362, nr 6, s. 485-493Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    BACKGROUND: The effect of childhood risk factors for cardiovascular disease on adult mortality is poorly understood. METHODS: In a cohort of 4857 American Indian children without diabetes (mean age, 11.3 years; 12,659 examinations) who were born between 1945 and 1984, we assessed whether body-mass index (BMI), glucose tolerance, and blood pressure and cholesterol levels predicted premature death. Risk factors were standardized according to sex and age. Proportional-hazards models were used to assess whether each risk factor was associated with time to death occurring before 55 years of age. Models were adjusted for baseline age, sex, birth cohort, and Pima or Tohono O'odham Indian heritage. RESULTS: There were 166 deaths from endogenous causes (3.4% of the cohort) during a median follow-up period of 23.9 years. Rates of death from endogenous causes among children in the highest quartile of BMI were more than double those among children in the lowest BMI quartile (incidence-rate ratio, 2.30; 95% confidence interval [CI], 1.46 to 3.62). Rates of death from endogenous causes among children in the highest quartile of glucose intolerance were 73% higher than those among children in the lowest quartile (incidence-rate ratio, 1.73; 95% CI, 1.09 to 2.74). No significant associations were seen between rates of death from endogenous or external causes and childhood cholesterol levels or systolic or diastolic blood-pressure levels on a continuous scale, although childhood hypertension was significantly associated with premature death from endogenous causes (incidence-rate ratio, 1.57; 95% CI, 1.10 to 2.24). CONCLUSIONS: Obesity, glucose intolerance, and hypertension in childhood were strongly associated with increased rates of premature death from endogenous causes in this population. In contrast, childhood hypercholesterolemia was not a major predictor of premature death from endogenous causes.

  • 85.
    Franks, Paul W
    et al.
    Umeå universitet, Medicinska fakulteten, Institutionen för folkhälsa och klinisk medicin, Medicin. Diabetes Prevention Program Coordinating Center, The Biostatistics Center, George Washington University, 6110 Executive Blvd, Suite 750, Rockville, MD 20852, USA.
    Jablonski, KA
    Delahanty, LM
    McAteer, JB
    Kahn, SE
    Knowler, WC
    Florez, JC
    Assessing gene-treatment interactions at the FTO and INSIG2 loci on obesity-related traits in the Diabetes Prevention Program2008Ingår i: Diabetologia, ISSN 0012-186X, E-ISSN 1432-0428, Vol. 51, nr 12, s. 2214-2223Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    AIMS/HYPOTHESIS: The single nucleotide polymorphism (SNP) rs9939609 in the fat mass and obesity associated gene (FTO) and the rs7566605 SNP located 10 kb upstream of the insulin-induced gene 2 gene (INSIG2) have been proposed as risk factors for common obesity.

    METHODS: We tested for genotype-treatment interactions on changes in obesity-related traits in the Diabetes Prevention Program (DPP). The DPP is a randomised controlled trial of 3,548 high-risk individuals from 27 participating centres throughout the USA who were originally randomised to receive metformin, troglitazone, intensive lifestyle modification or placebo to prevent the development of type 2 diabetes. Measures of adiposity from computed tomography were available in a subsample (n = 908). This report focuses on the baseline and 1 year results.

    RESULTS: The minor A allele at FTO rs9939609 was positively associated with baseline BMI (p = 0.003), but not with baseline adiposity or the change at 1 year in any anthropometric trait. For the INSIG2 rs7566605 genotype, the minor C allele was associated with more subcutaneous adiposity (second and third lumbar vertebrae [L2/3]) at baseline (p = 0.04). During follow-up, CC homozygotes lost more weight than G allele carriers (p = 0.009). In an additive model, we observed nominally significant gene-lifestyle interactions on weight change (p = 0.02) and subcutaneous (L2/3 [p = 0.01] and L4/5 [p = 0.03]) and visceral (L2/3 [p = 0.02]) adipose areas. No statistical evidence of association with physical activity energy expenditure or energy intake was observed for either genotype.

    CONCLUSIONS/INTERPRETATION: Within the DPP study population, common variants in FTO and INSIG2 are nominally associated with quantitative measures of obesity, directly and possibly by interacting with metformin or lifestyle intervention.

  • 86.
    Franks, Paul W
    et al.
    Umeå universitet, Medicinska fakulteten, Institutionen för folkhälsa och klinisk medicin, Medicin.
    Ling, Charlotte
    Epigenetics and obesity: the devil is in the details2010Ingår i: BMC Medicine, ISSN 1741-7015, E-ISSN 1741-7015, Vol. 8, s. 88-Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Obesity is a complex disease with multiple well-defined risk factors. Nevertheless, susceptibility to obesity and its sequelae within obesogenic environments varies greatly from one person to the next, suggesting a role for gene × environment interactions in the etiology of the disorder. Epigenetic regulation of the human genome provides a putative mechanism by which specific environmental exposures convey risk for obesity and other human diseases and is one possible mechanism that underlies the gene × environment/treatment interactions observed in epidemiological studies and clinical trials. A study published in BMC Medicine this month by Wang et al. reports on an examination of DNA methylation in peripheral blood leukocytes of lean and obese adolescents, comparing methylation patterns between the two groups. The authors identified two genes that were differentially methylated, both of which have roles in immune function. Here we overview the findings from this study in the context of those emerging from other recent genetic and epigenetic studies, discuss the strengths and weaknesses of the study and speculate on the future of epigenetics in chronic disease research.

