138 results on '"Kobes S"'
Search Results
2. Variants in ACAD10 are associated with type 2 diabetes, insulin resistance and lipid oxidation in Pima Indians
- Author
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Bian, L., Hanson, R. L., Muller, Y. L., Ma, L., Kobes, S., Knowler, W. C., Bogardus, C., Baier, L. J., and MAGIC Investigators
- Published
- 2010
- Full Text
- View/download PDF
3. Protein tyrosine phosphatase 1B is not a major susceptibility gene for type 2 diabetes mellitus or obesity among Pima Indians
- Author
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Traurig, M., Hanson, R. L., Kobes, S., Bogardus, C., and Baier, L. J.
- Published
- 2007
- Full Text
- View/download PDF
4. A Genome-Wide Association Study Using a Custom Genotyping Array Identifies Variants in GPR158 Associated With Reduced Energy Expenditure in American Indians
- Author
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Piaggi, P, Masindova, I, Muller, Yl, Mercader, J, Wiessner, Gb, Chen, P, Sigma Type, 2 Diabetes Consortium, Kobes, S, Hsueh, Wc, Mongalo, M, Knowler, Wc, Krakoff, J, Hanson, Rl, Bogardus, C, and Baier, Lj
- Subjects
Male ,0301 basic medicine ,North American/*genetics Male Middle Aged Receptors ,Endocrinology, Diabetes and Metabolism ,Genome-wide association study ,Body Mass Index ,Receptors, G-Protein-Coupled ,0302 clinical medicine ,Type 2/ethnology/genetics Energy Metabolism/*genetics Female Genetic Variation/*genetics Genome-Wide Association Study Humans Indians ,Adiposity ,Genetics ,education.field_of_study ,Arizona ,Genetics/Genomes/Proteomics/Metabolomics ,Middle Aged ,Adiposity/genetics Adult Alleles Arizona Basal Metabolism/genetics Body Mass Index Calorimetry/methods Diabetes Mellitus, Type 2/ethnology/genetics Energy Metabolism/*genetics Female Genetic Variation/*genetics Genome-Wide Association Study Humans Indians, North American/*genetics Male Middle Aged Receptors, G-Protein-Coupled/*genetics ,Female ,medicine.symptom ,Adult ,medicine.medical_specialty ,Population ,030209 endocrinology & metabolism ,Calorimetry ,Biology ,03 medical and health sciences ,Internal medicine ,Internal Medicine ,medicine ,Humans ,Allele ,education ,Adiposity/genetics Adult Alleles Arizona Basal Metabolism/genetics Body Mass Index Calorimetry/methods Diabetes Mellitus ,G-Protein-Coupled/*genetics ,Genotyping ,Alleles ,Genetic Variation ,nutritional and metabolic diseases ,Heritability ,medicine.disease ,Obesity ,030104 developmental biology ,Endocrinology ,Diabetes Mellitus, Type 2 ,Basal metabolic rate ,Indians, North American ,Basal Metabolism ,Energy Metabolism ,Weight gain ,Genome-Wide Association Study - Abstract
Pima Indians living in Arizona have a high prevalence of obesity, and we have previously shown that a relatively lower energy expenditure (EE) predicts weight and fat mass gain in this population. EE is a familial trait (heritability = 0.52); therefore, in the current study, we aimed to identify genetic variants that affect EE and thereby influence BMI and body fatness in Pima Indians. Genotypic data from 491,265 variants were analyzed for association with resting metabolic rate (RMR) and 24-h EE assessed in a whole-room calorimeter in 507 and 419 Pima Indians, respectively. Variants associated with both measures of EE were analyzed for association with maximum BMI and percent body fat (PFAT) in 5,870 and 912 Pima Indians, respectively. rs11014566 nominally associated with both measures of EE and both measures of adiposity in Pima Indians, where the G allele (frequency: Pima Indians = 0.60, Europeans
- Published
- 2017
5. Assessing Variation across Eight Established East Asian Loci for Type 2 Diabetes in American Indians: Suggestive Evidence for New Sex-specific Diabetes Signals in GLIS3 and ZFAND3
- Author
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Muller, Yl, Piaggi, P, Chen, P, Wiessner, G, Okani, C, Kobes, S, Knowler, Wc, Bogardus, C, Hanson, Rl, and Baier, Lj
- Published
- 2017
6. Secular changes in physical growth and obesity among southwestern American Indian children over four decades
- Author
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Vijayakumar, P., primary, Wheelock, K. M., additional, Kobes, S., additional, Nelson, R. G., additional, Hanson, R. L., additional, Knowler, W. C., additional, and Sinha, M., additional
- Published
- 2016
- Full Text
- View/download PDF
7. Use of Whole Genome Sequence Data to Design a Custom Genotyping Array for American Indians
- Author
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Baier, Lj, Muller, Y, Huang, K, Nair, A, Hsueh, Wc, Chen, P, Piaggi, P, Knowler, Wc, Kobes, S, Hanson, Rl, and Bogardus, C
- Published
- 2015
8. Identification of a New Signal for Type 2 Diabetes in the Previously Established GLIS3 Gene
- Author
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Muller, Y, Hanson, Rl, Piaggi, P, Wiessner, G, Okani, C, Kobes, S, Knowler, Wc, Bogar-Dus, C, and Baier, Lj
- Published
- 2015
9. Variants Associated with HDL-C Levels: Assessment for Association with Type 2 Diabetes in American Indians
- Author
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Nair, Ak, Mclean, N, Piaggi, P, Kaur, M, Hsueh, Wc, Kobes, S, Knowler, Wc, Bogardus, C, Hanson, Rl, and Baier, Lj
- Published
- 2015
10. Genome wide linkage analysis assessing parent-of-origin effects in the inheritance of birth weight
- Author
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Lindsay, R.S., Kobes, S., Knowler, W.C., and Hanson, R.L.
- Subjects
Genetic disorders -- Research ,Linkage (Genetics) -- Analysis ,Birth weight -- Genetic aspects ,Biological sciences - Published
- 2001
11. Comparison of Haseman-Elston Methods for Assessing Parent-of-Origin Effects in Linkage Analysis
- Author
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Hanson, R.L., Kobes, S., Lindsay, R.S., and Knowler, W.C.
- Subjects
Human genetics -- Research ,Genetic disorders -- Research ,Clans -- Genetic aspects ,Analysis of variance -- Usage ,Biological sciences - Published
- 2001
12. Linkage Disequilibrium Mapping of a Putative Type 2 Diabetes Locus (1q21-q23) using Pooled DNA
- Author
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Wolford, J.K., Kobes, S., Hanson, R.L., Bogardus, C., and Prochazka, M.
- Subjects
Pimas -- Diseases ,Type 2 diabetes -- Genetic aspects ,Linkage (Genetics) -- Research ,Genetic disorders -- Research ,Biological sciences - Published
- 2001
13. Assessing Parent-of-Origin Effects in Linkage Analysis of Quantitative Traits
- Author
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Hanson, R.L., Lindsay, R.S., Kobes, S., and Knowler, W.C.
- Subjects
Genetic research -- Analysis ,Human genetics -- Research ,Linkage (Genetics) -- Research ,Biological sciences - Published
- 2000
14. A variance components method of linkage analysis to assess parent-of-origin effects on body mass index
- Author
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Lindsay, R.S., Kobes, S., Knowler, W.C., and Hanson, R.L.
