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1. Understanding the genetic complexity of puberty timing across the allele frequency spectrum

Author: Kentistou, KA, Kaisinger, LR, Stankovic, S, Vaudel, M, Mendes de Oliveira, E, Messina, A, Walters, RG, Liu, X, Busch, AS, Helgason, H, Thompson, DJ, Santoni, F, Petricek, KM, Zouaghi, Y, Huang-Doran, I, Gudbjartsson, DF, Bratland, E, Lin, K, Gardner, EJ, Zhao, Y, Jia, RY, Terao, C, Riggan, MJ, Bolla, MK, Yazdanpanah, M, Yazdanpanah, N, Bradfield, JP, Broer, L, Campbell, A, Chasman, DI, Cousminer, DL, Franceschini, N, Franke, LH, Girotto, G, He, C, Järvelin, M-R, Joshi, PK, Kamatani, Y, Karlsson, R, Luan, J, Lunetta, KL, Mägi, R, Mangino, M, Medland, SE, Meisinger, C, Noordam, R, Nutile, T, Concas, MP, Polašek, O, Porcu, E, Ring, SM, Sala, C, Smith, AV, Tanaka, T, van der Most, PJ, Vitart, V, Wang, CA, Willemsen, G, Zygmunt, M, Ahearn, TU, Andrulis, IL, Anton-Culver, H, Antoniou, AC, Auer, PL, Barnes, CLK, Beckmann, MW, Berrington de Gonzalez, A, Bogdanova, NV, Bojesen, SE, Brenner, H, Buring, JE, Canzian, F, Chang-Claude, J, Couch, FJ, Cox, A, Crisponi, L, Czene, K, Daly, MB, Demerath, EW, Dennis, J, Devilee, P, De Vivo, I, Dörk, T, Dunning, AM, Dwek, M, Eriksson, JG, Fasching, PA, Fernandez-Rhodes, L, Ferreli, L, Fletcher, O, Gago-Dominguez, M, García-Closas, M, García-Sáenz, JA, González-Neira, A, Grallert, H, Guénel, P, Haiman, CA, Hall, P, Hamann, U, Hakonarson, H, Hart, RJ, Hickey, M, Hooning, MJ, Hoppe, R, Hopper, JL, Hottenga, J-J, Hu, FB, Huebner, H, Hunter, DJ, ABCTB Investigators, Jernström, H, John, EM, Karasik, D, Khusnutdinova, EK, Kristensen, VN, Lacey, JV, Lambrechts, D, Launer, LJ, Lind, PA, Lindblom, A, Magnusson, PKE, Mannermaa, A, McCarthy, MI, Meitinger, T, Menni, C, Michailidou, K, Millwood, IY, Milne, RL, Montgomery, GW, Nevanlinna, H, Nolte, IM, Nyholt, DR, Obi, N, O'Brien, KM, Offit, K, Oldehinkel, AJ, Ostrowski, SR, Palotie, A, Pedersen, OB, Peters, A, Pianigiani, G, Plaseska-Karanfilska, D, Pouta, A, Pozarickij, A, Radice, P, Rennert, G, Rosendaal, FR, Ruggiero, D, Saloustros, E, Sandler, DP, Schipf, S, Schmidt, CO, Schmidt, MK, Small, K, Spedicati, B, Stampfer, M, Stone, J, Tamimi, RM, Teras, LR, Tikkanen, E, Turman, C, Vachon, CM, Wang, Q, Winqvist, R, Wolk, A, Zemel, BS, Zheng, W, van Dijk, KW, Alizadeh, BZ, Bandinelli, S, Boerwinkle, E, Boomsma, DI, Ciullo, M, Chenevix-Trench, G, Cucca, F, Esko, T, Gieger, C, Grant, SFA, Gudnason, V, Hayward, C, Kolčić, I, Kraft, P, Lawlor, DA, Martin, NG, Nøhr, EA, Pedersen, NL, Pennell, CE, Ridker, PM, Robino, A, Snieder, H, Sovio, U, Spector, TD, Stöckl, D, Sudlow, C, Timpson, NJ, Toniolo, D, Uitterlinden, A, Ulivi, S, Völzke, H, Wareham, NJ, Widen, E, Wilson, JF, Lifelines Cohort Study, Danish Blood Donor Study, Ovarian Cancer Association Consortium, Breast Cancer Association Consortium, Biobank Japan Project, China Kadoorie Biobank Collaborative Group, Pharoah, PDP, Li, L, Easton, DF, Njølstad, PR, Sulem, P, Murabito, JM, Murray, A, Manousaki, D, Juul, A, Erikstrup, C, Stefansson, K, Horikoshi, M, Chen, Z, Farooqi, IS, Pitteloud, N, Johansson, S, Day, FR, Perry, JRB, Ong, KK, Kentistou, KA, Kaisinger, LR, Stankovic, S, Vaudel, M, Mendes de Oliveira, E, Messina, A, Walters, RG, Liu, X, Busch, AS, Helgason, H, Thompson, DJ, Santoni, F, Petricek, KM, Zouaghi, Y, Huang-Doran, I, Gudbjartsson, DF, Bratland, E, Lin, K, Gardner, EJ, Zhao, Y, Jia, RY, Terao, C, Riggan, MJ, Bolla, MK, Yazdanpanah, M, Yazdanpanah, N, Bradfield, JP, Broer, L, Campbell, A, Chasman, DI, Cousminer, DL, Franceschini, N, Franke, LH, Girotto, G, He, C, Järvelin, M-R, Joshi, PK, Kamatani, Y, Karlsson, R, Luan, J, Lunetta, KL, Mägi, R, Mangino, M, Medland, SE, Meisinger, C, Noordam, R, Nutile, T, Concas, MP, Polašek, O, Porcu, E, Ring, SM, Sala, C, Smith, AV, Tanaka, T, van der Most, PJ, Vitart, V, Wang, CA, Willemsen, G, Zygmunt, M, Ahearn, TU, Andrulis, IL, Anton-Culver, H, Antoniou, AC, Auer, PL, Barnes, CLK, Beckmann, MW, Berrington de Gonzalez, A, Bogdanova, NV, Bojesen, SE, Brenner, H, Buring, JE, Canzian, F, Chang-Claude, J, Couch, FJ, Cox, A, Crisponi, L, Czene, K, Daly, MB, Demerath, EW, Dennis, J, Devilee, P, De Vivo, I, Dörk, T, Dunning, AM, Dwek, M, Eriksson, JG, Fasching, PA, Fernandez-Rhodes, L, Ferreli, L, Fletcher, O, Gago-Dominguez, M, García-Closas, M, García-Sáenz, JA, González-Neira, A, Grallert, H, Guénel, P, Haiman, CA, Hall, P, Hamann, U, Hakonarson, H, Hart, RJ, Hickey, M, Hooning, MJ, Hoppe, R, Hopper, JL, Hottenga, J-J, Hu, FB, Huebner, H, Hunter, DJ, ABCTB Investigators, Jernström, H, John, EM, Karasik, D, Khusnutdinova, EK, Kristensen, VN, Lacey, JV, Lambrechts, D, Launer, LJ, Lind, PA, Lindblom, A, Magnusson, PKE, Mannermaa, A, McCarthy, MI, Meitinger, T, Menni, C, Michailidou, K, Millwood, IY, Milne, RL, Montgomery, GW, Nevanlinna, H, Nolte, IM, Nyholt, DR, Obi, N, O'Brien, KM, Offit, K, Oldehinkel, AJ, Ostrowski, SR, Palotie, A, Pedersen, OB, Peters, A, Pianigiani, G, Plaseska-Karanfilska, D, Pouta, A, Pozarickij, A, Radice, P, Rennert, G, Rosendaal, FR, Ruggiero, D, Saloustros, E, Sandler, DP, Schipf, S, Schmidt, CO, Schmidt, MK, Small, K, Spedicati, B, Stampfer, M, Stone, J, Tamimi, RM, Teras, LR, Tikkanen, E, Turman, C, Vachon, CM, Wang, Q, Winqvist, R, Wolk, A, Zemel, BS, Zheng, W, van Dijk, KW, Alizadeh, BZ, Bandinelli, S, Boerwinkle, E, Boomsma, DI, Ciullo, M, Chenevix-Trench, G, Cucca, F, Esko, T, Gieger, C, Grant, SFA, Gudnason, V, Hayward, C, Kolčić, I, Kraft, P, Lawlor, DA, Martin, NG, Nøhr, EA, Pedersen, NL, Pennell, CE, Ridker, PM, Robino, A, Snieder, H, Sovio, U, Spector, TD, Stöckl, D, Sudlow, C, Timpson, NJ, Toniolo, D, Uitterlinden, A, Ulivi, S, Völzke, H, Wareham, NJ, Widen, E, Wilson, JF, Lifelines Cohort Study, Danish Blood Donor Study, Ovarian Cancer Association Consortium, Breast Cancer Association Consortium, Biobank Japan Project, China Kadoorie Biobank Collaborative Group, Pharoah, PDP, Li, L, Easton, DF, Njølstad, PR, Sulem, P, Murabito, JM, Murray, A, Manousaki, D, Juul, A, Erikstrup, C, Stefansson, K, Horikoshi, M, Chen, Z, Farooqi, IS, Pitteloud, N, Johansson, S, Day, FR, Perry, JRB, and Ong, KK
Abstract: Pubertal timing varies considerably and is associated with later health outcomes. We performed multi-ancestry genetic analyses on ~800,000 women, identifying 1,080 signals for age at menarche. Collectively, these explained 11% of trait variance in an independent sample. Women at the top and bottom 1% of polygenic risk exhibited ~11 and ~14-fold higher risks of delayed and precocious puberty, respectively. We identified several genes harboring rare loss-of-function variants in ~200,000 women, including variants in ZNF483, which abolished the impact of polygenic risk. Variant-to-gene mapping approaches and mouse gonadotropin-releasing hormone neuron RNA sequencing implicated 665 genes, including an uncharacterized G-protein-coupled receptor, GPR83, which amplified the signaling of MC3R, a key nutritional sensor. Shared signals with menopause timing at genes involved in DNA damage response suggest that the ovarian reserve might signal centrally to trigger puberty. We also highlight body size-dependent and independent mechanisms that potentially link reproductive timing to later life disease.
Published: 2024

2. Clustered Mendelian Randomization analyses identifies distinct and opposing pathways in the causal association between insulin-like growth factor-1 and type 2 diabetes mellitus

Author: Tesfay Eb, van Dijk Kw, Raymond Noordam, Bartke A, van Heemst D, and Wang W
Subjects: Oncology, medicine.medical_specialty, medicine.medical_treatment, Type 2 Diabetes Mellitus, Single-nucleotide polymorphism, Type 2 diabetes, Biology, Lower risk, medicine.disease, Pathogenesis, Insulin-like growth factor, Internal medicine, Relative risk, Mendelian randomization, medicine
Abstract: Aims/hypothesisThere is inconsistent evidence for the causal role of serum insulin-like growth factor-1 (IGF-1) concentration in the pathogenesis of type 2 diabetes. Here, we investigated the association between IGF-1 and type 2 diabetes using a combination of multivariable-adjusted and (clustered) Mendelian Randomization (MR) analyses in the UK Biobank.MethodsWe conducted Cox proportional hazard analyses in 451,232 European-ancestry individuals of the UK Biobank (55.3% women, mean age at recruitment 56.6 years), among which 13,247 individuals developed type 2 diabetes during up to 12 years of follow-up. In addition, we conducted two-sample MR analyses based on independent SNPs associated with IGF-1. Given the heterogeneity between the causal estimates of individual instruments (P-value for Q statistic=4.03e-145), we also conducted clustered MR analyses. Biological pathway analyses of the identified clusters were performed by overrepresentation analyses.ResultsIn the Cox proportional hazard models, with IGF-1 concentrations stratified in quintiles, we observed that participants in the lowest quintile had the highest relative risk of type 2 diabetes (HR: 1.31; CI: 1.23-1.39). In contrast, in the two-sample MR analyses, higher genetically-influenced IGF-1 was associated with a higher risk of type 2 diabetes. Based on the heterogeneous distribution of causal effect estimates, six clusters associated either with a lower or a higher risk of type 2 diabetes were identified. The main clusters in which a higher IGF-1 was associated with a lower risk of type 2 diabetes consisted of instruments mapping to genes in the growth-hormone signaling pathway, whereas the main clusters in which a higher IGF-1 was associated with a higher risk of type 2 diabetes consisted of instruments mapping to genes in pathways related to amino acid metabolism and genomic integrity.ConclusionThe IGF-1 associated SNPs used as genetic instruments in MR analyses showed a heterogeneous distribution of causal effect estimates on the risk of type 2 diabetes. This was likely explained by differences in the underlying molecular pathways that increase IGF-1 concentration and differentially mediate the effects of IGF-1 on type 2 diabetes.
Published: 2021
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3. Genome-wide analysis of 102,084 migraine cases identifies 123 risk loci and subtype-specific risk alleles

Author: Tobias Kurth, Nadine Pelzer, Hautakangas H, Artto, O T Raitakari, Julie E. Buring, Penninx Bwjh., Christian Erikstrup, Mikko Kallela, Matti Pirinen, Christian Benner, Daniel I. Chasman, Kristian Hveem, Kristoffersen Es, Torben Hansen, Olafur A. Sveinsson, Mika Kähönen, van den Maagdenberg Amjm., Mona Ameri Chalmer, Aarno Palotie, Elisabeth Widen, Thorgeir E. Thorgeirsson, Hreinn Stefansson, Lighart L, Paul M. Ridker, de Boer I, Gisela M. Terwindt, Garbrielsen Me, Jouke J. Hottenga, Kogelman Lja., Arpo Aromaa, Bendik S. Winsvold, Dale R. Nyholt, Ikram Ma, van Dijk Kw, Harder Ave, Martin Dichgans, Rainer Malik, Mohsen Ghanbari, Marianne Bakke Johnsen, Marjo-Ritta Jarvelin, Samuli Ripatti, Paavo Häppölä, Gyda Bjornsdottir, Martti Färkkilä, Anne Heidi Skogholt, Terho Lehtimäki, Sanni Ruotsalainen, Frits R. Rosendaal, Gudrun R. Sigurdardottir, Headache Ha, Gormley P, Kari Stefansson, J.A. Zwart, Oluf Pedersen, Raymond Noordam, Ben Michael Brumpton, Laurent F. Thomas, Karina Banasik, M.G. Hrafnsdottir, Lisanne S. Vijfhuizen, Sølvsten Burgdorf K, Jes Olesen, Sigurdur H. Magnusson, T. Freilinger, Kristen Hagen, Andrea Carmine Belin, Dorret I. Boomsma, Henrik Ullum, Maija Wessman, Ran C, and Anna Bjornsdottir
Subjects: Genetics, 0303 health sciences, Aura, Genome wide analysis, Specific risk, Genome-wide association study, Biology, medicine.disease, Migraine with aura, 3. Good health, 03 medical and health sciences, 0302 clinical medicine, Migraine, medicine, medicine.symptom, Allele, Gene, 030217 neurology & neurosurgery, 030304 developmental biology
Abstract: Migraine affects over a billion individuals worldwide but its genetic underpinning remains largely unknown. This genome-wide association study (GWAS) of 102,084 migraine cases and 771,257 controls identified 123 loci of which 86 are novel. The loci provide an opportunity to evaluate shared and distinct genetic components in the two main migraine subtypes: migraine with aura and migraine without aura. A stratification of the risk loci using 29,679 cases with subtype information, of which approximately half have never been used in a GWAS before, indicated three risk variants that appear specific for migraine with aura (in HMOX2, CACNA1A and MPPED2), two that appear specific for migraine without aura (near SPINK2 and near FECH), and nine that increase susceptibility for migraine regardless of subtype. The new risk loci include genes encoding recent migraine-specific drug targets, namely calcitonin gene-related peptide (CALCA/CALCB) and serotonin 1F receptor (HTR1F). Overall, genomic annotations among migraine-associated variants were enriched in both vascular and central nervous system tissue/cell types supporting unequivocally that neurovascular mechanisms underlie migraine pathophysiology.
Published: 2021
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4. Genetic insights into biological mechanisms governing human ovarian ageing

Author: Ruth, KS, Day, FR, Hussain, J, Martinez-Marchal, A, Aiken, CE, Azad, A, Thompson, DJ, Knoblochova, L, Abe, H, Tarry-Adkins, JL, Gonzalez, JM, Fontanillas, P, Claringbould, A, Bakker, OB, Sulem, P, Walters, RG, Terao, C, Turon, S, Horikoshi, M, Lin, K, Onland-Moret, NC, Sankar, A, Hertz, EPT, Timshel, PN, Shukla, V, Borup, R, Olsen, KW, Aguilera, P, Ferrer-Roda, M, Huang, Y, Stankovic, S, Timmers, PRHJ, Ahearn, TU, Alizadeh, BZ, Naderi, E, Andrulis, IL, Arnold, AM, Aronson, KJ, Augustinsson, A, Bandinelli, S, Barbieri, CM, Beaumont, RN, Becher, H, Beckmann, MW, Benonisdottir, S, Bergmann, S, Bochud, M, Boerwinkle, E, Bojesen, SE, Bolla, MK, Boomsma, DI, Bowker, N, Brody, JA, Broer, L, Buring, JE, Campbell, A, Campbell, H, Castelao, JE, Catamo, E, Chanock, SJ, Chenevix-Trench, G, Ciullo, M, Corre, T, Couch, FJ, Cox, A, Crisponi, L, Cross, SS, Cucca, F, Czene, K, Smith, GD, de Geus, EJCN, de Mutsert, R, De Vivo, I, Demerath, EW, Dennis, J, Dunning, AM, Dwek, M, Eriksson, M, Esko, T, Fasching, PA, Faul, JD, Ferrucci, L, Franceschini, N, Frayling, TM, Gago-Dominguez, M, Mezzavilla, M, Garcia-Closas, M, Gieger, C, Giles, GG, Grallert, H, Gudbjartsson, DF, Gudnason, V, Guenel, P, Haiman, CA, Hakansson, N, Hall, P, Hayward, C, He, C, He, W, Heiss, G, Hoffding, MK, Hopper, JL, Hottenga, JJ, Hu, F, Hunter, D, Ikram, MA, Jackson, RD, Joaquim, MDR, John, EM, Joshi, PK, Karasik, D, Kardia, SLR, Kartsonaki, C, Karlsson, R, Kitahara, CM, Kolcic, I, Kooperberg, C, Kraft, P, Kurian, AW, Kutalik, Z, La Bianca, M, LaChance, G, Langenberg, C, Launer, LJ, Laven, JSE, Lawlor, DA, Le Marchand, L, Li, J, Lindblom, A, Lindstrom, S, Lindstrom, T, Linet, M, Liu, Y, Liu, S, Luan, J, Magi, R, Magnusson, PKE, Mangino, M, Mannermaa, A, Marco, B, Marten, J, Martin, NG, Mbarek, H, McKnight, B, Medland, SE, Meisinger, C, Meitinger, T, Menni, C, Metspalu, A, Milani, L, Milne, RL, Montgomery, GW, Mook-Kanamori, DO, Mulas, A, Mulligan, AM, Murray, A, Nalls, MA, Newman, A, Noordam, R, Nutile, T, Nyholt, DR, Olshan, AF, Olsson, H, Painter, JN, Patel, AV, Pedersen, NL, Perjakova, N, Peters, A, Peters, U, Pharoah, PDP, Polasek, O, Porcu, E, Psaty, BM, Rahman, I, Rennert, G, Rennert, HS, Ridker, PM, Ring, SM, Robino, A, Rose, LM, Rosendaal, FR, Rossouw, J, Rudan, I, Rueedi, R, Ruggiero, D, Sala, CF, Saloustros, E, Sandler, DP, Sanna, S, Sawyer, EJ, Sarnowski, C, Schlessinger, D, Schmidt, MK, Schoemaker, MJ, Schraut, KE, Scott, C, Shekari, S, Shrikhande, A, Smith, AV, Smith, BH, Smith, JA, Sorice, R, Southey, MC, Spector, TD, Spinelli, JJ, Stampfer, M, Stoeckl, D, van Meurs, JBJ, Strauch, K, Styrkarsdottir, U, Swerdlow, AJ, Tanaka, T, Teras, LR, Teumer, A, thorsteinsdottir, U, Timpson, NJ, Toniolo, D, Traglia, M, Troester, MA, Truong, T, Tyrrell, J, Uitterlinden, AG, Ulivi, S, Vachon, CM, Vitart, V, Voelker, U, Vollenweider, P, Voelzke, H, Wang, Q, Wareham, NJ, Weinberg, CR, Weir, DR, Wilcox, AN, van Dijk, KW, Willemsen, G, Wilson, JF, Wolffenbuttel, BHR, Wolk, A, Wood, AR, Zhao, W, Zygmunt, M, Chen, Z, Li, L, Franke, L, Burgess, S, Deelen, P, Pers, TH, Grondahl, ML, Andersen, CY, Pujol, A, Lopez-Contreras, AJ, Daniel, JA, Stefansson, K, Chang-Claude, J, van der Schouw, YT, Lunetta, KL, Chasman, DI, Easton, DF, Visser, JA, Ozanne, SE, Namekawa, SH, Solc, P, Murabito, JM, Ong, KK, Hoffmann, ER, Roig, I, Perry, JRB, Ruth, KS, Day, FR, Hussain, J, Martinez-Marchal, A, Aiken, CE, Azad, A, Thompson, DJ, Knoblochova, L, Abe, H, Tarry-Adkins, JL, Gonzalez, JM, Fontanillas, P, Claringbould, A, Bakker, OB, Sulem, P, Walters, RG, Terao, C, Turon, S, Horikoshi, M, Lin, K, Onland-Moret, NC, Sankar, A, Hertz, EPT, Timshel, PN, Shukla, V, Borup, R, Olsen, KW, Aguilera, P, Ferrer-Roda, M, Huang, Y, Stankovic, S, Timmers, PRHJ, Ahearn, TU, Alizadeh, BZ, Naderi, E, Andrulis, IL, Arnold, AM, Aronson, KJ, Augustinsson, A, Bandinelli, S, Barbieri, CM, Beaumont, RN, Becher, H, Beckmann, MW, Benonisdottir, S, Bergmann, S, Bochud, M, Boerwinkle, E, Bojesen, SE, Bolla, MK, Boomsma, DI, Bowker, N, Brody, JA, Broer, L, Buring, JE, Campbell, A, Campbell, H, Castelao, JE, Catamo, E, Chanock, SJ, Chenevix-Trench, G, Ciullo, M, Corre, T, Couch, FJ, Cox, A, Crisponi, L, Cross, SS, Cucca, F, Czene, K, Smith, GD, de Geus, EJCN, de Mutsert, R, De Vivo, I, Demerath, EW, Dennis, J, Dunning, AM, Dwek, M, Eriksson, M, Esko, T, Fasching, PA, Faul, JD, Ferrucci, L, Franceschini, N, Frayling, TM, Gago-Dominguez, M, Mezzavilla, M, Garcia-Closas, M, Gieger, C, Giles, GG, Grallert, H, Gudbjartsson, DF, Gudnason, V, Guenel, P, Haiman, CA, Hakansson, N, Hall, P, Hayward, C, He, C, He, W, Heiss, G, Hoffding, MK, Hopper, JL, Hottenga, JJ, Hu, F, Hunter, D, Ikram, MA, Jackson, RD, Joaquim, MDR, John, EM, Joshi, PK, Karasik, D, Kardia, SLR, Kartsonaki, C, Karlsson, R, Kitahara, CM, Kolcic, I, Kooperberg, C, Kraft, P, Kurian, AW, Kutalik, Z, La Bianca, M, LaChance, G, Langenberg, C, Launer, LJ, Laven, JSE, Lawlor, DA, Le Marchand, L, Li, J, Lindblom, A, Lindstrom, S, Lindstrom, T, Linet, M, Liu, Y, Liu, S, Luan, J, Magi, R, Magnusson, PKE, Mangino, M, Mannermaa, A, Marco, B, Marten, J, Martin, NG, Mbarek, H, McKnight, B, Medland, SE, Meisinger, C, Meitinger, T, Menni, C, Metspalu, A, Milani, L, Milne, RL, Montgomery, GW, Mook-Kanamori, DO, Mulas, A, Mulligan, AM, Murray, A, Nalls, MA, Newman, A, Noordam, R, Nutile, T, Nyholt, DR, Olshan, AF, Olsson, H, Painter, JN, Patel, AV, Pedersen, NL, Perjakova, N, Peters, A, Peters, U, Pharoah, PDP, Polasek, O, Porcu, E, Psaty, BM, Rahman, I, Rennert, G, Rennert, HS, Ridker, PM, Ring, SM, Robino, A, Rose, LM, Rosendaal, FR, Rossouw, J, Rudan, I, Rueedi, R, Ruggiero, D, Sala, CF, Saloustros, E, Sandler, DP, Sanna, S, Sawyer, EJ, Sarnowski, C, Schlessinger, D, Schmidt, MK, Schoemaker, MJ, Schraut, KE, Scott, C, Shekari, S, Shrikhande, A, Smith, AV, Smith, BH, Smith, JA, Sorice, R, Southey, MC, Spector, TD, Spinelli, JJ, Stampfer, M, Stoeckl, D, van Meurs, JBJ, Strauch, K, Styrkarsdottir, U, Swerdlow, AJ, Tanaka, T, Teras, LR, Teumer, A, thorsteinsdottir, U, Timpson, NJ, Toniolo, D, Traglia, M, Troester, MA, Truong, T, Tyrrell, J, Uitterlinden, AG, Ulivi, S, Vachon, CM, Vitart, V, Voelker, U, Vollenweider, P, Voelzke, H, Wang, Q, Wareham, NJ, Weinberg, CR, Weir, DR, Wilcox, AN, van Dijk, KW, Willemsen, G, Wilson, JF, Wolffenbuttel, BHR, Wolk, A, Wood, AR, Zhao, W, Zygmunt, M, Chen, Z, Li, L, Franke, L, Burgess, S, Deelen, P, Pers, TH, Grondahl, ML, Andersen, CY, Pujol, A, Lopez-Contreras, AJ, Daniel, JA, Stefansson, K, Chang-Claude, J, van der Schouw, YT, Lunetta, KL, Chasman, DI, Easton, DF, Visser, JA, Ozanne, SE, Namekawa, SH, Solc, P, Murabito, JM, Ong, KK, Hoffmann, ER, Roig, I, and Perry, JRB
Abstract: Reproductive longevity is essential for fertility and influences healthy ageing in women1,2, but insights into its underlying biological mechanisms and treatments to preserve it are limited. Here we identify 290 genetic determinants of ovarian ageing, assessed using normal variation in age at natural menopause (ANM) in about 200,000 women of European ancestry. These common alleles were associated with clinical extremes of ANM; women in the top 1% of genetic susceptibility have an equivalent risk of premature ovarian insufficiency to those carrying monogenic FMR1 premutations3. The identified loci implicate a broad range of DNA damage response (DDR) processes and include loss-of-function variants in key DDR-associated genes. Integration with experimental models demonstrates that these DDR processes act across the life-course to shape the ovarian reserve and its rate of depletion. Furthermore, we demonstrate that experimental manipulation of DDR pathways highlighted by human genetics increases fertility and extends reproductive life in mice. Causal inference analyses using the identified genetic variants indicate that extending reproductive life in women improves bone health and reduces risk of type 2 diabetes, but increases the risk of hormone-sensitive cancers. These findings provide insight into the mechanisms that govern ovarian ageing, when they act, and how they might be targeted by therapeutic approaches to extend fertility and prevent disease.
Published: 2021

5. Investigating the relationships between unfavourable habitual sleep and metabolomic traits: evidence from multi-cohort multivariable regression and Mendelian randomization analyses

Author: Bos, Maxime, Goulding, NJ, Lee, MA, Hofman, Bert, Bot, M, Pool, R, Vijfhuizen, LS, Zhang, K, Li, CH, Mustafa, R, Neville, MJ, Li-Gao, R, Trompet, S, Beekman, M, Biermasz, NR, Boomsma, DI, Boer, I, Christodoulides, C, Dehghan, A, van Dijk, KW, Ford, I, Ghanbari, Mohsen, Heijmans, BT, Ikram, Arfan, Jukema, JW, Mook, Dennis, Karpe, F, Luik, Annemarie, Lumey, LH, van den Maagdenberg, A, Mooijaart, SP, de Mutsert, R, Penninx, B, Rensen, PCN, Richmond, Rebecca, Rosendaal, FR, Sattar, N, Schoevers, RA, Slagboom, PE, Terwindt, GM, Thesing, CS, Wade, KH, Wijsman, CA, Willemsen, G, Zwinderman, AH, van Heemst, D, Noordam, R, Lawlor, DA, Bos, Maxime, Goulding, NJ, Lee, MA, Hofman, Bert, Bot, M, Pool, R, Vijfhuizen, LS, Zhang, K, Li, CH, Mustafa, R, Neville, MJ, Li-Gao, R, Trompet, S, Beekman, M, Biermasz, NR, Boomsma, DI, Boer, I, Christodoulides, C, Dehghan, A, van Dijk, KW, Ford, I, Ghanbari, Mohsen, Heijmans, BT, Ikram, Arfan, Jukema, JW, Mook, Dennis, Karpe, F, Luik, Annemarie, Lumey, LH, van den Maagdenberg, A, Mooijaart, SP, de Mutsert, R, Penninx, B, Rensen, PCN, Richmond, Rebecca, Rosendaal, FR, Sattar, N, Schoevers, RA, Slagboom, PE, Terwindt, GM, Thesing, CS, Wade, KH, Wijsman, CA, Willemsen, G, Zwinderman, AH, van Heemst, D, Noordam, R, and Lawlor, DA
Abstract: Background: Sleep traits are associated with cardiometabolic disease risk, with evidence from Mendelian randomization (MR) suggesting that insomnia symptoms and shorter sleep duration increase coronary artery disease risk. We combined adjusted multivariable regression (AMV) and MR analyses of phenotypes of unfavourable sleep on 113 metabolomic traits to investigate possible biochemical mechanisms linking sleep to cardiovascular disease. Methods: We used AMV (N = 17,368) combined with two-sample MR (N = 38,618) to examine effects of self-reported insomnia symptoms, total habitual sleep duration, and chronotype on 113 metabolomic traits. The AMV analyses were conducted on data from 10 cohorts of mostly Europeans, adjusted for age, sex, and body mass index. For the MR analyses, we used summary results from published European-ancestry genome-wide association studies of self-reported sleep traits and of nuclear magnetic resonance (NMR) serum metabolites. We used the inverse-variance weighted (IVW) method and complemented this with sensitivity analyses to assess MR assumptions. Results: We found consistent evidence from AMV and MR analyses for associations of usual vs. sometimes/rare/never insomnia symptoms with lower citrate (− 0.08 standard deviation (SD)[95% confidence interval (CI) − 0.12, − 0.03] in AMV and − 0.03SD [− 0.07, − 0.003] in MR), higher glycoprotein acetyls (0.08SD [95% CI 0.03, 0.12] in AMV and 0.06SD [0.03, 0.10) in MR]), lower total very large HDL particles (− 0.04SD [− 0.08, 0.00] in AMV and − 0.05SD [− 0.09, − 0.02] in MR), and lower phospholipids in very large HDL particles (− 0.04SD [− 0.08, 0.002] in AMV and − 0.05SD [− 0.08, − 0.02] in MR). Longer total sleep duration associated with higher creatinine concentrations using both methods (0.02SD per 1 h [0.01, 0.03] in AMV and 0.15SD [0.02, 0.29] in MR) and with isoleucine in MR analyses (0.22SD [0.08, 0.35]). No consistent evidence was observed for effects of chronotype on metabolomic measures.
Published: 2021