  • 87.
    Franks, Paul W.
    et al.
    Umeå universitet, Medicinska fakulteten, Institutionen för folkhälsa och klinisk medicin, Medicin. Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University, Malmö, Sweden; Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA.
    McCarthy, Mark I.
    Exposing the exposures responsible for type 2 diabetes and obesity2016Ingår i: Science, ISSN 0036-8075, E-ISSN 1095-9203, Vol. 354, nr 6308, s. 69-73Artikel, forskningsöversikt (Refereegranskat)
    Abstract [en]

    The rising prevalences of type 2 diabetes and obesity constitutemajor threats to human health globally. Powerful social and economic factors influence the distribution of these diseases among and within populations. These factors act on a substrate of individual predisposition derived from the composite effects of inherited DNA variation and a range of environmental exposures experienced throughout the life course. Although "Western" lifestyle represents a convenient catch-all culprit for such exposures, effective treatment and prevention will be informed by characterization of the most critical, causal environmental factors. In this Review, we examine how burgeoning understanding of the genetic basis of type 2 diabetes and obesity can highlight nongenetic exposures that drive development of these conditions.

  • 88.
    Franks, Paul W.
    et al.
    Umeå universitet, Medicinska fakulteten, Institutionen för folkhälsa och klinisk medicin, Avdelningen för medicin.
    Merino, Jordi
    Gene-lifestyle interplay in type 2 diabetes2018Ingår i: Current Opinion in Genetics and Development, ISSN 0959-437X, E-ISSN 1879-0380, Vol. 50, s. 35-40Artikel, forskningsöversikt (Refereegranskat)
    Abstract [en]

    Type 2 diabetes (T2D) is widespread, affecting the health of hundreds of millions worldwide. The disease results from the complex interplay of lifestyle factors acting on a backdrop of inherited DNA risk variants. Detecting and understanding biomarkers, whether genotypes or other downstream biological features that dictate a person's phenotypic response to different lifestyle exposures, may have tremendous utility in the prevention of T2D. Here, we explore (i) evidence of how human genetic adaptation to diverse local environments might interact with lifestyle factors in T2D, (ii) the key challenges facing the research area of gene x lifestyle interactions in T2D, and (iii) the solutions that might be pursued in future studies. Overall, many preliminary examples of such interactions exist, but none is sufficient to have a major impact on clinical decision making. Future studies, integrating genetics and other biological markers into regulatory networks, are likely to be necessary to facilitate the integration of genomics into lifestyle medicine in T2D.

  • 89.
    Franks, Paul W
    et al.
    Umeå universitet, Medicinska fakulteten, Institutionen för folkhälsa och klinisk medicin, Medicin.
    Nettleton, Jennifer A
    Invited commentary: gene X lifestyle interactions and complex disease traits-inferring cause and effect from observational data, sine qua non2010Ingår i: American Journal of Epidemiology, ISSN 0002-9262, E-ISSN 1476-6256, Vol. 172, nr 9, s. 992-997Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    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 A₁(c) 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.

  • 90.
    Franks, Paul W.
    et al.
    Umeå universitet, Medicinska fakulteten, Institutionen för folkhälsa och klinisk medicin, Medicin. Department of Nutrition, Harvard School of Public Health, Boston, USA.
    Pare, Guillaume
    Putting the Genome in Context: Gene-Environment Interactions in Type 2 Diabetes2016Ingår i: Current Diabetes Reports, ISSN 1534-4827, E-ISSN 1539-0829, Vol. 16, nr 7, artikel-id 57Artikel, forskningsöversikt (Refereegranskat)
    Abstract [en]

    The genome is often the conduit through which environmental exposures convey their effects on health and disease. Whilst not all diseases act by directly perturbing the genome, the phenotypic responses are often genetically determined. Hence, whilst diseases are often defined has having differing degrees of genetic determination, genetic and environmental factors are, with few exceptions, inseparable features of most diseases, not least type 2 diabetes. It follows that to optimize diabetes, prevention and treatment will require that the etiological roles of genetic and environmental risk factors be jointly considered. As we discuss here, studies focused on quantifying gene-environment and gene-treatment interactions are gathering momentum and may eventually yield data that helps guide health-related choices and medical interventions for type 2 diabetes and other complex diseases.

  • 91.
    Franks, Paul W.
    et al.
    Umeå universitet, Medicinska fakulteten, Institutionen för folkhälsa och klinisk medicin, Medicin.
    Pearson, Ewan
    Florez, Jose C.
    Gene-Environment and Gene-Treatment Interactions in Type 2 Diabetes2013Ingår i: Diabetes Care, ISSN 0149-5992, E-ISSN 1935-5548, Vol. 36, nr 5, s. 1413-1421Artikel, forskningsöversikt (Refereegranskat)
  • 92.
    Franks, Paul W
    et al.
    Umeå universitet, Medicinska fakulteten, Institutionen för folkhälsa och klinisk medicin, Medicin.
    Poveda, Alaitz
    Gene-lifestyle and gene-pharmacotherpy interactions in obesity and its cardiovascular consequences2011Ingår i: Current vascular pharmacology, ISSN 1875-6212, Vol. 9, nr 4, s. 401-456Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Obesity is a highly prevalent complex trait that raises the risk of other chronic diseases such as type 2 diabetes, certain cancers, sleep apnea, and cardiovascular disease, and shortens lifespan. Clinical intervention studies focused on weight loss and epidemiological studies of obesity indicate that genetic variation may modify the relationship between lifestyle behaviors and weight loss or weight gain. Similar observations have also emerged from pharmacogenetic studies. The literature includes several reports from these studies, but few examples of interactions have been adequately replicated. In this review we introduce the topics of population genetics research and gene x environment interaction. We also provide a systematic review of the published literature on gene x lifestyle (physical activity and dietary factors) and gene x drug interactions in relation to obesity. Finally, we overview the scope and findings from these studies and discuss some of their strengths and limitations.