- Subjects
Genetic research -- Analysis ,Human genetics -- Research ,Biological sciences - Published
- 2000
15. Variants in the Promoter of STAM2 Are Associated with Type 2 Diabetes in Pima Indians
- Author
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Nair, Ak, Hanson, Rl, Piaggi, P, Mclean, N, Muller, Y, Huang, K, Kobes, S, Knowler, Wc, Bogardus, C, and Baier, Lj
- Published
- 2014
16. The transcriptional landscape of age in human peripheral blood
- Author
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Peters, MJ, Joehanes, R, Pilling, LC, Schurmann, C, Conneely, KN, Powell, J, Reinmaa, E, Sutphin, GL, Zhernakova, A, Schramm, K, Wilson, YA, Kobes, S, Tukiainen, T, Ramos, YF, Göring, HHH, Fornage, M, Liu, Y, Gharib, SA, Stranger, BE, De Jager, PL, Aviv, A, Levy, D, Murabito, JM, Munson, PJ, Huan, T, Hofman, A, Uitterlinden, AG, Rivadeneira, F, Van Rooij, J, Stolk, L, Broer, L, Verbiest, MMPJ, Jhamai, M, Arp, P, Metspalu, A, Tserel, L, Milani, L, Samani, NJ, Peterson, P, Kasela, S, Codd, V, Peters, A, Ward-Caviness, CK, Herder, C, Waldenberger, M, Roden, M, Singmann, P, Zeilinger, S, Illig, T, Homuth, G, Grabe, HJ, Völzke, H, Steil, L, Kocher, T, Murray, A, Melzer, D, Yaghootkar, H, Bandinelli, S, Moses, EK, Kent, JW, Curran, JE, Johnson, MP, Williams-Blangero, S, Westra, HJ, McRae, AF, Smith, JA, Kardia, SLR, Hovatta, I, Perola, M, Ripatti, S, Salomaa, V, Henders, AK, Martin, NG, Smith, AK, Mehta, D, Binder, EB, Nylocks, KM, Kennedy, EM, Klengel, T, Ding, J, Suchy-Dicey, AM, Enquobahrie, DA, Brody, J, Rotter, JI, Chen, YDI, Houwing-Duistermaat, J, Kloppenburg, M, Slagboom, PE, Helmer, Q, Den Hollander, W, Bean, S, Raj, T, Bakhshi, N, Wang, QP, Oyston, LJ, Psaty, BM, Tracy, RP, Montgomery, GW, Turner, ST, Blangero, J, Peters, MJ, Joehanes, R, Pilling, LC, Schurmann, C, Conneely, KN, Powell, J, Reinmaa, E, Sutphin, GL, Zhernakova, A, Schramm, K, Wilson, YA, Kobes, S, Tukiainen, T, Ramos, YF, Göring, HHH, Fornage, M, Liu, Y, Gharib, SA, Stranger, BE, De Jager, PL, Aviv, A, Levy, D, Murabito, JM, Munson, PJ, Huan, T, Hofman, A, Uitterlinden, AG, Rivadeneira, F, Van Rooij, J, Stolk, L, Broer, L, Verbiest, MMPJ, Jhamai, M, Arp, P, Metspalu, A, Tserel, L, Milani, L, Samani, NJ, Peterson, P, Kasela, S, Codd, V, Peters, A, Ward-Caviness, CK, Herder, C, Waldenberger, M, Roden, M, Singmann, P, Zeilinger, S, Illig, T, Homuth, G, Grabe, HJ, Völzke, H, Steil, L, Kocher, T, Murray, A, Melzer, D, Yaghootkar, H, Bandinelli, S, Moses, EK, Kent, JW, Curran, JE, Johnson, MP, Williams-Blangero, S, Westra, HJ, McRae, AF, Smith, JA, Kardia, SLR, Hovatta, I, Perola, M, Ripatti, S, Salomaa, V, Henders, AK, Martin, NG, Smith, AK, Mehta, D, Binder, EB, Nylocks, KM, Kennedy, EM, Klengel, T, Ding, J, Suchy-Dicey, AM, Enquobahrie, DA, Brody, J, Rotter, JI, Chen, YDI, Houwing-Duistermaat, J, Kloppenburg, M, Slagboom, PE, Helmer, Q, Den Hollander, W, Bean, S, Raj, T, Bakhshi, N, Wang, QP, Oyston, LJ, Psaty, BM, Tracy, RP, Montgomery, GW, Turner, ST, and Blangero, J
- Abstract
Disease incidences increase with age, but the molecular characteristics of ageing that lead to increased disease susceptibility remain inadequately understood. Here we perform a whole-blood gene expression meta-analysis in 14,983 individuals of European ancestry (including replication) and identify 1,497 genes that are differentially expressed with chronological age. The age-associated genes do not harbor more age-associated CpG-methylation sites than other genes, but are instead enriched for the presence of potentially functional CpG-methylation sites in enhancer and insulator regions that associate with both chronological age and gene expression levels. We further used the gene expression profiles to calculate the 'transcriptomic age' of an individual, and show that differences between transcriptomic age and chronological age are associated with biological features linked to ageing, such as blood pressure, cholesterol levels, fasting glucose, and body mass index. The transcriptomic prediction model adds biological relevance and complements existing epigenetic prediction models, and can be used by others to calculate transcriptomic age in external cohorts.
- Published
- 2015
17. The transcriptional landscape of age in human peripheral blood
- Author
-
Peters, M.J. (Marjolein), Joehanes, R. (Roby), Pilling, L.C. (Luke), Schurmann, C. (Claudia), Conneely, K.N. (Karen N.), Powell, J.E. (Joseph), Reinmaa, E. (Eva), Sutphin, G.L. (George L.), Zhernakova, A. (Alexandra), Schramm, K. (Katharina), Wilson, Y.A. (Yana A.), Kobes, S. (Sayuko), Tukiainen, T. (Taru), Ramos, Y.F.M. (Yolande), Göring, H.H.H. (Harald H.), Fornage, M. (Myriam), Liu, Y. (YongMei), Gharib, S.A. (Sina), Stranger, B.E. (Barbara), Jager, P.L. (Philip) de, Aviv, A. (Abraham), Levy, D. (Daniel), Murabito, J. (Joanne), Munson, P.J. (Peter J.), Huan, T. (Tianxiao), Hofman, A. (Albert), Uitterlinden, A.G. (André), Rivadeneira Ramirez, F. (Fernando), Rooij, J.G.J. (Jeroen) van, Stolk, L. (Lisette), Broer, L. (Linda), Verbiest, M.M.P.J. (Michael), Jhamai, M. (Mila), Arp, P.P. (Pascal), Metspalu, A. (Andres), Tserel, L. (Liina), Milani, L. (Lili), Samani, N.J. (Nilesh), Peterson, P. (Pärt), Kasela, S. (Silva), Codd, V. (Veryan), Peters, A. (Annette), Ward-Caviness, C.K. (Cavin K.), Herder, C. (Christian), Waldenberger, M. (Melanie), Roden, M. (Michael), Singmann, P. (Paula), Zeilinger, S. (Sonja), Illig, T. (Thomas), Homuth, G. (Georg), Grabe, H.J. (Hans Jörgen), Völzke, H. (Henry), Steil, L. (Leif), Kocher, T. (Thomas), Murray, A. (Anna), Melzer, D. (David), Yaghootkar, H. (Hanieh), Bandinelli, S., Moses, E.K. (Eric), Kent, J.W. (Jack), Curran, J.E. (Joanne), Johnson, M.P. (Matthew), Williams-Blangero, S. (Sarah), Westra, H.J. (Harm-Jan), McRae, A.F. (Allan F.), Smith, J.A. (Jennifer A), Kardia, S.L.R. (Sharon), Hovatta, I. (Iiris), Perola, M. (Markus), Ripatti, S. (Samuli), Salomaa, V. (Veikko), Henders, A.K. (Anjali), Martin, N.G. (Nicholas), Smith, A.K. (Alicia K.), Mehta, D. (Divya), Binder, E.B. (Elisabeth B.), Nylocks, K.M. (K. Maria), Kennedy, E.M. (Elizabeth M.), Klengel, T. (Torsten), Ding, J. (Jingzhong), Suchy-Dicey, A. (Astrid), Enquobahrie, D., Brody, J.A. (Jennifer A.), Rotter, J.I. (Jerome I.), Chen, Y.