6. Genomic analyses for age at menarche identify 389 independent signals and indicate BMI-independent effects of puberty timing on cancer susceptibility

Author: Nora Franceschini, Rico Rueedi, Gerardo Heiss, Behrooz Z. Alizadeh, Marike Gabrielson, Henry Völzke, Daniela Ruggiero, Judith S. Brand, Stig E. Bojesen, Iffat Rahman, Fergus J. Couch, Patrick Sulem, Harold Snieder, Jenny Chang-Claude, Caterina Barbieri, Ute Hamann, Hinds D, Pascal Guénel, Amanda B. Spurdle, Paul M. Ridker, Ulla Sovio, Roger L. Milne, Hamdi Mbarek, H Brenner, Hilary K. Finucane, Maristella Steri, Lude Franke, Emmi Tikkanen, Ivana Kolcic, Vitart, Ken K. Ong, Alison M. Dunning, Aarno Palotie, Caroline Hayward, Jouke J. Hottenga, Abhishek Sarkar, Stefania Lenarduzzi, Nicholas G. Martin, Katharina E. Schraut, Eva Albrecht, Hiltrud Brauch, Lisette Stolk, Joop S.E. Laven, Penelope A. Lind, Ilaria Gandin, Patrik K. E. Magnusson, Ellen A. Nohr, Tanguy Corre, Jing Hua Zhao, N J Timpson, Jenny A. Visser, Harald Grallert, P. A. Fasching, Susan M. Ring, Stöckl D, Grant W. Montgomery, Marzyeh Amini, Velez Edwards Dr, Thomas Meitinger, Qinghua Wang, David Karasik, Daniel I. Chasman, Nicholas J. Wareham, Alexander Teumer, Mellissa C. Southey, Kathryn L. Lunetta, S. E. Medland, Dieter Flesch-Janys, Maartje J. Hooning, Lili Milani, D Lambrechts, Ozren Polasek, Po-Ru Loh, James F. Wilson, Campbell A, Julian Peto, Ellen W. Demerath, Christian Gieger, de Geus Ej, Cox A, Javier Benítez, Mitul Shah, Eric Boerwinkle, Matthias W. Beckmann, Thorsteindottir U, Julie E. Buring, De Vivo I, Hannes Helgason, Paolo Radice, Tracy A. O'Mara, L. J. Launer, D. F. Gudbjartsson, Frits R. Rosendaal, C.A. Hartman, Stefania Bandinelli, Felix R. Day, Lynda M. Rose, van Dijk Kw, Natalia Perjakova, Anneli Pouta, Igor Rudan, Sven Bergmann, Kamila Czene, Georgia Chenevix-Trench, Pau Navarro, Sean Whalen, Heli Nevanlinna, Teresa Nutile, Diana L. Cousminer, Albert V. Smith, Massimo Mangino, Uwe Völker, Michela Traglia, Lindsay Fernández-Rhodes, Ayush Giri, Linda Broer, Albertine J. Oldehinkel, Isabel dos-Santos-Silva, Peter Vollenweider, Jian'an Luan, Nancy L. Pedersen, Irene L. Andrulis, Reedik Mägi, Robert Winqvist, Gonneke Willemsen, John L. Hopper, Gudnason, Marjanka K. Schmidt, David G. Hunter, Robert A. Scott, T.B. Harris, Joanne M. Murabito, David J. Porteous, Harry Campbell, Eleonora Porcu, D.I. Boomsma, Thibaud Boutin, M. A. Ikram, Doug Easton, Magdalena Zoledziewska, Meir J. Stampfer, Katherine S. Pollard, Eulalia Catamo, Tõnu Esko, M-R Jarvelin, Laura Crisponi, Claudia Langenberg, Marek Zygmunt, Antonietta Robino, Emily Hallberg, Manjeet K. Bolla, Ruth Ks, Bruce H W Wolffenbuttel, Lavinia Paternoster, Tyrer Jp, P. Kraft, George Davey-Smith, Robert Karlsson, Graham G. Giles, Jingmei Li, Pharoah Pd, Segrè Av, Marina Ciullo, Perry, Brumat Marco, Peter K. Joshi, Chunyan He, Sara Lindström, Joe Dennis, Thérèse Truong, Yongmei Liu, Anna Marie Mulligan, Mike A. Nalls, Cinzia Sala, K. Stefansson, Murray A, Debbie A Lawlor, Tung Jy, Deborah J. Thompson, Dennis O. Mook-Kanamori, Daniela Toniolo, Luigi Ferrucci, Peter Devilee, S. Chanock, Cristina Menni, George McMahon, Murielle Bochud, A. Metspalu, Tomohiro Tanaka, E. Widen, Hae Kyung Im, Dale R. Nyholt, Ilja M. Nolte, Thomas Brüning, Christina Meisinger, Annette Peters, Kyriaki Michailidou, Per Hall, Rossella Sorice, Genevieve Lachance, Johan G. Eriksson, Francesco Cucca, A.G. Uitterlinden, Z. Kutalik, Mark I. McCarthy, Frank B. Hu, Konstantin Strauch, Tim D. Spector, Elisabeth Altmaier, S. Ulivi, Alkes L. Price, Arto Mannermaa, Raymond Noordam, and de Mutsert R
Subjects: Genetics, 0303 health sciences, 03 medical and health sciences, 0302 clinical medicine, 030220 oncology & carcinogenesis, Genotype, Menarche, Trait, Cancer susceptibility, Genomics, Biology, 030304 developmental biology
Abstract: The timing of puberty is a highly polygenic childhood trait that is epidemiologically associated with various adult diseases. Here, we analyse 1000-Genome reference panel imputed genotype data on up to ~370,000 women and identify 389 independent signals (all P−8) for age at menarche, a notable milestone in female pubertal development. In Icelandic data from deCODE, these signals explain ~7.4% of the population variance in age at menarche, corresponding to one quarter of the estimated heritability. We implicate over 250 genes via coding variation or associated gene expression, and demonstrate enrichment across genes active in neural tissues. We identify multiple rare variants near the imprinted genes MKRN3 and DLK1 that exhibit large effects on menarche only when paternally inherited. Disproportionate effects of variants on early or late puberty timing are observed: single variant and heritability estimates are larger for early than late puberty timing in females. The opposite pattern is seen in males, with larger estimates for late than early puberty timing. Mendelian randomization analyses indicate causal inverse associations, independent of BMI, between puberty timing and risks for breast and endometrial cancers in women, and prostate cancer in men. In aggregate, our findings reveal new complexity in the genetic regulation of puberty timing and support new causal links with adult cancer risks.
Published: 2016
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7. Pleiotropy among common genetic loci identified for cardiometabolic disorders and C-reactive protein

Author: Ligthart, S, De Vries, PS, Uitterlinden, AG, Hofman, A, Franco, OH, Chasman, DI, Dehghan, A, Dupuis, J, Barbalic, M, Bis, JC, Eiriksdottir, G, Lu, C, Pellikka, N, Wallaschofski, H, Kettunen, J, Henneman, P, Baumert, J, Strachan, DP, Fuchsberger, C, Vitart, V, Wilson, JF, Paré, G, Naitza, S, Rudock, ME, Surakka, I, De Geus, EJC, Alizadeh, BZ, Guralnik, JMD, Shuldiner, A, Tanaka, T, Zee, RYL, Schnabel, RB, Nambi, V, Kavousi, M, Ripatti, S, Nauck, M, Smith, NL, Smith, AV, Sundvall, J, Scheet, P, Liu, Y, Ruokonen, A, Rose, LM, Larson, MG, Hoogeveen, RC, Freimer, NB, Teumer, A, Tracy, RP, Launer, LJ, Buring, JE, Yamamoto, JF, Folsom, AR, Sijbrands, EJG, Pankow, J, Elliott, P, Keaney, JF, Sun, W, Sarin, AP, Fontes, JD, Badola, S, Astor, BC, Pouta, A, Werda, K, Greiser, KH, Kuss, O, Schwabedissen, HEMZ, Thiery, J, Jamshidi, Y, Nolte, IM, Soranzo, N, Spector, TD, Völzke, H, Parker, AN, Aspelund, T, Bates, D, Young, L, Tsui, K, Siscovick, DS, Guo, X, Rotter, JI, Uda, M, Schlessinger, D, Rudan, I, Hicks, AA, Penninx, BW, Thorand, B, Gieger, C, Coresh, J, Willemsen, G, Harris, TB, Järvelin, MR, Rice, K, Radke, D, Salomaa, V, Van Dijk, KW, Boerwinkle, E, Vasan, RS, Ferrucci, L, and Gibson, QD
Abstract: © 2015 Ligthart et al. Pleiotropic genetic variants have independent effects on different phenotypes. C-reactive protein (CRP) is associated with several cardiometabolic phenotypes. Shared genetic backgrounds may partially underlie these associations. We conducted a genome-wide analysis to identify the shared genetic background of inflammation and cardiometabolic phenotypes using published genome-wide association studies (GWAS). We also evaluated whether the pleiotropic effects of such loci were biological or mediated in nature. First, we examined whether 283 common variants identified for 10 cardiometabolic phenotypes in GWAS are associated with CRP level. Second, we tested whether 18 variants identified for serum CRP are associated with 10 cardiometabolic phenotypes. We used a Bonferroni corrected p-value of 1.1×10-04 (0.05/463) as a threshold of significance. We evaluated the independent pleiotropic effect on both phenotypes using individual level data from the Women Genome Health Study. Evaluating the genetic overlap between inflammation and cardiometabolic phenotypes, we found 13 pleiotropic regions. Additional analyses showed that 6 regions (APOC1, HNF1A, IL6R, PPP1R3B, HNF4A and IL1F10) appeared to have a pleiotropic effect on CRP independent of the effects on the cardiometabolic phenotypes. These included loci where individuals carrying the risk allele for CRP encounter higher lipid levels and risk of type 2 diabetes. In addition, 5 regions (GCKR, PABPC4, BCL7B, FTO and TMEM18) had an effect on CRP largely mediated through the cardiometabolic phenotypes. In conclusion, our results show genetic pleiotropy among inflammation and cardiometabolic phenotypes. In addition to reverse causation, our data suggests that pleiotropic genetic variants partially underlie the association between CRP and cardiometabolic phenotypes.
Published: 2015
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8. Metabolomics based markers predict type 2 diabetes in a 14-year follow-up study

Author: Liu, Jun, Semiz, S, van der Lee, Sven, van der Spek, Ashley, Verhoeven, Adrie, van Klinken, JB, Sijbrands, E.J.G., Harms, AC, Hankemeier, T, van Dijk, KW, Duijn, Cornelia, Demirkan, Ayse, Liu, Jun, Semiz, S, van der Lee, Sven, van der Spek, Ashley, Verhoeven, Adrie, van Klinken, JB, Sijbrands, E.J.G., Harms, AC, Hankemeier, T, van Dijk, KW, Duijn, Cornelia, and Demirkan, Ayse
Published: 2017

9. Genome-wide meta-analysis uncovers novel loci influencing circulating leptin levels

Author: Kilpelainen, TO, Carli, J F M, Skowronski, A A, Sun, Q, Kriebel, J, Feitosa, MF, Hedman, AK, Drong, AW, Hayes, J E, Zhao, JH, Pers, TH, Schick, U, Grarup, N, Kutalik, Z, Trompet, S, Mangino, M, Kristiansson, K, Beekman, M, Lyytikainen, LP, Eriksson, J, Henneman, Peter, Lahti, J, Tanaka, T, Luan, JA, Del Greco, F, Pasko, D, Renstrom, F, Willems, SM, Mahajan, A, Rose, LM, Guo, XQ, Liu, YM, Kleber, ME, Perusse, L, Gaunt, T, Ahluwalia, TS, Sung, YJ, Ramos, YF, Amin, Najaf, Amuzu, A, Barroso, I, Bellis, C, Blangero, J, Buckley, BM, Bohringer, S, Chen, YDI, de Craen, A J N, Crosslin, DR, Dale, CE, Dastani, Z, Day, FR, Deelen, J, Delgado, GE, Demirkan, Ayse, Finucane, FM, Ford, I, Garcia, ME, Gieger, C, Gustafsson, S, Hallmans, G, Hankinson, SE, Havulinna, AS, Herder, Cindy, Hernandez, D, Hicks, AA, Hunter, DJ, Illig, T, Ingelsson, E, Ioan-Facsinay, A, Jansson, JO, Jenny, NS, Jorgensen, ME, Jorgensen, T, Karlsson, M, Koenig, W, Kraft, P, Kwekkeboom, J, Laatikainen, T, Ladwig, KH, LeDuc, C A, Lowe, G, Lu, YC, Marques-Vidal, P, Meisinger, C, Menni, C, Morris, AP, Myers, RH, Mannisto, S, Nalls, MA, Paternoster, L, Peters, A, Pradhan, A D, Rankinen, T, Rasmussen-Torvik, LJ, Rathmann, W, Rice, TK, Richards, JB, Ridker, PM, Sattar, N, Savage, DB, Soderberg, S, Timpson, NJ, Vandenput, L, van Heemst, D, Uh, HW, Vohl, MC, Walker, M, Wichmann, HE, Widen, E, Wood, AR, Yao, J, Zeller, T, Zhang, YY, Meulenbelt, I, Kloppenburg, M, Astrup, A, Sorensen, TIA, Sarzynski, MA, Rao, DC, Jousilahti, P, Vartiainen, E, Hofman, Bert, Rivadeneira, Fernando, Uitterlinden, André, Kajantie, E, Osmond, C, Palotie, A, Eriksson, JG, Heliovaara, M, Knekt, PB, Koskinen, S, Jula, A, Perola, M, Huupponen, R K, Viikari, JS, Kahonen, M, Lehtimaki, T, Raitakari, OT, Mellstrom, D, Lorentzon, M, Casas, JP, Bandinelli, S, Marz, W, Isaacs, Aaron, van Dijk, KW, Duijn, Cornelia, Harris, TB, Bouchard, C, Allison, MA, Chasman, DI, Ohlsson, C, Lind, L, Scott, RA, Langenberg, C, Wareham, NJ, Ferrucci, L, Frayling, TM, Pramstaller, PP, Borecki, IB, Waterworth, DM, Bergmann, S, Waeber, G, Vollenweider, P, Vestergaard, H, Hansen, T, Pedersen, O, Hu, FB, Slagboom, PE (Eline), Grallert, H, Spector, TD, Jukema, JW, Klein, RJ, Schadt, EE, Franks, PW, Lindgren, CM, Leibel, R L, Loos, RJF, Kilpelainen, TO, Carli, J F M, Skowronski, A A, Sun, Q, Kriebel, J, Feitosa, MF, Hedman, AK, Drong, AW, Hayes, J E, Zhao, JH, Pers, TH, Schick, U, Grarup, N, Kutalik, Z, Trompet, S, Mangino, M, Kristiansson, K, Beekman, M, Lyytikainen, LP, Eriksson, J, Henneman, Peter, Lahti, J, Tanaka, T, Luan, JA, Del Greco, F, Pasko, D, Renstrom, F, Willems, SM, Mahajan, A, Rose, LM, Guo, XQ, Liu, YM, Kleber, ME, Perusse, L, Gaunt, T, Ahluwalia, TS, Sung, YJ, Ramos, YF, Amin, Najaf, Amuzu, A, Barroso, I, Bellis, C, Blangero, J, Buckley, BM, Bohringer, S, Chen, YDI, de Craen, A J N, Crosslin, DR, Dale, CE, Dastani, Z, Day, FR, Deelen, J, Delgado, GE, Demirkan, Ayse, Finucane, FM, Ford, I, Garcia, ME, Gieger, C, Gustafsson, S, Hallmans, G, Hankinson, SE, Havulinna, AS, Herder, Cindy, Hernandez, D, Hicks, AA, Hunter, DJ, Illig, T, Ingelsson, E, Ioan-Facsinay, A, Jansson, JO, Jenny, NS, Jorgensen, ME, Jorgensen, T, Karlsson, M, Koenig, W, Kraft, P, Kwekkeboom, J, Laatikainen, T, Ladwig, KH, LeDuc, C A, Lowe, G, Lu, YC, Marques-Vidal, P, Meisinger, C, Menni, C, Morris, AP, Myers, RH, Mannisto, S, Nalls, MA, Paternoster, L, Peters, A, Pradhan, A D, Rankinen, T, Rasmussen-Torvik, LJ, Rathmann, W, Rice, TK, Richards, JB, Ridker, PM, Sattar, N, Savage, DB, Soderberg, S, Timpson, NJ, Vandenput, L, van Heemst, D, Uh, HW, Vohl, MC, Walker, M, Wichmann, HE, Widen, E, Wood, AR, Yao, J, Zeller, T, Zhang, YY, Meulenbelt, I, Kloppenburg, M, Astrup, A, Sorensen, TIA, Sarzynski, MA, Rao, DC, Jousilahti, P, Vartiainen, E, Hofman, Bert, Rivadeneira, Fernando, Uitterlinden, André, Kajantie, E, Osmond, C, Palotie, A, Eriksson, JG, Heliovaara, M, Knekt, PB, Koskinen, S, Jula, A, Perola, M, Huupponen, R K, Viikari, JS, Kahonen, M, Lehtimaki, T, Raitakari, OT, Mellstrom, D, Lorentzon, M, Casas, JP, Bandinelli, S, Marz, W, Isaacs, Aaron, van Dijk, KW, Duijn, Cornelia, Harris, TB, Bouchard, C, Allison, MA, Chasman, DI, Ohlsson, C, Lind, L, Scott, RA, Langenberg, C, Wareham, NJ, Ferrucci, L, Frayling, TM, Pramstaller, PP, Borecki, IB, Waterworth, DM, Bergmann, S, Waeber, G, Vollenweider, P, Vestergaard, H, Hansen, T, Pedersen, O, Hu, FB, Slagboom, PE (Eline), Grallert, H, Spector, TD, Jukema, JW, Klein, RJ, Schadt, EE, Franks, PW, Lindgren, CM, Leibel, R L, and Loos, RJF
Published: 2016

10. Meta-analysis of 49 549 individuals imputed with the 1000 Genomes Project reveals an exonic damaging variant in ANGPTL4 determining fasting TG levels

Author: Leeuwen, Elisa, Sabo, A, Bis, JC, Huffman, JE, Manichaikul, A, Smith, AV, Feitosa, MF, Demissie, S, Joshi, PK, Duan, Q, Marten, J, van Klinken, JB, Surakka, I, Nolte, IM, Zhang, WH, Mbarek, H, Li-Gao, RF, Trompet, S, Verweij, N, Evangelou, E, Lyytikainen, LP, Tayo, BO, Deelen, J, van der Most, PJ, van der Laan, SW, Arking, DE, Morrison, A, Dehghan, Abbas, Franco Duran, OH, Hofman, Bert, Rivadeneira, Fernando, Sijbrands, E.J.G., Uitterlinden, André, Mychaleckyj, JC, Campbell, A (Archie), Hocking, LJ, Padmanabhan, S, Brody, JA, Rice, KM, White, CC, Harris, T, Isaacs, Aaron, Campbell, H, Lange, LA, Rudan, I, Kolcic, I, Navarro, P, Zemunik, T, Salomaa, V, Kooner, AS, Kooner, JS, Lehne, B, Scott, WR, Tan, ST, de Geus, EJ, Milaneschi, Y, Penninx, BWJH, Willemsen, G, de Mutsert, R, Ford, I, Gansevoort, RT, Segura-Lepe, M P, Raitakari, OT, Viikari, JS, Nikus, K, Forrester, T, McKenzie, CA, de Craen, AJM, den Ruijter, HM, Pasterkamp, G, Snieder, H, Oldehinkel, AJ, Slagboom, PE (Eline), Cooper, RS, Kahonen, M, Lehtimaki, T, Elliott, P, van der Harst, P, Jukema, JW, Mook-Kanamori, DO, Boomsma, DI, Chambers, JC, Swertz, M, Ripatti, S, van Dijk, KW, Vitart, V, Polasek, O, Hayward, C, Wilson, JG, Wilson, JF, Gudnason, V, Rich, SS, Psaty, BM, Borecki, IB, Boerwinkle, E, Rotter, JI, Cupples, LA, Duijn, Cornelia, Leeuwen, Elisa, Sabo, A, Bis, JC, Huffman, JE, Manichaikul, A, Smith, AV, Feitosa, MF, Demissie, S, Joshi, PK, Duan, Q, Marten, J, van Klinken, JB, Surakka, I, Nolte, IM, Zhang, WH, Mbarek, H, Li-Gao, RF, Trompet, S, Verweij, N, Evangelou, E, Lyytikainen, LP, Tayo, BO, Deelen, J, van der Most, PJ, van der Laan, SW, Arking, DE, Morrison, A, Dehghan, Abbas, Franco Duran, OH, Hofman, Bert, Rivadeneira, Fernando, Sijbrands, E.J.G., Uitterlinden, André, Mychaleckyj, JC, Campbell, A (Archie), Hocking, LJ, Padmanabhan, S, Brody, JA, Rice, KM, White, CC, Harris, T, Isaacs, Aaron, Campbell, H, Lange, LA, Rudan, I, Kolcic, I, Navarro, P, Zemunik, T, Salomaa, V, Kooner, AS, Kooner, JS, Lehne, B, Scott, WR, Tan, ST, de Geus, EJ, Milaneschi, Y, Penninx, BWJH, Willemsen, G, de Mutsert, R, Ford, I, Gansevoort, RT, Segura-Lepe, M P, Raitakari, OT, Viikari, JS, Nikus, K, Forrester, T, McKenzie, CA, de Craen, AJM, den Ruijter, HM, Pasterkamp, G, Snieder, H, Oldehinkel, AJ, Slagboom, PE (Eline), Cooper, RS, Kahonen, M, Lehtimaki, T, Elliott, P, van der Harst, P, Jukema, JW, Mook-Kanamori, DO, Boomsma, DI, Chambers, JC, Swertz, M, Ripatti, S, van Dijk, KW, Vitart, V, Polasek, O, Hayward, C, Wilson, JG, Wilson, JF, Gudnason, V, Rich, SS, Psaty, BM, Borecki, IB, Boerwinkle, E, Rotter, JI, Cupples, LA, and Duijn, Cornelia
Published: 2016

11. Genome-wide association study identifies six new loci influencing pulse pressure and mean arterial pressure

Author: Wain, LV, Verwoert, GC, O'Reilly, PF, Shi, G, Johnson, T, Johnson, AD, Bochud, M, Rice, KM, Henneman, P, Smith, AV, Ehret, GB, Amin, N, Larson, MG, Mooser, V, Hadley, D, Dörr, M, Bis, JC, Aspelund, T, Esko, T, Janssens, AC, Zhao, JH, Heath, S, Laan, M, Fu, J, Pistis, G, Luan, J, Arora, P, Lucas, G, Pirastu, N, Pichler, I, Jackson, AU, Webster, RJ, Zhang, F, Peden, JF, Schmidt, H, Tanaka, T, Campbell, H, Igl, W, Milaneschi, Y, Hottenga, JJ, Vitart, V, Chasman, DI, Trompet, S, Bragg-Gresham, JL, Alizadeh, BZ, Chambers, JC, Guo, X, Lehtimäki, T, Kühnel, B, Lopez, LM, Polašek, O, Boban, M, Nelson, CP, Morrison, AC, Pihur, V, Ganesh, SK, Hofman, A, Kundu, S, Mattace-Raso, FU, Rivadeneira, F, Sijbrands, EJ, Uitterlinden, AG, Hwang, SJ, Vasan, RS, Wang, TJ, Bergmann, S, Vollenweider, P, Waeber, G, Laitinen, J, Pouta, A, Zitting, P, McArdle, WL, Kroemer, HK, Völker, U, Völzke, H, Glazer, NL, Taylor, KD, Harris, TB, Alavere, H, Haller, T, Keis, A, Tammesoo, ML, Aulchenko, Y, Barroso, I, Khaw, KT, Galan, P, Hercberg, S, Lathrop, M, Eyheramendy, S, Org, E, Sõber, S, Lu, X, Nolte, IM, Penninx, BW, Corre, T, Masciullo, C, Sala, C, Groop, L, Voight, BF, Melander, O, O'Donnell, CJ, Salomaa, V, d'Adamo, AP, Fabretto, A, Faletra, F, Ulivi, S, Del Greco, F, Facheris, M, Collins, FS, Bergman, RN, Beilby, JP, Hung, J, Musk, AW, Mangino, M, Shin, SY, Soranzo, N, Watkins, H, Goel, A, Hamsten, A, Gider, P, Loitfelder, M, Zeginigg, M, Hernandez, D, Najjar, SS, Navarro, P, Wild, SH, Corsi, AM, Singleton, A, de Geus, EJ, Willemsen, G, Parker, AN, Rose, LM, Buckley, B, Stott, D, Orru, M, Uda, M, LifeLines Cohort Study, van der Klauw, MM, Zhang, W, Li, X, Scott, J, Chen, YD, Burke, GL, Kähönen, M, Viikari, J, Döring, A, Meitinger, T, Davies, G, Starr, JM, Emilsson, V, Plump, A, Lindeman, JH, Hoen, PA, König, IR, EchoGen consortium, Felix, JF, Clarke, R, Hopewell, JC, Ongen, H, Breteler, M, Debette, S, Destefano, AL, Fornage, M, AortaGen Consortium, Mitchell, GF, CHARGE Consortium Heart Failure Working Group, Smith, NL, KidneyGen consortium, Holm, H, Stefansson, K, Thorleifsson, G, Thorsteinsdottir, U, CKDGen consortium, Cardiogenics consortium, CardioGram, Samani, NJ, Preuss, M, Rudan, I, Hayward, C, Deary, IJ, Wichmann, HE, Raitakari, OT, Palmas, W, Kooner, JS, Stolk, RP, Jukema, JW, Wright, AF, Boomsma, DI, Bandinelli, S, Gyllensten, UB, Wilson, JF, Ferrucci, L, Schmidt, R, Farrall, M, Spector, TD, Palmer, LJ, Tuomilehto, J, Pfeufer, A, Gasparini, P, Siscovick, D, Altshuler, D, Loos, RJ, Toniolo, D, Snieder, H, Gieger, C, Meneton, P, Wareham, NJ, Oostra, BA, Metspalu, A, Launer, L, Rettig, R, Strachan, DP, Beckmann, JS, Witteman, JC, Erdmann, J, van Dijk, KW, Boerwinkle, E, Boehnke, M, Ridker, PM, Jarvelin, MR, Chakravarti, A, Abecasis, GR, Gudnason, V, Newton-Cheh, C, Levy, D, Munroe, PB, Psaty, BM, Caulfield, MJ, Rao, DC, Tobin, MD, Elliott, P, and van Duijn, CM
Published: 2011

12. Insight in Genome-Wide Association of Metabolite Quantitative Traits by Exome Sequence Analyses

Author: Demirkan, Ayse, Henneman, Peter, Verhoeven, Adrie, Dharuri, H, Amin, Najaf, van Klinken, JB, Karssen, Lennart, de Vries, B, Meissner, A, Goraler, S, Maagdenberg, AMJM, Deelder, AM, 't Hoen, PAC, Duijn, Cornelia, van Dijk, KW, Demirkan, Ayse, Henneman, Peter, Verhoeven, Adrie, Dharuri, H, Amin, Najaf, van Klinken, JB, Karssen, Lennart, de Vries, B, Meissner, A, Goraler, S, Maagdenberg, AMJM, Deelder, AM, 't Hoen, PAC, Duijn, Cornelia, and van Dijk, KW
Abstract: Metabolite quantitative traits carry great promise for epidemiological studies, and their genetic background has been addressed using Genome-Wide Association Studies (GWAS). Thus far, the role of less common variants has not been exhaustively studied. Here, we set out a GWAS for metabolite quantitative traits in serum, followed by exome sequence analysis to zoom in on putative causal variants in the associated genes. H-1 Nuclear Magnetic Resonance (H-1-NMR) spectroscopy experiments yielded successful quantification of 42 unique metabolites in 2,482 individuals from The Erasmus Rucphen Family (ERF) study. Heritability of metabolites were estimated by SOLAR. GWAS was performed by linear mixed models, using HapMap imputations. Based on physical vicinity and pathway analyses, candidate genes were screened for coding region variation using exome sequence data. Heritability estimates for metabolites ranged between 10% and 52%. GWAS replicated three known loci in the metabolome wide significance: CPS1 with glycine (P-value = 1.27x10(-32)), PRODH with proline (P-value = 1.11x10(-19)), SLC16A9 with carnitine level (P-value = 4.81x10(-14)) and uncovered a novel association between DMGDH and dimethyl-glycine (P-value = 1.65x10(-19)) level. In addition, we found three novel, suggestively significant loci: TNP1 with pyruvate (P-value = 1.26x10(-8)), KCNJ16 with 3-hydroxybutyrate (P-value = 1.65x10(-8)) and 2p12 locus with valine (P-value = 3.49x10(-8)). Exome sequence analysis identified potentially causal coding and regulatory variants located in the genes CPS1, KCNJ2 and PRODH, and revealed allelic heterogeneity for CPS1 and PRODH. Combined GWAS and exome analyses of metabolites detected by high-resolution H-1-NMR is a robust approach to uncover metabolite quantitative trait loci (mQTL), and the likely causative variants in these loci. It is anticipated that insight in the genetics of intermediate phenotypes will provide additional insight into the genetics of complex traits.
Published: 2015

13. Znf202 Affects High Density Lipoprotein Cholesterol Levels and Promotes Hepatosteatosis in Hyperlipidemic Mice

Author: Vrins, CLJ, Out, R, van Santbrink, P, van der Zee, A, Mahmoudi, Tokameh, Groenendijk, M (Martine), Havekes, LM, Van Berkel, TJC, van Dijk, KW, Biessen, EAL, Vrins, CLJ, Out, R, van Santbrink, P, van der Zee, A, Mahmoudi, Tokameh, Groenendijk, M (Martine), Havekes, LM, Van Berkel, TJC, van Dijk, KW, and Biessen, EAL
Abstract: Background: The zinc finger protein Znf202 is a transcriptional suppressor of lipid related genes and has been linked to hypoalphalipoproteinemia. A functional role of Znf202 in lipid metabolism in vivo still remains to be established. Methodology and Principal Findings: We generated mouse Znf202 expression vectors, the functionality of which was established in several in vitro systems. Next, effects of adenoviral znf202 overexpression in vivo were determined in normoas well as hyperlipidemic mouse models. Znf202 overexpression in mouse hepatoma cells mhAT3F2 resulted in downregulation of members of the Apoe/c1/c2 and Apoa1/c3/a4 gene cluster. The repressive activity of Znf202 was firmly confirmed in an apoE reporter assay and Conclusion/Significance: Znf202 overexpression in vivo reveals an important role of this transcriptional regulator in liver lipid homeostasis, while firmly establishing the proposed key role in the control of HDL levels.
Published: 2013

14. Automated workflow-based exploitation of pathway databases provides new insights into genetic associations of metabolite profiles

Author: Dharuri, H, Henneman, Peter, Demirkan, Ayse, van Klinken, JB, Mook, Dennis, Wang-Sattler, R, Gieger, C, Adamski, J, Hettne, K, Roos, M (Marco), Suhre, K, Duijn, Cornelia, van Dijk, KW, 't Hoen, PAC, Dharuri, H, Henneman, Peter, Demirkan, Ayse, van Klinken, JB, Mook, Dennis, Wang-Sattler, R, Gieger, C, Adamski, J, Hettne, K, Roos, M (Marco), Suhre, K, Duijn, Cornelia, van Dijk, KW, and 't Hoen, PAC
Abstract: Background: Genome-wide association studies (GWAS) have identified many common single nucleotide polymorphisms (SNPs) that associate with clinical phenotypes, but these SNPs usually explain just a small part of the heritability and have relatively modest effect sizes. In contrast, SNPs that associate with metabolite levels generally explain a higher percentage of the genetic variation and demonstrate larger effect sizes. Still, the discovery of SNPs associated with metabolite levels is challenging since testing all metabolites measured in typical metabolomics studies with all SNPs comes with a severe multiple testing penalty. We have developed an automated workflow approach that utilizes prior knowledge of biochemical pathways present in databases like KEGG and BioCyc to generate a smaller SNP set relevant to the metabolite. This paper explores the opportunities and challenges in the analysis of GWAS of metabolomic phenotypes and provides novel insights into the genetic basis of metabolic variation through the re-analysis of published GWAS datasets. Results: Re-analysis of the published GWAS dataset from Illig et al. (Nature Genetics, 2010) using a pathway-based workflow (http://www.myexperiment.org/packs/319.html), confirmed previously identified hits and identified a new locus of human metabolic individuality, associating Aldehyde dehydrogenase family1 L1 (ALDH1L1) with serine/glycine ratios in blood. Replication in an independent GWAS dataset of phospholipids (Demirkan et al., PLoS Genetics, 2012) identified two novel loci supported by a Conclusions: We demonstrate the utility of automated exploitation of background knowledge present in pathway databases for the analysis of GWAS datasets of metabolomic phenotypes. We report novel loci and potential biochemical mechanisms that contribute to our understanding of the genetic basis of metabolic variation and its relationship to disease development and progression.
Published: 2013

15. Genome-wide association for abdominal subcutaneous and visceral adipose reveals a novel locus for visceral fat in women.