  • 93.
    Franks, Paul W.
    et al.
    Umeå universitet, Medicinska fakulteten, Institutionen för folkhälsa och klinisk medicin, Medicin. Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Clinical Research Centre, Lund University, Skåne University Hospital, Malmö, Sweden; Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA ; Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Clinical Research Centre, Lund University.
    Poveda, Alaitz
    Lifestyle and precision diabetes medicine: will genomics help optimise the prediction, prevention and treatment of type 2 diabetes through lifestyle therapy?2017Ingår i: Diabetologia, ISSN 0012-186X, E-ISSN 1432-0428, Vol. 60, nr 5, s. 784-792Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Precision diabetes medicine, the optimisation of therapy using patient-level biomarker data, has stimulated enormous interest throughout society as it provides hope of more effective, less costly and safer ways of preventing, treating, and perhaps even curing the disease. While precision diabetes medicine is often framed in the context of pharmacotherapy, using biomarkers to personalise lifestyle recommendations, intended to lower type 2 diabetes risk or to slow progression, is also conceivable. There are at least four ways in which this might work: (1) by helping to predict a person's susceptibility to adverse lifestyle exposures; (2) by facilitating the stratification of type 2 diabetes into subclasses, some of which may be prevented or treated optimally with specific lifestyle interventions; (3) by aiding the discovery of prognostic biomarkers that help guide timing and intensity of lifestyle interventions; (4) by predicting treatment response. In this review we overview the rationale for precision diabetes medicine, specifically as it relates to lifestyle; we also scrutinise existing evidence, discuss the barriers germane to research in this field and consider how this work is likely to proceed.

  • 94.
    Franks, Paul W.
    et al.
    Umeå universitet, Medicinska fakulteten, Institutionen för folkhälsa och klinisk medicin.
    Roth, Stephen M.
    Interaction Between Physical Activity and Genetic Factors in Complex Metabolic Disease2008Ingår i: Energy Metabolism and Obesity: Research and Clinical Applications / [ed] Patricia A. Donohoue MD, Totowa: Humana Press, 2008, s. 155-173Kapitel i bok, del av antologi (Övrigt vetenskapligt)
    Abstract [en]

    Obesity and diabetes have become increasingly prevalent during the past century. Concomitant with this rise, the consumption of trans-fatty acids and processed carbohydrates is likely to have increased and physical activity levels declined. However, the rates at which obesity and diabetes have increased differ across people of varying ethnicities living in the same environment, suggesting the presence of interaction between ethnic-specific factors, such as genes, and changing environments and lifestyles. Quantifying these interactions is difficult because the interaction effect is often small, and precise measurement of lifestyle factors, such as diet and habitual physical activity, is difficult. Conventional interaction studies aim to test whether the magnitude of the association between the lifestyle exposures and the disease outcome is different in those who carry the variant allele at a given locus by comparison with those who do not. Because exercising skeletal muscle is a major site for glucose and lipid metabolism, variants in the genes that are located within muscle and that are up-regulated in response to physical activity present interesting candidates for testing in studies of gene x physical activity interaction in diabetes. However, numerous methodological limitations seriously hinder attempts to test such hypotheses. This chapter describes (1) a brief review of studies that provide evidence of gene x physical activity interaction in diabetes (and related traits), (2) functional evidence for interaction between genetic factors and physical activity in metabolic dysregulation, and (3) some common methodological issues that face the study of gene x environment interaction in human populations.

  • 95.
    Franks, Paul W.
    et al.
    Umeå universitet, Medicinska fakulteten, Institutionen för folkhälsa och klinisk medicin, Avdelningen för medicin. Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Skåne University Hospital, Malmö, Sweden; Oxford Centre for Diabetes, Endocrinology, and Metabolism, University of Oxford, United Kingdom; Department of Nutrition, Harvard TH Chan School of Public Health, Boston, MA.
    Timpson, Nicholas J.
    Genotype-Based Recall Studies in Complex Cardiometabolic Traits2018Ingår i: Circulation: Genomic and Precision Medicine, ISSN 2574-8300, Vol. 11, nr 8, artikel-id e001947Artikel, forskningsöversikt (Refereegranskat)
    Abstract [en]

    In genotype-based recall (GBR) studies, people (or their biological samples) who carry genotypes of special interest for a given hypothesis test are recalled from a larger cohort (or biobank) for more detailed investigations. There are several GBR study designs that offer a range of powerful options to elucidate (1) genotype-phenotype associations (by increasing the efficiency of genetic association studies, thereby allowing bespoke phenotyping in relatively small cohorts), (2) the effects of environmental exposures (within the Mendelian randomization framework), and (3) gene-treatment interactions (within the setting of GBR interventional trials). In this review, we overview the literature on GBR studies as applied to cardiometabolic health outcomes. We also review the GBR approaches used to date and outline new methods and study designs that might enhance the utility of GBR-focused studies. Specifically, we highlight how GBR methods have the potential to augment randomized controlled trials, providing an alternative application for the now increasingly accepted Mendelian randomization methods usually applied to large-scale population-based data sets. Further to this, we consider how functional and basic science approaches alongside GBR designs offer intellectually intriguing and potentially powerful ways to explore the implications of alterations to specific (and potentially druggable) biological pathways.