-D.I. (Yii-Der I.), Houwing-Duistermaat, J.J. (Jeanine), Kloppenburg, M. (Margreet), Slagboom, P.E. (Eline), Helmer, Q. (Quinta), Hollander, W. (Wouter) den, Bean, S. (Shannon), Raj, T. (Towfique), Bakhshi, N. (Noman), Wang, Q.P. (Qiao Ping), Oyston, L.J. (Lisa J.), Psaty, B.M. (Bruce), Tracy, R.P. (Russell), Montgomery, G.W. (Grant), Turner, S.T. (Stephen), Blangero, J. (John), Meulenbelt, I. (Ingrid), Ressler, K.J. (Kerry), Yang, J. (Jian), Franke, L. (Lude), Kettunen, J. (Johannes), Visscher, P.M. (Peter), Neely, G.G. (G. Gregory), Korstanje, R. (Ron), Hanson, R.L. (Robert L.), Prokisch, H. (Holger), Ferrucci, L. (Luigi), Esko, T. (Tõnu), Teumer, A. (Alexander), Meurs, J.B.J. (Joyce) van, Johnson, A.D. (Andrew D.), Nalls, M.A. (Michael), Hernandez, D.G. (Dena), Cookson, M.R. (Mark), Gibbs, R.J. (Raphael J.), Hardy, J. (John), Ramasamy, A. (Adaikalavan), Zonderman, A.B. (Alan B.), Dillman, A. (Allissa), Traynor, B. (Bryan), Smith, C. (Colin), Longo, D.L. (Dan L.), Trabzuni, D. (Danyah), Troncoso, J.C. (Juan), Brug, M.P. (Marcel) van der, Weale, M.E. (Michael), O'Brien, R. (Richard), Johnson, R. (Robert), Walker, R. (Robert), Zielke, R.H. (Ronald H.), Arepalli, S. (Sampath), Ryten, M. (Mina), Singleton, A., Peters, M.J. (Marjolein), Joehanes, R. (Roby), Pilling, L.C. (Luke), Schurmann, C. (Claudia), Conneely, K.N. (Karen N.), Powell, J.E. (Joseph), Reinmaa, E. (Eva), Sutphin, G.L. (George L.), Zhernakova, A. (Alexandra), Schramm, K. (Katharina), Wilson, Y.A. (Yana A.), Kobes, S. (Sayuko), Tukiainen, T. (Taru), Ramos, Y.F.M. (Yolande), Göring, H.H.H. (Harald H.), Fornage, M. (Myriam), Liu, Y. (YongMei), Gharib, S.A. (Sina), Stranger, B.E. (Barbara), Jager, P.L. (Philip) de, Aviv, A. (Abraham), Levy, D. (Daniel), Murabito, J. (Joanne), Munson, P.J. (Peter J.), Huan, T. (Tianxiao), Hofman, A. (Albert), Uitterlinden, A.G. (André), Rivadeneira Ramirez, F. (Fernando), Rooij, J.G.J. (Jeroen) van, Stolk, L. (Lisette), Broer, L. (Linda), Verbiest, M.M.P.J. (Michael), Jhamai, M. (Mila), Arp, P.P. (Pascal), Metspalu, A. (Andres), Tserel, L. (Liina), Milani, L. (Lili), Samani, N.J. (Nilesh), Peterson, P. (Pärt), Kasela, S. (Silva), Codd, V. (Veryan), Peters, A. (Annette), Ward-Caviness, C.K. (Cavin K.), Herder, C. (Christian), Waldenberger, M. (Melanie), Roden, M. (Michael), Singmann, P. (Paula), Zeilinger, S. (Sonja), Illig, T. (Thomas), Homuth, G. (Georg), Grabe, H.J. (Hans Jörgen), Völzke, H. (Henry), Steil, L. (Leif), Kocher, T. (Thomas), Murray, A. (Anna), Melzer, D. (David), Yaghootkar, H. (Hanieh), Bandinelli, S., Moses, E.K. (Eric), Kent, J.W. (Jack), Curran, J.E. (Joanne), Johnson, M.P. (Matthew), Williams-Blangero, S. (Sarah), Westra, H.J. (Harm-Jan), McRae, A.F. (Allan F.), Smith, J.A. (Jennifer A), Kardia, S.L.R. (Sharon), Hovatta, I. (Iiris), Perola, M. (Markus), Ripatti, S. (Samuli), Salomaa, V. (Veikko), Henders, A.K. (Anjali), Martin, N.G. (Nicholas), Smith, A.K. (Alicia K.), Mehta, D. (Divya), Binder, E.B. (Elisabeth B.), Nylocks, K.M. (K. Maria), Kennedy, E.M. (Elizabeth M.), Klengel, T. (Torsten), Ding, J. (Jingzhong), Suchy-Dicey, A. (Astrid), Enquobahrie, D., Brody, J.A. (Jennifer A.), Rotter, J.I. (Jerome I.), Chen, Y.-D.I. (Yii-Der I.), Houwing-Duistermaat, J.J. (Jeanine), Kloppenburg, M. (Margreet), Slagboom, P.E. (Eline), Helmer, Q. (Quinta), Hollander, W. (Wouter) den, Bean, S. (Shannon), Raj, T. (Towfique), Bakhshi, N. (Noman), Wang, Q.P. (Qiao Ping), Oyston, L.J. (Lisa J.), Psaty, B.M. (Bruce), Tracy, R.P. (Russell), Montgomery, G.W. (Grant), Turner, S.T. (Stephen), Blangero, J. (John), Meulenbelt, I. (Ingrid), Ressler, K.J. (Kerry), Yang, J. (Jian), Franke, L. (Lude), Kettunen, J. (Johannes), Visscher, P.M. (Peter), Neely, G.G. (G. Gregory), Korstanje, R. (Ron), Hanson, R.L. (Robert L.), Prokisch, H. (Holger), Ferrucci, L. (Luigi), Esko, T. (Tõnu), Teumer, A. (Alexander), Meurs, J.B.J. (Joyce) van, Johnson, A.D. (Andrew D.), Nalls, M.A. (Michael), Hernandez, D.G. (Dena), Cookson, M.R. (Mark), Gibbs, R.J. (Raphael J.), Hardy, J. (John), Ramasamy, A. (Adaikalavan), Zonderman, A.B. (Alan B.), Dillman, A. (Allissa), Traynor, B. (Bryan), Smith, C. (Colin), Longo, D.L. (Dan L.), Trabzuni, D. (Danyah), Troncoso, J.C. (Juan), Brug, M.P. (Marcel) van der, Weale, M.E. (Michael), O'Brien, R. (Richard), Johnson, R. (Robert), Walker, R. (Robert), Zielke, R.H. (Ronald H.), Arepalli, S. (Sampath), Ryten, M. (Mina), and Singleton, A.
- Abstract
Disease incidences increase with age, but the molecular characteristics of ageing that lead to increased disease susceptibility remain inadequately understood. Here we perform a whole-blood gene expression meta-analysis in 14,983 individuals of European ancestry (including replication) and identify 1,497 genes that are differentially expressed with chronological age. The age-associated genes do not harbor more age-associated CpG-methylation sites than other genes, but are instead enriched for the presence of potentially functional CpG-methylation sites in enhancer and insulator regions that associate with both chronological age and gene expression levels. We further used the gene expression profiles to calculate the 'transcriptomic age' of an individual, and show that differences between transcriptomic age and chronological age are associated with biological features linked to ageing, such as blood pressure, cholesterol levels, fasting glucose, and body mass index. The transcriptomic prediction model adds biological relevance and complements existing epigenetic prediction models, and can be used by others to calculate transcriptomic age in external cohorts.