Author: GIANT Consortium, MAGIC Consortium, GLGC Consortium, Dupuis, J., Langenberg, C., Prokopenko, I., Saxena, R., Soranzo, N., Jackson, AU., Wheeler, E., Glazer, NL., Bouatia-Naji, N., Gloyn, AL., Lindgren, CM., Mägi, R., Morris, AP., Randall, J., Johnson, T., Elliott, P., Rybin, D., Thorleifsson, G., Steinthorsdottir, V., Henneman, P., Grallert, H., Dehghan, A., Hottenga, JJ., Franklin, CS., Navarro, P., Song, K., Goel, A., Perry, JR., Egan, JM., Lajunen, T., Grarup, N., Sparsø, T., Doney, A., Voight, BF., Stringham, HM., Li, M., Kanoni, S., Shrader, P., Cavalcanti-Proença, C., Kumari, M., Qi, L., Timpson, NJ., Gieger, C., Zabena, C., Rocheleau, G., Ingelsson, E., An, P., O'Connell, J., Luan£££Jian'an£££ J., Elliott, A., McCarroll, SA., Payne, F., Roccasecca, RM., Pattou, F., Sethupathy, P., Ardlie, K., Ariyurek, Y., Balkau, B., Barter, P., Beilby, JP., Ben-Shlomo, Y., Benediktsson, R., Bennett, AJ., Bergmann, S., Bochud, M., Boerwinkle, E., Bonnefond, A., Bonnycastle, LL., Borch-Johnsen, K., Böttcher, Y., Brunner, E., Bumpstead, SJ., Charpentier, G., Chen, YD., Chines, P., Clarke, R., Coin, LJ., Cooper, MN., Cornelis, M., Crawford, G., Crisponi, L., Day, IN., de Geus EJ., Delplanque, J., Dina, C., Erdos, MR., Fedson, AC., Fischer-Rosinsky, A., Forouhi, NG., Fox, CS., Frants, R., Franzosi, MG., Galan, P., Goodarzi, MO., Graessler, J., Groves, CJ., Grundy, S., Gwilliam, R., Gyllensten, U., Hadjadj, S., Hallmans, G., Hammond, N., Han, X., Hartikainen, AL., Hassanali, N., Hayward, C., Heath, SC., Hercberg, S., Herder, C., Hicks, AA., Hillman, DR., Hingorani, AD., Hofman, A., Hui, J., Hung, J., Isomaa, B., Johnson, PR., Jørgensen, T., Jula, A., Kaakinen, M., Kaprio, J., Kesaniemi, YA., Kivimaki, M., Knight, B., Koskinen, S., Kovacs, P., Kyvik, KO., Lathrop, GM., Lawlor, DA., Le Bacquer, O., Lecoeur, C., Li, Y., Lyssenko, V., Mahley, R., Mangino, M., Manning, AK., Martínez-Larrad£££María Teresa£££ MT., McAteer, JB., McCulloch, LJ., McPherson, R., Meisinger, C., Melzer, D., Meyre, D., Mitchell, BD., Morken, MA., Mukherjee, S., Naitza, S., Narisu, N., Neville, MJ., Oostra, BA., Orrù, M., Pakyz, R., Palmer, CN., Paolisso, G., Pattaro, C., Pearson, D., Peden, JF., Pedersen, NL., Perola, M., Pfeiffer, AF., Pichler, I., Polasek, O., Posthuma, D., Potter, SC., Pouta, A., Province, MA., Psaty, BM., Rathmann, W., Rayner, NW., Rice, K., Ripatti, S., Rivadeneira, F., Roden, M., Rolandsson, O., Sandbaek, A., Sandhu, M., Sanna, S., Sayer, AA., Scheet, P., Scott, LJ., Seedorf, U., Sharp, SJ., Shields, B., Sigurðsson, G., Sijbrands, EJ., Silveira, A., Simpson, L., Singleton, A., Smith, NL., Sovio, U., Swift, A., Syddall, H., Syvänen, AC., Tanaka, T., Thorand, B., Tichet, J., Tönjes, A., Tuomi, T., Uitterlinden, AG., van Dijk KW., van Hoek, M., Varma, D., Visvikis-Siest, S., Vitart, V., Vogelzangs, N., Waeber, G., Wagner, PJ., Walley, A., Walters, GB., Ward, KL., Watkins, H., Weedon, MN., Wild, SH., Willemsen, G., Witteman, JC., Yarnell, JW., Zeggini, E., Zelenika, D., Zethelius, B., Zhai, G., Zhao, JH., Zillikens, MC., Borecki, IB., Loos, RJ., Meneton, P., Magnusson, PK., Nathan, DM., Williams, GH., Hattersley, AT., Silander, K., Salomaa, V., Smith, GD., Bornstein, SR., Schwarz, P., Spranger, J., Karpe, F., Shuldiner, AR., Cooper, C., Dedoussis, GV., Serrano-Ríos, M., Morris, AD., Lind, L., Palmer, LJ., Hu, FB., Franks, PW., Ebrahim, S., Marmot, M., Kao, WH., Pankow, JS., Sampson, MJ., Kuusisto, J., Laakso, M., Hansen, T., Pedersen, O., Pramstaller, PP., Wichmann, HE., Illig, T., Rudan, I., Wright, AF., Stumvoll, M., Campbell, H., Wilson, JF., Hamsten, A., Bergman, RN., Buchanan, TA., Collins, FS., Mohlke, KL., Tuomilehto, J., Valle, TT., Altshuler, D., Rotter, JI., Siscovick, DS., Penninx, BW., Boomsma, ID., Deloukas, P., Spector, TD., Frayling, TM., Ferrucci, L., Kong, A., Thorsteinsdottir, U., Stefansson, K., van Duijn CM., Aulchenko, YS., Cao, A., Scuteri, A., Schlessinger, D., Uda, M., Ruokonen, A., Jarvelin, MR., Waterworth, DM., Vollenweider, P., Peltonen, L., Mooser, V., Abecasis, RG., Wareham, NJ., Sladek, R., Froguel, P., Watanabe, RM., Meigs, JB., Groop, L., Boehnke, M., McCarthy, MI., Florez, JC., Barroso£££Inês£££ I., Fox, C.S., Liu, Y., White, C.C., Feitosa, M., Smith, A.V., Heard-Costa, N., Lohman, K., Johnson, A.D., Foster, M.C., Greenawalt, D.M., Griffin, P., Ding, J., Newman, A.B., Tylavsky, F., Miljkovic, I., Kritchevsky, S.B., Launer, L., Garcia, M., Eiriksdottir, G., Carr, J.J., Gudnason, V., Harris, T.B., Cupples, L.A., Borecki, I.B., GIANT Consortium, MAGIC Consortium, GLGC Consortium, Dupuis, J., Langenberg, C., Prokopenko, I., Saxena, R., Soranzo, N., Jackson, AU., Wheeler, E., Glazer, NL., Bouatia-Naji, N., Gloyn, AL., Lindgren, CM., Mägi, R., Morris, AP., Randall, J., Johnson, T., Elliott, P., Rybin, D., Thorleifsson, G., Steinthorsdottir, V., Henneman, P., Grallert, H., Dehghan, A., Hottenga, JJ., Franklin, CS., Navarro, P., Song, K., Goel, A., Perry, JR., Egan, JM., Lajunen, T., Grarup, N., Sparsø, T., Doney, A., Voight, BF., Stringham, HM., Li, M., Kanoni, S., Shrader, P., Cavalcanti-Proença, C., Kumari, M., Qi, L., Timpson, NJ., Gieger, C., Zabena, C., Rocheleau, G., Ingelsson, E., An, P., O'Connell, J., Luan£££Jian'an£££ J., Elliott, A., McCarroll, SA., Payne, F., Roccasecca, RM., Pattou, F., Sethupathy, P., Ardlie, K., Ariyurek, Y., Balkau, B., Barter, P., Beilby, JP., Ben-Shlomo, Y., Benediktsson, R., Bennett, AJ., Bergmann, S., Bochud, M., Boerwinkle, E., Bonnefond, A., Bonnycastle, LL., Borch-Johnsen, K., Böttcher, Y., Brunner, E., Bumpstead, SJ., Charpentier, G., Chen, YD., Chines, P., Clarke, R., Coin, LJ., Cooper, MN., Cornelis, M., Crawford, G., Crisponi, L., Day, IN., de Geus EJ., Delplanque, J., Dina, C., Erdos, MR., Fedson, AC., Fischer-Rosinsky, A., Forouhi, NG., Fox, CS., Frants, R., Franzosi, MG., Galan, P., Goodarzi, MO., Graessler, J., Groves, CJ., Grundy, S., Gwilliam, R., Gyllensten, U., Hadjadj, S., Hallmans, G., Hammond, N., Han, X., Hartikainen, AL., Hassanali, N., Hayward, C., Heath, SC., Hercberg, S., Herder, C., Hicks, AA., Hillman, DR., Hingorani, AD., Hofman, A., Hui, J., Hung, J., Isomaa, B., Johnson, PR., Jørgensen, T., Jula, A., Kaakinen, M., Kaprio, J., Kesaniemi, YA., Kivimaki, M., Knight, B., Koskinen, S., Kovacs, P., Kyvik, KO., Lathrop, GM., Lawlor, DA., Le Bacquer, O., Lecoeur, C., Li, Y., Lyssenko, V., Mahley, R., Mangino, M., Manning, AK., Martínez-Larrad£££María Teresa£££ MT., McAteer, JB., McCulloch, LJ., McPherson, R., Meisinger, C., Melzer, D., Meyre, D., Mitchell, BD., Morken, MA., Mukherjee, S., Naitza, S., Narisu, N., Neville, MJ., Oostra, BA., Orrù, M., Pakyz, R., Palmer, CN., Paolisso, G., Pattaro, C., Pearson, D., Peden, JF., Pedersen, NL., Perola, M., Pfeiffer, AF., Pichler, I., Polasek, O., Posthuma, D., Potter, SC., Pouta, A., Province, MA., Psaty, BM., Rathmann, W., Rayner, NW., Rice, K., Ripatti, S., Rivadeneira, F., Roden, M., Rolandsson, O., Sandbaek, A., Sandhu, M., Sanna, S., Sayer, AA., Scheet, P., Scott, LJ., Seedorf, U., Sharp, SJ., Shields, B., Sigurðsson, G., Sijbrands, EJ., Silveira, A., Simpson, L., Singleton, A., Smith, NL., Sovio, U., Swift, A., Syddall, H., Syvänen, AC., Tanaka, T., Thorand, B., Tichet, J., Tönjes, A., Tuomi, T., Uitterlinden, AG., van Dijk KW., van Hoek, M., Varma, D., Visvikis-Siest, S., Vitart, V., Vogelzangs, N., Waeber, G., Wagner, PJ., Walley, A., Walters, GB., Ward, KL., Watkins, H., Weedon, MN., Wild, SH., Willemsen, G., Witteman, JC., Yarnell, JW., Zeggini, E., Zelenika, D., Zethelius, B., Zhai, G., Zhao, JH., Zillikens, MC., Borecki, IB., Loos, RJ., Meneton, P., Magnusson, PK., Nathan, DM., Williams, GH., Hattersley, AT., Silander, K., Salomaa, V., Smith, GD., Bornstein, SR., Schwarz, P., Spranger, J., Karpe, F., Shuldiner, AR., Cooper, C., Dedoussis, GV., Serrano-Ríos, M., Morris, AD., Lind, L., Palmer, LJ., Hu, FB., Franks, PW., Ebrahim, S., Marmot, M., Kao, WH., Pankow, JS., Sampson, MJ., Kuusisto, J., Laakso, M., Hansen, T., Pedersen, O., Pramstaller, PP., Wichmann, HE., Illig, T., Rudan, I., Wright, AF., Stumvoll, M., Campbell, H., Wilson, JF., Hamsten, A., Bergman, RN., Buchanan, TA., Collins, FS., Mohlke, KL., Tuomilehto, J., Valle, TT., Altshuler, D., Rotter, JI., Siscovick, DS., Penninx, BW., Boomsma, ID., Deloukas, P., Spector, TD., Frayling, TM., Ferrucci, L., Kong, A., Thorsteinsdottir, U., Stefansson, K., van Duijn CM., Aulchenko, YS., Cao, A., Scuteri, A., Schlessinger, D., Uda, M., Ruokonen, A., Jarvelin, MR., Waterworth, DM., Vollenweider, P., Peltonen, L., Mooser, V., Abecasis, RG., Wareham, NJ., Sladek, R., Froguel, P., Watanabe, RM., Meigs, JB., Groop, L., Boehnke, M., McCarthy, MI., Florez, JC., Barroso£££Inês£££ I., Fox, C.S., Liu, Y., White, C.C., Feitosa, M., Smith, A.V., Heard-Costa, N., Lohman, K., Johnson, A.D., Foster, M.C., Greenawalt, D.M., Griffin, P., Ding, J., Newman, A.B., Tylavsky, F., Miljkovic, I., Kritchevsky, S.B., Launer, L., Garcia, M., Eiriksdottir, G., Carr, J.J., Gudnason, V., Harris, T.B., Cupples, L.A., and Borecki, I.B.
Abstract: Body fat distribution, particularly centralized obesity, is associated with metabolic risk above and beyond total adiposity. We performed genome-wide association of abdominal adipose depots quantified using computed tomography (CT) to uncover novel loci for body fat distribution among participants of European ancestry. Subcutaneous and visceral fat were quantified in 5,560 women and 4,997 men from 4 population-based studies. Genome-wide genotyping was performed using standard arrays and imputed to ~2.5 million Hapmap SNPs. Each study performed a genome-wide association analysis of subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), VAT adjusted for body mass index, and VAT/SAT ratio (a metric of the propensity to store fat viscerally as compared to subcutaneously) in the overall sample and in women and men separately. A weighted z-score meta-analysis was conducted. For the VAT/SAT ratio, our most significant p-value was rs11118316 at LYPLAL1 gene (p = 3.1 × 10E-09), previously identified in association with waist-hip ratio. For SAT, the most significant SNP was in the FTO gene (p = 5.9 × 10E-08). Given the known gender differences in body fat distribution, we performed sex-specific analyses. Our most significant finding was for VAT in women, rs1659258 near THNSL2 (p = 1.6 × 10-08), but not men (p = 0.75). Validation of this SNP in the GIANT consortium data demonstrated a similar sex-specific pattern, with observed significance in women (p = 0.006) but not men (p = 0.24) for BMI and waist circumference (p = 0.04 [women], p = 0.49 [men]). Finally, we interrogated our data for the 14 recently published loci for body fat distribution (measured by waist-hip ratio adjusted for BMI); associations were observed at 7 of these loci. In contrast, we observed associations at only 7/32 loci previously identified in association with BMI; the majority of overlap was observed with SAT. Genome-wide association for visceral and subcutaneous fat revealed a SNP for VAT in
Published: 2012

16. The Role of Body Mass Index, Insulin, and Adiponectin in the Relation Between Fat Distribution and Bone Mineral Density

Author: Zillikens, M.C., Uitterlinden, André, van Leeuwen, Hans, Berends, Annelous, Henneman, Peter, van Dijk, KW, Oostra, Ben, Duijn, Cornelia, Pols, Huib, Rivadeneira, Fernando, Zillikens, M.C., Uitterlinden, André, van Leeuwen, Hans, Berends, Annelous, Henneman, Peter, van Dijk, KW, Oostra, Ben, Duijn, Cornelia, Pols, Huib, and Rivadeneira, Fernando
Abstract: Despite the positive association between body mass index (BMI) and bone mineral density (BMD) and content (BMC), the role of fat distribution in BMD/BMC remains unclear. We examined relationships between BMD/BMC and various measurements of fat distribution and studied the role of BMI, insulin, and adiponectin in these relations. Using a cross-sectional investigation of 2631 participants from the Erasmus Rucphen Family study, we studied associations between BMD (using dual-energy X-ray absorptiometry (DXA]) at the hip, lumbar spine, total body (BMD and BMC), and fat distribution by the waist-to-hip ratio (WHR), waist-to-thigh ratio (WTR), and DXA-based trunk-to-leg fat ratio and android-to-gynoid fat ratio. Analyses were stratified by gender and median age (48.0 years in women and 49.2 years in men) and were performed with and without adjustment for BMI, fasting insulin, and adiponectin. Using linear regression (adjusting for age, height, smoking, and use of alcohol), most relationships between fat distribution and BMD and BMC were positive, except for WTR. After BMI adjustment, most correlations were negative except for trunk-to-leg fat ratio in both genders. No consistent influence of age or menopausal status was found. Insulin and adiponectin levels did not explain either positive or negative associations. In conclusion, positive associations between android fat distribution and BMD/BMC are explained by higher BMI but not by higher insulin and/or lower adiponectin levels. Inverse associations after adjustment for BMI suggest that android fat deposition as measured by the WHR, WTR, and DXA-based android-to-gynoid fat ratio is not beneficial and possibly even deleterious for bone.
Published: 2010

17. Novel DNA methylation sites of glucose and insulin homeostasis: an integrative cross-omics analysis

Author: James B. Meigs, Jordana T. Bell, Marjolein J. Peters, James S. Pankow, Symen Ligthart, Dorret I. Boomsma, Toshiko Tanaka, Marie-France Hivert, Eric J.G. Sijbrands, Bertha Hidalgo, Joyce B. J. van Meurs, Elena Carnero-Montoro, Pei-Chien Tsai, Lifang Hou, van Dijk Kw, Cornelia M. van Duijn, Ko Willems van Dijk, Harald Grallert, Najaf Amin, Abbas Dehghan, Ann Zenobia Moore, Janine F. Felix, Tiphaine Martin, Aaron Isaacs, Jerome I. Rotter, Pooja R. Mandaviya, Rozenn N. Lemaitre, Henning Tiemeier, Eric A. Whitsel, Min A. Jhun, Ivana Nedeljkovic, Themistocles L. Assimes, Nona Sotoodehnia, Rick Jansen, Liesbeth Duijts, Shih-Jen Hwang, Jan Bressler, Vincent W. V. Jaddoe, Daniel Levy, André G. Uitterlinden, Audrey Y. Chu, Tim D. Spector, Luigi Ferrucci, Josée Dupuis, Ayse Demirkan, Samantha Lent, Rahul Gondalia, Donna K. Arnett, Oscar H. Franco, Jun Liu, Stefania Bandinelli, Jennifer A. Smith, Andrea A. Baccarelli, Elias Salfati, and Christian Herder
Subjects: In silico, Diabetes mellitus, Insulin, medicine.medical_treatment, DNA methylation, medicine, Genomics, Methylation, Type 2 diabetes, Computational biology, Biology, medicine.disease, Epigenomics
Abstract: Despite existing reports on differential DNA methylation in type 2 diabetes (T2D) and obesity, our understanding of the functional relevance of the phenomenon remains limited. Because obesity is the main risk factor for T2D and a driver of methylation from previous study, we aimed to explore the effect of DNA methylation in the early phases of T2D pathology while accounting for body mass index (BMI). We performed a blood-based epigenome-wide association study (EWAS) of fasting glucose and insulin among 4,808 non-diabetic European individuals and replicated the findings in an independent sample consisting of 11,750 non-diabetic subjects. We integrated blood-based in silico cross-omics databases comprising genomics, epigenomics and transcriptomics collected by BIOS project of the Biobanking and BioMolecular resources Research Infrastructure of the Netherlands (BBMRI-NL), the Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC), the DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) consortium, and the tissue-specific Genotype-Tissue Expression (GTEx) project. We identified and replicated nine novel differentially methylated sites in whole blood (P-value < 1.27 × 10-7): sites in LETM1, RBM20, IRS2, MAN2A2 genes and 1q25.3 region were associated with fasting insulin; sites in FCRL6, SLAMF1, APOBEC3H genes and 15q26.1 region were associated with fasting glucose. The association between SLAMF1, APOBEC3H and 15q26.1 methylation sites and glucose emerged only when accounted for BMI. Follow-up in silico cross-omics analyses indicate that the cis-acting meQTLs near SLAMF1 and SLAMF1 expression are involved in glucose level regulation. Moreover, our data suggest that differential methylation in FCRL6 may affect glucose level and the risk of T2D by regulating FCLR6 expression in the liver. In conclusion, the present study provided nine new DNA methylation sites associated with glycemia homeostasis and also provided new insights of glycemia related loci into the genetics, epigenetics and transcriptomics pathways based on the integration of cross-omics data in silico.

18. Estrogen induced hypertriglyceridemia in an apolipoprotein AV deficient patient.

Author: Henneman P, Schaap FG, Rensen PC, van Dijk KW, Smelt AH, Henneman, P, Schaap, F G, Rensen, P C N, van Dijk, K W, and Smelt, A H M
Published: 2008
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19. A Genome-Wide Association Search for Type 2 Diabetes Genes in African Americans