  • 96. Fretts, Amanda M.
    et al.
    Follis, Jack L.
    Nettleton, Jennifer A.
    Lemaitre, Rozenn N.
    Ngwa, Julius S.
    Wojczynski, Mary K.
    Kalafati, Ioanna Panagiota
    Varga, Tibor V.
    Frazier-Wood, Alexis C.
    Houston, Denise K.
    Lahti, Jari
    Ericson, Ulrika
    van den Hooven, Edith H.
    Mikkilae, Vera
    Kiefte-de Jong, Jessica C.
    Mozaffarian, Dariush
    Rice, Kenneth
    Renström, Frida
    Umeå universitet, Medicinska fakulteten, Enheten för biobanksforskning. Umeå universitet, Medicinska fakulteten, Institutionen för folkhälsa och klinisk medicin, Näringsforskning. Department of Clinical Sciences Genetic and Molecular Epidemiology Unit, Lund University, Malmö, Sweden.
    North, Kari E.
    McKeown, Nicola M.
    Feitosa, Mary F.
    Kanoni, Stavroula
    Smith, Caren E.
    Garcia, Melissa E.
    Tiainen, Anna-Maija
    Sonestedt, Emily
    Manichaikul, Ani
    van Rooij, Frank J. A.
    Dimitriou, Maria
    Raitakari, Olli
    Pankow, James S.
    Djousse, Luc
    Province, Michael A.
    Hu, Frank B.
    Lai, Chao-Qiang
    Keller, Margaux F.
    Peraelae, Mia-Maria
    Rotter, Jerome I.
    Hofman, Albert
    Graff, Misa
    Kaehoenen, Mika
    Mukamal, Kenneth
    Johansson, Ingegerd
    Umeå universitet, Medicinska fakulteten, Institutionen för odontologi. Umeå universitet, Medicinska fakulteten, Institutionen för folkhälsa och klinisk medicin, Näringsforskning. Umeå universitet, Medicinska fakulteten, Enheten för biobanksforskning.
    Ordovas, Jose M.
    Liu, Yongmei
    Maennistoe, Satu
    Uitterlinden, Andre G.
    Deloukas, Panos
    Seppaelae, Ilkka
    Psaty, Bruce M.
    Cupples, L. Adrienne
    Borecki, Ingrid B.
    Franks, Paul W.
    Umeå universitet, Medicinska fakulteten, Institutionen för folkhälsa och klinisk medicin, Medicin. Department of Clinical Sciences Genetic and Molecular Epidemiology Unit, Lund University, Malmö, Sweden; Department of Nutrition, Harvard School of Public Health, Boston, MA.
    Arnett, Donna K.
    Nalls, Mike A.
    Eriksson, Johan G.
    Orho-Melander, Marju
    Franco, Oscar H.
    Lehtimaeki, Terho
    Dedoussis, George V.
    Meigs, James B.
    Siscovick, David S.
    Consumption of meat is associated with higher fasting glucose and insulin concentrations regardless of glucose and insulin genetic risk scores: a meta-analysis of 50,345 Caucasians2015Ingår i: American Journal of Clinical Nutrition, ISSN 0002-9165, E-ISSN 1938-3207, Vol. 102, nr 5, s. 1266-1278Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Background: Recent studies suggest that meat intake is associated with diabetes-related phenotypes. However, whether the associations of meat intake and glucose and insulin homeostasis are modified by genes related to glucose and insulin is unknown. Objective: We investigated the associations of meat intake and the interaction of meat with genotype on fasting glucose and insulin concentrations in Caucasians free of diabetes mellitus. Design: Fourteen studies that are part of the Cohorts for Heart and Aging Research in Genomic Epidemiology consortium participated in the analysis. Data were provided for up to 50,345 participants. Using linear regression within studies and a fixed-effects meta-analysis across studies, we examined l) the associations of processed meat and unprocessed red meat intake with fasting glucose and insulin concentrations; and 2) the interactions of processed meat and unprocessed red meat with genetic risk score related to fasting glucose or insulin resistance on fasting glucose and insulin concentrations. Results: Processed meat was associated with higher fasting glucose, and unprocessed red meat was associated with both higher fasting glucose and fasting insulin concentrations after adjustment for potential confounders [not including body mass index (BMI)]. For every additional 50-g serving of processed meat per day, fasting glucose was 0.021 mmol/L (95% CI: 0.011, 0.030 mmol/L) higher. Every additional 100-g serving of unprocessed red meat per day was associated with a 0.037-mmol/L (95% CI: 0.023, 0.051-mmol/L) higher fasting glucose concentration and a 0.049-1n-pmon (95% CI: 0.035, 0.063-1n-pmol/L) higher fasting insulin concentration. After additional adjustment for BMI, observed associations were attenuated and no longer statistically significant. The association of processed meat and fasting insulin did not reach statistical significance after correction for multiple comparisons. Observed associations were not modified by genetic loci known to influence fasting glucose or insulin resistance. Conclusion: The association of higher fasting glucose and insulin concentrations with meat consumption was not modified by an index of glucose- and insulin-related single-nucleotide polymorphisms.