- Published
- 2015
- Full Text
- View/download PDF
18. The transcriptional landscape of age in human peripheral blood
- Author
-
Peters, Marjolein, Joehanes, R, Pilling, LC, Schurmann, C, Conneely, KN, Powell, J, Reinmaa, E, Sutphin, GL, Zhernakova, A, Schramm, K, Wilson, YA, Kobes, S, Tukiainen, T, Ramos, YF, Goring, HHH, Fornage, M, Liu, YM, Gharib, SA, Stranger, BE, De Jager, PL, Aviv, A, Levy, D, Murabito, JM, Munson, PJ, Huan, T, Hofman, Bert, Uitterlinden, André, Rivadeneira, Fernando, van Rooij, J, Stolk, Lisette, Broer, Linda, Verbiest, Michael, Jhamai, M, Arp, Pascal, Metspalu, A, Tserel, L, Milani, L, Samani, NJ, Peterson, P, Kasela, S, Codd, V, Peters, A, Ward-Caviness, CK, Herder, Cindy, Waldenberger, M, Roden, M, Singmann, P, Zeilinger, S, Illig, T, Homuth, G, Grabe, HJ, Voelzke, H, Steil, L, Kocher, T, Murray, A, Melzer, D, Yaghootkar, H, Bandinelli, S, Moses, EK, Kent, JW, Curran, JE, Johnson, MP, Williams-Blangero, S, Westra, HJ, Mcrae, AF, Smith, JA, Kardia, SLR, Hovatta, I, Perola, M, Ripatti, S, Salomaa, V, Henders, AK, Martin, NG, Smith, AK, Mehta, D, Binder, EB, Nylocks, KM, Kennedy, EM, Klengel, T, Ding, J, Suchy-Dicey, AM, Enquobahrie, DA, Brody, J, Rotter, JI, Chen, YDI, Houwing-Duistermaat, J, Kloppenburg, M, Slagboom, PE (Eline), Helmer, Q, den Hollander, W, Bean, S, Raj, T, Bakhshi, N, Wang, QP, Oyston, LJ, Psaty, BM, Tracy, RP, Montgomery, GW, Turner, ST, Blangero, J, Meulenbelt, I, Ressler, KJ, Yang, Jiaqi, Franke, L, Kettunen, J, Visscher, PM, Neely, GG, Korstanje, R, Hanson, RL, Prokisch, H, Ferrucci, L, Esko, T, Teumer, A, van Meurs, Joyce, Andrew, D, Peters, Marjolein, Joehanes, R, Pilling, LC, Schurmann, C, Conneely, KN, Powell, J, Reinmaa, E, Sutphin, GL, Zhernakova, A, Schramm, K, Wilson, YA, Kobes, S, Tukiainen, T, Ramos, YF, Goring, HHH, Fornage, M, Liu, YM, Gharib, SA, Stranger, BE, De Jager, PL, Aviv, A, Levy, D, Murabito, JM, Munson, PJ, Huan, T, Hofman, Bert, Uitterlinden, André, Rivadeneira, Fernando, van Rooij, J, Stolk, Lisette, Broer, Linda, Verbiest, Michael, Jhamai, M, Arp, Pascal, Metspalu, A, Tserel, L, Milani, L, Samani, NJ, Peterson, P, Kasela, S, Codd, V, Peters, A, Ward-Caviness, CK, Herder, Cindy, Waldenberger, M, Roden, M, Singmann, P, Zeilinger, S, Illig, T, Homuth, G, Grabe, HJ, Voelzke, H, Steil, L, Kocher, T, Murray, A, Melzer, D, Yaghootkar, H, Bandinelli, S, Moses, EK, Kent, JW, Curran, JE, Johnson, MP, Williams-Blangero, S, Westra, HJ, Mcrae, AF, Smith, JA, Kardia, SLR, Hovatta, I, Perola, M, Ripatti, S, Salomaa, V, Henders, AK, Martin, NG, Smith, AK, Mehta, D, Binder, EB, Nylocks, KM, Kennedy, EM, Klengel, T, Ding, J, Suchy-Dicey, AM, Enquobahrie, DA, Brody, J, Rotter, JI, Chen, YDI, Houwing-Duistermaat, J, Kloppenburg, M, Slagboom, PE (Eline), Helmer, Q, den Hollander, W, Bean, S, Raj, T, Bakhshi, N, Wang, QP, Oyston, LJ, Psaty, BM, Tracy, RP, Montgomery, GW, Turner, ST, Blangero, J, Meulenbelt, I, Ressler, KJ, Yang, Jiaqi, Franke, L, Kettunen, J, Visscher, PM, Neely, GG, Korstanje, R, Hanson, RL, Prokisch, H, Ferrucci, L, Esko, T, Teumer, A, van Meurs, Joyce, and Andrew, D
- Abstract
Disease incidences increase with age, but the molecular characteristics of ageing that lead to increased disease susceptibility remain inadequately understood. Here we perform a whole-blood gene expression meta-analysis in 14,983 individuals of European ancestry (including replication) and identify 1,497 genes that are differentially expressed with chronological age. The age-associated genes do not harbor more age-associated CpG-methylation sites than other genes, but are instead enriched for the presence of potentially functional CpG-methylation sites in enhancer and insulator regions that associate with both chronological age and gene expression levels. We further used the gene expression profiles to calculate the 'transcriptomic age' of an individual, and show that differences between transcriptomic age and chronological age are associated with biological features linked to ageing, such as blood pressure, cholesterol levels, fasting glucose, and body mass index. The transcriptomic prediction model adds biological relevance and complements existing epigenetic prediction models, and can be used by others to calculate transcriptomic age in external cohorts.
- Published
- 2015
19. The transcriptional landscape of age in human peripheral blood
- Author
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Peters, M., Joehanes, R., Pilling, L., Schurmann, C., Conneely, K., Powell, J., Reinmaa, E., Sutphin, G., Zhernakova, A., Schramm, K., Wilson, Y., Kobes, S., Tukiainen, T., Ramos, Y., Göring, H., Fornage, M., Liu, Y., Gharib, S., Stranger, B., De Jager, P., Aviv, A., Levy, D., Murabito, J., Munson, P., Huan, T., Hofman, A., Uitterlinden, A., Rivadeneira, F., Van Rooij, J., Stolk, L., Broer, L., Verbiest, M., Jhamai, M., Arp, P., Metspalu, A., Tserel, L., Milani, L., Samani, N., Peterson, P., Kasela, S., Codd, V., Peters, A., Ward-Caviness, C., Herder, C., Waldenberger, M., Roden, M., Singmann, P., Zeilinger, S., Illig, T., Homuth, G., Grabe, H., Völzke, H., Steil, L., Kocher, T., Murray, A., Melzer, D., Yaghootkar, H., Bandinelli, S., Moses, Eric, Kent, J., Curran, J., Johnson, M., Williams-Blangero, S., Westra, H., McRae, A., Smith, J., Kardia, S., Hovatta, I., Perola, M., Ripatti, S., Salomaa, V., Henders, A., Peters, M., Joehanes, R., Pilling, L., Schurmann, C., Conneely, K., Powell, J., Reinmaa, E., Sutphin, G., Zhernakova, A., Schramm, K., Wilson, Y., Kobes, S., Tukiainen, T., Ramos, Y., Göring, H., Fornage, M., Liu, Y., Gharib, S., Stranger, B., De Jager, P., Aviv, A., Levy, D., Murabito, J., Munson, P., Huan, T., Hofman, A., Uitterlinden, A., Rivadeneira, F., Van Rooij, J., Stolk, L., Broer, L., Verbiest, M., Jhamai, M., Arp, P., Metspalu, A., Tserel, L., Milani, L., Samani, N., Peterson, P., Kasela, S., Codd, V., Peters, A., Ward-Caviness, C., Herder, C., Waldenberger, M., Roden, M., Singmann, P., Zeilinger, S., Illig, T., Homuth, G., Grabe, H., Völzke, H., Steil, L., Kocher, T., Murray, A., Melzer, D., Yaghootkar, H., Bandinelli, S., Moses, Eric, Kent, J., Curran, J., Johnson, M., Williams-Blangero, S., Westra, H., McRae, A., Smith, J., Kardia, S., Hovatta, I., Perola, M., Ripatti, S., Salomaa, V., and Henders, A.
- Abstract
Disease incidences increase with age, but the molecular characteristics of ageing that lead to increased disease susceptibility remain inadequately understood. Here we perform a whole-blood gene expression meta-analysis in 14,983 individuals of European ancestry (including replication) and identify 1,497 genes that are differentially expressed with chronological age. The age-associated genes do not harbor more age-associated CpG-methylation sites than other genes, but are instead enriched for the presence of potentially functional CpG-methylation sites in enhancer and insulator regions that associate with both chronological age and gene expression levels. We further used the gene expression profiles to calculate the ‘transcriptomic age’ of an individual, and show that differences between transcriptomic age and chronological age are associated with biological features linked to ageing, such as blood pressure, cholesterol levels, fasting glucose, and body mass index. The transcriptomic prediction model adds biological relevance and complements existing epigenetic prediction models, and can be used by others to calculate transcriptomic age in external cohorts.