Author: Palmer, Nicholette D, McDonough, Caitrin W, Hicks, Pamela J, Roh, Bong H, Wing, Maria R, An, S Sandy, Hester, Jessica M, Cooke, Jessica N, Bostrom, Meredith A, Rudock, Megan E, Talbert, Matthew E, Lewis, Joshua P, DIAGRAM Consortium, MAGIC Investigators, Ferrara, Assiamira, Lu, Lingyi, Ziegler, Julie T, Sale, Michele M, Divers, Jasmin, Shriner, Daniel, Adeyemo, Adebowale, Rotimi, Charles N, Ng, Maggie CY, Langefeld, Carl D, Freedman, Barry I, Bowden, Donald W, Voight, Benjamin F, Scott, Laura J, Steinthorsdottir, Valgerdur, Morris, Andrew P, Dina, Christian, Welch, Ryan P, Zeggini, Eleftheria, Huth, Cornelia, Aulchenko, Yurii S, Thorleifsson, Gudmar, McCulloch, Laura J, Ferreira, Teresa, Grallert, Harald, Amin, Najaf, Wu, Guanming, Willer, Cristen J, Raychaudhuri, Soumya, McCarroll, Steve A, Langenberg, Claudia, Hofmann, Oliver M, Dupuis, Josée, Qi, Lu, Segrè, Ayellet V, Van Hoek, Mandy, Navarro, Pau, Ardlie, Kristin, Balkau, Beverley, Benediktsson, Rafn, Bennett, Amanda J, Blagieva, Roza, Boerwinkle, Eric, Bonnycastle, Lori L, Boström, Kristina Bengtsson, Bravenboer, Bert, Bumpstead, Suzannah, Burtt, Noël P, Charpentier, Guillaume, Chines, Peter S, Cornelis, Marilyn, Couper, David J, Crawford, Gabe, Doney, Alex SF, Elliott, Katherine S, Elliott, Amanda L, Erdos, Michael R, Fox, Caroline S, Franklin, Christopher S, Ganser, Martha, Gieger, Christian, Grarup, Niels, Green, Todd, Griffin, Simon, Groves, Christopher J, Guiducci, Candace, Hadjadj, Samy, Hassanali, Neelam, Herder, Christian, Isomaa, Bo, Jackson, Anne U, Johnson, Paul RV, Jørgensen, Torben, Kao, Wen HL, Klopp, Norman, Kong, Augustine, Kraft, Peter, Kuusisto, Johanna, Lauritzen, Torsten, Li, Man, Lieverse, Aloysius, Lindgren, Cecilia M, Lyssenko, Valeriya, Marre, Michel, Meitinger, Thomas, Midthjell, Kristian, Morken, Mario A, Narisu, Narisu, Nilsson, Peter, Owen, Katharine R, Payne, Felicity, Perry, John RB, Petersen, Ann-Kristin, Platou, Carl, Proença, Christine, Prokopenko, Inga, Rathmann, Wolfgang, Rayner, N William, Robertson, Neil R, Rocheleau, Ghislain, Roden, Michael, Sampson, Michael J, Saxena, Richa, Shields, Beverley M, Shrader, Peter, Sigurdsson, Gunnar, Sparsø, Thomas, Strassburger, Klaus, Stringham, Heather M, Sun, Qi, Swift, Amy J, Thorand, Barbara, Tichet, Jean, Tuomi, Tiinamaija, Van Dam, Rob M, Van Haeften, Timon W, Van Herpt, Thijs, Van Vliet-Ostaptchouk, Jana V, Walters, G Bragi, Weedon, Michael N, Wijmenga, Cisca, Witteman, Jacqueline, Bergman, Richard N, Cauchi, Stephane, Collins, Francis S, Gloyn, Anna L, Gyllensten, Ulf, Hansen, Torben, Hide, Winston A, Hitman, Graham A, Hofman, Albert, Hunter, David J, Hveem, Kristian, Laakso, Markku, Mohlke, Karen L, Morris, Andrew D, Palmer, Colin NA, Pramstaller, Peter P, Rudan, Igor, Sijbrands, Eric, Stein, Lincoln D, Tuomilehto, Jaakko, Uitterlinden, Andre, Walker, Mark, Wareham, Nicholas J, Watanabe, Richard M, Abecasis, Goncalo R, Boehm, Bernhard O, Campbell, Harry, Daly, Mark J, Hattersley, Andrew T, Hu, Frank B, Meigs, James B, Pankow, James S, Pedersen, Oluf, Wichmann, H-Erich, Barroso, Inês, Florez, Jose C, Frayling, Timothy M, Groop, Leif, Sladek, Rob, Thorsteinsdottir, Unnur, Wilson, James F, Illig, Thomas, Froguel, Philippe, Van Duijn, Cornelia M, Stefansson, Kari, Altshuler, David, Boehnke, Michael, McCarthy, Mark I, Soranzo, Nicole, Wheeler, Eleanor, Glazer, Nicole L, Bouatia-Naji, Nabila, Mägi, Reedik, Randall, Joshua, Johnson, Toby, Elliott, Paul, Rybin, Denis, Henneman, Peter, Dehghan, Abbas, Hottenga, Jouke Jan, Song, Kijoung, Goel, Anuj, Egan, Josephine M, Lajunen, Taina, Doney, Alex, Kanoni, Stavroula, Cavalcanti-Proença, Christine, Kumari, Meena, Timpson, Nicholas J, Zabena, Carina, Ingelsson, Erik, An, Ping, O'Connell, Jeffrey, Luan, Jian'an, Elliott, Amanda, McCarroll, Steven A, Roccasecca, Rosa Maria, Pattou, François, Sethupathy, Praveen, Ariyurek, Yavuz, Barter, Philip, Beilby, John P, Ben-Shlomo, Yoav, Bergmann, Sven, Bochud, Murielle, Bonnefond, Amélie, Borch-Johnsen, Knut, Böttcher, Yvonne, Brunner, Eric, Bumpstead, Suzannah J, Chen, Yii-Der Ida, Chines, Peter, Clarke, Robert, Coin, Lachlan JM, Cooper, Matthew N, Crisponi, Laura, Day, Ian NM, De Geus, Eco JC, Delplanque, Jerome, Fedson, Annette C, Fischer-Rosinsky, Antje, Forouhi, Nita G, Frants, Rune, Franzosi, Maria Grazia, Galan, Pilar, Goodarzi, Mark O, Graessler, Jürgen, Grundy, Scott, Gwilliam, Rhian, Hallmans, Göran, Hammond, Naomi, Han, Xijing, Hartikainen, Anna-Liisa, Hayward, Caroline, Heath, Simon C, Hercberg, Serge, Hicks, Andrew A, Hillman, David R, Hingorani, Aroon D, Hui, Jennie, Hung, Joe, Jula, Antti, Kaakinen, Marika, Kaprio, Jaakko, Kesaniemi, Y Antero, Kivimaki, Mika, Knight, Beatrice, Koskinen, Seppo, Kovacs, Peter, Kyvik, Kirsten Ohm, Lathrop, G Mark, Lawlor, Debbie A, Le Bacquer, Olivier, Lecoeur, Cécile, Li, Yun, Mahley, Robert, Mangino, Massimo, Manning, Alisa K, Martínez-Larrad, María Teresa, McAteer, Jarred B, McPherson, Ruth, Meisinger, Christa, Melzer, David, Meyre, David, Mitchell, Braxton D, Mukherjee, Sutapa, Naitza, Silvia, Neville, Matthew J, Oostra, Ben A, Orrù, Marco, Pakyz, Ruth, Paolisso, Giuseppe, Pattaro, Cristian, Pearson, Daniel, Peden, John F, Pedersen, Nancy L, Perola, Markus, Pfeiffer, Andreas FH, Pichler, Irene, Polasek, Ozren, Posthuma, Danielle, Potter, Simon C, Pouta, Anneli, Province, Michael A, Psaty, Bruce M, Rayner, Nigel W, Rice, Kenneth, Ripatti, Samuli, Rivadeneira, Fernando, Rolandsson, Olov, Sandbaek, Annelli, Sandhu, Manjinder, Sanna, Serena, Sayer, Avan Aihie, Scheet, Paul, Seedorf, Udo, Sharp, Stephen J, Shields, Beverley, Sijbrands, Eric JG, Silveira, Angela, Simpson, Laila, Singleton, Andrew, Smith, Nicholas L, Sovio, Ulla, Swift, Amy, Syddall, Holly, Syvänen, Ann-Christine, Tanaka, Toshiko, Tönjes, Anke, Uitterlinden, André G, Van Dijk, Ko Willems, Varma, Dhiraj, Visvikis-Siest, Sophie, Vitart, Veronique, Vogelzangs, Nicole, Waeber, Gérard, Wagner, Peter J, Walley, Andrew, Ward, Kim L, Watkins, Hugh, Wild, Sarah H, Willemsen, Gonneke, Witteman, Jaqueline CM, Yarnell, John WG, Zelenika, Diana, Zethelius, Björn, Zhai, Guangju, Zhao, Jing Hua, Zillikens, M Carola, Borecki, Ingrid B, Loos, Ruth JF, Meneton, Pierre, Magnusson, Patrik KE, Nathan, David M, Williams, Gordon H, Silander, Kaisa, Salomaa, Veikko, Smith, George Davey, Bornstein, Stefan R, Schwarz, Peter, Spranger, Joachim, Karpe, Fredrik, Shuldiner, Alan R, Cooper, Cyrus, Dedoussis, George V, Serrano-Ríos, Manuel, Lind, Lars, Palmer, Lyle J, Franks, Paul W, Ebrahim, Shah, Marmot, Michael, Kao, WH Linda, Pramstaller, Peter Paul, Wright, Alan F, Stumvoll, Michael, Hamsten, Anders, Buchanan, Thomas A, Valle, Timo T, Rotter, Jerome I, Siscovick, David S, Penninx, Brenda WJH, Boomsma, Dorret I, Deloukas, Panos, Spector, Timothy D, Ferrucci, Luigi, Cao, Antonio, Scuteri, Angelo, Schlessinger, David, Uda, Manuela, Ruokonen, Aimo, Jarvelin, Marjo-Riitta, Waterworth, Dawn M, Vollenweider, Peter, Peltonen, Leena, Mooser, Vincent, Sladek, Robert, Center for Liver, Digestive and Metabolic Diseases (CLDM), Palmer, Nd, Mcdonough, Cw, Hicks, Pj, Roh, Bh, Wing, Mr, An, S, Hester, Jm, Cooke, Jn, Bostrom, Ma, Rudock, Me, Talbert, Me, Lewis, Jp, Diagram, Consortium, Magic, Investigator, Ferrara, A, Lu, L, Ziegler, Jt, Sale, Mm, Divers, J, Shriner, D, Adeyemo, A, Rotimi, Cn, Ng, Mc, Langefeld, Cd, Freedman, Bi, Bowden, Dw, Voight, Bf, Scott, Lj, Steinthorsdottir, V, Morris, Ap, Dina, C, Welch, Rp, Zeggini, E, Huth, C, Aulchenko, Y, Thorleifsson, G, Mcculloch, Lj, Ferreira, T, Grallert, H, Amin, N, Wu, G, Willer, Cj, Raychaudhuri, S, Mccarroll, Sa, Langenberg, C, Hofmann, Om, Dupuis, J, Qi, L, Segrè, Av, van Hoek, M, Navarro, P, Ardlie, K, Balkau, B, Benediktsson, R, Bennett, Aj, Blagieva, R, Boerwinkle, E, Bonnycastle, Ll, Boström, Kb, Bravenboer, B, Bumpstead, S, Burtt, Np, Charpentier, G, Chines, P, Cornelis, M, Couper, Dj, Crawford, G, Doney, A, Elliott, K, Elliott, Al, Erdos, Mr, Fox, C, Franklin, C, Ganser, M, Gieger, C, Grarup, N, Green, T, Griffin, S, Groves, Cj, Guiducci, C, Hadjadj, S, Hassanali, N, Herder, C, Isomaa, B, Jackson, Au, Johnson, Pr, Jørgensen, T, Kao, Wh, Klopp, N, Kong, A, Kraft, P, Kuusisto, J, Lauritzen, T, Li, M, Lieverse, A, Lindgren, Cm, Lyssenko, V, Marre, M, Meitinger, T, Midthjell, K, Morken, Ma, Narisu, N, Nilsson, P, Owen, Kr, Payne, F, Perry, Jr, Petersen, Ak, Platou, C, Proença, C, Prokopenko, I, Rathmann, W, Rayner, Nw, Robertson, Nr, Rocheleau, G, Roden, M, Sampson, Mj, Saxena, R, Shields, Bm, Shrader, P, Sigurdsson, G, Sparsø, T, Strassburger, K, Stringham, Hm, Sun, Q, Swift, Aj, Thorand, B, Tichet, J, Tuomi, T, van Dam, Rm, van Haeften, Tw, van Herpt, T, van Vliet Ostaptchouk, Jv, Walters, Gb, Weedon, Mn, Wijmenga, C, Witteman, J, Bergman, Rn, Cauchi, S, Collins, F, Gloyn, Al, Gyllensten, U, Hansen, T, Hide, Wa, Hitman, Ga, Hofman, A, Hunter, Dj, Hveem, K, Laakso, M, Mohlke, Kl, Morris, Ad, Palmer, Cn, Pramstaller, Pp, Rudan, I, Sijbrands, E, Stein, Ld, Tuomilehto, J, Uitterlinden, A, Walker, M, Wareham, Nj, Watanabe, Rm, Abecasis, Gr, Boehm, Bo, Campbell, H, Daly, Mj, Hattersley, At, Hu, Fb, Meigs, Jb, Pankow, J, Pedersen, O, Wichmann, He, Barroso, I, Florez, Jc, Frayling, Tm, Groop, L, Sladek, R, Thorsteinsdottir, U, Wilson, Jf, Illig, T, Froguel, P, van Duijn, Cm, Stefansson, K, Altshuler, D, Boehnke, M, Mccarthy, Mi, Soranzo, N, Wheeler, E, Glazer, Nl, Bouatia Naji, N, Mägi, R, Randall, J, Johnson, T, Elliott, P, Rybin, D, Henneman, P, Dehghan, A, Hottenga, Jj, Song, K, Goel, A, Egan, Jm, Lajunen, T, Kanoni, S, Cavalcanti Proença, C, Kumari, M, Timpson, Nj, Zabena, C, Ingelsson, E, An, P, O'Connell, J, Luan, J, Elliott, A, Roccasecca, Rm, Pattou, F, Sethupathy, P, Ariyurek, Y, Barter, P, Beilby, Jp, Ben Shlomo, Y, Bergmann, S, Bochud, M, Bonnefond, A, Borch Johnsen, K, Böttcher, Y, Brunner, E, Bumpstead, Sj, Chen, Yd, Clarke, R, Coin, Lj, Cooper, Mn, Crisponi, L, Day, In, de Geus, Ej, Delplanque, J, Fedson, Ac, Fischer Rosinsky, A, Forouhi, Ng, Frants, R, Franzosi, Mg, Galan, P, Goodarzi, Mo, Graessler, J, Grundy, S, Gwilliam, R, Hallmans, G, Hammond, N, Han, X, Hartikainen, Al, Hayward, C, Heath, Sc, Hercberg, S, Hicks, Aa, Hillman, Dr, Hingorani, Ad, Hui, J, Hung, J, Jula, A, Kaakinen, M, Kaprio, J, Kesaniemi, Ya, Kivimaki, M, Knight, B, Koskinen, S, Kovacs, P, Kyvik, Ko, Lathrop, Gm, Lawlor, Da, Le Bacquer, O, Lecoeur, C, Li, Y, Mahley, R, Mangino, M, Manning, Ak, Martínez Larrad, Mt, Mcateer, Jb, Mcpherson, R, Meisinger, C, Melzer, D, Meyre, D, Mitchell, Bd, Mukherjee, S, Naitza, S, Neville, Mj, Oostra, Ba, Orrù, M, Pakyz, R, Paolisso, Giuseppe, Pattaro, C, Pearson, D, Peden, Jf, Pedersen, Nl, Perola, M, Pfeiffer, Af, Pichler, I, Polasek, O, Posthuma, D, Potter, Sc, Pouta, A, Province, Ma, Psaty, Bm, Rice, K, Ripatti, S, Rivadeneira, F, Rolandsson, O, Sandbaek, A, Sandhu, M, Sanna, S, Sayer, Aa, Scheet, P, Seedorf, U, Sharp, Sj, Shields, B, Sijbrands, Ej, Silveira, A, Simpson, L, Singleton, A, Smith, Nl, Sovio, U, Swift, A, Syddall, H, Syvänen, Ac, Tanaka, T, Tönjes, A, Uitterlinden, Ag, van Dijk, Kw, Varma, D, Visvikis Siest, S, Vitart, V, Vogelzangs, N, Waeber, G, Wagner, Pj, Walley, A, Ward, Kl, Watkins, H, Wild, Sh, Willemsen, G, Witteman, Jc, Yarnell, Jw, Zelenika, D, Zethelius, B, Zhai, G, Zhao, Jh, Zillikens, Mc, Borecki, Ib, Loos, Rj, Meneton, P, Magnusson, Pk, Nathan, Dm, Williams, Gh, Silander, K, Salomaa, V, Smith, Gd, Bornstein, Sr, Schwarz, P, Spranger, J, Karpe, F, Shuldiner, Ar, Cooper, C, Dedoussis, Gv, Serrano Ríos, M, Lind, L, Palmer, Lj, Franks, Pw, Ebrahim, S, Marmot, M, Wright, Af, Stumvoll, M, Hamsten, A, Buchanan, Ta, Valle, Tt, Rotter, Ji, Siscovick, D, Penninx, Bw, Boomsma, Di, Deloukas, P, Spector, Td, Ferrucci, L, Cao, A, Scuteri, A, Schlessinger, D, Uda, M, Ruokonen, A, Jarvelin, Mr, Waterworth, Dm, Vollenweider, P, Peltonen, L, Mooser, V, Sladek, R., Medical Research Council (MRC), Human genetics, Psychiatry, NCA - Attention & Cognition, EMGO - Lifestyle, overweight and diabetes, Biological Psychology, Functional Genomics, Neuroscience Campus Amsterdam - Attention & Cognition, EMGO+ - Lifestyle, Overweight and Diabetes, Langenberg, Claudia [0000-0002-5017-7344], Griffin, Simon [0000-0002-2157-4797], Wareham, Nicholas [0000-0003-1422-2993], Soranzo, Nicole [0000-0003-1095-3852], Wheeler, Eleanor [0000-0002-8616-6444], Luan, Jian'an [0000-0003-3137-6337], Forouhi, Nita [0000-0002-5041-248X], Sharp, Stephen [0000-0003-2375-1440], Sovio, Ulla [0000-0002-0799-1105], Apollo - University of Cambridge Repository, DIAGRAM Consortium, MAGIC Investigators, Johnson, T., Bergman, S., Bochud, M., Waeber, G., and Vollenweider, P.
Subjects: Netherlands Twin Register (NTR), Male, Adult, African Americans/genetics, Aged, Case-Control Studies, Cohort Studies, Diabetes Mellitus, Type 2/ethnology, Diabetes Mellitus, Type 2/genetics, Female, Genetic Predisposition to Disease, Genome-Wide Association Study, Genotype, Humans, Meta-Analysis as Topic, Middle Aged, Polymorphism, Single Nucleotide, Validation Studies as Topic, Medicin och hälsovetenskap, Linkage disequilibrium, Genetic Screens, endocrine system diseases, lcsh:Medicine, Genome-wide association study, Medical and Health Sciences, 0302 clinical medicine, Endocrinology, Genome Sequencing, lcsh:Science, Medicine(all), Genetics, African Americans, 0303 health sciences, education.field_of_study, INSULIN-RESISTANCE, Multidisciplinary, Agricultural and Biological Sciences(all), LARGE-SCALE ASSOCIATION, STAGE RENAL-DISEASE, COMMON VARIANTS, Genomics, Medicine, Research Article, SUSCEPTIBILITY LOCI, General Science & Technology, Population, Single-nucleotide polymorphism, Biology, DIAGRAM Consortium, 03 medical and health sciences, MAGIC Investigators, SDG 3 - Good Health and Well-being, Genetic linkage, MD Multidisciplinary, Genome-Wide Association Studies, SNP, ddc:610, education, Genotyping, 030304 developmental biology, Diabetic Endocrinology, LINKAGE ANALYSIS, Biochemistry, Genetics and Molecular Biology(all), lcsh:R, TCF7L2 GENE, Case-control study, Computational Biology, nutritional and metabolic diseases, Human Genetics, Diabetes Mellitus Type 2, Stage renal-disease, large-scale association, Susceptibility loci, Insulin-resistance, Fasting glucose, Tissue factor, Homeodomain protein, Linkage analysis, Common variants, TCF7L2 gene, Black or African American, Diabetes Mellitus, Type 2, TISSUE FACTOR, Genetics of Disease, HOMEODOMAIN PROTEIN, Genetic Polymorphism, lcsh:Q, Genome Expression Analysis, 030217 neurology & neurosurgery, Population Genetics, FASTING GLUCOSE
Abstract: African Americans are disproportionately affected by type 2 diabetes (T2DM) yet few studies have examined T2DM using genome-wide association approaches in this ethnicity. The aim of this study was to identify genes associated with T2DM in the African American population. We performed a Genome Wide Association Study (GWAS) using the Affymetrix 6.0 array in 965 African-American cases with T2DM and end-stage renal disease (T2DM-ESRD) and 1029 population-based controls. The most significant SNPs (n = 550 independent loci) were genotyped in a replication cohort and 122 SNPs (n = 98 independent loci) were further tested through genotyping three additional validation cohorts followed by meta-analysis in all five cohorts totaling 3,132 cases and 3,317 controls. Twelve SNPs had evidence of association in the GWAS (P
Published: 2012
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20. Genome-wide association for abdominal subcutaneous and visceral adipose reveals a novel locus for visceral fat in women

Author: Amelie Bonnefond, Nancy Heard-Costa, Eleftheria Zeggini, Anna Gloyn, LAURA CRISPONI, Eco De Geus, Michael Weedon, Torben Jørgensen, Jaakko Kaprio, Iva Miljkovic, Amanda Bennett, Yurii Aulchenko, Michael Stumvoll, Andrew Hattersley, Christian Dina, Sophie Visvikis-Siest, Udo Seedorf, Vilmundur Gudnason, Michael Marmot, Mika Kivimaki, Mary Feitosa, Danielle Posthuma, Claudia Langenberg, Stavroula Kanoni, Cyrus Cooper, Meena Kumari, Peter Kovacs, Debbie A Lawlor, Inga Prokopenko, Rafn Benediktsson, Stephen Kritchevsky, Philippe Froguel, Cornelia Van Duijn, Nita Forouhi, Anne B. Newman, Ko Willems van Dijk, John Carr, Reedik Mägi, Sutapa Mukherjee, Lyle John Palmer, Francois Pattou, L. Adrienne Cupples, Paul Franks, Paul Elliott, Albert Vernon Smith, MANUELA UDA, Kirsten Ohm Kyvik, Manuel Serrano Ríos, Richard Bergman, Gonneke Willemsen, Cecilia Lindgren, Igor Rudan, Human genetics, NCA - Attention & Cognition, EMGO - Lifestyle, overweight and diabetes, Institute for Molecular Medicine Finland, Research Group Ripatti Samuli, Hjelt Institute (-2014), Department of Public Health, Biostatistics Helsinki, Complex Disease Genetics, Genetic Epidemiology, Biological Psychology, Functional Genomics, Neuroscience Campus Amsterdam - Attention & Cognition, EMGO+ - Lifestyle, Overweight and Diabetes, GIANT Consortium, MAGIC Consortium, GLGC Consortium, Dupuis, J., Langenberg, C., Prokopenko, I., Saxena, R., Soranzo, N., Jackson, AU., Wheeler, E., Glazer, NL., Bouatia-Naji, N., Gloyn, AL., Lindgren, CM., Mägi, R., Morris, AP., Randall, J., Johnson, T., Elliott, P., Rybin, D., Thorleifsson, G., Steinthorsdottir, V., Henneman, P., Grallert, H., Dehghan, A., Hottenga, JJ., Franklin, CS., Navarro, P., Song, K., Goel, A., Perry, JR., Egan, JM., Lajunen, T., Grarup, N., Sparsø, T., Doney, A., Voight, BF., Stringham, HM., Li, M., Kanoni, S., Shrader, P., Cavalcanti-Proença, C., Kumari, M., Qi, L., Timpson, NJ., Gieger, C., Zabena, C., Rocheleau, G., Ingelsson, E., An, P., O'Connell, J., Luan£££Jian'an£££ J., Elliott, A., McCarroll, SA., Payne, F., Roccasecca, RM., Pattou, F., Sethupathy, P., Ardlie, K., Ariyurek, Y., Balkau, B., Barter, P., Beilby, JP., Ben-Shlomo, Y., Benediktsson, R., Bennett, AJ., Bergmann, S., Bochud, M., Boerwinkle, E., Bonnefond, A., Bonnycastle, LL., Borch-Johnsen, K., Böttcher, Y., Brunner, E., Bumpstead, SJ., Charpentier, G., Chen, YD., Chines, P., Clarke, R., Coin, LJ., Cooper, MN., Cornelis, M., Crawford, G., Crisponi, L., Day, IN., de Geus EJ., Delplanque, J., Dina, C., Erdos, MR., Fedson, AC., Fischer-Rosinsky, A., Forouhi, NG., Fox, CS., Frants, R., Franzosi, MG., Galan, P., Goodarzi, MO., Graessler, J., Groves, CJ., Grundy, S., Gwilliam, R., Gyllensten, U., Hadjadj, S., Hallmans, G., Hammond, N., Han, X., Hartikainen, AL., Hassanali, N., Hayward, C., Heath, SC., Hercberg, S., Herder, C., Hicks, AA., Hillman, DR., Hingorani, AD., Hofman, A., Hui, J., Hung, J., Isomaa, B., Johnson, PR., Jørgensen, T., Jula, A., Kaakinen, M., Kaprio, J., Kesaniemi, YA., Kivimaki, M., Knight, B., Koskinen, S., Kovacs, P., Kyvik, KO., Lathrop, GM., Lawlor, DA., Le Bacquer, O., Lecoeur, C., Li, Y., Lyssenko, V., Mahley, R., Mangino, M., Manning, AK., Martínez-Larrad£££María Teresa£££ MT., McAteer, JB., McCulloch, LJ., McPherson, R., Meisinger, C., Melzer, D., Meyre, D., Mitchell, BD., Morken, MA., Mukherjee, S., Naitza, S., Narisu, N., Neville, MJ., Oostra, BA., Orrù, M., Pakyz, R., Palmer, CN., Paolisso, G., Pattaro, C., Pearson, D., Peden, JF., Pedersen, NL., Perola, M., Pfeiffer, AF., Pichler, I., Polasek, O., Posthuma, D., Potter, SC., Pouta, A., Province, MA., Psaty, BM., Rathmann, W., Rayner, NW., Rice, K., Ripatti, S., Rivadeneira, F., Roden, M., Rolandsson, O., Sandbaek, A., Sandhu, M., Sanna, S., Sayer, AA., Scheet, P., Scott, LJ., Seedorf, U., Sharp, SJ., Shields, B., Sigurðsson, G., Sijbrands, EJ., Silveira, A., Simpson, L., Singleton, A., Smith, NL., Sovio, U., Swift, A., Syddall, H., Syvänen, AC., Tanaka, T., Thorand, B., Tichet, J., Tönjes, A., Tuomi, T., Uitterlinden, AG., van Dijk KW., van Hoek, M., Varma, D., Visvikis-Siest, S., Vitart, V., Vogelzangs, N., Waeber, G., Wagner, PJ., Walley, A., Walters, GB., Ward, KL., Watkins, H., Weedon, MN., Wild, SH., Willemsen, G., Witteman, JC., Yarnell, JW., Zeggini, E., Zelenika, D., Zethelius, B., Zhai, G., Zhao, JH., Zillikens, MC., Borecki, IB., Loos, RJ., Meneton, P., Magnusson, PK., Nathan, DM., Williams, GH., Hattersley, AT., Silander, K., Salomaa, V., Smith, GD., Bornstein, SR., Schwarz, P., Spranger, J., Karpe, F., Shuldiner, AR., Cooper, C., Dedoussis, GV., Serrano-Ríos, M., Morris, AD., Lind, L., Palmer, LJ., Hu, FB., Franks, PW., Ebrahim, S., Marmot, M., Kao, WH., Pankow, JS., Sampson, MJ., Kuusisto, J., Laakso, M., Hansen, T., Pedersen, O., Pramstaller, PP., Wichmann, HE., Illig, T., Rudan, I., Wright, AF., Stumvoll, M., Campbell, H., Wilson, JF., Hamsten, A., Bergman, RN., Buchanan, TA., Collins, FS., Mohlke, KL., Tuomilehto, J., Valle, TT., Altshuler, D., Rotter, JI., Siscovick, DS., Penninx, BW., Boomsma, ID., Deloukas, P., Spector, TD., Frayling, TM., Ferrucci, L., Kong, A., Thorsteinsdottir, U., Stefansson, K., van Duijn CM., Aulchenko, YS., Cao, A., Scuteri, A., Schlessinger, D., Uda, M., Ruokonen, A., Jarvelin, MR., Waterworth, DM., Vollenweider, P., Peltonen, L., Mooser, V., Abecasis, RG., Wareham, NJ., Sladek, R., Froguel, P., Watanabe, RM., Meigs, JB., Groop, L., Boehnke, M., McCarthy, MI., Florez, JC., Barroso£££Inês£££ I., Feitosa, Mary [0000-0002-0933-2410], Heard-Costa, Nancy [0000-0001-9730-0306], Newman, Anne B [0000-0002-0106-1150], Miljkovic, Iva [0000-0002-3155-9777], Kritchevsky, Stephen B [0000-0003-3336-6781], Carr, J Jeffrey [0000-0002-4398-8237], Gudnason, Vilmunder [0000-0001-5696-0084], Cupples, L Adrienne [0000-0003-0273-7965], Apollo - University of Cambridge Repository, and Medical Research Council (MRC)
Subjects: Male, Netherlands Twin Register (NTR), Cancer Research, Adipose tissue, Genome-wide association study, QH426-470, FTO gene, Body Mass Index, 0302 clinical medicine, Genetics (clinical), TISSUE DISTRIBUTION, GENE-EXPRESSION, 2. Zero hunger, ATHEROSCLEROSIS MESA, 0303 health sciences, INSULIN-RESISTANCE, Sex Characteristics, MAGIC Consortium, Adult, Aged, Cytokines/genetics, European Continental Ancestry Group, Female, Genome-Wide Association Study, HapMap Project, Humans, Intra-Abdominal Fat, Lysophospholipase/genetics, Middle Aged, Phenotype, Polymorphism, Single Nucleotide, Proteins/genetics, Subcutaneous Fat, Abdominal, 3142 Public health care science, environmental and occupational health, CARDIOVASCULAR-DISEASE, OBESITY, Cytokines, Medicine, Lysophospholipase, Research Article, medicine.medical_specialty, Waist, Alpha-Ketoglutarate-Dependent Dioxygenase FTO, 030209 endocrinology & metabolism, HIP RATIO, Biology, White People, GLGC Consortium, 03 medical and health sciences, Insulin resistance, Internal medicine, GIANT Consortium, medicine, Genetics, CORONARY-HEART-DISEASE, ddc:610, Molecular Biology, PERICARDIAL FAT, Ecology, Evolution, Behavior and Systematics, 030304 developmental biology, 0604 Genetics, Proteins, nutritional and metabolic diseases, medicine.disease, Obesity, Endocrinology, RISK-FACTORS, Body mass index, Developmental Biology
Abstract: Body fat distribution, particularly centralized obesity, is associated with metabolic risk above and beyond total adiposity. We performed genome-wide association of abdominal adipose depots quantified using computed tomography (CT) to uncover novel loci for body fat distribution among participants of European ancestry. Subcutaneous and visceral fat were quantified in 5,560 women and 4,997 men from 4 population-based studies. Genome-wide genotyping was performed using standard arrays and imputed to ∼2.5 million Hapmap SNPs. Each study performed a genome-wide association analysis of subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), VAT adjusted for body mass index, and VAT/SAT ratio (a metric of the propensity to store fat viscerally as compared to subcutaneously) in the overall sample and in women and men separately. A weighted z-score meta-analysis was conducted. For the VAT/SAT ratio, our most significant p-value was rs11118316 at LYPLAL1 gene (p = 3.1×10E-09), previously identified in association with waist–hip ratio. For SAT, the most significant SNP was in the FTO gene (p = 5.9×10E-08). Given the known gender differences in body fat distribution, we performed sex-specific analyses. Our most significant finding was for VAT in women, rs1659258 near THNSL2 (p = 1.6×10-08), but not men (p = 0.75). Validation of this SNP in the GIANT consortium data demonstrated a similar sex-specific pattern, with observed significance in women (p = 0.006) but not men (p = 0.24) for BMI and waist circumference (p = 0.04 [women], p = 0.49 [men]). Finally, we interrogated our data for the 14 recently published loci for body fat distribution (measured by waist–hip ratio adjusted for BMI); associations were observed at 7 of these loci. In contrast, we observed associations at only 7/32 loci previously identified in association with BMI; the majority of overlap was observed with SAT. Genome-wide association for visceral and subcutaneous fat revealed a SNP for VAT in women. More refined phenotypes for body composition and fat distribution can detect new loci not previously uncovered in large-scale GWAS of anthropometric traits., Author Summary Body fat distribution, particularly centralized obesity, is associated with metabolic risk above and beyond total adiposity. We performed genome-wide association of abdominal adipose depots quantified using computed tomography (CT) to uncover novel loci for body fat distribution among participants of European ancestry. We quantified subcutaneous and visceral fat in more than 10,000 women and men who also had genome-wide association data available. Given the known gender differences in body fat distribution, we performed sex-specific analyses. Our most significant finding was for VAT in women, near the THNSL2 gene. These findings were not observed in men. We also interrogated our data for the 14 recently published loci for body fat distribution (measured by waist–hip ratio adjusted for BMI); associations were observed for 7 of these loci, most notably for VAT/SAT ratio. We conclude that genome-wide association for visceral and subcutaneous fat revealed a SNP for VAT in women. More refined phenotypes for body composition and fat distribution can detect new loci not uncovered in large-scale GWAS of anthropometric traits.
Published: 2012
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21. Novel Loci for Adiponectin Levels and Their Influence on Type 2 Diabetes and Metabolic Traits: A Multi-Ethnic Meta-Analysis of 45,891 Individuals