  • 97. Fuchsberger, Christian
    et al.
    Flannick, Jason
    Teslovich, Tanya M.
    Mahajan, Anubha
    Agarwala, Vineeta
    Gaulton, Kyle J.
    Ma, Clement
    Fontanillas, Pierre
    Moutsianas, Loukas
    McCarthy, Davis J.
    Rivas, Manuel A.
    Perry, John R. B.
    Sim, Xueling
    Blackwell, Thomas W.
    Robertson, Neil R.
    Rayner, N. William
    Cingolani, Pablo
    Locke, Adam E.
    Tajes, Juan Fernandez
    Highland, Heather M.
    Dupuis, Josee
    Chines, Peter S.
    Lindgren, Cecilia M.
    Hartl, Christopher
    Jackson, Anne U.
    Chen, Han
    Huyghe, Jeroen R.
    van de Bunt, Martijn
    Pearson, Richard D.
    Kumar, Ashish
    Mueller-Nurasyid, Martina
    Grarup, Niels
    Stringham, Heather M.
    Gamazon, Eric R.
    Lee, Jaehoon
    Chen, Yuhui
    Scott, Robert A.
    Below, Jennifer E.
    Chen, Peng
    Huang, Jinyan
    Go, Min Jin
    Stitzel, Michael L.
    Pasko, Dorota
    Parker, Stephen C. J.
    Varga, Tibor V.
    Green, Todd
    Beer, Nicola L.
    Day-Williams, Aaron G.
    Ferreira, Teresa
    Fingerlin, Tasha
    Horikoshi, Momoko
    Hu, Cheng
    Huh, Iksoo
    Ikram, Mohammad Kamran
    Kim, Bong-Jo
    Kim, Yongkang
    Kim, Young Jin
    Kwon, Min-Seok
    Lee, Juyoung
    Lee, Selyeong
    Lin, Keng-Han
    Maxwell, Taylor J.
    Nagai, Yoshihiko
    Wang, Xu
    Welch, Ryan P.
    Yoon, Joon
    Zhang, Weihua
    Barzilai, Nir
    Voight, Benjamin F.
    Han, Bok-Ghee
    Jenkinson, Christopher P.
    Kuulasmaa, Teemu
    Kuusisto, Johanna
    Manning, Alisa
    Ng, Maggie C. Y.
    Palmer, Nicholette D.
    Balkau, Beverley
    Stancakova, Alena
    Abboud, Hanna E.
    Boeing, Heiner
    Giedraitis, Vilmantas
    Prabhakaran, Dorairaj
    Gottesman, Omri
    Scott, James
    Carey, Jason
    Kwan, Phoenix
    Grant, George
    Smith, Joshua D.
    Neale, Benjamin M.
    Purcell, Shaun
    Butterworth, Adam S.
    Howson, Joanna M. M.
    Lee, Heung Man
    Lu, Yingchang
    Kwak, Soo-Heon
    Zhao, Wei
    Danesh, John
    Lam, Vincent K. L.
    Park, Kyong Soo
    Saleheen, Danish
    So, Wing Yee
    Tam, Claudia H. T.
    Afzal, Uzma
    Aguilar, David
    Arya, Rector
    Aung, Tin
    Chan, Edmund
    Navarro, Carmen
    Cheng, Ching-Yu
    Palli, Domenico
    Correa, Adolfo
    Curran, Joanne E.
    Rybin, Denis
    Farook, Vidya S.
    Fowler, Sharon P.
    Freedman, Barry I.
    Griswold, Michael
    Hale, Daniel Esten
    Hicks, Pamela J.
    Khor, Chiea-Chuen
    Kumar, Satish
    Lehne, Benjamin
    Thuillier, Dorothee
    Lim, Wei Yen
    Liu, Jianjun
    van der Schouw, Yvonne T.
    Loh, Marie
    Musani, Solomon K.
    Puppala, Sobha
    Scott, William R.
    Yengo, Loic
    Tan, Sian-Tsung
    Taylor, Herman A., Jr.
    Thameem, Farook
    Wilson, Gregory, Sr.
    Wong, Tien Yin
    Njolstad, Pal Rasmus
    Levy, Jonathan C.
    Mangino, Massimo
    Bonnycastle, Lori L.
    Schwarzmayr, Thomas
    Fadista, Joao
    Surdulescu, Gabriela L.
    Herder, Christian
    Groves, Christopher J.
    Wieland, Thomas
    Bork-Jensen, Jette
    Brandslund, Ivan
    Christensen, Cramer
    Koistinen, Heikki A.
    Doney, Alex S. F.
    Kinnunen, Leena
    Esko, Tonu
    Farmer, Andrew J.
    Hakaste, Liisa
    Hodgkiss, Dylan
    Kravic, Jasmina
    Lyssenko, Valeriya
    Hollensted, Mette
    Jorgensen, Marit E.
    Jorgensen, Torben
    Ladenvall, Claes
    Justesen, Johanne Marie
    Karajamaki, Annemari
    Kriebel, Jennifer
    Rathmann, Wolfgang
    Lannfelt, Lars
    Lauritzen, Torsten
    Narisu, Narisu
    Linneberg, Allan
    Melander, Olle
    Milani, Lili
    Neville, Matt
    Orho-Melander, Marju
    Qi, Lu
    Qi, Qibin
    Roden, Michael
    Rolandsson, Olov
    Umeå universitet, Medicinska fakulteten, Institutionen för folkhälsa och klinisk medicin, Allmänmedicin.
    Swift, Amy
    Rosengren, Anders H.
    Stirrups, Kathleen
    Wood, Andrew R.
    Mihailov, Evelin
    Blancher, Christine
    Carneiro, Mauricio O.
    Maguire, Jared
    Poplin, Ryan
    Shakir, Khalid
    Fennell, Timothy
    DePristo, Mark
    de Angelis, Martin Hrabe
    Deloukas, Panos
    Gjesing, Anette P.
    Jun, Goo
    Nilsson, Peter
    Murphy, Jacquelyn
    Onofrio, Robert
    Thorand, Barbara
    Hansen, Torben
    Meisinger, Christa
    Hu, Frank B.
    Isomaa, Bo
    Karpe, Fredrik
    Liang, Liming
    Peters, Annette
    Huth, Cornelia
    O'Rahilly, Stephen P.
    Palmer, Colin N. A.
    Pedersen, Oluf
    Rauramaa, Rainer
    Tuomilehto, Jaakko
    Salomaa, Veikko
    Watanabe, Richard M.
    Syvanen, Ann-Christine
    Bergman, Richard N.
    Bharadwaj, Dwaipayan
    Bottinger, Erwin P.
    Cho, Yoon Shin
    Chandak, Giriraj R.
    Chan, Juliana C. N.
    Chia, Kee Seng
    Daly, Mark J.
    Ebrahim, Shah B.
    Langenberg, Claudia
    Elliott, Paul
    Jablonski, Kathleen A.
    Lehman, Donna M.
    Jia, Weiping
    Ma, Ronald C. W.
    Pollin, Toni I.
    Sandhu, Manjinder
    Tandon, Nikhil
    Froguel, Philippe
    Barroso, Ines
    Teo, Yik Ying
    Zeggini, Eleftheria
    Loos, Ruth J. F.
    Small, Kerrin S.
    Ried, Janina S.
    DeFronzo, Ralph A.
    Grallert, Harald
    Glaser, Benjamin
    Metspalu, Andres
    Wareham, Nicholas J.
    Walker, Mark
    Banks, Eric
    Gieger, Christian
    Ingelsson, Erik
    Im, Hae Kyung
    Illig, Thomas
    Franks, Paul W.
    Umeå universitet, Medicinska fakulteten, Institutionen för folkhälsa och klinisk medicin, Medicin. Department of Clinical Sciences, Lund University Diabetes Centre, Genetic and Molecular Epidemiology Unit, Lund University, Malmö, Sweden; Department of Nutrition, Harvard School of Public Health, Boston, Massachusetts, USA.
    Buck, Gemma
    Trakalo, Joseph
    Buck, David
    Prokopenko, Inga
    Magi, Reedik
    Lind, Lars
    Farjoun, Yossi
    Owen, Katharine R.
    Gloyn, Anna L.
    Strauch, Konstantin
    Tuomi, Tiinamaija
    Kooner, Jaspal Singh
    Lee, Jong-Young
    Park, Taesung
    Donnelly, Peter
    Morris, Andrew D.
    Hattersley, Andrew T.
    Bowden, Donald W.
    Collins, Francis S.
    Atzmon, Gil
    Chambers, John C.
    Spector, Timothy D.
    Laakso, Markku
    Strom, Tim M.
    Bell, Graeme I.
    Blangero, John
    Duggirala, Ravindranath
    Tai, E. Shyong
    McVean, Gilean
    Hanis, Craig L.
    Wilson, James G.
    Seielstad, Mark
    Frayling, Timothy M.
    Meigs, James B.
    Cox, Nancy J.
    Sladek, Rob
    Lander, Eric S.
    Gabriel, Stacey
    Burtt, Noel P.
    Mohlke, Karen L.
    Meitinger, Thomas
    Groop, Leif
    Abecasis, Goncalo
    Florez, Jose C.
    Scott, Laura J.
    Morris, Andrew P.
    Kang, Hyun Min
    Boehnke, Michael
    Altshuler, David
    McCarthy, Mark I.
    The genetic architecture of type 2 diabetes2016Ingår i: Nature, ISSN 0028-0836, E-ISSN 1476-4687, Vol. 536, nr 7614, s. 41-47Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The genetic architecture of common traits, including the number, frequency, and effect sizes of inherited variants that contribute to individual risk, has been long debated. Genome-wide association studies have identified scores of common variants associated with type 2 diabetes, but in aggregate, these explain only a fraction of the heritability of this disease. Here, to test the hypothesis that lower-frequency variants explain much of the remainder, the GoT2D and T2D-GENES consortia performed whole-genome sequencing in 2,657 European individuals with and without diabetes, and exome sequencing in 12,940 individuals from five ancestry groups. To increase statistical power, we expanded the sample size via genotyping and imputation in a further 111,548 subjects. Variants associated with type 2 diabetes after sequencing were overwhelmingly common and most fell within regions previously identified by genome-wide association studies. Comprehensive enumeration of sequence variation is necessary to identify functional alleles that provide important clues to disease pathophysiology, but large-scale sequencing does not support the idea that lower-frequency variants have a major role in predisposition to type 2 diabetes.