- Published
- 2015
20. MAP2K3 is associated with body mass index in American Indians and Caucasians and may mediate hypothalamic inflammation
- Author
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Bian, L., primary, Traurig, M., additional, Hanson, R. L., additional, Marinelarena, A., additional, Kobes, S., additional, Muller, Y. L., additional, Malhotra, A., additional, Huang, K., additional, Perez, J., additional, Gale, A., additional, Knowler, W. C., additional, Bogardus, C., additional, and Baier, L. J., additional
- Published
- 2013
- Full Text
- View/download PDF
21. An autosomal genomic scan for loci linked to plasma leptin concentration in Pima Indians
- Author
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Walder, K., Hanson, R. L., Kobes, S., Knowler, W. C., Ravussin, E., Walder, K., Hanson, R. L., Kobes, S., Knowler, W. C., and Ravussin, E.
- Published
- 2000
22. Comparison of the Effect of Plasma Glucose Concentrations on Microvascular Disease Between Pima Indian Youths and Adults
- Author
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Krakoff, J., primary, Hanson, R. L., additional, Kobes, S., additional, and Knowler, W. C., additional
- Published
- 2001
- Full Text
- View/download PDF
23. Segregation analysis of diabetic nephropathy in Pima Indians.
- Author
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Imperatore, G, primary, Knowler, W C, additional, Pettitt, D J, additional, Kobes, S, additional, Bennett, P H, additional, and Hanson, R L, additional
- Published
- 2000
- Full Text
- View/download PDF
24. An autosomal genomic scan for loci linked to plasma leptin concentration in Pima Indians
- Author
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Walder, K, primary, Hanson, RL, additional, Kobes, S, additional, Knowler, WC, additional, and Ravussin, E, additional
- Published
- 2000
- Full Text
- View/download PDF
25. Sib-pair linkage analysis for susceptibility genes for microvascular complications among Pima Indians with type 2 diabetes. Pima Diabetes Genes Group.
- Author
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Imperatore, G, primary, Hanson, R L, additional, Pettitt, D J, additional, Kobes, S, additional, Bennett, P H, additional, and Knowler, W C, additional
- Published
- 1998
- Full Text
- View/download PDF
26. TCF7L2 is not a major susceptibility gene for type 2 diabetes in Pima Indians: analysis of 3,501 individuals.
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Guo T, Hanson RL, Traurig M, Muller YL, Ma L, Mack J, Kobes S, Knowler WC, Bogardus C, and Baier LJ
- Subjects
OBESITY complications ,PROTEINS ,OBESITY ,REFERENCE values ,GENETICS ,TYPE 2 diabetes ,DISEASE susceptibility ,PIMA (North American people) ,BODY mass index ,LONGITUDINAL method ,DISEASE complications - Abstract
OBJECTIVE: The transcription factor 7-like 2 (TCF7L2) gene was initially reported to be associated with type 2 diabetes in Icelandic, Danish, and U.S. populations. We investigated whether TCF7L2 also has a role in type 2 diabetes susceptibility in Pima Indians. RESEARCH DESIGN AND METHODS: The six variants reported to be associated with type 2 diabetes in the Icelandic study were genotyped in a population-based sample of 3,501 Pima Indians (1,561 subjects had type 2 diabetes, and 1,940 did not have diabetes). In addition, the coding and promoter regions of TCF7L2 were sequenced in 24 Pima subjects. The one variant identified by sequencing, 35 additional database variants positioned in introns, and the six variants reported in the Icelandic study were genotyped in Pima families to determine the haplotype structure of TCF7L2 among Pima Indians. Fourteen representative variants were selected and genotyped in 3,501 Pima Indians. RESULTS: The six variants initially reported to be associated with type 2 diabetes were less common in Pima Indians compared with samples of European origin, and none were associated with type 2 diabetes. One representative variant, rs1225404, was nominally associated with type 2 diabetes in a general model (additive P = 0.03, dominant P = 0.005) but not in a within-family analysis (additive P = 0.2, dominant P = 0.07). However, several variants were associated with BMI; in particular, rs12255372 was associated in both general and within-family analyses (both P = 0.0007). Modest associations were also found with traits predictive for type 2 diabetes. CONCLUSIONS: Variation within TCF7L2 does not confer major risk for type 2 diabetes among the Pima Indian population. [ABSTRACT FROM AUTHOR]
- Published
- 2007
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- View/download PDF
27. Identification of PVT1 as a candidate gene for end-stage renal disease in type 2 diabetes using a pooling-based genome-wide single nucleotide plymorphism association study.
- Author
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Hanson RL, Craig DW, Millis MP, Yeatts KA, Kobes S, Pearson JV, Lee AM, Knowler WC, Nelson RG, and Wolford JK
- Abstract
To identify genetic variants contributing to end-stage renal disease (ESRD) in type 2 diabetes, we performed a genome-wide analysis of 115,352 single nucleotide polymorphisms (SNPs) in pools of 105 unrelated case subjects with ESRD and 102 unrelated control subjects who have had type 2 diabetes for >/=10 years without macroalbuminuria. Using a sliding window statistic of ranked SNPs, we identified a 200-kb region on 8q24 harboring three SNPs showing substantial differences in allelic frequency between case and control pools. These SNPs were genotyped in individuals comprising each pool, and strong evidence for association was found with rs2720709 (P = 0.000021; odds ratio 2.57 [95% CI 1.66-3.96]), which is located in the plasmacytoma variant translocation gene PVT1. We sequenced all exons, exon-intron boundaries, and the promoter of PVT1 and identified 47 variants, 11 of which represented nonredundant markers with minor allele frequency >/=0.05. We subsequently genotyped these 11 variants and an additional 87 SNPs identified through public databases in 319-kb flanking rs2720709 ( approximately 1 SNP/3.5 kb); 23 markers were associated with ESRD at P < 0.01. The strongest evidence for association was found for rs2648875 (P = 0.0000018; 2.97 [1.90-4.65]), which maps to intron 8 of PVT1. Together, these results suggest that PVT1 may contribute to ESRD susceptibility in diabetes. [ABSTRACT FROM AUTHOR]
- Published
- 2007
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- View/download PDF
28. Genome-wide linkage analysis assessing parent-of-origin effects in the inheritance of type 2 diabetes and BMI in Pima Indians.
- Author
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Lindsay, Robert S., Kobes, Sayuko, Knowler, William C., Bennett, Peter H., Hanson, Robert L., Lindsay, R S, Kobes, S, Knowler, W C, Bennett, P H, and Hanson, R L
- Subjects
GENES ,TYPE 2 diabetes ,OBESITY ,PIMA (North American people) - Abstract
We examined the hypothesis that imprinted genes may affect the propensity to type 2 diabetes and obesity in Pima Indians. Multipoint variance component methods were used to assess linkage of BMI (kg/m(2)) and age-adjusted diabetes to loci derived from either father (LOD(FA)) or mother (LOD(MO)) in a genome-wide scan. Tentative evidence of loci where imprinted genes might be acting was found for diabetes with maternally derived alleles on chromosomes 5 (LOD(MO) = 1.5) and 14 (LOD(MO) = 1.6). Evidence of linkage of BMI to maternally derived alleles was found on chromosome 5 (LOD(MO) = 1.7) and to paternally derived alleles on chromosome 10p (LOD(FA) = 1.7). Additional analyses of sibling pairs who were affected by diabetes and younger than 25 years of age showed an increase of sharing of maternally derived alleles on chromosome 6 (LOD(MO) = 3.0). We also examined sites of a priori interest where action of imprinted genes has been proposed in diabetes or obesity. We found no evidence of parent-specific linkage (of either diabetes or BMI) on chromosome 11p, a region that contains several imprinted genes, but observed weak evidence of linkage of diabetes to paternally derived alleles (LOD(FA) = 0.9) in the region of chromosome 6q, believed to contain an exclusively paternally expressed gene or genes that cause transient neonatal diabetes mellitus. In conclusion, we determined regions of interest on chromosomes 5, 6, and 10 where imprinted genes might be affecting the risk of type 2 diabetes or obesity in Pima Indians. [ABSTRACT FROM AUTHOR]
- Published
- 2001
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29. An E115A Missense Variant in CERS2 Is Associated With Increased Sleeping Energy Expenditure and Hepatic Insulin Resistance in American Indians.