Author: Seppo Koskinen, Christian Herder, Daniel I. Chasman, Andrew R. Wood, Jonna L. Grimsby, J.F. Wilson, Day Inm., Massimo Mangino, Gonneke Willemsen, Robert W. Mahley, Cristian Pattaro, Nicole L. Glazer, T.B. Harris, Irene Pichler, M S Sandhu, D. van Heemst, Christine Proença, Martha Ganser, Robert A. Hegele, Richa Saxena, Eleftheria Zeggini, Markku Laakso, Peter Kraft, Judith B. Borja, Karen L. Mohlke, J B Richards, de Geus Ejc., Robert Sladek, Cristen J. Willer, Samy Hadjadj, S.M. Boekholdt, Gina M. Peloso, Kijoung Song, Sutapa Mukherjee, Gudmar Thorleifsson, Winston Hide, Mark I. McCarthy, Ruth E. Pakyz, Marian Beekman, Ayellet V. Segrè, Inga Prokopenko, Ping An, George Dedoussis, Danielle Posthuma, Jeanette Erdmann, Simon J. Griffin, Nilesh J. Samani, Inke R. König, Frank B. Hu, Lokki M-L., David M. Evans, Xiaohui Li, Valgerdur Steinthorsdottir, Aimo Ruokonen, A Pouta, Kerrin S. Small, Cecilia M. Lindgren, O Le Bacquer, Xijing Han, Florian Kronenberg, E Katsareli, Christian Dina, S. Gabriel, Jochen Spranger, James S. Pankow, M. Kloppenburg, Penninx Bwjh., Torben Hansen, Josh Smith, Jennie Hui, Gordon H. Williams, Mark Seielstad, Ingrid B. Borecki, Weihua Zhang, Peter P. Pramstaller, Stephen J. Sharp, Neil R. Robertson, Zee Ryl., Mike Sampson, Angela Silveira, C.M. van Duijn, Anders Hamsten, Peter Shrader, Denis Rybin, Chen Y-Di., Gunnar Sigurdsson, Michael Stumvoll, Russel Tracy, Mark O. Goodarzi, Göran Hallmans, Michael R. Erdos, Valeriya Lyssenko, Juha Saharinen, Sven Bergmann, Jeffrey R. O'Connell, Debbie A Lawlor, Thomas Meitinger, Yvonne Böttcher, Jérôme Delplanque, Sarah G. Buxbaum, Silvia Naitza, Shah Ebrahim, Graham A. Hitman, Angelo Scuteri, Aroon D. Hingorani, Heribert Schunkert, François Pattou, Claudia Lamina, A L Elliott, Sekar Kathiresan, Dawn M. Waterworth, Jennifer A. Brody, Thomas Quertermous, Leena Peltonen, Josephine M. Egan, Daniel J. Rader, J F Peden, Yarnell Jwg., Daniel S. Pearson, Pfeiffer Afh., P S Chines, N Vogelzangs, Susan Redline, Alka M. Kanaya, T B Harris, J. V. van Vliet-Ostaptchouk, Ghislain Rocheleau, Rune R. Frants, Olga D. Carlson, James G. Wilson, Melissa Garcia, Ong Rt-H., Mark J. Caulfield, Tanya M. Teslovich, Loo B-M., Beatrice Knight, Andreas Ziegler, Claudia Langenberg, Yoon Shin Cho, Paul M. Ridker, Mark J. Rieder, Praveen Sethupathy, Bert Bravenboer, J. Viikari, Matt Neville, Ioannis M. Stylianou, Andrew Walley, Jarvelin M-R., Jarred B. McAteer, Ronald M. Krauss, Augustine Kong, Oluf Pedersen, Mark J. Daly, Andrew P. Morris, Anna F. Dominiczak, Stéphane Cauchi, Michael Boehnke, Christopher J. O'Donnell, Barbara Thorand, Peter M. Nilsson, Aaron Isaacs, Deborah A. Nickerson, Roza Blagieva, Mary F. Feitosa, Nicholas J. Wareham, Robert Roberts, J S Kooner, K W van Dijk, Tiinamaija Tuomi, Paul Scheet, Lynda M. Rose, Albert V. Smith, Rafn Benediktsson, Chiara Sabatti, Candace Guiducci, Lee M. Kaplan, Aki S. Havulinna, Toby Johnson, Samuli Ripatti, Erik Ingelsson, Mario A. Morken, Carl G. P. Platou, Anke Tönjes, Qi Sun, Narisu Narisu, S J Bumpstead, Jose M. Ordovas, Alan B. Feranil, L Groop, P Chines, Sara M. Willems, Perry Jrb., Matthew A. Allison, Jan Scott, Cécile Lecoeur, Kastelein Jjp., Herman A. Taylor, Anyuan Cao, Christopher J. Groves, Lincoln D. Stein, Laura J. Scott, John Beilby, Kristin G. Ardlie, Christopher S. Franklin, Yoav Ben-Shlomo, B M Shields, N J Timpson, Marco Orrù, Amélie Bonnefond, Kiran Musunuru, Murielle Bochud, Udo Seedorf, Yongmei Liu, Guillaume Lettre, Lee J-Y., Alan R. Shuldiner, Ryan P. Welch, David J. Hunter, John Whitfield, Klaus Strassburger, Khaw K-T., Hartikainen A-L., Gunnar Sigurðsson, Lu Qi, Richard N. Bergman, G M Lathrop, Sigrid W. Fouchier, T van Herpt, David S. Siscovick, Igor Rudan, Richard M. Watanabe, Themistocles L. Assimes, Nicholas G. Martin, Ozren Polasek, Dhiraj Varma, K Kim, Oliver Hofmann, Nicholas D. Hastie, S Bumpstead, Jose C. Florez, Fernando Rivadeneira, Katharine R. Owen, Braxton D. Mitchell, Alisa K. Manning, Abbas Dehghan, Bruce Bartholow Duncan, Cisca Wijmenga, Timo T. Valle, Jaakko Kaprio, Mika Kivimäki, B Shields, Laila Simpson, Tim D. Spector, Paul W. Franks, Guangju Zhai, María Teresa Martínez-Larrad, Janssens Acjw., Kim L. Ward, Inês Barroso, Xiuqing Guo, Rosa Maria Roccasecca, Zari Dastani, Reijo Laaksonen, Wilmar Igl, Vincent Mooser, Niels Grarup, Cornelia Huth, Christian Gieger, Fabio Marroni, Jaakko Tuomilehto, Doney Asf., Andrew C. Edmondson, Christian Fuchsberger, Meena Kumari, David M. Nathan, Reedik Mägi, Solomon K. Musani, U de Faire, Knut Borch-Johnsen, Masahiro Koseki, Giuseppe Paolisso, Norman Klopp, Caroline S. Fox, Nelson B. Freimer, Mika Kähönen, Peter Henneman, Diana Zelenika, K Willems-Vandijk, Steven A. McCarroll, Paul Elliott, Wichmann H-E., J. C. Bis, Nita G. Forouhi, Antti Jula, Witteman Jcm., Fredrik Karpe, Joseph Hung, Antje Fischer-Rosinsky, Eric J. Brunner, Elena Gonzalez, Soumya Raychaudhuri, Jian'an Luan, Josée Dupuis, Joshua C. Randall, Taesung Park, Francis S. Collins, Lori L. Bonnycastle, Andrew A. Hicks, Peter Kovacs, Thomas Illig, Maja Barbalić, David Couper, Jaspal S. Kooner, Damien C. Croteau-Chonka, Gavin Lucas, P J Wagner, Young-Jin Kim, Yurii S. Aulchenko, Aurelian Bidulescu, Ingrid Meulenbelt, Pilar Galan, Iris M. Heid, Michael N. Weedon, Serena Sanna, Sarah H. Wild, Hivert M-F., Patricia B. Munroe, Johan G. Eriksson, Teresa Ferreira, Robert A. Scott, A. Sandbaek, Kenneth Rice, Veronique Vitart, Xin Yuan, Leslie A. Lange, Hilma Holm, Jorge R. Kizer, Timothy M. Frayling, Marika Kaakinen, Liu C-T., Petersen A-K., Peter Schwarz, G B Walters, Palmer Cna., Jean Tichet, Bernhard Paulweber, Ying Wu, Alyson Hall, Christopher T. Johansen, David Masson, Martin Ladouceur, Christie M. Ballantyne, Tai E-S., Robert Luben, Guillaume Charpentier, Angela Döring, Philip J. Barter, Ruth McPherson, Benjamin F. Voight, Wolfgang Rathmann, Mark Walker, Markus Perola, M. A. Province, Veikko Salomaa, James B. Meigs, George Davey Smith, Robert Clarke, Gerard Waeber, Stefania Bandinelli, Sally L. Ricketts, Kaisa Silander, Loos Rjf., Amanda J. Bennett, John C. Chambers, Marilyn C. Cornelis, L A Cupples, Andrew T. Hattersley, M Sandhu, Marju Orho-Melander, C M van Duijn, Olli T. Raitakari, David Meyre, Ida Surakka, Jouke-Jan Hottenga, Uh H-W., Kari Stefansson, David Melzer, P E Slagboom, Kristian Midthjell, Robert K. Semple, James P. Pirruccello, Aloysius G Lieverse, Åsa Johansson, Michael Roden, Felicity Payne, Eric J.G. Sijbrands, N P Burtt, David R. Hillman, Michael Marmot, Todd Green, Eric E. Schadt, Sijbrands Ejg., Tien Yin Wong, Coin Ljm., K B Boström, Olov Rolandsson, A D Morris, David Altshuler, Harald Grallert, L C Groop, Alan F. Wright, Karen Kapur, Xueling Sim, Philippe Froguel, K O Kyvik, T. Lauritzen, Linda S. Adair, Yavuz Ariyurek, Talin Haritunians, Toshiko Tanaka, Albert Hofman, MariaGrazia Franzosi, Nicholas L. Smith, Laura Crisponi, Andrew B. Singleton, A Uitterlinden, Bo Isomaa, Y A Kesaniemi, Anne U. Jackson, Christa Meisinger, Holly E. Syddall, Dorret I. Boomsma, Harry Campbell, Gonçalo R. Abecasis, Lyudmyla Kedenko, Christine Cavalcanti-Proença, G Crawford, Scott M. Grundy, Johnson Prv., Nuotio M-L., I Chen, J.H. Smit, Anuj Goel, M Li, David P. Strachan, Kenechi Ejebe, Beverley Balkau, Neelam Hassanali, Kristian Hveem, Pierre Meneton, R. Gwilliam, A J Swift, Caroline Hayward, J. Graessler, Carina Zabena, B. St Pourcain, Michel Marre, Margot Haun, Lyytikäinen L-P., Ben A. Oostra, Stefan Coassin, M. van Hoek, Nigel W. Rayner, John R. Thompson, Kurt Lohman, Ulla Sovio, Unnur Thorsteinsdottir, Naveed Sattar, Lyle J. Palmer, Ulf Gyllensten, A Elliott, Muredach P. Reilly, A Swift, Luigi Ferrucci, Syvänen A-C., Simon C. Potter, T.W. van Haeften, G Wu, Stefan Böhringer, Grant W. Montgomery, Edward G. Lakatta, Serkalem Demissie, Alex S. F. Doney, Najaf Amin, Lenore J. Launer, Hugh Watkins, Johanna Kuusisto, Lars Lind, Stefan R. Bornstein, Laura J. Rasmussen-Torvik, Terho Lehtimäki, Guillaume Paré, Sophie Visvikis-Siest, S C Heath, David Schlessinger, Juha Sinisalo, Kao Whl., Mark E. Cooper, Kati Kristiansson, Thomas W. Winkler, Thomas Sparsø, Laura J. McCulloch, Taina K. Lajunen, Alex N. Parker, Nabila Bouatia-Naji, Markku S. Nieminen, Peter Vollenweider, Wendy L. McArdle, G K Hovingh, Thomas A. Buchanan, Avan Aihie Sayer, M C Zillikens, Jing Hua Zhao, Naomi Hammond, Vilmundur Gudnason, Björn Zethelius, Panos Deloukas, Jacqueline C. M. Witteman, Eric Boerwinkle, Manuel Serrano-Ríos, Anna L. Gloyn, Katherine S. Elliott, A C Fedson, Torben Jørgensen, Nicole Soranzo, Heather M. Stringham, Bruce M. Psaty, A G Uitterlinden, Stavroula Kanoni, Christian Hengstenberg, Yun Li, Olle Melander, Alan R. Tall, Manuela Uda, Magnusson Pke., Christopher W. Kuzawa, V Mooser, R. M. van Dam, Jerome I. Rotter, Greenwood Cmt., Cyrus Cooper, Pau Navarro, Min Jin Go, Nancy L. Pedersen, Serge Hercberg, Bernhard O. Boehm, Eleanor Wheeler, Epidemiology, Medical Microbiology & Infectious Diseases, Clinical Genetics, Dastani, Z, Hivert, Mf, Timpson, N, Perry, Jr, Yuan, X, Scott, Ra, Henneman, P, Heid, Im, Kizer, Jr, Lyytikäinen, Lp, Fuchsberger, C, Tanaka, T, Morris, Ap, Small, K, Isaacs, A, Beekman, M, Coassin, S, Lohman, K, Qi, L, Kanoni, S, Pankow, J, Uh, Hw, Wu, Y, Bidulescu, A, Rasmussen Torvik, Lj, Greenwood, Cm, Ladouceur, M, Grimsby, J, Manning, Ak, Liu, Ct, Kooner, J, Mooser, Ve, Vollenweider, P, Kapur, Ka, Chambers, J, Wareham, Nj, Langenberg, C, Frants, R, Willems Vandijk, K, Oostra, Ba, Willems, Sm, Lamina, C, Winkler, Tw, Psaty, Bm, Tracy, Rp, Brody, J, Chen, I, Viikari, J, Kähönen, M, Pramstaller, Pp, Evans, Dm, St Pourcain, B, Sattar, N, Wood, Ar, Bandinelli, S, Carlson, Od, Egan, Jm, Böhringer, S, van Heemst, D, Kedenko, L, Kristiansson, K, Nuotio, Ml, Loo, Bm, Harris, T, Garcia, M, Kanaya, A, Haun, M, Klopp, N, Wichmann, He, Deloukas, P, Katsareli, E, Couper, Dj, Duncan, Bb, Kloppenburg, M, Adair, L, Borja, Jb, DIAGRAM+, Consortium, Magic, Consortium, Glgc, Investigator, Muther, Consortium, Wilson, Jg, Musani, S, Guo, X, Johnson, T, Semple, R, Teslovich, Tm, Allison, Ma, Redline, S, Buxbaum, Sg, Mohlke, Kl, Meulenbelt, I, Ballantyne, Cm, Dedoussis, Gv, Hu, Fb, Liu, Y, Paulweber, B, Spector, Td, Slagboom, Pe, Ferrucci, L, Jula, A, Perola, M, Raitakari, O, Florez, Jc, Salomaa, V, Eriksson, Jg, Frayling, Tm, Hicks, Aa, Lehtimäki, T, Smith, Gd, Siscovick, D, Kronenberg, F, van Duijn, C, Loos, Rj, Waterworth, Dm, Meigs, Jb, Dupuis, J, Richards, Jb, Voight, Bf, Scott, Lj, Steinthorsdottir, V, Dina, C, Welch, Rp, Zeggini, E, Huth, C, Aulchenko, Y, Thorleifsson, G, Mcculloch, Lj, Ferreira, T, Grallert, H, Amin, N, Wu, G, Willer, Cj, Raychaudhuri, S, Mccarroll, Sa, Hofmann, Om, Segrè, Av, van Hoek, M, Navarro, P, Ardlie, K, Balkau, B, Benediktsson, R, Bennett, Aj, Blagieva, R, Boerwinkle, E, Bonnycastle, Ll, Boström, Kb, Bravenboer, B, Bumpstead, S, Burtt, Np, Charpentier, G, Chines, P, Cornelis, M, Crawford, G, Doney, A, Elliott, K, Elliott, Al, Erdos, Mr, Fox, C, Franklin, C, Ganser, M, Gieger, C, Grarup, N, Green, T, Griffin, S, Groves, Cj, Guiducci, C, Hadjadj, S, Hassanali, N, Herder, C, Isomaa, B, Jackson, Au, Johnson, Pr, Jørgensen, T, Kao, Wh, Kong, A, Kraft, P, Kuusisto, J, Lauritzen, T, Li, M, Lieverse, A, Lindgren, Cm, Lyssenko, V, Marre, M, Meitinger, T, Midthjell, K, Morken, Ma, Narisu, N, Nilsson, P, Owen, Kr, Payne, F, Petersen, Ak, Platou, C, Proença, C, Prokopenko, I, Rathmann, W, Rayner, Nw, Robertson, Nr, Rocheleau, G, Roden, M, Sampson, Mj, Saxena, R, Shields, Bm, Shrader, P, Sigurdsson, G, Sparsø, T, Strassburger, K, Stringham, Hm, Sun, Q, Swift, Aj, Thorand, B, Tichet, J, Tuomi, T, van Dam, Rm, van Haeften, Tw, van Herpt, T, van Vliet Ostaptchouk, Jv, Walters, Gb, Weedon, Mn, Wijmenga, C, Witteman, J, Bergman, Rn, Cauchi, S, Collins, F, Gloyn, Al, Gyllensten, U, Hansen, T, Hide, Wa, Hitman, Ga, Hofman, A, Hunter, Dj, Hveem, K, Laakso, M, Morris, Ad, Palmer, Cn, Rudan, I, Sijbrands, E, Stein, Ld, Tuomilehto, J, Uitterlinden, A, Walker, M, Watanabe, Rm, Abecasis, Gr, Boehm, Bo, Campbell, H, Daly, Mj, Hattersley, At, Pedersen, O, Barroso, I, Groop, L, Sladek, R, Thorsteinsdottir, U, Wilson, Jf, Illig, T, Froguel, P, van Duijn, Cm, Stefansson, K, Altshuler, D, Boehnke, M, Mccarthy, Mi, Soranzo, N, Wheeler, E, Glazer, Nl, Bouatia Naji, N, Mägi, R, Randall, J, Elliott, P, Rybin, D, Dehghan, A, Hottenga, Jj, Song, K, Goel, A, Lajunen, T, Cavalcanti Proença, C, Kumari, M, Timpson, Nj, Zabena, C, Ingelsson, E, An, P, O'Connell, J, Luan, J, Elliott, A, Roccasecca, Rm, Pattou, F, Sethupathy, P, Ariyurek, Y, Barter, P, Beilby, Jp, Ben Shlomo, Y, Bergmann, S, Bochud, M, Bonnefond, A, Borch Johnsen, K, Böttcher, Y, Brunner, E, Bumpstead, Sj, Chen, Yd, Clarke, R, Coin, Lj, Cooper, Mn, Crisponi, L, Day, In, de Geus, Ej, Delplanque, J, Fedson, Ac, Fischer Rosinsky, A, Forouhi, Ng, Franzosi, Mg, Galan, P, Goodarzi, Mo, Graessler, J, Grundy, S, Gwilliam, R, Hallmans, G, Hammond, N, Han, X, Hartikainen, Al, Hayward, C, Heath, Sc, Hercberg, S, Hillman, Dr, Hingorani, Ad, Hui, J, Hung, J, Kaakinen, M, Kaprio, J, Kesaniemi, Ya, Kivimaki, M, Knight, B, Koskinen, S, Kovacs, P, Kyvik, Ko, Lathrop, Gm, Lawlor, Da, Le Bacquer, O, Lecoeur, C, Li, Y, Mahley, R, Mangino, M, Martínez Larrad, Mt, Mcateer, Jb, Mcpherson, R, Meisinger, C, Melzer, D, Meyre, D, Mitchell, Bd, Mukherjee, S, Naitza, S, Neville, Mj, Orrù, M, Pakyz, R, Paolisso, Giuseppe, Pattaro, C, Pearson, D, Peden, Jf, Pedersen, Nl, Pfeiffer, Af, Pichler, I, Polasek, O, Posthuma, D, Potter, Sc, Pouta, A, Province, Ma, Rice, K, Ripatti, S, Rivadeneira, F, Rolandsson, O, Sandbaek, A, Sandhu, M, Sanna, S, Sayer, Aa, Scheet, P, Seedorf, U, Sharp, Sj, Shields, B, Sigurðsson, G, Sijbrands, Ej, Silveira, A, Simpson, L, Singleton, A, Smith, Nl, Sovio, U, Swift, A, Syddall, H, Syvänen, Ac, Tönjes, A, Uitterlinden, Ag, van Dijk, Kw, Varma, D, Visvikis Siest, S, Vitart, V, Vogelzangs, N, Waeber, G, Wagner, Pj, Walley, A, Ward, Kl, Watkins, H, Wild, Sh, Willemsen, G, Witteman, Jc, Yarnell, Jw, Zelenika, D, Zethelius, B, Zhai, G, Zhao, Jh, Zillikens, Mc, Diagram, Consortium, Giant, Consortium, Global B., Pgen Consortium, Borecki, Ib, Meneton, P, Magnusson, Pk, Nathan, Dm, Williams, Gh, Silander, K, Bornstein, Sr, Schwarz, P, Spranger, J, Karpe, F, Shuldiner, Ar, Cooper, C, Serrano Ríos, M, Lind, L, Palmer, Lj, Hu FB, 1st, Franks, Pw, Ebrahim, S, Marmot, M, Wright, Af, Stumvoll, M, Hamsten, A, Procardis, Consortium, Buchanan, Ta, Valle, Tt, Rotter, Ji, Penninx, Bw, Boomsma, Di, Cao, A, Scuteri, A, Schlessinger, D, Uda, M, Ruokonen, A, Jarvelin, Mr, Peltonen, L, Mooser, V, Magic, Investigator, Glgc, Consortium, Musunuru, K, Smith, Av, Edmondson, Ac, Stylianou, Im, Koseki, M, Pirruccello, Jp, Chasman, Di, Johansen, Ct, Fouchier, Sw, Peloso, Gm, Barbalic, M, Ricketts, Sl, Bis, Jc, Feitosa, Mf, Orho Melander, M, Melander, O, Li, X, Cho, Y, Go, Mj, Kim, Yj, Lee, Jy, Park, T, Kim, K, Sim, X, Ong, Rt, Croteau Chonka, Dc, Lange, La, Smith, Jd, Ziegler, A, Zhang, W, Zee, Ry, Whitfield, Jb, Thompson, Jr, Surakka, I, Smit, Jh, Sinisalo, J, Scott, J, Saharinen, J, Sabatti, C, Rose, Lm, Roberts, R, Rieder, M, Parker, An, Pare, G, O'Donnell, Cj, Nieminen, M, Nickerson, Da, Montgomery, Gw, Mcardle, W, Masson, D, Martin, Ng, Marroni, F, Lucas, G, Luben, R, Lokki, Ml, Lettre, G, Launer, Lj, Lakatta, Eg, Laaksonen, R, König, Ir, Khaw, Kt, Kaplan, Lm, Johansson, Å, Janssens, Ac, Igl, W, Hovingh, Gk, Hengstenberg, C, Havulinna, A, Hastie, Nd, Harris, Tb, Haritunians, T, Hall, A, Groop, Lc, Gonzalez, E, Freimer, Nb, Erdmann, J, Ejebe, Kg, Döring, A, Dominiczak, Af, Demissie, S, de Faire, U, Caulfield, Mj, Boekholdt, Sm, Assimes, Tl, Quertermous, T, Seielstad, M, Wong, Ty, Tai, E, Feranil, Ab, Kuzawa, Cw, Taylor HA, Jr, Gabriel, Sb, Holm, H, Gudnason, V, Krauss, Rm, Ordovas, Jm, Munroe, Pb, Tall, Ar, Hegele, Ra, Kastelein, Jj, Schadt, Ee, Strachan, Dp, Reilly, Mp, Samani, Nj, Schunkert, H, Cupples, La, Ridker, Pm, Rader, Dj, Kathiresan, S., Medical Research Council (MRC), Perry, John [0000-0001-6483-3771], Wareham, Nicholas [0000-0003-1422-2993], Langenberg, Claudia [0000-0002-5017-7344], Semple, Robert [0000-0001-6539-3069], Griffin, Simon [0000-0002-2157-4797], Barroso, Ines [0000-0001-5800-4520], Soranzo, Nicole [0000-0003-1095-3852], Wheeler, Eleanor [0000-0002-8616-6444], Luan, Jian'an [0000-0003-3137-6337], Forouhi, Nita [0000-0002-5041-248X], Sharp, Stephen [0000-0003-2375-1440], Sovio, Ulla [0000-0002-0799-1105], Zhao, Jing Hua [0000-0003-4930-3582], Luben, Robert [0000-0002-5088-6343], Khaw, Kay-Tee [0000-0002-8802-2903], Sandhu, Manjinder [0000-0002-2725-142X], Apollo - University of Cambridge Repository, Biological Psychology, Functional Genomics, Neuroscience Campus Amsterdam - Attention & Cognition, EMGO+ - Lifestyle, Overweight and Diabetes, Other departments, Experimental Vascular Medicine, ACS - Amsterdam Cardiovascular Sciences, Vascular Medicine, Cardiology, Human genetics, Psychiatry, NCA - Attention & Cognition, EMGO - Lifestyle, overweight and diabetes, Lääketieteen yksikkö - School of Medicine, University of Tampere, Institute for Molecular Medicine Finland, Hjelt Institute (-2014), Clinicum, Department of General Practice and Primary Health Care, Department of Public Health, Haartman Institute (-2014), Transplantation Laboratory, Biostatistics Helsinki, Quantitative Genetics, Complex Disease Genetics, Genetic Epidemiology, DIAGRAM+ Consortium, MAGIC Consortium, GLGC Investigators, MuTHER Consortium, DIAGRAM Consortium, GIANT Consortium, Global B Pgen Consortium, Procardis Consortium, MAGIC investigators, GLGC Consortium, Olson, J., Kronmal, R., Robbins, J., Chaves, PH., Burke, G., Kuller, LH., Tracy, R., Gottdiener, J., Prineas, R., Becker, JT., Enright, P., Klein, R., and O'Leary, DH.
Subjects: Netherlands Twin Register (NTR), Male, Insulin Resistance/genetics, VARIANTS, 0302 clinical medicine, POPULATION, African Americans, blood/genetics, 0303 health sciences, education.field_of_study, Adiponectin/blood, Adiponectin/genetics, Asian Continental Ancestry Group, Cholesterol, HDL/genetics, Diabetes Mellitus, Type 2/genetics, European Continental Ancestry Group, Female, Gene Expression, Genetic Predisposition to Disease, Genome-Wide Association Study, Glucose Tolerance Test, Humans, Metabolic Networks and Pathways, Polymorphism, Single Nucleotide, Waist-Hip Ratio, Global B Pgen Consortium, MAGIC investigators, 3. Good health, Cholesterol, Medicine, Adiponectin, Type 2, medicine.medical_specialty, HDL, Biolääketieteet - Biomedicine, Single-nucleotide polymorphism, DIAGRAM Consortium, White People, Molecular Genetics, GLGC Consortium, 03 medical and health sciences, Asian People, SDG 3 - Good Health and Well-being, GIANT Consortium, Diabetes Mellitus, Genetics, DIAGRAM+ Consortium, GENOME-WIDE ASSOCIATION, Polymorphism, education, Biology, Molecular Biology, Ecology, Evolution, Behavior and Systematics, 0604 Genetics, Science & Technology, GLGC Investigators, nutritional and metabolic diseases, ta3121, medicine.disease, Obesity, Black or African American, blood/genetics, African Americans, Asian Continental Ancestry Group, Cholesterol, genetics, Diabetes Mellitus, genetics, European Continental Ancestry Group, Female, Gene Expression, Genetic Predisposition to Disease, Genome-Wide Association Study, Glucose Tolerance Test, Humans, Insulin Resistance, genetics, Male, Metabolic Networks and Pathways, Polymorphism, Single Nucleotide, Waist-Hip Ratio, Endocrinology, Diabetes Mellitus, Type 2, Developmental Biology, Type 2/genetics, Cancer Research, Type 2 diabetes, QH426-470, 030204 cardiovascular system & hematology, LIPID CONCENTRATIONS, GENETICS & HEREDITY, Genetics (clinical), RISK, 2. Zero hunger, INSULIN-RESISTANCE, Glucose tolerance test, medicine.diagnostic_test, MAGIC Consortium, Single Nucleotide, ADIPOSE-TISSUE, CORONARY-ARTERY-DISEASE, Life Sciences & Biomedicine, Research Article, Clinical Research Design, GENETIC-BASIS, Population, Insulin resistance, Internal medicine, Diabetes mellitus, medicine, ddc:610, 030304 developmental biology, RECEPTOR, Cholesterol, HDL, Human Genetics, HDL/genetics, 3121 General medicine, internal medicine and other clinical medicine, MuTHER Consortium, 3111 Biomedicine, Procardis Consortium, Insulin Resistance
Abstract: Circulating levels of adiponectin, a hormone produced predominantly by adipocytes, are highly heritable and are inversely associated with type 2 diabetes mellitus (T2D) and other metabolic traits. We conducted a meta-analysis of genome-wide association studies in 39,883 individuals of European ancestry to identify genes associated with metabolic disease. We identified 8 novel loci associated with adiponectin levels and confirmed 2 previously reported loci (P = 4.5×10−8–1.2×10−43). Using a novel method to combine data across ethnicities (N = 4,232 African Americans, N = 1,776 Asians, and N = 29,347 Europeans), we identified two additional novel loci. Expression analyses of 436 human adipocyte samples revealed that mRNA levels of 18 genes at candidate regions were associated with adiponectin concentrations after accounting for multiple testing (p, Author Summary Serum adiponectin levels are highly heritable and are inversely correlated with the risk of type 2 diabetes (T2D), coronary artery disease, stroke, and several metabolic traits. To identify common genetic variants associated with adiponectin levels and risk of T2D and metabolic traits, we conducted a meta-analysis of genome-wide association studies of 45,891 multi-ethnic individuals. In addition to confirming that variants at the ADIPOQ and CDH13 loci influence adiponectin levels, our analyses revealed that 10 new loci also affecting circulating adiponectin levels. We demonstrated that expression levels of several genes in these candidate regions are associated with serum adiponectin levels. Using a powerful novel method to assess the contribution of the identified variants with other traits using summary-level results from large-scale GWAS consortia, we provide evidence that the risk alleles for adiponectin are associated with deleterious changes in T2D risk and metabolic syndrome traits (triglycerides, HDL, post-prandial glucose, insulin, and waist-to-hip ratio), demonstrating that the identified loci, taken together, impact upon metabolic disease.
Published: 2012
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22. Genetic drivers of heterogeneity in type 2 diabetes pathophysiology.

Author: Suzuki K, Hatzikotoulas K, Southam L, Taylor HJ, Yin X, Lorenz KM, Mandla R, Huerta-Chagoya A, Melloni GEM, Kanoni S, Rayner NW, Bocher O, Arruda AL, Sonehara K, Namba S, Lee SSK, Preuss MH, Petty LE, Schroeder P, Vanderwerff B, Kals M, Bragg F, Lin K, Guo X, Zhang W, Yao J, Kim YJ, Graff M, Takeuchi F, Nano J, Lamri A, Nakatochi M, Moon S, Scott RA, Cook JP, Lee JJ, Pan I, Taliun D, Parra EJ, Chai JF, Bielak LF, Tabara Y, Hai Y, Thorleifsson G, Grarup N, Sofer T, Wuttke M, Sarnowski C, Gieger C, Nousome D, Trompet S, Kwak SH, Long J, Sun M, Tong L, Chen WM, Nongmaithem SS, Noordam R, Lim VJY, Tam CHT, Joo YY, Chen CH, Raffield LM, Prins BP, Nicolas A, Yanek LR, Chen G, Brody JA, Kabagambe E, An P, Xiang AH, Choi HS, Cade BE, Tan J, Broadaway KA, Williamson A, Kamali Z, Cui J, Thangam M, Adair LS, Adeyemo A, Aguilar-Salinas CA, Ahluwalia TS, Anand SS, Bertoni A, Bork-Jensen J, Brandslund I, Buchanan TA, Burant CF, Butterworth AS, Canouil M, Chan JCN, Chang LC, Chee ML, Chen J, Chen SH, Chen YT, Chen Z, Chuang LM, Cushman M, Danesh J, Das SK, de Silva HJ, Dedoussis G, Dimitrov L, Doumatey AP, Du S, Duan Q, Eckardt KU, Emery LS, Evans DS, Evans MK, Fischer K, Floyd JS, Ford I, Franco OH, Frayling TM, Freedman BI, Genter P, Gerstein HC, Giedraitis V, González-Villalpando C, González-Villalpando ME, Gordon-Larsen P, Gross M, Guare LA, Hackinger S, Hakaste L, Han S, Hattersley AT, Herder C, Horikoshi M, Howard AG, Hsueh W, Huang M, Huang W, Hung YJ, Hwang MY, Hwu CM, Ichihara S, Ikram MA, Ingelsson M, Islam MT, Isono M, Jang HM, Jasmine F, Jiang G, Jonas JB, Jørgensen T, Kamanu FK, Kandeel FR, Kasturiratne A, Katsuya T, Kaur V, Kawaguchi T, Keaton JM, Kho AN, Khor CC, Kibriya MG, Kim DH, Kronenberg F, Kuusisto J, Läll K, Lange LA, Lee KM, Lee MS, Lee NR, Leong A, Li L, Li Y, Li-Gao R, Ligthart S, Lindgren CM, Linneberg A, Liu CT, Liu J, Locke AE, Louie T, Luan J, Luk AO, Luo X, Lv J, Lynch JA, Lyssenko V, Maeda S, Mamakou V, Mansuri SR, Matsuda K, Meitinger T, Melander O, Metspalu A, Mo H, Morris AD, Moura FA, Nadler JL, Nalls MA, Nayak U, Ntalla I, Okada Y, Orozco L, Patel SR, Patil S, Pei P, Pereira MA, Peters A, Pirie FJ, Polikowsky HG, Porneala B, Prasad G, Rasmussen-Torvik LJ, Reiner AP, Roden M, Rohde R, Roll K, Sabanayagam C, Sandow K, Sankareswaran A, Sattar N, Schönherr S, Shahriar M, Shen B, Shi J, Shin DM, Shojima N, Smith JA, So WY, Stančáková A, Steinthorsdottir V, Stilp AM, Strauch K, Taylor KD, Thorand B, Thorsteinsdottir U, Tomlinson B, Tran TC, Tsai FJ, Tuomilehto J, Tusie-Luna T, Udler MS, Valladares-Salgado A, van Dam RM, van Klinken JB, Varma R, Wacher-Rodarte N, Wheeler E, Wickremasinghe AR, van Dijk KW, Witte DR, Yajnik CS, Yamamoto K, Yamamoto K, Yoon K, Yu C, Yuan JM, Yusuf S, Zawistowski M, Zhang L, Zheng W, Raffel LJ, Igase M, Ipp E, Redline S, Cho YS, Lind L, Province MA, Fornage M, Hanis CL, Ingelsson E, Zonderman AB, Psaty BM, Wang YX, Rotimi CN, Becker DM, Matsuda F, Liu Y, Yokota M, Kardia SLR, Peyser PA, Pankow JS, Engert JC, Bonnefond A, Froguel P, Wilson JG, Sheu WHH, Wu JY, Hayes MG, Ma RCW, Wong TY, Mook-Kanamori DO, Tuomi T, Chandak GR, Collins FS, Bharadwaj D, Paré G, Sale MM, Ahsan H, Motala AA, Shu XO, Park KS, Jukema JW, Cruz M, Chen YI, Rich SS, McKean-Cowdin R, Grallert H, Cheng CY, Ghanbari M, Tai ES, Dupuis J, Kato N, Laakso M, Köttgen A, Koh WP, Bowden DW, Palmer CNA, Kooner JS, Kooperberg C, Liu S, North KE, Saleheen D, Hansen T, Pedersen O, Wareham NJ, Lee J, Kim BJ, Millwood IY, Walters RG, Stefansson K, Ahlqvist E, Goodarzi MO, Mohlke KL, Langenberg C, Haiman CA, Loos RJF, Florez JC, Rader DJ, Ritchie MD, Zöllner S, Mägi R, Marston NA, Ruff CT, van Heel DA, Finer S, Denny JC, Yamauchi T, Kadowaki T, Chambers JC, Ng MCY, Sim X, Below JE, Tsao PS, Chang KM, McCarthy MI, Meigs JB, Mahajan A, Spracklen CN, Mercader JM, Boehnke M, Rotter JI, Vujkovic M, Voight BF, Morris AP, and Zeggini E
Subjects: Humans, Adipocytes metabolism, Chromatin genetics, Chromatin metabolism, Coronary Artery Disease complications, Coronary Artery Disease genetics, Diabetic Nephropathies complications, Diabetic Nephropathies genetics, Endothelial Cells metabolism, Enteroendocrine Cells, Epigenomics, Islets of Langerhans metabolism, Multifactorial Inheritance genetics, Peripheral Arterial Disease complications, Peripheral Arterial Disease genetics, Single-Cell Analysis, Diabetes Mellitus, Type 2 classification, Diabetes Mellitus, Type 2 complications, Diabetes Mellitus, Type 2 genetics, Diabetes Mellitus, Type 2 pathology, Diabetes Mellitus, Type 2 physiopathology, Disease Progression, Genetic Predisposition to Disease genetics, Genome-Wide Association Study
Abstract: Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes 1,2 and molecular mechanisms that are often specific to cell type 3,4 . Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P < 5 × 10 -8 ) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores 5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care., (© 2024. The Author(s).)
Published: 2024
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23. A novel approach for pharmacological substantiation of safety signals using plasma concentrations of medication and administrative/healthcare databases: A case study using Danish registries for an FDA warning on lamotrigine.