  • 98. Gaulton, Kyle J.
    et al.
    Ferreira, Teresa
    Lee, Yeji
    Raimondo, Anne
    Maegi, Reedik
    Reschen, Michael E.
    Mahajan, Anubha
    Locke, Adam
    Rayner, N. William
    Robertson, Neil
    Scott, Robert A.
    Prokopenko, Inga
    Scott, Laura J.
    Green, Todd
    Sparso, Thomas
    Thuillier, Dorothee
    Yengo, Loic
    Grallert, Harald
    Wahl, Simone
    Franberg, Mattias
    Strawbridge, Rona J.
    Kestler, Hans
    Chheda, Himanshu
    Eisele, Lewin
    Gustafsson, Stefan
    Steinthorsdottir, Valgerdur
    Thorleifsson, Gudmar
    Qi, Lu
    Karssen, Lennart C.
    van Leeuwen, Elisabeth M.
    Willems, Sara M.
    Li, Man
    Chen, Han
    Fuchsberger, Christian
    Kwan, Phoenix
    Ma, Clement
    Linderman, Michael
    Lu, Yingchang
    Thomsen, Soren K.
    Rundle, Jana K.
    Beer, Nicola L.
    van de Bunt, Martijn
    Chalisey, Anil
    Kang, Hyun Min
    Voight, Benjamin F.
    Abecasis, Goncalo R.
    Almgren, Peter
    Baldassarre, Damiano
    Balkau, Beverley
    Benediktsson, Rafn
    Blueher, Matthias
    Boeing, Heiner
    Bonnycastle, Lori L.
    Bottinger, Erwin P.
    Burtt, Noel P.
    Carey, Jason
    Charpentier, Guillaume
    Chines, Peter S.
    Cornelis, Marilyn C.
    Couper, David J.
    Crenshaw, Andrew T.
    van Dam, Rob M.
    Doney, Alex S. F.
    Dorkhan, Mozhgan
    Edkins, Sarah
    Eriksson, Johan G.
    Esko, Tonu
    Eury, Elodie
    Fadista, Joao
    Flannick, Jason
    Fontanillas, Pierre
    Fox, Caroline
    Franks, Paul W.
    Umeå universitet, Medicinska fakulteten, Institutionen för folkhälsa och klinisk medicin, Medicin. Department of Nutrition, Harvard School of Public Health, Boston, Massachusetts, USA; Lund University Diabetes Centre, Department of Clinical Science Malmö, Scania University Hospital, Lund University, Malmö, Sweden; Department of Clinical Sciences, Lund University, Malmö, Sweden..
    Gertow, Karl
    Gieger, Christian
    Gigante, Bruna
    Gottesman, Omri
    Grant, George B.
    Grarup, Niels
    Groves, Christopher J.
    Hassinen, Maija
    Have, Christian T.
    Herder, Christian
    Holmen, Oddgeir L.
    Hreidarsson, Astradur B.
    Humphries, Steve E.
    Hunter, David J.
    Jackson, Anne U.
    Jonsson, Anna
    Jorgensen, Marit E.
    Jorgensen, Torben
    Kao, Wen-Hong L.
    Kerrison, Nicola D.
    Kinnunen, Leena
    Klopp, Norman
    Kong, Augustine
    Kovacs, Peter
    Kraft, Peter
    Kravic, Jasmina
    Langford, Cordelia
    Leander, Karin
    Liang, Liming
    Lichtner, Peter
    Lindgren, Cecilia M.
    Lindholm, Eero
    Linneberg, Allan
    Liu, Ching-Ti
    Lobbens, Stephane
    Luan, Jian'an
    Lyssenko, Valeriya
    Mannisto, Satu
    McLeod, Olga
    Meyer, Julia
    Mihailov, Evelin
    Mirza, Ghazala
    Muehleisen, Thomas W.
    Mueller-Nurasyid, Martina
    Navarro, Carmen
    Noethen, Markus M.
    Oskolkov, Nikolay N.
    Owen, Katharine R.
    Palli, Domenico
    Pechlivanis, Sonali
    Peltonen, Leena
    Perry, John R. B.
    Platou, Carl G. P.
    Roden, Michael
    Ruderfer, Douglas
    Rybin, Denis
    van der Schouw, Yvonne T.
    Sennblad, Bengt
    Sigurdsson, Gunnar
    Stancakova, Alena
    Steinbach, Gerald
    Storm, Petter
    Strauch, Konstantin
    Stringham, Heather M.
    Sun, Qi
    Thorand, Barbara
    Tikkanen, Emmi
    Tonjes, Anke
    Trakalo, Joseph
    Tremoli, Elena
    Tuomi, Tiinamaija
    Wennauer, Roman
    Wiltshire, Steven
    Wood, Andrew R.
    Zeggini, Eleftheria
    Dunham, Ian
    Birney, Ewan
    Pasquali, Lorenzo
    Ferrer, Jorge
    Loos, Ruth J. F.
    Dupuis, Josee
    Florez, Jose C.
    Boerwinkle, Eric
    Pankow, James S.
    van Duijn, Cornelia
    Sijbrands, Eric
    Meigs, James B.
    Hu, Frank B.
    Thorsteinsdottir, Unnur
    Stefansson, Kari
    Lakka, Timo A.
    Rauramaa, Rainer
    Stumvoll, Michael
    Pedersen, Nancy L.
    Lind, Lars
    Keinanen-Kiukaanniemi, Sirkka M.
    Korpi-Hyovalti, Eeva
    Saaristo, Timo E.
    Saltevo, Juha
    Kuusisto, Johanna
    Laakso, Markku
    Metspalu, Andres
    Erbel, Raimund
    Joecke, Karl-Heinz
    Moebus, Susanne
    Ripatti, Samuli
    Salomaa, Veikko
    Ingelsson, Erik
    Boehm, Bernhard O.
    Bergman, Richard N.
    Collins, Francis S.
    Mohlke, Karen L.
    Koistinen, Heikki
    Tuomilehto, Jaakko
    Hveem, Kristian
    Njolstad, Inger
    Deloukas, Panagiotis
    Donnelly, Peter J.
    Frayling, Timothy M.
    Hattersley, Andrew T.
    de Faire, Ulf
    Hamsten, Anders
    Illig, Thomas
    Peters, Annette
    Cauchi, Stephane
    Sladek, Rob
    Froguel, Philippe
    Hansen, Torben
    Pedersen, Oluf
    Morris, Andrew D.
    Palmer, Collin N. A.
    Kathiresan, Sekar
    Melander, Olle
    Nilsson, Peter M.
    Groop, Leif C.
    Barroso, Ines
    Langenberg, Claudia
    Wareham, Nicholas J.
    O'Callaghan, Christopher A.
    Gloyn, Anna L.
    Altshuler, David
    Boehnke, Michael
    Teslovich, Tanya M.
    McCarthy, Mark I.
    Morris, Andrew P.
    Genetic fine mapping and genomic annotation defines causal mechanisms at type 2 diabetes susceptibility loci2015Ingår i: Nature Genetics, ISSN 1061-4036, E-ISSN 1546-1718, Vol. 47, nr 12, s. 1415-1425Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    We performed fine mapping of 39 established type 2 diabetes (T2D) loci in 27,206 cases and 57,574 controls of European ancestry. We identified 49 distinct association signals at these loci, including five mapping in or near KCNQ1. 'Credible sets' of the variants most likely to drive each distinct signal mapped predominantly to noncoding sequence, implying that association with T2D is mediated through gene regulation. Credible set variants were enriched for overlap with FOXA2 chromatin immunoprecipitation binding sites in human islet and liver cells, including at MTNR1B, where fine mapping implicated rs10830963 as driving T2D association. We confirmed that the T2D risk allele for this SNP increases FOXA2-bound enhancer activity in islet- and liver-derived cells. We observed allele-specific differences in NEUROD1 binding in islet-derived cells, consistent with evidence that the T2D risk allele increases islet MTNR1B expression. Our study demonstrates how integration of genetic and genomic information can define molecular mechanisms through which variants underlying association signals exert their effects on disease.