- Author
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Heinitz S, Traurig M, Krakoff J, Rabe P, Stäubert C, Kobes S, Hanson RL, Stumvoll M, Blüher M, Bogardus C, Baier L, and Piaggi P
- Subjects
- Adult, Female, Humans, Male, Middle Aged, Glucose Clamp Technique, Hep G2 Cells, Mutation, Missense, Sleep genetics, Sleep physiology, Tumor Suppressor Proteins genetics, Tumor Suppressor Proteins metabolism, Energy Metabolism genetics, Indians, North American genetics, Insulin Resistance genetics, Liver metabolism, Membrane Proteins genetics, Membrane Proteins metabolism, Sphingosine N-Acyltransferase genetics, Sphingosine N-Acyltransferase metabolism
- Abstract
Genetic determinants of interindividual differences in energy expenditure (EE) are largely unknown. Sphingolipids, such as ceramides, have been implicated in the regulation of human EE via mitochondrial uncoupling. In this study, we investigated whether genetic variants within enzymes involved in sphingolipid synthesis and degradation affect EE and insulin-related traits in a cohort of American Indians informative for 24-h EE and glucose disposal rates during a hyperinsulinemic-euglycemic clamp. Association analysis of 10,084 genetic variants within 28 genes involved in sphingolipid pathways identified a missense variant (rs267738, A>C, E115A) in exon 4 of CERS2 that was associated with higher sleeping EE (116 kcal/day) and increased rates of endogenous glucose production during basal (5%) and insulin-stimulated (43%) conditions, both indicators of hepatic insulin resistance. The rs267738 variant did not affect ceramide synthesis in HepG2 cells but resulted in a 30% decrease in basal mitochondrial respiration. In conclusion, we provide evidence that the CERS2 rs267738 missense variant may influence hepatic glucose production and postabsorptive sleeping metabolic rate., (© 2024 by the American Diabetes Association.)
- Published
- 2024
- Full Text
- View/download PDF
30. Author Correction: Trans-ancestral genome-wide association study of longitudinal pubertal height growth and shared heritability with adult health outcomes.
- Author
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Bradfeld JP, Kember RL, Ulrich A, Balkhiyarova Z, Alyass A, Aris IM, Bell JA, Broadaway KA, Chen Z, Chai JF, Davies NM, Fernandez-Orth D, Bustamante M, Fore R, Ganguli A, Heiskala A, Hottenga JJ, Íñiguez C, Kobes S, Leinonen J, Lowry E, Lyytikainen LP, Mahajan A, Pitkänen N, Schnurr TM, Have CT, Strachan DP, Thiering E, Vogelezang S, Wade KH, Wang CA, Wong A, Holm LA, Chesi A, Choong C, Cruz M, Elliott P, Franks S, Frithiof-Bøjsøe C, Gauderman WJ, Glessner JT, Gilsanz V, Griesman K, Hanson RL, Kaakinen M, Kalkwarf H, Kelly A, Kindler J, Kähönen M, Lanca C, Lappe J, Lee NR, McCormack S, Mentch FD, Mitchell JA, Mononen N, Niinikoski H, Oken E, Pahkala K, Sim X, Teo YY, Baier LJ, van Beijsterveldt T, Adair LS, Boomsma DI, de Geus E, Guxens M, Eriksson JG, Felix JF, Gilliland FD, Hansen T, Hardy R, Hivert MF, Holm JC, Jaddoe VWV, Järvelin MR, Lehtimäki T, Mackey DA, Meyre D, Mohlke KL, Mykkänen J, Oberfeld S, Pennell CE, Perry JRB, Raitakari O, Rivadeneira F, Saw SM, Sebert S, Shepherd JA, Standl M, Sørensen TIA, Timpson NJ, Torrent M, Willemsen G, Hypponen E, Power C, McCarthy MI, Freathy RM, Widén E, Hakonarson H, Prokopenko I, Voight BF, Zemel BS, Grant SFA, and Cousminer DL
- Published
- 2024
- Full Text
- View/download PDF
31. Novel signals and polygenic score for height are associated with pubertal growth traits in Southwestern American Indians.
- Author
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Ramírez-Luzuriaga MJ, Kobes S, Hsueh WC, Baier LJ, and Hanson RL
- Subjects
- Humans, Male, Female, Adolescent, Arizona, Longitudinal Studies, Child, Genotype, Body Height genetics, Genome-Wide Association Study, Polymorphism, Single Nucleotide, Multifactorial Inheritance genetics, Indians, North American genetics, Puberty genetics
- Abstract
Most genetic variants associated with adult height have been identified through large genome-wide association studies (GWASs) in European-ancestry cohorts. However, it is unclear how these variants influence linear growth during adolescence. This study uses anthropometric and genotypic data from a longitudinal study conducted in an American Indian community in Arizona between 1965-2007. Growth parameters (i.e. height, velocity, and timing of growth spurt) were derived from the Preece-Baines growth model, a parametric growth curve fitted to longitudinal height data, in 787 participants with height measurements spanning the whole period of growth. Heritability estimates suggested that genetic factors could explain 25% to 71% of the variance of pubertal growth traits. We performed a GWAS of growth parameters, testing their associations with 5 077 595 imputed or directly genotyped variants. Six variants associated with height at peak velocity (P < 5 × 10-8, adjusted for sex, birth year and principal components). Implicated genes include NUDT3, previously associated with adult height, and PACSIN1. Two novel variants associated with duration of growth spurt (P < 5 × 10-8) in LOC105375344, an uncharacterized gene with unknown function. We finally examined the association of growth parameters with a polygenic score for height derived from 9557 single nucleotide polymorphisms (SNPs) identified in the GIANT meta-analysis for which genotypic data were available for the American Indian study population. Height polygenic score was correlated with the magnitude and velocity of height growth that occurred before and at the peak of the adolescent growth spurt, indicating overlapping genetic architecture, with no influence on the timing of adolescent growth., (Published by Oxford University Press 2024.)
- Published
- 2024
- Full Text
- View/download PDF
32. Trans-ancestral genome-wide association study of longitudinal pubertal height growth and shared heritability with adult health outcomes.
- Author
-
Bradfield JP, Kember RL, Ulrich A, Balkhiyarova Z, Alyass A, Aris IM, Bell JA, Broadaway KA, Chen Z, Chai JF, Davies NM, Fernandez-Orth D, Bustamante M, Fore R, Ganguli A, Heiskala A, Hottenga JJ, Íñiguez C, Kobes S, Leinonen J, Lowry E, Lyytikainen LP, Mahajan A, Pitkänen N, Schnurr TM, Have CT, Strachan DP, Thiering E, Vogelezang S, Wade KH, Wang CA, Wong A, Holm LA, Chesi A, Choong C, Cruz M, Elliott P, Franks S, Frithioff-Bøjsøe C, Gauderman WJ, Glessner JT, Gilsanz V, Griesman K, Hanson RL, Kaakinen M, Kalkwarf H, Kelly A, Kindler J, Kähönen M, Lanca C, Lappe J, Lee NR, McCormack S, Mentch FD, Mitchell JA, Mononen N, Niinikoski H, Oken E, Pahkala K, Sim X, Teo YY, Baier LJ, van Beijsterveldt T, Adair LS, Boomsma DI, de Geus E, Guxens M, Eriksson JG, Felix JF, Gilliland FD, Biobank PM, Hansen T, Hardy R, Hivert MF, Holm JC, Jaddoe VWV, Järvelin MR, Lehtimäki T, Mackey DA, Meyre D, Mohlke KL, Mykkänen J, Oberfield S, Pennell CE, Perry JRB, Raitakari O, Rivadeneira F, Saw SM, Sebert S, Shepherd JA, Standl M, Sørensen TIA, Timpson NJ, Torrent M, Willemsen G, Hypponen E, Power C, McCarthy MI, Freathy RM, Widén E, Hakonarson H, Prokopenko I, Voight BF, Zemel BS, Grant SFA, and Cousminer DL
- Subjects
- Adult, Adolescent, Humans, Child, Child, Preschool, Puberty genetics, Phenotype, Body Height genetics, Outcome Assessment, Health Care, Longitudinal Studies, Genome-Wide Association Study, Diabetes Mellitus, Type 2
- Abstract
Background: Pubertal growth patterns correlate with future health outcomes. However, the genetic mechanisms mediating growth trajectories remain largely unknown. Here, we modeled longitudinal height growth with Super-Imposition by Translation And Rotation (SITAR) growth curve analysis on ~ 56,000 trans-ancestry samples with repeated height measurements from age 5 years to adulthood. We performed genetic analysis on six phenotypes representing the magnitude, timing, and intensity of the pubertal growth spurt. To investigate the lifelong impact of genetic variants associated with pubertal growth trajectories, we performed genetic correlation analyses and phenome-wide association studies in the Penn Medicine BioBank and the UK Biobank., Results: Large-scale growth modeling enables an unprecedented view of adolescent growth across contemporary and 20th-century pediatric cohorts. We identify 26 genome-wide significant loci and leverage trans-ancestry data to perform fine-mapping. Our data reveals genetic relationships between pediatric height growth and health across the life course, with different growth trajectories correlated with different outcomes. For instance, a faster tempo of pubertal growth correlates with higher bone mineral density, HOMA-IR, fasting insulin, type 2 diabetes, and lung cancer, whereas being taller at early puberty, taller across puberty, and having quicker pubertal growth were associated with higher risk for atrial fibrillation., Conclusion: We report novel genetic associations with the tempo of pubertal growth and find that genetic determinants of growth are correlated with reproductive, glycemic, respiratory, and cardiac traits in adulthood. These results aid in identifying specific growth trajectories impacting lifelong health and show that there may not be a single "optimal" pubertal growth pattern., (© 2023. The Author(s).)