Author: Wang W, Battini V, Carnovale C, Noordam R, van Dijk KW, Kragholm KH, van Heemst D, Soeorg H, and Sessa M
Subjects: United States, Humans, Aged, Lamotrigine adverse effects, United States Food and Drug Administration, Delivery of Health Care, Denmark epidemiology, Triazines adverse effects, Anticonvulsants therapeutic use
Abstract: PHARMACOM-EPI is a novel framework to predict plasma concentrations of drugs at the time of occurrence of clinical outcomes. In early 2021, the U.S. Food and Drug Administration (FDA) issued a warning on the antiseizure drug lamotrigine claiming that it has the potential to increase the risk of arrhythmias and related sudden cardiac death due to a pharmacological sodium channel-blocking effect. We hypothesized that the risk of arrhythmias and related death is due to toxicity. We used the PHARMACOM-EPI framework to investigate the relationship between lamotrigine's plasma concentrations and the risk of death in older patients using real-world data. Danish nationwide administrative and healthcare registers were used as data sources and individuals aged 65 years or older during the period 1996 - 2018 were included in the study. According to the PHARMACOM-EPI framework, plasma concentrations of lamotrigine were predicted at the time of death and patients were categorized into non-toxic and toxic groups based on the therapeutic range of lamotrigine (3-15 mg/L). Over 1 year of treatment, the incidence rate ratio (IRR) of all-cause mortality was calculated between the propensities score matched toxic and non-toxic groups. In total, 7286 individuals were diagnosed with epilepsy and were exposed to lamotrigine, 432 of which had at least one plasma concentration measurement The pharmacometric model by Chavez et al. was used to predict lamotrigine's plasma concentrations considering the lowest absolute percentage error among identified models (14.25 %, 95 % CI: 11.68-16.23). The majority of lamotrigine associated deaths were cardiovascular-related and occurred among individuals with plasma concentrations in the toxic range. The IRR of mortality between the toxic group and non-toxic group was 3.37 [95 % CI: 1.44-8.32] and the cumulative incidence of all-cause mortality exponentially increased in the toxic range. Application of our novel framework PHARMACOM-EPI provided strong evidence to support our hypothesis that the increased risk of all-cause and cardiovascular death was associated with a toxic plasma concentration level of lamotrigine among older lamotrigine users., Competing Interests: Declaration of Competing Interest All authors declare that there is no conflict of interest., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)
Published: 2023
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24. Author Correction: The power of genetic diversity in genome-wide association studies of lipids.

Author: Graham SE, Clarke SL, Wu KH, Kanoni S, Zajac GJM, Ramdas S, Surakka I, Ntalla I, Vedantam S, Winkler TW, Locke AE, Marouli E, Hwang MY, Han S, Narita A, Choudhury A, Bentley AR, Ekoru K, Verma A, Trivedi B, Martin HC, Hunt KA, Hui Q, Klarin D, Zhu X, Thorleifsson G, Helgadottir A, Gudbjartsson DF, Holm H, Olafsson I, Akiyama M, Sakaue S, Terao C, Kanai M, Zhou W, Brumpton BM, Rasheed H, Ruotsalainen SE, Havulinna AS, Veturi Y, Feng Q, Rosenthal EA, Lingren T, Pacheco JA, Pendergrass SA, Haessler J, Giulianini F, Bradford Y, Miller JE, Campbell A, Lin K, Millwood IY, Hindy G, Rasheed A, Faul JD, Zhao W, Weir DR, Turman C, Huang H, Graff M, Mahajan A, Brown MR, Zhang W, Yu K, Schmidt EM, Pandit A, Gustafsson S, Yin X, Luan J, Zhao JH, Matsuda F, Jang HM, Yoon K, Medina-Gomez C, Pitsillides A, Hottenga JJ, Willemsen G, Wood AR, Ji Y, Gao Z, Haworth S, Mitchell RE, Chai JF, Aadahl M, Yao J, Manichaikul A, Warren HR, Ramirez J, Bork-Jensen J, Kårhus LL, Goel A, Sabater-Lleal M, Noordam R, Sidore C, Fiorillo E, McDaid AF, Marques-Vidal P, Wielscher M, Trompet S, Sattar N, Møllehave LT, Thuesen BH, Munz M, Zeng L, Huang J, Yang B, Poveda A, Kurbasic A, Lamina C, Forer L, Scholz M, Galesloot TE, Bradfield JP, Daw EW, Zmuda JM, Mitchell JS, Fuchsberger C, Christensen H, Brody JA, Feitosa MF, Wojczynski MK, Preuss M, Mangino M, Christofidou P, Verweij N, Benjamins JW, Engmann J, Kember RL, Slieker RC, Lo KS, Zilhao NR, Le P, Kleber ME, Delgado GE, Huo S, Ikeda DD, Iha H, Yang J, Liu J, Leonard HL, Marten J, Schmidt B, Arendt M, Smyth LJ, Cañadas-Garre M, Wang C, Nakatochi M, Wong A, Hutri-Kähönen N, Sim X, Xia R, Huerta-Chagoya A, Fernandez-Lopez JC, Lyssenko V, Ahmed M, Jackson AU, Yousri NA, Irvin MR, Oldmeadow C, Kim HN, Ryu S, Timmers PRHJ, Arbeeva L, Dorajoo R, Lange LA, Chai X, Prasad G, Lorés-Motta L, Pauper M, Long J, Li X, Theusch E, Takeuchi F, Spracklen CN, Loukola A, Bollepalli S, Warner SC, Wang YX, Wei WB, Nutile T, Ruggiero D, Sung YJ, Hung YJ, Chen S, Liu F, Yang J, Kentistou KA, Gorski M, Brumat M, Meidtner K, Bielak LF, Smith JA, Hebbar P, Farmaki AE, Hofer E, Lin M, Xue C, Zhang J, Concas MP, Vaccargiu S, van der Most PJ, Pitkänen N, Cade BE, Lee J, van der Laan SW, Chitrala KN, Weiss S, Zimmermann ME, Lee JY, Choi HS, Nethander M, Freitag-Wolf S, Southam L, Rayner NW, Wang CA, Lin SY, Wang JS, Couture C, Lyytikäinen LP, Nikus K, Cuellar-Partida G, Vestergaard H, Hildalgo B, Giannakopoulou O, Cai Q, Obura MO, van Setten J, Li X, Schwander K, Terzikhan N, Shin JH, Jackson RD, Reiner AP, Martin LW, Chen Z, Li L, Highland HM, Young KL, Kawaguchi T, Thiery J, Bis JC, Nadkarni GN, Launer LJ, Li H, Nalls MA, Raitakari OT, Ichihara S, Wild SH, Nelson CP, Campbell H, Jäger S, Nabika T, Al-Mulla F, Niinikoski H, Braund PS, Kolcic I, Kovacs P, Giardoglou T, Katsuya T, Bhatti KF, de Kleijn D, de Borst GJ, Kim EK, Adams HHH, Ikram MA, Zhu X, Asselbergs FW, Kraaijeveld AO, Beulens JWJ, Shu XO, Rallidis LS, Pedersen O, Hansen T, Mitchell P, Hewitt AW, Kähönen M, Pérusse L, Bouchard C, Tönjes A, Chen YI, Pennell CE, Mori TA, Lieb W, Franke A, Ohlsson C, Mellström D, Cho YS, Lee H, Yuan JM, Koh WP, Rhee SY, Woo JT, Heid IM, Stark KJ, Völzke H, Homuth G, Evans MK, Zonderman AB, Polasek O, Pasterkamp G, Hoefer IE, Redline S, Pahkala K, Oldehinkel AJ, Snieder H, Biino G, Schmidt R, Schmidt H, Chen YE, Bandinelli S, Dedoussis G, Thanaraj TA, Kardia SLR, Kato N, Schulze MB, Girotto G, Jung B, Böger CA, Joshi PK, Bennett DA, De Jager PL, Lu X, Mamakou V, Brown M, Caulfield MJ, Munroe PB, Guo X, Ciullo M, Jonas JB, Samani NJ, Kaprio J, Pajukanta P, Adair LS, Bechayda SA, de Silva HJ, Wickremasinghe AR, Krauss RM, Wu JY, Zheng W, den Hollander AI, Bharadwaj D, Correa A, Wilson JG, Lind L, Heng CK, Nelson AE, Golightly YM, Wilson JF, Penninx B, Kim HL, Attia J, Scott RJ, Rao DC, Arnett DK, Hunt SC, Walker M, Koistinen HA, Chandak GR, Yajnik CS, Mercader JM, Tusié-Luna T, Aguilar-Salinas CA, Villalpando CG, Orozco L, Fornage M, Tai ES, van Dam RM, Lehtimäki T, Chaturvedi N, Yokota M, Liu J, Reilly DF, McKnight AJ, Kee F, Jöckel KH, McCarthy MI, Palmer CNA, Vitart V, Hayward C, Simonsick E, van Duijn CM, Lu F, Qu J, Hishigaki H, Lin X, März W, Parra EJ, Cruz M, Gudnason V, Tardif JC, Lettre G, 't Hart LM, Elders PJM, Damrauer SM, Kumari M, Kivimaki M, van der Harst P, Spector TD, Loos RJF, Province MA, Psaty BM, Brandslund I, Pramstaller PP, Christensen K, Ripatti S, Widén E, Hakonarson H, Grant SFA, Kiemeney LALM, de Graaf J, Loeffler M, Kronenberg F, Gu D, Erdmann J, Schunkert H, Franks PW, Linneberg A, Jukema JW, Khera AV, Männikkö M, Jarvelin MR, Kutalik Z, Cucca F, Mook-Kanamori DO, van Dijk KW, Watkins H, Strachan DP, Grarup N, Sever P, Poulter N, Rotter JI, Dantoft TM, Karpe F, Neville MJ, Timpson NJ, Cheng CY, Wong TY, Khor CC, Sabanayagam C, Peters A, Gieger C, Hattersley AT, Pedersen NL, Magnusson PKE, Boomsma DI, de Geus EJC, Cupples LA, van Meurs JBJ, Ghanbari M, Gordon-Larsen P, Huang W, Kim YJ, Tabara Y, Wareham NJ, Langenberg C, Zeggini E, Kuusisto J, Laakso M, Ingelsson E, Abecasis G, Chambers JC, Kooner JS, de Vries PS, Morrison AC, North KE, Daviglus M, Kraft P, Martin NG, Whitfield JB, Abbas S, Saleheen D, Walters RG, Holmes MV, Black C, Smith BH, Justice AE, Baras A, Buring JE, Ridker PM, Chasman DI, Kooperberg C, Wei WQ, Jarvik GP, Namjou B, Hayes MG, Ritchie MD, Jousilahti P, Salomaa V, Hveem K, Åsvold BO, Kubo M, Kamatani Y, Okada Y, Murakami Y, Thorsteinsdottir U, Stefansson K, Ho YL, Lynch JA, Rader DJ, Tsao PS, Chang KM, Cho K, O'Donnell CJ, Gaziano JM, Wilson P, Rotimi CN, Hazelhurst S, Ramsay M, Trembath RC, van Heel DA, Tamiya G, Yamamoto M, Kim BJ, Mohlke KL, Frayling TM, Hirschhorn JN, Kathiresan S, Boehnke M, Natarajan P, Peloso GM, Brown CD, Morris AP, Assimes TL, Deloukas P, Sun YV, and Willer CJ
Published: 2023
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25. Normal range CAG repeat size variations in the HTT gene are associated with an adverse lipoprotein profile partially mediated by body mass index.

Author: Faquih TO, Aziz NA, Gardiner SL, Li-Gao R, de Mutsert R, Milaneschi Y, Trompet S, Jukema JW, Rosendaal FR, van Hylckama Vlieg A, van Dijk KW, and Mook-Kanamori DO
Subjects: Humans, Body Mass Index, Reference Values, Huntingtin Protein genetics, Lipoproteins, Lipoproteins, LDL genetics, Trinucleotide Repeat Expansion genetics, Diabetes Mellitus, Type 2 genetics, Huntington Disease pathology
Abstract: Tandem cytosine-adenine-guanine (CAG) repeat sizes of 36 or more in the huntingtin gene (HTT) cause Huntington's disease (HD). Apart from neuropsychiatric complications, the disease is also accompanied by metabolic dysregulation and weight loss, which contribute to a progressive functional decline. Recent studies also reported an association between repeats below the pathogenic threshold (<36) for HD and body mass index (BMI), suggesting that HTT repeat sizes in the non-pathogenic range are associated with metabolic dysregulation. In this study, we hypothesized that HTT repeat sizes < 36 are associated with metabolite levels, possibly mediated through reduced BMI. We pooled data from three European cohorts (n = 10 228) with genotyped HTT CAG repeat size and metabolomic measurements. All 145 metabolites were measured on the same targeted platform in all studies. Multilevel mixed-effects analysis using the CAG repeat size in HTT identified 67 repeat size metabolite associations. Overall, the metabolomic profile associated with larger CAG repeat sizes in HTT were unfavorable-similar to those of higher risk of coronary artery disease and type 2 diabetes-and included elevated levels of amino acids, fatty acids, low-density lipoprotein (LDL)-, very low-density lipoprotein- and intermediate density lipoprotein (IDL)-related metabolites while with decreased levels of very large high-density lipoprotein (HDL)-related metabolites. Furthermore, the associations of 50 metabolites, in particular, specific very large HDL-related metabolites, were mediated by lower BMI. However, no mediation effect was found for 17 metabolites related to LDL and IDL. In conclusion, our findings indicate that large non-pathogenic CAG repeat sizes in HTT are associated with an unfavorable metabolomic profile despite their association with a lower BMI., (© The Author(s) 2023. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)
Published: 2023
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26. Low leukocyte mitochondrial DNA abundance drives atherosclerotic cardiovascular diseases: a cohort and Mendelian randomization study.

Author: Luo J, Noordam R, Jukema JW, van Dijk KW, Hägg S, Grassmann F, le Cessie S, and van Heemst D
Subjects: Humans, DNA, Mitochondrial genetics, Mendelian Randomization Analysis, Cohort Studies, Mitochondria genetics, Genome-Wide Association Study, Polymorphism, Single Nucleotide, Cardiovascular Diseases genetics, Coronary Artery Disease genetics, Atherosclerosis
Abstract: Aim: Mitochondrial DNA dysfunction has been implicated in the pathogenesis of cardiovascular diseases. We aimed to investigate the associations between leukocyte mitochondrial DNA (mtDNA) abundance, as a proxy of mitochondrial function, and coronary artery disease (CAD) and heart failure (HF) in a cohort study and approximate the causal nature of these relationships using Mendelian randomization (MR) in genetic studies., Methods and Results: Multivariable-adjusted Cox regression analyses were conducted in 273 619 unrelated participants of European ancestry from the UK Biobank (UKB). For genetic studies, we first performed MR analyses with individual-level data from the UKB using a weighted genetic risk score (GRS); two-sample MR analyses were subsequently performed using summary-level data from the publicly available three consortia/biobank for CAD and two for HF. MR analyses were performed per database separately and results were subsequently meta-analysed using fixed-effects models. During a median follow-up of 11.8 years, restricted cubic spline Cox regression analyses showed associations between lower mtDNA abundance and higher risk of CAD and HF. Hazard ratios for participants in the lowest quintile of mtDNA abundance compared with those in the highest quintile were 1.08 (95% confidence interval: 1.03, 1.14) and 1.15 (1.05, 1.24) for CAD and HF. Genetically, no evidence was observed for a possible non-linear causal effect using individual-level weighted genetic risk scores calculated in the UKB on the study outcomes; the pooled odds ratios (95% confidence interval) from two-sample MR of genetically predicted per one-SD decrease in mtDNA abundance were 1.09 (1.03, 1.16) for CAD and 0.99 (0.92, 1.08) for HF, respectively., Conclusion: Our findings support a possible causal role of lower leukocyte mtDNA abundance in higher CAD risk, but not in HF., Competing Interests: Conflict of interest: None declared., (© The Author(s) 2022. Published by Oxford University Press on behalf of the European Society of Cardiology. All rights reserved. For permissions, please email: journals.permissions@oup.com.)
Published: 2023
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27. Dietary butyrate ameliorates metabolic health associated with selective proliferation of gut Lachnospiraceae bacterium 28-4.

Author: Li Z, Zhou E, Liu C, Wicks H, Yildiz S, Razack F, Ying Z, Kooijman S, Koonen DPY, Heijink M, Kostidis S, Giera M, Sanders IMJG, Kuijper EJ, Smits WK, van Dijk KW, Rensen PCN, and Wang Y
Subjects: Humans, Animals, Mice, Obesity metabolism, RNA, Ribosomal, 16S, Weight Gain, Cell Proliferation, Butyrates adverse effects, Metabolic Syndrome
Abstract: Short-chain fatty acids, including butyrate, have multiple metabolic benefits in individuals who are lean but not in individuals with metabolic syndrome, with the underlying mechanisms still being unclear. We aimed to investigate the role of gut microbiota in the induction of metabolic benefits of dietary butyrate. We performed antibiotic-induced microbiota depletion of the gut and fecal microbiota transplantation (FMT) in APOE*3-Leiden.CETP mice, a well-established translational model for developing human-like metabolic syndrome, and revealed that dietary butyrate reduced appetite and ameliorated high-fat diet-induced (HFD-induced) weight gain dependent on the presence of gut microbiota. FMT from butyrate-treated lean donor mice, but not butyrate-treated obese donor mice, into gut microbiota-depleted recipient mice reduced food intake, attenuated HFD-induced weight gain, and improved insulin resistance. 16S rRNA and metagenomic sequencing on cecal bacterial DNA of recipient mice implied that these effects were accompanied by the selective proliferation of Lachnospiraceae bacterium 28-4 in the gut as induced by butyrate. Collectively, our findings reveal a crucial role of gut microbiota in the beneficial metabolic effects of dietary butyrate as strongly associated with the abundance of Lachnospiraceae bacterium 28-4.
Published: 2023
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28. Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis.

Author: Kanoni S, Graham SE, Wang Y, Surakka I, Ramdas S, Zhu X, Clarke SL, Bhatti KF, Vedantam S, Winkler TW, Locke AE, Marouli E, Zajac GJM, Wu KH, Ntalla I, Hui Q, Klarin D, Hilliard AT, Wang Z, Xue C, Thorleifsson G, Helgadottir A, Gudbjartsson DF, Holm H, Olafsson I, Hwang MY, Han S, Akiyama M, Sakaue S, Terao C, Kanai M, Zhou W, Brumpton BM, Rasheed H, Havulinna AS, Veturi Y, Pacheco JA, Rosenthal EA, Lingren T, Feng Q, Kullo IJ, Narita A, Takayama J, Martin HC, Hunt KA, Trivedi B, Haessler J, Giulianini F, Bradford Y, Miller JE, Campbell A, Lin K, Millwood IY, Rasheed A, Hindy G, Faul JD, Zhao W, Weir DR, Turman C, Huang H, Graff M, Choudhury A, Sengupta D, Mahajan A, Brown MR, Zhang W, Yu K, Schmidt EM, Pandit A, Gustafsson S, Yin X, Luan J, Zhao JH, Matsuda F, Jang HM, Yoon K, Medina-Gomez C, Pitsillides A, Hottenga JJ, Wood AR, Ji Y, Gao Z, Haworth S, Yousri NA, Mitchell RE, Chai JF, Aadahl M, Bjerregaard AA, Yao J, Manichaikul A, Hwu CM, Hung YJ, Warren HR, Ramirez J, Bork-Jensen J, Kårhus LL, Goel A, Sabater-Lleal M, Noordam R, Mauro P, Matteo F, McDaid AF, Marques-Vidal P, Wielscher M, Trompet S, Sattar N, Møllehave LT, Munz M, Zeng L, Huang J, Yang B, Poveda A, Kurbasic A, Lamina C, Forer L, Scholz M, Galesloot TE, Bradfield JP, Ruotsalainen SE, Daw E, Zmuda JM, Mitchell JS, Fuchsberger C, Christensen H, Brody JA, Vazquez-Moreno M, Feitosa MF, Wojczynski MK, Wang Z, Preuss MH, Mangino M, Christofidou P, Verweij N, Benjamins JW, Engmann J, Tsao NL, Verma A, Slieker RC, Lo KS, Zilhao NR, Le P, Kleber ME, Delgado GE, Huo S, Ikeda DD, Iha H, Yang J, Liu J, Demirkan A, Leonard HL, Marten J, Frank M, Schmidt B, Smyth LJ, Cañadas-Garre M, Wang C, Nakatochi M, Wong A, Hutri-Kähönen N, Sim X, Xia R, Huerta-Chagoya A, Fernandez-Lopez JC, Lyssenko V, Nongmaithem SS, Bayyana S, Stringham HM, Irvin MR, Oldmeadow C, Kim HN, Ryu S, Timmers PRHJ, Arbeeva L, Dorajoo R, Lange LA, Prasad G, Lorés-Motta L, Pauper M, Long J, Li X, Theusch E, Takeuchi F, Spracklen CN, Loukola A, Bollepalli S, Warner SC, Wang YX, Wei WB, Nutile T, Ruggiero D, Sung YJ, Chen S, Liu F, Yang J, Kentistou KA, Banas B, Nardone GG, Meidtner K, Bielak LF, Smith JA, Hebbar P, Farmaki AE, Hofer E, Lin M, Concas MP, Vaccargiu S, van der Most PJ, Pitkänen N, Cade BE, van der Laan SW, Chitrala KN, Weiss S, Bentley AR, Doumatey AP, Adeyemo AA, Lee JY, Petersen ERB, Nielsen AA, Choi HS, Nethander M, Freitag-Wolf S, Southam L, Rayner NW, Wang CA, Lin SY, Wang JS, Couture C, Lyytikäinen LP, Nikus K, Cuellar-Partida G, Vestergaard H, Hidalgo B, Giannakopoulou O, Cai Q, Obura MO, van Setten J, Li X, Liang J, Tang H, Terzikhan N, Shin JH, Jackson RD, Reiner AP, Martin LW, Chen Z, Li L, Kawaguchi T, Thiery J, Bis JC, Launer LJ, Li H, Nalls MA, Raitakari OT, Ichihara S, Wild SH, Nelson CP, Campbell H, Jäger S, Nabika T, Al-Mulla F, Niinikoski H, Braund PS, Kolcic I, Kovacs P, Giardoglou T, Katsuya T, de Kleijn D, de Borst GJ, Kim EK, Adams HHH, Ikram MA, Zhu X, Asselbergs FW, Kraaijeveld AO, Beulens JWJ, Shu XO, Rallidis LS, Pedersen O, Hansen T, Mitchell P, Hewitt AW, Kähönen M, Pérusse L, Bouchard C, Tönjes A, Chen YI, Pennell CE, Mori TA, Lieb W, Franke A, Ohlsson C, Mellström D, Cho YS, Lee H, Yuan JM, Koh WP, Rhee SY, Woo JT, Heid IM, Stark KJ, Zimmermann ME, Völzke H, Homuth G, Evans MK, Zonderman AB, Polasek O, Pasterkamp G, Hoefer IE, Redline S, Pahkala K, Oldehinkel AJ, Snieder H, Biino G, Schmidt R, Schmidt H, Bandinelli S, Dedoussis G, Thanaraj TA, Kardia SLR, Peyser PA, Kato N, Schulze MB, Girotto G, Böger CA, Jung B, Joshi PK, Bennett DA, De Jager PL, Lu X, Mamakou V, Brown M, Caulfield MJ, Munroe PB, Guo X, Ciullo M, Jonas JB, Samani NJ, Kaprio J, Pajukanta P, Tusié-Luna T, Aguilar-Salinas CA, Adair LS, Bechayda SA, de Silva HJ, Wickremasinghe AR, Krauss RM, Wu JY, Zheng W, Hollander AI, Bharadwaj D, Correa A, Wilson JG, Lind L, Heng CK, Nelson AE, Golightly YM, Wilson JF, Penninx B, Kim HL, Attia J, Scott RJ, Rao DC, Arnett DK, Hunt SC, Walker M, Koistinen HA, Chandak GR, Mercader JM, Costanzo MC, Jang D, Burtt NP, Villalpando CG, Orozco L, Fornage M, Tai E, van Dam RM, Lehtimäki T, Chaturvedi N, Yokota M, Liu J, Reilly DF, McKnight AJ, Kee F, Jöckel KH, McCarthy MI, Palmer CNA, Vitart V, Hayward C, Simonsick E, van Duijn CM, Jin ZB, Qu J, Hishigaki H, Lin X, März W, Gudnason V, Tardif JC, Lettre G, Hart LM', Elders PJM, Damrauer SM, Kumari M, Kivimaki M, van der Harst P, Spector TD, Loos RJF, Province MA, Parra EJ, Cruz M, Psaty BM, Brandslund I, Pramstaller PP, Rotimi CN, Christensen K, Ripatti S, Widén E, Hakonarson H, Grant SFA, Kiemeney LALM, de Graaf J, Loeffler M, Kronenberg F, Gu D, Erdmann J, Schunkert H, Franks PW, Linneberg A, Jukema JW, Khera AV, Männikkö M, Jarvelin MR, Kutalik Z, Francesco C, Mook-Kanamori DO, van Dijk KW, Watkins H, Strachan DP, Grarup N, Sever P, Poulter N, Chuang LM, Rotter JI, Dantoft TM, Karpe F, Neville MJ, Timpson NJ, Cheng CY, Wong TY, Khor CC, Li H, Sabanayagam C, Peters A, Gieger C, Hattersley AT, Pedersen NL, Magnusson PKE, Boomsma DI, Willemsen AHM, Cupples L, van Meurs JBJ, Ghanbari M, Gordon-Larsen P, Huang W, Kim YJ, Tabara Y, Wareham NJ, Langenberg C, Zeggini E, Kuusisto J, Laakso M, Ingelsson E, Abecasis G, Chambers JC, Kooner JS, de Vries PS, Morrison AC, Hazelhurst S, Ramsay M, North KE, Daviglus M, Kraft P, Martin NG, Whitfield JB, Abbas S, Saleheen D, Walters RG, Holmes MV, Black C, Smith BH, Baras A, Justice AE, Buring JE, Ridker PM, Chasman DI, Kooperberg C, Tamiya G, Yamamoto M, van Heel DA, Trembath RC, Wei WQ, Jarvik GP, Namjou B, Hayes MG, Ritchie MD, Jousilahti P, Salomaa V, Hveem K, Åsvold BO, Kubo M, Kamatani Y, Okada Y, Murakami Y, Kim BJ, Thorsteinsdottir U, Stefansson K, Zhang J, Chen Y, Ho YL, Lynch JA, Rader DJ, Tsao PS, Chang KM, Cho K, O'Donnell CJ, Gaziano JM, Wilson PWF, Frayling TM, Hirschhorn JN, Kathiresan S, Mohlke KL, Sun YV, Morris AP, Boehnke M, Brown CD, Natarajan P, Deloukas P, Willer CJ, Assimes TL, and Peloso GM
Subjects: Humans, Sex Characteristics, Phenotype, Lipids genetics, Polymorphism, Single Nucleotide, Genetic Pleiotropy, Genome-Wide Association Study, Genetic Predisposition to Disease
Abstract: Background: Genetic variants within nearly 1000 loci are known to contribute to modulation of blood lipid levels. However, the biological pathways underlying these associations are frequently unknown, limiting understanding of these findings and hindering downstream translational efforts such as drug target discovery., Results: To expand our understanding of the underlying biological pathways and mechanisms controlling blood lipid levels, we leverage a large multi-ancestry meta-analysis (N = 1,654,960) of blood lipids to prioritize putative causal genes for 2286 lipid associations using six gene prediction approaches. Using phenome-wide association (PheWAS) scans, we identify relationships of genetically predicted lipid levels to other diseases and conditions. We confirm known pleiotropic associations with cardiovascular phenotypes and determine novel associations, notably with cholelithiasis risk. We perform sex-stratified GWAS meta-analysis of lipid levels and show that 3-5% of autosomal lipid-associated loci demonstrate sex-biased effects. Finally, we report 21 novel lipid loci identified on the X chromosome. Many of the sex-biased autosomal and X chromosome lipid loci show pleiotropic associations with sex hormones, emphasizing the role of hormone regulation in lipid metabolism., Conclusions: Taken together, our findings provide insights into the biological mechanisms through which associated variants lead to altered lipid levels and potentially cardiovascular disease risk., (© 2022. The Author(s).)
Published: 2022
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29. The APOE locus is linked to decline in general cognitive function: 20-years follow-up in the Doetinchem Cohort Study.