  • 99. Golubic, Rajna
    et al.
    May, Anne M.
    Borch, Kristin Benjaminsen
    Overvad, Kim
    Charles, Marie-Aline
    Tormo Diaz, Maria Jose
    Amiano, Pilar
    Palli, Domenico
    Valanou, Elisavet
    Vigl, Matthaeus
    Franks, Paul W.
    Umeå universitet, Medicinska fakulteten, Institutionen för folkhälsa och klinisk medicin, Medicin.
    Wareham, Nicholas
    Ekelund, Ulf
    Brage, Soren
    Validity of Electronically Administered Recent Physical Activity Questionnaire (RPAQ) in Ten European Countries2014Ingår i: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 9, nr 3, s. e92829-Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Objective: To examine the validity of the Recent Physical Activity Questionnaire (RPAQ) which assesses physical activity (PA) in 4 domains (leisure, work, commuting, home) during past month. Methods: 580 men and 1343 women from 10 European countries attended 2 visits at which PA energy expenditure (PAEE), time at moderate-to-vigorous PA (MVPA) and sedentary time were measured using individually-calibrated combined heart-rate and movement sensing. At the second visit, RPAQ was administered electronically. Validity was assessed using agreement analysis. Results: RPAQ significantly underestimated PAEE in women [median(IQR) 34.1 (22.1, 52.2) vs. 40.6 (32.4, 50.9) kJ/kg/day, 95%LoA: -44.4, 63.4 kJ/kg/day) and in men (43.7 (29.0, 69.0) vs. 45.5 (34.1, 57.6) kJ/kg/day, 95%LoA: -47.2, 101.3 kJ/kg/day]. Using individualised definition of 1MET, RPAQ significantly underestimated MVPA in women [median(IQR): 62.1 (29.4, 124.3) vs. 73.6 (47.8, 107.2) min/day, 95%LoA: -130.5, 305.3 min/day] and men [82.7 (38.8, 185.6) vs. 83.3 (55.1, 125.0) min/day, 95%LoA: -136.4, 400.1 min/day]. Correlations (95%CI) between subjective and objective estimates were statistically significant [PAEE: women, rho = 0.20 (0.15-0.26); men, rho = 0.37 (0.30-0.44); MVPA: women, rho = 0.18 (0.13-0.23); men, rho = 0.31 (0.24-0.39)]. When using non-individualised definition of 1MET (3.5 mlO(2)/kg/min), MVPA was substantially overestimated (similar to 30 min/day). Revisiting occupational intensity assumptions in questionnaire estimation algorithms with occupational group-level empirical distributions reduced median PAEE-bias in manual (25.1 kJ/kg/day vs. 29.0 kJ/kg/day, p<0.001) and heavy manual workers (64.1 vs. -4.6 kJ/kg/day, p<0.001) in an independent hold-out sample. Conclusion: Relative validity of RPAQ-derived PAEE and MVPA is comparable to previous studies but underestimation of PAEE is smaller. Electronic RPAQ may be used in large-scale epidemiological studies including surveys, providing information on all domains of PA.