- Published
- 2024
- Full Text
- View/download PDF
33. Adolescent Growth Spurt and Type 2 Diabetes Risk in Southwestern American Indians.
- Author
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Ramirez-Luzuriaga MJ, Kobes S, Sinha M, Knowler WC, and Hanson RL
- Subjects
- Adolescent, Female, Humans, Male, American Indian or Alaska Native, Body Height, Body Mass Index, Longitudinal Studies, Puberty, Risk Factors, Diabetes Mellitus, Type 2 epidemiology, Adolescent Development
- Abstract
Early puberty onset is associated with higher risk of diabetes, but most studies have not accounted for childhood factors that may confound the association. Using data from a study conducted in an Indigenous community in Arizona (1965-2007), we examined associations of timing and velocity of the adolescent growth spurt with type 2 diabetes, and whether these associations are mediated by childhood body mass index and insulinemia. Adolescent growth parameters were derived from the Preece-Baines growth model, a parametric growth curve fitted to longitudinal height data, for 861 participants with height measurements spanning the whole period of growth. In males, older age at take-off, age at peak velocity, and age at maturation were associated with decreased prevalence of diabetes (odds ratio (OR) = 0.43 per year, 95% confidence interval (CI): 0.27, 0.69; OR = 0.50, 95% CI: 0.35, 0.72; OR = 0.58, 95% CI: 0.41, 0.83, respectively), while higher velocity at take-off was associated with increased risk (OR = 3.47 per cm/year, 95% CI: 1.87, 6.42) adjusting for age, birth year, and maternal diabetes. Similar results were observed with incident diabetes. Our findings suggest that an early and accelerated adolescent growth spurt is a risk factor for diabetes, at least in males. These associations are only partially explained by measures of adiposity and insulinemia., (Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health 2023. This work is written by (a) US Government employee(s) and is in the public domain in the US.)
- Published
- 2023
- Full Text
- View/download PDF
34. The utility of a type 2 diabetes polygenic score in addition to clinical variables for prediction of type 2 diabetes incidence in birth, youth and adult cohorts in an Indigenous study population.
- Author
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Wedekind LE, Mahajan A, Hsueh WC, Chen P, Olaiya MT, Kobes S, Sinha M, Baier LJ, Knowler WC, McCarthy MI, and Hanson RL
- Subjects
- Humans, Adult, Adolescent, Young Adult, Child, Preschool, Child, Incidence, Longitudinal Studies, Genome-Wide Association Study, Risk Factors, Diabetes Mellitus, Type 2 epidemiology, Diabetes Mellitus, Type 2 genetics
- Abstract
Aims/hypothesis: There is limited information on how polygenic scores (PSs), based on variants from genome-wide association studies (GWASs) of type 2 diabetes, add to clinical variables in predicting type 2 diabetes incidence, particularly in non-European-ancestry populations., Methods: For participants in a longitudinal study in an Indigenous population from the Southwestern USA with high type 2 diabetes prevalence, we analysed ten constructions of PS using publicly available GWAS summary statistics. Type 2 diabetes incidence was examined in three cohorts of individuals without diabetes at baseline. The adult cohort, 2333 participants followed from age ≥20 years, had 640 type 2 diabetes cases. The youth cohort included 2229 participants followed from age 5-19 years (228 cases). The birth cohort included 2894 participants followed from birth (438 cases). We assessed contributions of PSs and clinical variables in predicting type 2 diabetes incidence., Results: Of the ten PS constructions, a PS using 293 genome-wide significant variants from a large type 2 diabetes GWAS meta-analysis in European-ancestry populations performed best. In the adult cohort, the AUC of the receiver operating characteristic curve for clinical variables for prediction of incident type 2 diabetes was 0.728; with the PS, 0.735. The PS's HR was 1.27 per SD (p=1.6 × 10
-8 ; 95% CI 1.17, 1.38). In youth, corresponding AUCs were 0.805 and 0.812, with HR 1.49 (p=4.3 × 10-8 ; 95% CI 1.29, 1.72). In the birth cohort, AUCs were 0.614 and 0.685, with HR 1.48 (p=2.8 × 10-16 ; 95% CI 1.35, 1.63). To further assess the potential impact of including PS for assessing individual risk, net reclassification improvement (NRI) was calculated: NRI for the PS was 0.270, 0.268 and 0.362 for adult, youth and birth cohorts, respectively. For comparison, NRI for HbA1c was 0.267 and 0.173 for adult and youth cohorts, respectively. In decision curve analyses across all cohorts, the net benefit of including the PS in addition to clinical variables was most pronounced at moderately stringent threshold probability values for instituting a preventive intervention., Conclusions/interpretation: This study demonstrates that a European-derived PS contributes significantly to prediction of type 2 diabetes incidence in addition to information provided by clinical variables in this Indigenous study population. Discriminatory power of the PS was similar to that of other commonly measured clinical variables (e.g. HbA1c ). Including type 2 diabetes PS in addition to clinical variables may be clinically beneficial for identifying individuals at higher risk for the disease, especially at younger ages., (© 2023. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)- Published
- 2023
- Full Text
- View/download PDF
35. Functional characterization of a novel p.Ser76Thr variant in IGFBP4 that associates with body mass index in American Indians.
- Author
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Muller YL, Saporito M, Day S, Bandesh K, Koroglu C, Kobes S, Knowler WC, Hanson RL, Van Hout CV, Shuldiner AR, Bogardus C, and Baier LJ
- Subjects
- Animals, Body Mass Index, Humans, Mice, Obesity genetics, PPAR gamma genetics, Phosphatidylinositol 3-Kinases genetics, Polymorphism, Single Nucleotide, Proto-Oncogene Proteins c-akt genetics, American Indian or Alaska Native, Indians, North American genetics, Insulin-Like Growth Factor Binding Protein 4 genetics
- Abstract
Insulin-like growth factor binding protein 4 (IGFBP4) is involved in adipogenesis, and IGFBP4 null mice have decreased body fat through decreased PPAR-γ expression. In the current study, we assessed whether variation in the IGFBP4 coding region influences body mass index (BMI) in American Indians who are disproportionately affected by obesity. Whole exome sequence data from a population-based sample of 6779 American Indians with longitudinal measures of BMI were used to identify variation in IGFBP4 that associated with BMI. A novel variant that predicts a p.Ser76Thr in IGFBP4 (Thr-allele frequency = 0.02) was identified which associated with the maximum BMI measured during adulthood (BMI 39.8 kg/m
2 for Thr-allele homozygotes combined with heterozygotes vs. 36.2 kg/m2 for Ser-allele homozygotes, β = 6.7% per Thr-allele, p = 8.0 × 10-5 , adjusted for age, sex, birth-year and the first five genetic principal components) and the maximum age- and sex-adjusted BMI z-score measured during childhood/adolescence (z-score 0.70 SD for Thr-allele heterozygotes vs. 0.32 SD for Ser-allele homozygotes, β = 0.37 SD per Thr-allele, p = 8.8 × 10-6 ). In vitro functional studies showed that IGFBP4 with the Thr-allele (BMI-increasing) had a 55% decrease (p = 0.0007) in FOXO-induced transcriptional activity, reflecting increased activation of the PI3K/AKT pathway mediated through increased IGF signaling. Over-expression and knock-down of IGFBP4 in OP9 cells during differentiation showed that IGFBP4 upregulates adipogenesis through PPARγ, CEBPα, AGPAT2 and SREBP1 expression. We propose that this American Indian specific variant in IGFBP4 affects obesity via an increase of IGF signaling., (© 2022. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)- Published
- 2022
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36. Increased Adiposity and Low Height-for-Age in Early Childhood Are Associated With Later Metabolic Risks in American Indian Children and Adolescents.