Author: Rietman ML, Onland-Moret NC, Nooyens ACJ, Ibi D, van Dijk KW, Samson LD, Pennings JLA, Schipper M, Wong A, Spijkerman AMW, Dollé MET, and Verschuren WMM
Subjects: Female, Humans, Male, Cognition physiology, Cohort Studies, Follow-Up Studies, Young Adult, Adult, Middle Aged, Apolipoproteins E genetics, Cognitive Dysfunction genetics
Abstract: Cognitive decline is part of the normal aging process. However, some people experience a more rapid decline than others due to environmental and genetic factors. Numerous single nucleotide polymorphisms (SNPs) have been linked to cognitive function, but only a few to cognitive decline. To understand whether cognitive function and cognitive decline are driven by the same mechanisms, we investigated whether 433 SNPs previously linked to cognitive function and 2 SNPs previously linked to cognitive decline are associated with both general cognitive functioning at baseline and general cognitive decline up to 20-years follow-up in the Doetinchem Cohort Study (DCS). The DCS is a longitudinal population-based study that enrolled men and women aged 20-59 years between 1987-1991, with follow-up examinations every 5 years. We used data of rounds 2-6 (1993-2017, n = 2559). General cognitive function was assessed using four cognition tests measuring memory, speed, fluency and flexibility. With these test scores, standardized residuals (adjusted for sex, age and examination round) were calculated for each cognition test at each round and subsequently combined into one general cognitive function measure using principal component analyses. None of the 435 previously identified variants were associated with baseline general cognitive function in the DCS. But rs429358-C, a coding apolipoprotein E (APOE) SNP and one of the variants previously associated with cognitive decline, was associated with general cognitive decline in our study as well (p-value = 1 × 10 -5 , Beta = -0.013). These findings suggest that decline of general cognitive function is influenced by other mechanisms than those that are involved in the regulation of general cognitive function., (© 2022. The Author(s).)
Published: 2022
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30. A Systems Analysis of Phenotype Heterogeneity in APOE*3Leiden.CETP Mice Induced by Long-Term High-Fat High-Cholesterol Diet Feeding.

Author: Paalvast Y, Zhou E, Rozendaal YJW, Wang Y, Gerding A, van Dijk TH, de Boer JF, Rensen PCN, van Dijk KW, Kuivenhoven JA, Bakker BM, van Riel NAW, and Groen AK
Subjects: Animals, Mice, Bile Acids and Salts metabolism, Cholesterol metabolism, Fatty Acids metabolism, Glucose metabolism, Liver metabolism, Longitudinal Studies, Phenotype, Systems Analysis, Triglycerides, Cholesterol Ester Transfer Proteins genetics, Cholesterol Ester Transfer Proteins metabolism, Diet, High-Fat adverse effects, Metabolic Syndrome genetics, Metabolic Syndrome metabolism, Apolipoprotein E3 genetics, Apolipoprotein E3 metabolism
Abstract: Within the human population, considerable variability exists between individuals in their susceptibility to develop obesity and dyslipidemia. In humans, this is thought to be caused by both genetic and environmental variation. APOE*3-Leiden.CETP mice, as part of an inbred mouse model in which mice develop the metabolic syndrome upon being fed a high-fat high-cholesterol diet, show large inter-individual variation in the parameters of the metabolic syndrome, despite a lack of genetic and environmental variation. In the present study, we set out to resolve what mechanisms could underlie this variation. We used measurements of glucose and lipid metabolism from a six-month longitudinal study on the development of the metabolic syndrome. Mice were classified as mice with either high plasma triglyceride (responders) or low plasma triglyceride (non-responders) at the baseline. Subsequently, we fitted the data to a dynamic computational model of whole-body glucose and lipid metabolism (MINGLeD) by making use of a hybrid modelling method called Adaptations in Parameter Trajectories (ADAPT). ADAPT integrates longitudinal data, and predicts how the parameters of the model must change through time in order to comply with the data and model constraints. To explain the phenotypic variation in plasma triglycerides, the ADAPT analysis suggested a decreased cholesterol absorption, higher energy expenditure and increased fecal fatty acid excretion in non-responders. While decreased cholesterol absorption and higher energy expenditure could not be confirmed, the experimental validation demonstrated that the non-responders were indeed characterized by increased fecal fatty acid excretion. Furthermore, the amount of fatty acids excreted strongly correlated with bile acid excretion, in particular deoxycholate. Since bile acids play an important role in the solubilization of lipids in the intestine, these results suggest that variation in bile acid homeostasis may in part drive the phenotypic variation in the APOE*3-Leiden.CETP mice.
Published: 2022
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31. Bone marrow transplantation induces changes in the gut microbiota that chronically increase the cytokine response pattern of splenocytes.

Author: Katiraei S, van Diepen JA, Tavares LP, Hoving LR, Pronk A, Verschueren I, Rensen PCN, Zwaginga JJ, Kostidis S, Giera M, Teixera M, van Dijk KW, Netea MG, Berbée JFP, and van Harmelen V
Subjects: Animals, Bone Marrow Transplantation adverse effects, Cytokines, Immune System, Mice, Spleen, Gastrointestinal Microbiome
Abstract: Bone marrow transplantation (BMT) involves conditioning regimens which acutely induce side effects, including systemic inflammation, intestinal damage and shifts in the gut microbial composition, some of which may persist chronically. As the gut microbiota affect systemic immune responses, we aimed to investigate whether, post-BMT, the peripheral immune system is modulated as a direct consequence of alterations in the gut microbiota. We show that 24 weeks post-BMT, splenocytes but not peritoneal macrophages display increased cytokine response patterns upon ex-vivo stimulation with various pathogens as compared to untreated controls. The pattern of BMT-induced cytokine responses was transferred to splenocytes, and not to peritoneal macrophages, of healthy controls via co-housing and transferred to germfree mice via transplantation of cecum content. Thus, BMT induces changes in gut microbiota that in their turn increase cytokine responsiveness of splenocytes. Thus, BMT establishes a dominant microbiota that attenuates normalization of the immune-response., (© 2022. The Author(s).)
Published: 2022
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32. Evaluation of the Value of Waist Circumference and Metabolomics in the Estimation of Visceral Adipose Tissue.

Author: Boone SC, van Smeden M, Rosendaal FR, le Cessie S, Groenwold RHH, Jukema JW, van Dijk KW, Lamb HJ, Greenland P, Neeland IJ, Allison MA, Criqui MH, Budoff MJ, Lind LL, Kullberg J, Ahlström H, Mook-Kanamori DO, and de Mutsert R
Subjects: Adipose Tissue, Body Mass Index, Humans, Metabolomics, Middle Aged, Prospective Studies, Waist Circumference, Intra-Abdominal Fat, Obesity epidemiology
Abstract: Visceral adipose tissue (VAT) is a strong prognostic factor for cardiovascular disease and a potential target for cardiovascular risk stratification. Because VAT is difficult to measure in clinical practice, we estimated prediction models with predictors routinely measured in general practice and VAT as outcome using ridge regression in 2,501 middle-aged participants from the Netherlands Epidemiology of Obesity study, 2008-2012. Adding waist circumference and other anthropometric measurements on top of the routinely measured variables improved the optimism-adjusted R2 from 0.50 to 0.58 with a decrease in the root-mean-square error (RMSE) from 45.6 to 41.5 cm2 and with overall good calibration. Further addition of predominantly lipoprotein-related metabolites from the Nightingale platform did not improve the optimism-corrected R2 and RMSE. The models were externally validated in 370 participants from the Prospective Investigation of Vasculature in Uppsala Seniors (PIVUS, 2006-2009) and 1,901 participants from the Multi-Ethnic Study of Atherosclerosis (MESA, 2000-2007). Performance was comparable to the development setting in PIVUS (R2 = 0.63, RMSE = 42.4 cm2, calibration slope = 0.94) but lower in MESA (R2 = 0.44, RMSE = 60.7 cm2, calibration slope = 0.75). Our findings indicate that the estimation of VAT with routine clinical measurements can be substantially improved by incorporating waist circumference but not by metabolite measurements., (© The Author(s) 2021. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health.)
Published: 2022
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33. A multi-omics study of circulating phospholipid markers of blood pressure.

Author: Liu J, de Vries PS, Del Greco M F, Johansson Å, Schraut KE, Hayward C, van Dijk KW, Franco OH, Hicks AA, Vitart V, Rudan I, Campbell H, Polašek O, Pramstaller PP, Wilson JF, Gyllensten U, van Duijn CM, Dehghan A, and Demirkan A
Subjects: Adult, Aged, Biomarkers blood, Cardiometabolic Risk Factors, Cohort Studies, Diastole, Female, Humans, Male, Mendelian Randomization Analysis, Middle Aged, Systole, Blood Pressure, Computational Biology, Hypertension blood, Phospholipids blood
Abstract: High-throughput techniques allow us to measure a wide-range of phospholipids which can provide insight into the mechanisms of hypertension. We aimed to conduct an in-depth multi-omics study of various phospholipids with systolic blood pressure (SBP) and diastolic blood pressure (DBP). The associations of blood pressure and 151 plasma phospholipids measured by electrospray ionization tandem mass spectrometry were performed by linear regression in five European cohorts (n = 2786 in discovery and n = 1185 in replication). We further explored the blood pressure-related phospholipids in Erasmus Rucphen Family (ERF) study by associating them with multiple cardiometabolic traits (linear regression) and predicting incident hypertension (Cox regression). Mendelian Randomization (MR) and phenome-wide association study (Phewas) were also explored to further investigate these association results. We identified six phosphatidylethanolamines (PE 38:3, PE 38:4, PE 38:6, PE 40:4, PE 40:5 and PE 40:6) and two phosphatidylcholines (PC 32:1 and PC 40:5) which together predicted incident hypertension with an area under the ROC curve (AUC) of 0.61. The identified eight phospholipids are strongly associated with triglycerides, obesity related traits (e.g. waist, waist-hip ratio, total fat percentage, body mass index, lipid-lowering medication, and leptin), diabetes related traits (e.g. glucose, insulin resistance and insulin) and prevalent type 2 diabetes. The genetic determinants of these phospholipids also associated with many lipoproteins, heart rate, pulse rate and blood cell counts. No significant association was identified by bi-directional MR approach. We identified eight blood pressure-related circulating phospholipids that have a predictive value for incident hypertension. Our cross-omics analyses show that phospholipid metabolites in the circulation may yield insight into blood pressure regulation and raise a number of testable hypothesis for future research., (© 2022. The Author(s).)
Published: 2022
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34. The power of genetic diversity in genome-wide association studies of lipids.

Author: Graham SE, Clarke SL, Wu KH, Kanoni S, Zajac GJM, Ramdas S, Surakka I, Ntalla I, Vedantam S, Winkler TW, Locke AE, Marouli E, Hwang MY, Han S, Narita A, Choudhury A, Bentley AR, Ekoru K, Verma A, Trivedi B, Martin HC, Hunt KA, Hui Q, Klarin D, Zhu X, Thorleifsson G, Helgadottir A, Gudbjartsson DF, Holm H, Olafsson I, Akiyama M, Sakaue S, Terao C, Kanai M, Zhou W, Brumpton BM, Rasheed H, Ruotsalainen SE, Havulinna AS, Veturi Y, Feng Q, Rosenthal EA, Lingren T, Pacheco JA, Pendergrass SA, Haessler J, Giulianini F, Bradford Y, Miller JE, Campbell A, Lin K, Millwood IY, Hindy G, Rasheed A, Faul JD, Zhao W, Weir DR, Turman C, Huang H, Graff M, Mahajan A, Brown MR, Zhang W, Yu K, Schmidt EM, Pandit A, Gustafsson S, Yin X, Luan J, Zhao JH, Matsuda F, Jang HM, Yoon K, Medina-Gomez C, Pitsillides A, Hottenga JJ, Willemsen G, Wood AR, Ji Y, Gao Z, Haworth S, Mitchell RE, Chai JF, Aadahl M, Yao J, Manichaikul A, Warren HR, Ramirez J, Bork-Jensen J, Kårhus LL, Goel A, Sabater-Lleal M, Noordam R, Sidore C, Fiorillo E, McDaid AF, Marques-Vidal P, Wielscher M, Trompet S, Sattar N, Møllehave LT, Thuesen BH, Munz M, Zeng L, Huang J, Yang B, Poveda A, Kurbasic A, Lamina C, Forer L, Scholz M, Galesloot TE, Bradfield JP, Daw EW, Zmuda JM, Mitchell JS, Fuchsberger C, Christensen H, Brody JA, Feitosa MF, Wojczynski MK, Preuss M, Mangino M, Christofidou P, Verweij N, Benjamins JW, Engmann J, Kember RL, Slieker RC, Lo KS, Zilhao NR, Le P, Kleber ME, Delgado GE, Huo S, Ikeda DD, Iha H, Yang J, Liu J, Leonard HL, Marten J, Schmidt B, Arendt M, Smyth LJ, Cañadas-Garre M, Wang C, Nakatochi M, Wong A, Hutri-Kähönen N, Sim X, Xia R, Huerta-Chagoya A, Fernandez-Lopez JC, Lyssenko V, Ahmed M, Jackson AU, Yousri NA, Irvin MR, Oldmeadow C, Kim HN, Ryu S, Timmers PRHJ, Arbeeva L, Dorajoo R, Lange LA, Chai X, Prasad G, Lorés-Motta L, Pauper M, Long J, Li X, Theusch E, Takeuchi F, Spracklen CN, Loukola A, Bollepalli S, Warner SC, Wang YX, Wei WB, Nutile T, Ruggiero D, Sung YJ, Hung YJ, Chen S, Liu F, Yang J, Kentistou KA, Gorski M, Brumat M, Meidtner K, Bielak LF, Smith JA, Hebbar P, Farmaki AE, Hofer E, Lin M, Xue C, Zhang J, Concas MP, Vaccargiu S, van der Most PJ, Pitkänen N, Cade BE, Lee J, van der Laan SW, Chitrala KN, Weiss S, Zimmermann ME, Lee JY, Choi HS, Nethander M, Freitag-Wolf S, Southam L, Rayner NW, Wang CA, Lin SY, Wang JS, Couture C, Lyytikäinen LP, Nikus K, Cuellar-Partida G, Vestergaard H, Hildalgo B, Giannakopoulou O, Cai Q, Obura MO, van Setten J, Li X, Schwander K, Terzikhan N, Shin JH, Jackson RD, Reiner AP, Martin LW, Chen Z, Li L, Highland HM, Young KL, Kawaguchi T, Thiery J, Bis JC, Nadkarni GN, Launer LJ, Li H, Nalls MA, Raitakari OT, Ichihara S, Wild SH, Nelson CP, Campbell H, Jäger S, Nabika T, Al-Mulla F, Niinikoski H, Braund PS, Kolcic I, Kovacs P, Giardoglou T, Katsuya T, Bhatti KF, de Kleijn D, de Borst GJ, Kim EK, Adams HHH, Ikram MA, Zhu X, Asselbergs FW, Kraaijeveld AO, Beulens JWJ, Shu XO, Rallidis LS, Pedersen O, Hansen T, Mitchell P, Hewitt AW, Kähönen M, Pérusse L, Bouchard C, Tönjes A, Chen YI, Pennell CE, Mori TA, Lieb W, Franke A, Ohlsson C, Mellström D, Cho YS, Lee H, Yuan JM, Koh WP, Rhee SY, Woo JT, Heid IM, Stark KJ, Völzke H, Homuth G, Evans MK, Zonderman AB, Polasek O, Pasterkamp G, Hoefer IE, Redline S, Pahkala K, Oldehinkel AJ, Snieder H, Biino G, Schmidt R, Schmidt H, Chen YE, Bandinelli S, Dedoussis G, Thanaraj TA, Kardia SLR, Kato N, Schulze MB, Girotto G, Jung B, Böger CA, Joshi PK, Bennett DA, De Jager PL, Lu X, Mamakou V, Brown M, Caulfield MJ, Munroe PB, Guo X, Ciullo M, Jonas JB, Samani NJ, Kaprio J, Pajukanta P, Adair LS, Bechayda SA, de Silva HJ, Wickremasinghe AR, Krauss RM, Wu JY, Zheng W, den Hollander AI, Bharadwaj D, Correa A, Wilson JG, Lind L, Heng CK, Nelson AE, Golightly YM, Wilson JF, Penninx B, Kim HL, Attia J, Scott RJ, Rao DC, Arnett DK, Hunt SC, Walker M, Koistinen HA, Chandak GR, Yajnik CS, Mercader JM, Tusié-Luna T, Aguilar-Salinas CA, Villalpando CG, Orozco L, Fornage M, Tai ES, van Dam RM, Lehtimäki T, Chaturvedi N, Yokota M, Liu J, Reilly DF, McKnight AJ, Kee F, Jöckel KH, McCarthy MI, Palmer CNA, Vitart V, Hayward C, Simonsick E, van Duijn CM, Lu F, Qu J, Hishigaki H, Lin X, März W, Parra EJ, Cruz M, Gudnason V, Tardif JC, Lettre G, 't Hart LM, Elders PJM, Damrauer SM, Kumari M, Kivimaki M, van der Harst P, Spector TD, Loos RJF, Province MA, Psaty BM, Brandslund I, Pramstaller PP, Christensen K, Ripatti S, Widén E, Hakonarson H, Grant SFA, Kiemeney LALM, de Graaf J, Loeffler M, Kronenberg F, Gu D, Erdmann J, Schunkert H, Franks PW, Linneberg A, Jukema JW, Khera AV, Männikkö M, Jarvelin MR, Kutalik Z, Cucca F, Mook-Kanamori DO, van Dijk KW, Watkins H, Strachan DP, Grarup N, Sever P, Poulter N, Rotter JI, Dantoft TM, Karpe F, Neville MJ, Timpson NJ, Cheng CY, Wong TY, Khor CC, Sabanayagam C, Peters A, Gieger C, Hattersley AT, Pedersen NL, Magnusson PKE, Boomsma DI, de Geus EJC, Cupples LA, van Meurs JBJ, Ghanbari M, Gordon-Larsen P, Huang W, Kim YJ, Tabara Y, Wareham NJ, Langenberg C, Zeggini E, Kuusisto J, Laakso M, Ingelsson E, Abecasis G, Chambers JC, Kooner JS, de Vries PS, Morrison AC, North KE, Daviglus M, Kraft P, Martin NG, Whitfield JB, Abbas S, Saleheen D, Walters RG, Holmes MV, Black C, Smith BH, Justice AE, Baras A, Buring JE, Ridker PM, Chasman DI, Kooperberg C, Wei WQ, Jarvik GP, Namjou B, Hayes MG, Ritchie MD, Jousilahti P, Salomaa V, Hveem K, Åsvold BO, Kubo M, Kamatani Y, Okada Y, Murakami Y, Thorsteinsdottir U, Stefansson K, Ho YL, Lynch JA, Rader DJ, Tsao PS, Chang KM, Cho K, O'Donnell CJ, Gaziano JM, Wilson P, Rotimi CN, Hazelhurst S, Ramsay M, Trembath RC, van Heel DA, Tamiya G, Yamamoto M, Kim BJ, Mohlke KL, Frayling TM, Hirschhorn JN, Kathiresan S, Boehnke M, Natarajan P, Peloso GM, Brown CD, Morris AP, Assimes TL, Deloukas P, Sun YV, and Willer CJ
Subjects: Genetic Predisposition to Disease genetics, Humans, Linkage Disequilibrium, Multifactorial Inheritance, Polymorphism, Single Nucleotide genetics, Population Groups, Cardiovascular Diseases genetics, Genome-Wide Association Study methods
Abstract: Increased blood lipid levels are heritable risk factors of cardiovascular disease with varied prevalence worldwide owing to different dietary patterns and medication use 1 . Despite advances in prevention and treatment, in particular through reducing low-density lipoprotein cholesterol levels 2 , heart disease remains the leading cause of death worldwide 3 . Genome-wideassociation studies (GWAS) of blood lipid levels have led to important biological and clinical insights, as well as new drug targets, for cardiovascular disease. However, most previous GWAS 4-23 have been conducted in European ancestry populations and may have missed genetic variants that contribute to lipid-level variation in other ancestry groups. These include differences in allele frequencies, effect sizes and linkage-disequilibrium patterns 24 . Here we conduct a multi-ancestry, genome-wide genetic discovery meta-analysis of lipid levels in approximately 1.65 million individuals, including 350,000 of non-European ancestries. We quantify the gain in studying non-European ancestries and provide evidence to support the expansion of recruitment of additional ancestries, even with relatively small sample sizes. We find that increasing diversity rather than studying additional individuals of European ancestry results in substantial improvements in fine-mapping functional variants and portability of polygenic prediction (evaluated in approximately 295,000 individuals from 7 ancestry groupings). Modest gains in the number of discovered loci and ancestry-specific variants were also achieved. As GWAS expand emphasis beyond the identification of genes and fundamental biology towards the use of genetic variants for preventive and precision medicine 25 , we anticipate that increased diversity of participants will lead to more accurate and equitable 26 application of polygenic scores in clinical practice., (© 2021. The Author(s), under exclusive licence to Springer Nature Limited.)
Published: 2021
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35. Adverse generational changes in obesity development converge at midlife without increased cardiometabolic risk.

Author: Ibi D, Rietman ML, Picavet HSJ, van Klinken JB, van Dijk KW, Dollé MET, and Verschuren WMM
Subjects: Adult, Body Mass Index, Cohort Studies, Female, Humans, Male, Middle Aged, Obesity epidemiology, Prevalence, Risk Factors, Waist Circumference, Young Adult, Cardiovascular Diseases epidemiology, Cardiovascular Diseases etiology, Diabetes Mellitus, Type 2 epidemiology, Diabetes Mellitus, Type 2 etiology
Abstract: Objective: Obesity is becoming a global public health problem, but it is unclear how it impacts different generations over the life course. Here, a descriptive analysis of the age-related changes in anthropometric measures and related cardiometabolic risk factors across different generations was performed., Methods: The development of anthropometric measures and related cardiometabolic risk factors was studied during 26 years of follow-up in the Doetinchem Cohort Study (N = 6,314 at baseline). All analyses were stratified by sex and generation, i.e., 10-year age groups (20-29, 30-39, 40-49, and 50-59 years) at baseline. Generalized estimating equations were used to test for generational differences., Results: Weight, BMI, waist circumference, and prevalence of overweight and obesity were higher, in general, in the younger generations during the first 10 to 15 years of follow-up. From age 50 to 59 years onward, these measures converged in all generations of men and women. Among cardiometabolic risk factors, only type 2 diabetes showed an unfavorable shift between the two oldest generations of men., Conclusions: It was observed that, compared with the older generations, the younger generations had obesity at an earlier age but did not reach higher levels at midlife and beyond. This increased exposure to obesity was not (yet) associated with increased prevalence of cardiometabolic risk factors., (© 2021 The Authors. Obesity published by Wiley Periodicals LLC on behalf of The Obesity Society (TOS).)
Published: 2021
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36. Genetic insights into biological mechanisms governing human ovarian ageing.

Author: Ruth KS, Day FR, Hussain J, Martínez-Marchal A, Aiken CE, Azad A, Thompson DJ, Knoblochova L, Abe H, Tarry-Adkins JL, Gonzalez JM, Fontanillas P, Claringbould A, Bakker OB, Sulem P, Walters RG, Terao C, Turon S, Horikoshi M, Lin K, Onland-Moret NC, Sankar A, Hertz EPT, Timshel PN, Shukla V, Borup R, Olsen KW, Aguilera P, Ferrer-Roda M, Huang Y, Stankovic S, Timmers PRHJ, Ahearn TU, Alizadeh BZ, Naderi E, Andrulis IL, Arnold AM, Aronson KJ, Augustinsson A, Bandinelli S, Barbieri CM, Beaumont RN, Becher H, Beckmann MW, Benonisdottir S, Bergmann S, Bochud M, Boerwinkle E, Bojesen SE, Bolla MK, Boomsma DI, Bowker N, Brody JA, Broer L, Buring JE, Campbell A, Campbell H, Castelao JE, Catamo E, Chanock SJ, Chenevix-Trench G, Ciullo M, Corre T, Couch FJ, Cox A, Crisponi L, Cross SS, Cucca F, Czene K, Smith GD, de Geus EJCN, de Mutsert R, De Vivo I, Demerath EW, Dennis J, Dunning AM, Dwek M, Eriksson M, Esko T, Fasching PA, Faul JD, Ferrucci L, Franceschini N, Frayling TM, Gago-Dominguez M, Mezzavilla M, García-Closas M, Gieger C, Giles GG, Grallert H, Gudbjartsson DF, Gudnason V, Guénel P, Haiman CA, Håkansson N, Hall P, Hayward C, He C, He W, Heiss G, Høffding MK, Hopper JL, Hottenga JJ, Hu F, Hunter D, Ikram MA, Jackson RD, Joaquim MDR, John EM, Joshi PK, Karasik D, Kardia SLR, Kartsonaki C, Karlsson R, Kitahara CM, Kolcic I, Kooperberg C, Kraft P, Kurian AW, Kutalik Z, La Bianca M, LaChance G, Langenberg C, Launer LJ, Laven JSE, Lawlor DA, Le Marchand L, Li J, Lindblom A, Lindstrom S, Lindstrom T, Linet M, Liu Y, Liu S, Luan J, Mägi R, Magnusson PKE, Mangino M, Mannermaa A, Marco B, Marten J, Martin NG, Mbarek H, McKnight B, Medland SE, Meisinger C, Meitinger T, Menni C, Metspalu A, Milani L, Milne RL, Montgomery GW, Mook-Kanamori DO, Mulas A, Mulligan AM, Murray A, Nalls MA, Newman A, Noordam R, Nutile T, Nyholt DR, Olshan AF, Olsson H, Painter JN, Patel AV, Pedersen NL, Perjakova N, Peters A, Peters U, Pharoah PDP, Polasek O, Porcu E, Psaty BM, Rahman I, Rennert G, Rennert HS, Ridker PM, Ring SM, Robino A, Rose LM, Rosendaal FR, Rossouw J, Rudan I, Rueedi R, Ruggiero D, Sala CF, Saloustros E, Sandler DP, Sanna S, Sawyer EJ, Sarnowski C, Schlessinger D, Schmidt MK, Schoemaker MJ, Schraut KE, Scott C, Shekari S, Shrikhande A, Smith AV, Smith BH, Smith JA, Sorice R, Southey MC, Spector TD, Spinelli JJ, Stampfer M, Stöckl D, van Meurs JBJ, Strauch K, Styrkarsdottir U, Swerdlow AJ, Tanaka T, Teras LR, Teumer A, Þorsteinsdottir U, Timpson NJ, Toniolo D, Traglia M, Troester MA, Truong T, Tyrrell J, Uitterlinden AG, Ulivi S, Vachon CM, Vitart V, Völker U, Vollenweider P, Völzke H, Wang Q, Wareham NJ, Weinberg CR, Weir DR, Wilcox AN, van Dijk KW, Willemsen G, Wilson JF, Wolffenbuttel BHR, Wolk A, Wood AR, Zhao W, Zygmunt M, Chen Z, Li L, Franke L, Burgess S, Deelen P, Pers TH, Grøndahl ML, Andersen CY, Pujol A, Lopez-Contreras AJ, Daniel JA, Stefansson K, Chang-Claude J, van der Schouw YT, Lunetta KL, Chasman DI, Easton DF, Visser JA, Ozanne SE, Namekawa SH, Solc P, Murabito JM, Ong KK, Hoffmann ER, Murray A, Roig I, and Perry JRB
Subjects: Adult, Alleles, Animals, Bone and Bones metabolism, Checkpoint Kinase 1 genetics, Checkpoint Kinase 2 genetics, Diabetes Mellitus, Type 2, Diet, Europe ethnology, Asia, Eastern ethnology, Female, Fertility genetics, Fragile X Mental Retardation Protein genetics, Genetic Predisposition to Disease, Genome-Wide Association Study, Healthy Aging genetics, Humans, Longevity genetics, Menopause genetics, Menopause, Premature genetics, Mice, Mice, Inbred C57BL, Middle Aged, Primary Ovarian Insufficiency genetics, Uterus, Aging genetics, Ovary metabolism
Abstract: Reproductive longevity is essential for fertility and influences healthy ageing in women 1,2 , but insights into its underlying biological mechanisms and treatments to preserve it are limited. Here we identify 290 genetic determinants of ovarian ageing, assessed using normal variation in age at natural menopause (ANM) in about 200,000 women of European ancestry. These common alleles were associated with clinical extremes of ANM; women in the top 1% of genetic susceptibility have an equivalent risk of premature ovarian insufficiency to those carrying monogenic FMR1 premutations 3 . The identified loci implicate a broad range of DNA damage response (DDR) processes and include loss-of-function variants in key DDR-associated genes. Integration with experimental models demonstrates that these DDR processes act across the life-course to shape the ovarian reserve and its rate of depletion. Furthermore, we demonstrate that experimental manipulation of DDR pathways highlighted by human genetics increases fertility and extends reproductive life in mice. Causal inference analyses using the identified genetic variants indicate that extending reproductive life in women improves bone health and reduces risk of type 2 diabetes, but increases the risk of hormone-sensitive cancers. These findings provide insight into the mechanisms that govern ovarian ageing, when they act, and how they might be targeted by therapeutic approaches to extend fertility and prevent disease., (© 2021. The Author(s), under exclusive licence to Springer Nature Limited.)
Published: 2021
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37. Lifestyle Risk Score: handling missingness of individual lifestyle components in meta-analysis of gene-by-lifestyle interactions.