  • 100.
    Gradmark, Anna M I
    et al.
    Umeå universitet, Medicinska fakulteten, Institutionen för folkhälsa och klinisk medicin, Medicin.
    Rydh, Anders
    Umeå universitet, Medicinska fakulteten, Institutionen för strålningsvetenskaper, Diagnostisk radiologi.
    Renström, Frida
    Umeå universitet, Medicinska fakulteten, Institutionen för folkhälsa och klinisk medicin, Medicin.
    De Lucia-Rolfe, Emanuella
    Sleigh, Alison
    Nordström, Peter
    Umeå universitet, Medicinska fakulteten, Institutionen för samhällsmedicin och rehabilitering, Geriatrik.
    Brage, Sören
    Franks, Paul W
    Umeå universitet, Medicinska fakulteten, Institutionen för folkhälsa och klinisk medicin, Medicin.
    Computed tomography-based validation of abdominal adiposity measurements from ultrasonography, dual-energy X-ray absorptiometry and anthropometry2010Ingår i: British Journal of Nutrition, ISSN 0007-1145, E-ISSN 1475-2662, Vol. 104, nr 4, s. 582-588Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Large-scale aetiological studies of obesity and its pathological consequences require accurate measurements of adipose mass, distribution and subtype. Here, we compared the validity of three abdominal obesity assessment methods (dual-energy X-ray absorptiometry (DXA), ultrasound and anthropometry) against the gold-standard method of computed tomography (CT) in twenty-nine non-diseased middle-aged men (BMI 26.5 (sd 3.1) kg/m(2)) and women (BMI 25.5 (sd 3.2) kg/m(2)). Assessments of adipose mass (kg) and distribution (total subcutaneous (TSAT), superficial subcutaneous (SSAT), deep subcutaneous (DSAT) and visceral (VAT)) were obtained. Spearman's correlations were performed adjusted for age and sex. VAT area that was assessed using ultrasound (r 0.79; P < 0.0001) and waist circumference (r 0.85; P < 0.0001) correlated highly with VAT from CT, as did BMI (r 0.67; P < 0.0001) and DXA (r 0.70; P < 0.0001). DXA (r 0.72; P = 0.0004), BMI (r 0.71; P = 0.0003), waist circumference (r 0.86; P < 0.0001) and ultrasound (r 0.52; P = 0.015) were less strongly correlated with CT TSAT. None of the comparison measures of DSAT was strongly correlated with CT DSAT (all r approximately 0.50; P < 0.02). BMI (r 0.76; P < 0.0001), waist circumference (r 0.65; P = 0.002) and DXA (r 0.75; P < 0.0001) were all fairly strongly correlated with the CT measure of SSAT, whereas ultrasound yielded a weaker yet statistically significant correlation (r 0.48; P = 0.03). Compared with CT, visceral and subcutaneous adiposity can be assessed with reasonable validity using waist circumference and BMI, respectively. Ultrasound or DXA does not generally provide substantially better measures of these traits. Highly valid assessments of DSAT do not appear to be possible with surrogate measures. These findings may help guide the selection of measures for epidemiological studies of obesity.

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