Author: Xu H, Schwander K, Brown MR, Wang W, Waken RJ, Boerwinkle E, Cupples LA, de las Fuentes L, van Heemst D, Osazuwa-Peters O, de Vries PS, van Dijk KW, Sung YJ, Zhang X, Morrison AC, Rao DC, Noordam R, and Liu CT
Subjects: Genome-Wide Association Study standards, Healthy Lifestyle, Humans, Hypertension epidemiology, Obesity epidemiology, Cardiometabolic Risk Factors, Gene-Environment Interaction, Genome-Wide Association Study methods, Hypertension genetics, Obesity genetics
Abstract: Recent studies consider lifestyle risk score (LRS), an aggregation of multiple lifestyle exposures, in identifying association of gene-lifestyle interaction with disease traits. However, not all cohorts have data on all lifestyle factors, leading to increased heterogeneity in the environmental exposure in collaborative meta-analyses. We compared and evaluated four approaches (Naïve, Safe, Complete and Moderator Approaches) to handle the missingness in LRS-stratified meta-analyses under various scenarios. Compared to "benchmark" results with all lifestyle factors available for all cohorts, the Complete Approach, which included only cohorts with all lifestyle components, was underpowered due to lower sample size, and the Naïve Approach, which utilized all available data and ignored the missingness, was slightly inflated. The Safe Approach, which used all data in LRS-exposed group and only included cohorts with all lifestyle factors available in the LRS-unexposed group, and the Moderator Approach, which handled missingness via moderator meta-regression, were both slightly conservative and yielded almost identical p values. We also evaluated the performance of the Safe Approach under different scenarios. We observed that the larger the proportion of cohorts without missingness included, the more accurate the results compared to "benchmark" results. In conclusion, we generally recommend the Safe Approach, a straightforward and non-inflated approach, to handle heterogeneity among cohorts in the LRS based genome-wide interaction meta-analyses.
Published: 2021
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38. Hepatic Scavenger Receptor Class B Type 1 Knockdown Reduces Atherosclerosis and Enhances the Antiatherosclerotic Effect of Brown Fat Activation in APOE*3-Leiden.CETP Mice.

Author: Zhou E, Li Z, Nakashima H, Liu C, Ying Z, Foks AC, Berbée JFP, van Dijk KW, Rensen PCN, and Wang Y
Subjects: Adipose Tissue, Brown drug effects, Animals, Apolipoprotein E3 genetics, Atherosclerosis genetics, Atherosclerosis metabolism, Atherosclerosis pathology, Biomarkers blood, Cholesterol Ester Transfer Proteins genetics, Disease Models, Animal, Humans, Lipolysis drug effects, Mice, Inbred C57BL, Mice, Transgenic, Plaque, Atherosclerotic, RNA Interference, RNA, Small Interfering genetics, RNA, Small Interfering metabolism, Scavenger Receptors, Class B genetics, Mice, Adipose Tissue, Brown metabolism, Adrenergic beta-3 Receptor Agonists pharmacology, Apolipoprotein E3 metabolism, Atherosclerosis prevention & control, Cholesterol Ester Transfer Proteins metabolism, Dioxoles pharmacology, Gene Knockdown Techniques, Lipids blood, Liver metabolism, Scavenger Receptors, Class B deficiency
Abstract: [Figure: see text].
Published: 2021
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39. Investigating the relationships between unfavourable habitual sleep and metabolomic traits: evidence from multi-cohort multivariable regression and Mendelian randomization analyses.

Author: Bos MM, Goulding NJ, Lee MA, Hofman A, Bot M, Pool R, Vijfhuizen LS, Zhang X, Li C, Mustafa R, Neville MJ, Li-Gao R, Trompet S, Beekman M, Biermasz NR, Boomsma DI, de Boer I, Christodoulides C, Dehghan A, van Dijk KW, Ford I, Ghanbari M, Heijmans BT, Ikram MA, Jukema JW, Mook-Kanamori DO, Karpe F, Luik AI, Lumey LH, van den Maagdenberg AMJM, Mooijaart SP, de Mutsert R, Penninx BWJH, Rensen PCN, Richmond RC, Rosendaal FR, Sattar N, Schoevers RA, Slagboom PE, Terwindt GM, Thesing CS, Wade KH, Wijsman CA, Willemsen G, Zwinderman AH, van Heemst D, Noordam R, and Lawlor DA
Subjects: Aged, Creatinine metabolism, Cross-Sectional Studies, Humans, Isoleucine metabolism, Phenotype, Polymorphism, Single Nucleotide, Risk Factors, Coronary Artery Disease epidemiology, Genome-Wide Association Study, Mendelian Randomization Analysis, Metabolic Diseases complications, Metabolic Diseases epidemiology, Sleep
Abstract: Background: Sleep traits are associated with cardiometabolic disease risk, with evidence from Mendelian randomization (MR) suggesting that insomnia symptoms and shorter sleep duration increase coronary artery disease risk. We combined adjusted multivariable regression (AMV) and MR analyses of phenotypes of unfavourable sleep on 113 metabolomic traits to investigate possible biochemical mechanisms linking sleep to cardiovascular disease., Methods: We used AMV (N = 17,368) combined with two-sample MR (N = 38,618) to examine effects of self-reported insomnia symptoms, total habitual sleep duration, and chronotype on 113 metabolomic traits. The AMV analyses were conducted on data from 10 cohorts of mostly Europeans, adjusted for age, sex, and body mass index. For the MR analyses, we used summary results from published European-ancestry genome-wide association studies of self-reported sleep traits and of nuclear magnetic resonance (NMR) serum metabolites. We used the inverse-variance weighted (IVW) method and complemented this with sensitivity analyses to assess MR assumptions., Results: We found consistent evidence from AMV and MR analyses for associations of usual vs. sometimes/rare/never insomnia symptoms with lower citrate (- 0.08 standard deviation (SD)[95% confidence interval (CI) - 0.12, - 0.03] in AMV and - 0.03SD [- 0.07, - 0.003] in MR), higher glycoprotein acetyls (0.08SD [95% CI 0.03, 0.12] in AMV and 0.06SD [0.03, 0.10) in MR]), lower total very large HDL particles (- 0.04SD [- 0.08, 0.00] in AMV and - 0.05SD [- 0.09, - 0.02] in MR), and lower phospholipids in very large HDL particles (- 0.04SD [- 0.08, 0.002] in AMV and - 0.05SD [- 0.08, - 0.02] in MR). Longer total sleep duration associated with higher creatinine concentrations using both methods (0.02SD per 1 h [0.01, 0.03] in AMV and 0.15SD [0.02, 0.29] in MR) and with isoleucine in MR analyses (0.22SD [0.08, 0.35]). No consistent evidence was observed for effects of chronotype on metabolomic measures., Conclusions: Whilst our results suggested that unfavourable sleep traits may not cause widespread metabolic disruption, some notable effects were observed. The evidence for possible effects of insomnia symptoms on glycoprotein acetyls and citrate and longer total sleep duration on creatinine and isoleucine might explain some of the effects, found in MR analyses of these sleep traits on coronary heart disease, which warrant further investigation.
Published: 2021
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40. Associations between Lifestyle Factors and Vitamin E Metabolites in the General Population.

Author: Martens LG, Luo J, Meulmeester FL, Ashrafi N, van Eekelen EW, de Mutsert R, Mook-Kanamori DO, Rosendaal FR, van Dijk KW, Mills K, Noordam R, and van Heemst D
Abstract: The antioxidant vitamin E (α-tocopherol, α-TOH) protects lipids from oxidation by reactive oxygen species. We hypothesized that lifestyle factors associate with vitamin E metabolism marked by urinary α-tocopheronolactone hydroquinone (α-TLHQ) and α-carboxymethyl-hydroxychroman (α-CEHC levels), as potential reflection of lipid oxidation. We conducted a cross-sectional study in the Netherlands Epidemiology of Obesity Study. Serum α-TOH, and urinary α-TLHQ and α-CEHC were quantified by liquid chromatography coupled with tandem mass spectrometry. Information on the lifestyle factors (sleep, physical activity (PA), smoking and alcohol) were collected through questionnaires. Multivariable linear regression analyses were performed to assess the associations between the lifestyle factors and α-TOH measures. A total of 530 participants (46% men) were included with mean (SD) age of 56 (6) years. Of the examined lifestyle factors, only poor sleep was associated with a higher serum α-TOH (mean difference: 4% (95% CI: 1, 7%)). Current smoking was associated with higher urinary α-CEHC (32%: (14%, 53%)), with evidence of a dose-response relationship with smoking intensity (low pack years, 24% (2, 52%); high pack years, 55% (25, 93%)). Moderate physical activity was associated with a lower α-TLHQ relative to α-CEHC (-17%: (-26, -6%), compared with low PA). Only specific lifestyle factors associate with vitamin E metabolism. Examining serum α-TOH does not provide complete insight in vitamin E antioxidant capacity.
Published: 2020
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41. Correction to: Fasting in diabetes treatment (FIT) trial: study protocol for a randomised, controlled, assessor-blinded intervention trial on the effects of intermittent use of a fasting-mimicking diet in patients with type 2 diabetes.

Author: van den Burg EL, Schoonakker MP, van Peet PG, van den Akker-van Marle ME, van Dijk KW, Longo VD, Lamb HJ, Numans ME, and Pijl H
Abstract: An amendment to this paper has been published and can be accessed via the original article.
Published: 2020
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42. How the COVID-19 pandemic highlights the necessity of animal research.

Author: Genzel L, Adan R, Berns A, van den Beucken JJJP, Blokland A, Boddeke EHWGM, Bogers WM, Bontrop R, Bulthuis R, Bousema T, Clevers H, Coenen TCJJ, van Dam AM, Deen PMT, van Dijk KW, Eggen BJL, Elgersma Y, Erdogan I, Englitz B, Fentener van Vlissingen JM, la Fleur S, Fouchier R, Fitzsimons CP, Frieling W, Haagmans B, Heesters BA, Henckens MJAG, Herfst S, Hol E, van den Hove D, de Jonge MI, Jonkers J, Joosten LAB, Kalsbeek A, Kamermans M, Kampinga HH, Kas MJ, Keijer J, Kersten S, Kiliaan AJ, Kooij TWA, Kooijman S, Koopman WJH, Korosi A, Krugers HJ, Kuiken T, Kushner SA, Langermans JAM, Lesscher HMB, Lucassen PJ, Lutgens E, Netea MG, Noldus LPJJ, van der Meer JWM, Meye FJ, Mul JD, van Oers K, Olivier JDA, Pasterkamp RJ, Philippens IHCHM, Prickaerts J, Pollux BJA, Rensen PCN, van Rheenen J, van Rij RP, Ritsma L, Rockx BHG, Roozendaal B, van Schothorst EM, Stittelaar K, Stockhofe N, Swaab DF, de Swart RL, Vanderschuren LJMJ, de Vries TJ, de Vrij F, van Wezel R, Wierenga CJ, Wiesmann M, Willuhn I, de Zeeuw CI, and Homberg JR
Published: 2020
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43. Genome-Wide Association Study of the Postprandial Triglyceride Response Yields Common Genetic Variation in LIPC (Hepatic Lipase).

Author: Ibi D, Noordam R, van Klinken JB, Li-Gao R, de Mutsert R, Trompet S, Christen T, Blauw LL, van Heemst D, Mook-Kanamori DO, Rosendaal FR, Jukema JW, Dollé MET, Rensen PCN, and van Dijk KW
Subjects: Female, Genetic Loci, Humans, Linear Models, Lipid Metabolism genetics, Lipoproteins, HDL blood, Lipoproteins, VLDL blood, Male, Middle Aged, Netherlands, Polymorphism, Single Nucleotide, Postprandial Period, Genetic Variation, Genome-Wide Association Study, Lipase genetics, Triglycerides blood
Abstract: Background: The increase in serum triglyceride (TG) concentrations in response to a meal is considered a risk factor for cardiovascular disease. We aimed to elucidate the genetics of the postprandial TG response through genome-wide association studies (GWAS)., Methods: Participants of the NEO (Netherlands Epidemiology of Obesity) study (n=5630) consumed a liquid mixed meal after an overnight fast. GWAS of fasting and postprandial serum TG at 150 minutes were performed. To identify genetic variation of postprandial TG independent of fasting TG, we calculated the TG response at 150 minutes by the residuals of a nonlinear regression that predicted TG at 150 minutes as a function of fasting TG. Association analyses were adjusted for age, sex, and principal components in a linear regression model. Next, using the identified variants as determinants, we performed linear regression analyses on the residuals of the postprandial response of 149 nuclear magnetic resonance-based metabolite measures., Results: GWAS of fasting TG and postprandial serum TG at 150 minutes resulted in completely overlapping loci, replicating previous GWAS. From GWAS of the TG response, we identified rs7350789-A (allele frequency=0.36), mapping to hepatic lipase ( LIPC ), to be associated with a smaller increase in TG concentrations at 150 minutes (β=-0.11; P -value=5.1×10 -8 ). Rs7350789-A was associated with responses of 33 metabolite measures ( P -value <1.34×10 -3 ), mainly smaller increases of the TG-component in almost all HDL (high-density lipoprotein) subparticles (HDL-TG), a smaller decrease of HDL diameter and smaller increases of most components of VLDL (very low density lipoprotein) subparticles., Conclusions: GWAS of the TG response identified a variant near LIPC as a main contributor to postprandial TG metabolism independent of fasting TG concentrations, resulting in smaller increases of HDL-TG and VLDL subparticles.
Published: 2020
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44. Study design of the Fasting In diabetes Treatment (FIT) trial: a randomised, controlled, assessor blinded intervention trial which examines the effect of intermittent use of a fasting mimicking diet in patients with type 2 diabetes.

Author: van den Burg E, Schoonakker M, van den Akker E, van Dijk KW, Lamb H, Pijl H, Numans M, and van Peet P
Abstract: Background: Caloric restriction is an effective way to treat type 2 diabetes (DM2). However, chronic restriction of food intake is difficult to sustain. Intermittent total fasting exerts similar metabolic effects, but may be even more challenging for most patients. A fasting mimicking diet (FMD) was designed to achieve the metabolic benefits of total fasting, despite considerable calorie content. The effects of a FMD in DM2 patients are still unknown., Aim: To determine the effect of intermittent use (5 consecutive days a month during a year) of a FMD in DM2 patients on metabolic parameters and DM2 medication use compared to usual care., Method: One hundred DM2 patients from general practices in the Netherlands with a BMI ≥ 27 kg/m 2 , treated with lifestyle advice only or metformin, will be randomised to receive the FMD plus usual care or usual care only. Primary outcomes are HbA1c and DM2 medication dosage. Secondary outcomes are anthropometrics, blood pressure, plasma lipid profiles, quality of life, treatment satisfaction, metabolomics, microbiome, MRI (for example, cardiac function and fat distribution), cost-effectiveness, and feasibility in clinical practice., Results: The first 70 patients are included. Follow up will be completed in April 2021., Conclusion: Our results will show whether monthly cycles of a FMD are feasible in clinical practice, if they improve metabolic parameters and/or reduce the need for medication in DM2 and if this is a cost-effective intervention., (© British Journal of General Practice 2020.)
Published: 2020
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45. Integration of epidemiologic, pharmacologic, genetic and gut microbiome data in a drug-metabolite atlas.

Author: Liu J, Lahousse L, Nivard MG, Bot M, Chen L, van Klinken JB, Thesing CS, Beekman M, van den Akker EB, Slieker RC, Waterham E, van der Kallen CJH, de Boer I, Li-Gao R, Vojinovic D, Amin N, Radjabzadeh D, Kraaij R, Alferink LJM, Murad SD, Uitterlinden AG, Willemsen G, Pool R, Milaneschi Y, van Heemst D, Suchiman HED, Rutters F, Elders PJM, Beulens JWJ, van der Heijden AAWA, van Greevenbroek MMJ, Arts ICW, Onderwater GLJ, van den Maagdenberg AMJM, Mook-Kanamori DO, Hankemeier T, Terwindt GM, Stehouwer CDA, Geleijnse JM, 't Hart LM, Slagboom PE, van Dijk KW, Zhernakova A, Fu J, Penninx BWJH, Boomsma DI, Demirkan A, Stricker BHC, and van Duijn CM
Subjects: Body Mass Index, Confounding Factors, Epidemiologic, Endophenotypes, Humans, Liver metabolism, Models, Biological, Protein Interaction Maps, Epidemiologic Studies, Gastrointestinal Microbiome genetics, Metabolome genetics, Pharmaceutical Preparations
Abstract: Progress in high-throughput metabolic profiling provides unprecedented opportunities to obtain insights into the effects of drugs on human metabolism. The Biobanking BioMolecular Research Infrastructure of the Netherlands has constructed an atlas of drug-metabolite associations for 87 commonly prescribed drugs and 150 clinically relevant plasma-based metabolites assessed by proton nuclear magnetic resonance. The atlas includes a meta-analysis of ten cohorts (18,873 persons) and uncovers 1,071 drug-metabolite associations after evaluation of confounders including co-treatment. We show that the effect estimates of statins on metabolites from the cross-sectional study are comparable to those from intervention and genetic observational studies. Further data integration links proton pump inhibitors to circulating metabolites, liver function, hepatic steatosis and the gut microbiome. Our atlas provides a tool for targeted experimental pharmaceutical research and clinical trials to improve drug efficacy, safety and repurposing. We provide a web-based resource for visualization of the atlas (http://bbmri.researchlumc.nl/atlas/).
Published: 2020
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46. The role of leptin and adiponectin as mediators in the relationship between adiposity and hand and knee osteoarthritis.

Author: Kroon FPB, Veenbrink AI, de Mutsert R, Visser AW, van Dijk KW, le Cessie S, Rosendaal FR, and Kloppenburg M
Subjects: Adiposity, Aged, Female, Humans, Logistic Models, Male, Middle Aged, Netherlands epidemiology, Obesity epidemiology, Osteoarthritis epidemiology, Osteoarthritis metabolism, Osteoarthritis, Knee epidemiology, Adiponectin metabolism, Hand Joints, Leptin metabolism, Obesity metabolism, Osteoarthritis, Knee metabolism
Abstract: Objectives: To investigate associations of leptin and adiponectin levels with knee and hand osteoarthritis, and explore whether these mediate the association between adiposity and osteoarthritis., Methods: This is a cross-sectional analysis of baseline data from the population-based Netherlands Epidemiology of Obesity study. Adiposity was assessed with body mass index (BMI) and percentage total body fat (%TBF). Osteoarthritis, defined as hand or knee osteoarthritis, was determined using American College of Rheumatology criteria. Fasting serum adipokine levels were measured using immunoassays. Associations between adiposity and osteoarthritis were examined with logistic regression, adjusted for age, sex, ethnicity and education, and additionally for leptin and adiponectin as potential mediators., Results: In 6408 participants (56% women, median age 56 years), prevalence of osteoarthritis was 22% (10% isolated knee and 8% isolated hand osteoarthritis). Leptin levels were positively associated with osteoarthritis, while adiponectin levels were not. Leptin partially mediated the association of adiposity with osteoarthritis (OR 1.40 (95%CI 1.30; 1.52) attenuated to 1.38 (1.24; 1.54) per 5 units BMI and OR 1.25 (1.17; 1.35) to 1.20 (1.10; 1.32) per 5 units %TBF, representing 4% and 17% mediation, respectively). Larger proportion mediation by leptin was found in knee (13%/27%) than in hand osteoarthritis (9%/18%). Sex-stratified analyses generally showed stronger associations between adiposity, leptin and osteoarthritis in women than in men., Conclusions: Serum leptin levels were associated with osteoarthritis, and partially mediated the association between adiposity and osteoarthritis, while adiponectin levels were not associated with osteoarthritis. These findings provide evidence for systemic effects of adipose tissue in osteoarthritis., (Copyright © 2019 Osteoarthritis Research Society International. Published by Elsevier Ltd. All rights reserved.)
Published: 2019
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47. Multi-ancestry sleep-by-SNP interaction analysis in 126,926 individuals reveals lipid loci stratified by sleep duration.

Author: Noordam R, Bos MM, Wang H, Winkler TW, Bentley AR, Kilpeläinen TO, de Vries PS, Sung YJ, Schwander K, Cade BE, Manning A, Aschard H, Brown MR, Chen H, Franceschini N, Musani SK, Richard M, Vojinovic D, Aslibekyan S, Bartz TM, de las Fuentes L, Feitosa M, Horimoto AR, Ilkov M, Kho M, Kraja A, Li C, Lim E, Liu Y, Mook-Kanamori DO, Rankinen T, Tajuddin SM, van der Spek A, Wang Z, Marten J, Laville V, Alver M, Evangelou E, Graff ME, He M, Kühnel B, Lyytikäinen LP, Marques-Vidal P, Nolte IM, Palmer ND, Rauramaa R, Shu XO, Snieder H, Weiss S, Wen W, Yanek LR, Adolfo C, Ballantyne C, Bielak L, Biermasz NR, Boerwinkle E, Dimou N, Eiriksdottir G, Gao C, Gharib SA, Gottlieb DJ, Haba-Rubio J, Harris TB, Heikkinen S, Heinzer R, Hixson JE, Homuth G, Ikram MA, Komulainen P, Krieger JE, Lee J, Liu J, Lohman KK, Luik AI, Mägi R, Martin LW, Meitinger T, Metspalu A, Milaneschi Y, Nalls MA, O'Connell J, Peters A, Peyser P, Raitakari OT, Reiner AP, Rensen PCN, Rice TK, Rich SS, Roenneberg T, Rotter JI, Schreiner PJ, Shikany J, Sidney SS, Sims M, Sitlani CM, Sofer T, Strauch K, Swertz MA, Taylor KD, Uitterlinden AG, van Duijn CM, Völzke H, Waldenberger M, Wallance RB, van Dijk KW, Yu C, Zonderman AB, Becker DM, Elliott P, Esko T, Gieger C, Grabe HJ, Lakka TA, Lehtimäki T, North KE, Penninx BWJH, Vollenweider P, Wagenknecht LE, Wu T, Xiang YB, Zheng W, Arnett DK, Bouchard C, Evans MK, Gudnason V, Kardia S, Kelly TN, Kritchevsky SB, Loos RJF, Pereira AC, Province M, Psaty BM, Rotimi C, Zhu X, Amin N, Cupples LA, Fornage M, Fox EF, Guo X, Gauderman WJ, Rice K, Kooperberg C, Munroe PB, Liu CT, Morrison AC, Rao DC, van Heemst D, and Redline S
Subjects: Adolescent, Adult, Aged, Aged, 80 and over, Chromosome Mapping, Female, Humans, Male, Middle Aged, Young Adult, Genetic Loci, Lipids genetics, Phylogeny, Polymorphism, Single Nucleotide genetics, Sleep genetics
Abstract: Both short and long sleep are associated with an adverse lipid profile, likely through different biological pathways. To elucidate the biology of sleep-associated adverse lipid profile, we conduct multi-ancestry genome-wide sleep-SNP interaction analyses on three lipid traits (HDL-c, LDL-c and triglycerides). In the total study sample (discovery + replication) of 126,926 individuals from 5 different ancestry groups, when considering either long or short total sleep time interactions in joint analyses, we identify 49 previously unreported lipid loci, and 10 additional previously unreported lipid loci in a restricted sample of European-ancestry cohorts. In addition, we identify new gene-sleep interactions for known lipid loci such as LPL and PCSK9. The previously unreported lipid loci have a modest explained variance in lipid levels: most notable, gene-short-sleep interactions explain 4.25% of the variance in triglyceride level. Collectively, these findings contribute to our understanding of the biological mechanisms involved in sleep-associated adverse lipid profiles.
Published: 2019
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48. Hepatic triglyceride content does not affect circulating CETP: lessons from a liraglutide intervention trial and a population-based cohort.

Author: van Eyk HJ, Blauw LL, Bizino MB, Wang Y, van Dijk KW, de Mutsert R, Smit JWA, Lamb HJ, Jazet IM, and Rensen PCN
Subjects: Adult, Aged, Diabetes Mellitus, Type 2 metabolism, Double Bind Interaction, Female, Humans, Hypoglycemic Agents pharmacology, Liraglutide pharmacology, Liver drug effects, Male, Middle Aged, Prospective Studies, Treatment Outcome, Young Adult, Cholesterol Ester Transfer Proteins blood, Diabetes Mellitus, Type 2 drug therapy, Hypoglycemic Agents administration & dosage, Liraglutide administration & dosage, Liver chemistry, Triglycerides metabolism
Abstract: Cholesteryl ester transfer protein (CETP) is mainly expressed by Kupffer cells in the liver. A reduction of hepatic triglyceride content (HTGC) by pioglitazone or caloric restriction is accompanied by a decrease in circulating CETP. Since GLP-1 analogues also reduce HTGC, we assessed whether liraglutide decreases CETP. Furthermore, we investigated the association between HTGC and CETP in a population-based cohort. In a placebo-controlled trial, 50 patients with type 2 diabetes were randomly assigned to treatment with liraglutide or placebo added to standard care. In this trial and in 1,611 participants of the Netherlands Epidemiology of Obesity (NEO) study, we measured HTGC and circulating CETP by proton magnetic resonance spectroscopy and ELISA, respectively. The HTGC was decreased in the liraglutide group (-6.3%; 95%CI of difference [-9.5, -3.0]) but also in the placebo group (-4.0%; 95%CI[-6.0, -2.0]), without between-group differences. CETP was not decreased by liraglutide (-0.05 µg/mL; 95%CI[-0.13, 0.04]) or placebo (-0.04 µg/mL; 95%CI[-0.12, 0.04]). No association was present between HTGC and CETP at baseline (β: 0.002 µg/mL per %TG, 95%CI[-0.005, 0.009]) and between the changes after treatment with liraglutide (β: 0.003 µg/mL per %TG, 95%CI[-0.010, 0.017]) or placebo (β: 0.006 µg/mL per %TG, 95%CI[-0.012,0.024]). Also, in the cohort n o association between HTGC and CETP was present (β: -0.001 µg/mL per SD TG, 95%CI[-0.005, 0.003]). A reduction of HTGC after treatment with liraglutide or placebo does not decrease circulating CETP. Also, no association between HTGC and CETP was present in a large cohort. These findings indicate that circulating CETP is not determined by HTGC.Clinical Trial Registration: Clinicaltrials.gov (NCT01761318).
Published: 2019
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49. An integrative cross-omics analysis of DNA methylation sites of glucose and insulin homeostasis.

Author: Liu J, Carnero-Montoro E, van Dongen J, Lent S, Nedeljkovic I, Ligthart S, Tsai PC, Martin TC, Mandaviya PR, Jansen R, Peters MJ, Duijts L, Jaddoe VWV, Tiemeier H, Felix JF, Willemsen G, de Geus EJC, Chu AY, Levy D, Hwang SJ, Bressler J, Gondalia R, Salfati EL, Herder C, Hidalgo BA, Tanaka T, Moore AZ, Lemaitre RN, Jhun MA, Smith JA, Sotoodehnia N, Bandinelli S, Ferrucci L, Arnett DK, Grallert H, Assimes TL, Hou L, Baccarelli A, Whitsel EA, van Dijk KW, Amin N, Uitterlinden AG, Sijbrands EJG, Franco OH, Dehghan A, Spector TD, Dupuis J, Hivert MF, Rotter JI, Meigs JB, Pankow JS, van Meurs JBJ, Isaacs A, Boomsma DI, Bell JT, Demirkan A, and van Duijn CM
Subjects: Adult, Aged, Aged, 80 and over, Computer Simulation, CpG Islands genetics, Diabetes Mellitus, Type 2 metabolism, Epigenesis, Genetic physiology, Epigenomics methods, Female, Gene Expression Profiling methods, Gene Expression Regulation genetics, Genome-Wide Association Study methods, Homeostasis genetics, Humans, Male, Metabolic Networks and Pathways genetics, Middle Aged, Obesity metabolism, Polymorphism, Single Nucleotide physiology, Young Adult, DNA Methylation physiology, Diabetes Mellitus, Type 2 genetics, Glucose metabolism, Insulin metabolism, Obesity genetics
Abstract: Despite existing reports on differential DNA methylation in type 2 diabetes (T2D) and obesity, our understanding of its functional relevance remains limited. Here we show the effect of differential methylation in the early phases of T2D pathology by a blood-based epigenome-wide association study of 4808 non-diabetic Europeans in the discovery phase and 11,750 individuals in the replication. We identify CpGs in LETM1, RBM20, IRS2, MAN2A2 and the 1q25.3 region associated with fasting insulin, and in FCRL6, SLAMF1, APOBEC3H and the 15q26.1 region with fasting glucose. In silico cross-omics analyses highlight the role of differential methylation in the crosstalk between the adaptive immune system and glucose homeostasis. The differential methylation explains at least 16.9% of the association between obesity and insulin. Our study sheds light on the biological interactions between genetic variants driving differential methylation and gene expression in the early pathogenesis of T2D.
Published: 2019
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50. Metabolomics Profiling of Visceral Adipose Tissue: Results From MESA and the NEO Study.

Author: Neeland IJ, Boone SC, Mook-Kanamori DO, Ayers C, Smit RAJ, Tzoulaki I, Karaman I, Boulange C, Vaidya D, Punjabi N, Allison M, Herrington DM, Jukema JW, Rosendaal FR, Lamb HJ, van Dijk KW, Greenland P, and de Mutsert R
Subjects: Aged, Aged, 80 and over, Amino Acids blood, Amino Acids, Branched-Chain blood, Cholesterol, HDL blood, Cholesterol, LDL blood, Female, Gluconeogenesis, Glutamine blood, Glycolysis, Humans, Intra-Abdominal Fat diagnostic imaging, Lactic Acid blood, Linear Models, Magnetic Resonance Imaging, Male, Metabolic Networks and Pathways, Middle Aged, Organ Size, Proton Magnetic Resonance Spectroscopy, Tomography, X-Ray Computed, Triglycerides blood, Intra-Abdominal Fat metabolism, Metabolomics
Abstract: Background Identifying associations between serum metabolites and visceral adipose tissue ( VAT ) could provide novel biomarkers of VAT and insights into the pathogenesis of obesity-related diseases. We aimed to discover and replicate metabolites reflecting pathways related to VAT . Methods and Results Associations between fasting serum metabolites and VAT area (by computed tomography or magnetic resonance imaging) were assessed with cross-sectional linear regression of individual-level data from participants in MESA (Multi-Ethnic Study of Atherosclerosis; discovery, N=1103) and the NEO (Netherlands Epidemiology of Obesity) study (replication, N=2537). Untargeted 1 H nuclear magnetic resonance metabolomics profiling of serum was performed in MESA, and metabolites were replicated in the NEO study using targeted 1 H nuclear magnetic resonance spectroscopy. A total of 30 590 metabolomic spectral variables were evaluated. After adjustment for age, sex, race/ethnicity, socioeconomic status, smoking, physical activity, glucose/lipid-lowering medication, and body mass index, 2104 variables representing 24 nonlipid and 49 lipid/lipoprotein subclass metabolites remained significantly associated with VAT ( P=4.88×10 -20 -1.16×10 -3 ). These included conventional metabolites, amino acids, acetylglycoproteins, intermediates of glucose and hepatic metabolism, organic acids, and subclasses of apolipoproteins, cholesterol, phospholipids, and triglycerides. Metabolites mapped to 31 biochemical pathways, including amino acid substrate use/metabolism and glycolysis/gluconeogenesis. In the replication cohort, acetylglycoproteins, branched-chain amino acids, lactate, glutamine (inversely), and atherogenic lipids remained associated with VAT ( P=1.90×10 -35 -8.46×10 -7 ), with most associations remaining after additional adjustment for surrogates of VAT (glucose level, waist circumference, and serum triglycerides), reflecting novel independent associations. Conclusions We identified and replicated a metabolite panel associated with VAT in 2 community-based cohorts. These findings persisted after adjustment for body mass index and appear to define a metabolic signature of visceral adiposity.
Published: 2019
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