- Author
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Ramírez-Luzuriaga MJ, Kobes S, Sinha M, Knowler WC, and Hanson RL
- Subjects
- Adiposity, Adolescent, Body Mass Index, Child, Child, Preschool, Female, Humans, Infant, Insulin, Longitudinal Studies, Male, Obesity complications, Overweight complications, Overweight epidemiology, Risk Factors, American Indian or Alaska Native, Cardiovascular Diseases epidemiology, Cardiovascular Diseases etiology, Diabetes Mellitus, Type 2 complications
- Abstract
Background: Growth abnormalities in childhood have been related to later cardiometabolic risks, but little is known about these associations in populations at high risk of type 2 diabetes., Objectives: We examined the associations of patterns of growth, including weight and height at ages 1-59 months, with cardiometabolic risk factors at ages 5-16 years., Methods: We linked anthropometric data collected at ages 1-59 months to cardiometabolic data obtained from a longitudinal study in a southwestern American Indian population at high risk of diabetes. Analyses included 701 children with ≥1 follow-up examination at ages 5-16 years. We derived age- and sex-specific weight-for-height z-scores (WHZ) and height-for-age z-scores (HAZ) at ages 1-59 months. We selected the highest observed WHZ and the lowest observed HAZ at ages 1-59 months and analyzed associations of z-scores and categories of WHZ and HAZ with cardiometabolic outcomes at ages 5-16 years. We used linear mixed-effects models to account for repeated measures., Results: Overweight/obesity (WHZ >2) at ages 1-59 months was significantly associated with increased BMI, fasting and 2-hour postload plasma glucose, fasting and 2-hour insulin, triglycerides, systolic blood pressure, diastolic blood pressure, and decreased HDL cholesterol at ages 5-16 years relative to normal weight (WHZ ≤1). For example, at ages 5-9 years, 2-hour glucose was 10.4 mg/dL higher (95% CI: 5.6-15.3 mg/dL) and fasting insulin was 4.29 μU/mL higher (95% CI: 2.96-5.71 μU/mL) in those with overweight/obesity in early childhood. Associations were attenuated and no longer significant when adjusted for concurrent BMI. A low height-for-age (HAZ < -2) at ages 1-59 months was associated with 5.37 mg/dL lower HDL (95% CI: 2.57-8.17 mg/dL) and 27.5 μU/mL higher 2-hour insulin (95% CI: 3.41-57.6 μU/mL) at ages 10-16 years relative to an HAZ ≥0., Conclusions: In this American Indian population, findings suggest a strong contribution of overweight/obesity in early childhood to cardiometabolic risks in later childhood and adolescence, mediated through persistent overweight/obesity into later ages. Findings also suggest potential adverse effects of low height-for-age, which require confirmation., (Published by Oxford University Press on behalf of the American Society for Nutrition 2022.)
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- 2022
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37. Association of protein function-altering variants with cardiometabolic traits: the strong heart study.
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Shan Y, Cole SA, Haack K, Melton PE, Best LG, Bizon C, Kobes S, Köroğlu Ç, Baier LJ, Hanson RL, Sanna S, Li Y, and Franceschini N
- Subjects
- Fasting, Genetic Predisposition to Disease, Glucose metabolism, Humans, Insulin genetics, Polymorphism, Single Nucleotide, Receptors, GABA genetics, Cardiovascular Diseases, Diabetes Mellitus, Type 2 metabolism
- Abstract
Clinical and biomarker phenotypic associations for carriers of protein function-altering variants may help to elucidate gene function and health effects in populations. We genotyped 1127 Strong Heart Family Study participants for protein function-altering single nucleotide variants (SNV) and indels selected from a low coverage whole exome sequencing of American Indians. We tested the association of each SNV/indel with 35 cardiometabolic traits. Among 1206 variants (average minor allele count = 20, range of 1 to 1064), ~ 43% were not present in publicly available repositories. We identified seven SNV-trait significant associations including a missense SNV at ABCA10 (rs779392624, p = 8 × 10
-9 ) associated with fasting triglycerides, which gene product is involved in macrophage lipid homeostasis. Among non-diabetic individuals, missense SNVs at four genes were associated with fasting insulin adjusted for BMI (PHIL, chr6:79,650,711, p = 2.1 × 10-6 ; TRPM3, rs760461668, p = 5 × 10-8 ; SPTY2D1, rs756851199, p = 1.6 × 10-8 ; and TSPO, rs566547284, p = 2.4 × 10-6 ). PHIL encoded protein is involved in pancreatic β-cell proliferation and survival, and TRPM3 protein mediates calcium signaling in pancreatic β-cells in response to glucose. A genetic risk score combining increasing insulin risk alleles of these four genes was associated with 53% (95% confidence interval 1.09, 2.15) increased odds of incident diabetes and 83% (95% confidence interval 1.35, 2.48) increased odds of impaired fasting glucose at follow-up. Our study uncovered novel gene-trait associations through the study of protein-coding variants and demonstrates the advantages of association screenings targeting diverse and high-risk populations to study variants absent in publicly available repositories., (© 2022. The Author(s).)- Published
- 2022
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38. A missense variant Arg611Cys in LIPE which encodes hormone sensitive lipase decreases lipolysis and increases risk of type 2 diabetes in American Indians.
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Muller YL, Sutherland J, Nair AK, Koroglu C, Kobes S, Knowler WC, Van Hout CV, Shuldiner AR, Hanson RL, Bogardus C, and Baier LJ
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- Animals, Humans, Insulin metabolism, Lipolysis genetics, American Indian or Alaska Native, Diabetes Mellitus, Type 2 genetics, Diabetes Mellitus, Type 2 metabolism, Sterol Esterase genetics, Sterol Esterase metabolism
- Abstract
Aims: Hormone sensitive lipase (HSL), encoded by the LIPE gene, is involved in lipolysis. Based on prior animal and human studies, LIPE was analysed as a candidate gene for the development of type 2 diabetes (T2D) in a community-based sample of American Indians., Materials and Methods: Whole-exome sequence data from 6782 participants with longitudinal clinical measures were used to identify variation in LIPE., Results: Amongst the 16 missense variants identified, an Arg611Cys variant (rs34052647; Cys-allele frequency = 0.087) significantly associated with T2D (OR [95% CI] = 1.38 [1.17-1.64], p = 0.0002, adjusted for age, sex, birth year, and the first five genetic principal components) and an earlier onset age of T2D (HR = 1.22 [1.09-1.36], p = 0.0005). This variant was further analysed for quantitative traits related to T2D. Amongst non-diabetic American Indians, those with the T2D risk Cys-allele had increased insulin levels during an oral glucose tolerance test (0.07 SD per Cys-allele, p = 0.04) and a mixed meal test (0.08 log
10 µU/ml per Cys-allele, p = 0.003), and had increased lipid oxidation rates post-absorptively and during insulin infusion (0.07 mg [kg estimated metabolic body size {EMBS}]-1 min-1 per Cys-allele for both, p = 0.01 and 0.009, respectively), compared to individuals with the non-risk Arg-allele. In vitro functional studies showed that cells expressing the Cys-allele had a 17.2% decrease in lipolysis under isoproterenol stimulation (p = 0.03) and a 21.3% decrease in lipase enzyme activity measured by using p-nitrophenyl butyrate as a substrate (p = 0.04) compared to the Arg-allele., Conclusion: The Arg611Cys variant causes a modest impairment in lipolysis, thereby affecting glucose homoeostasis and risk of T2D., (© 2021 John Wiley & Sons Ltd. This article has been contributed to by US Government employees and their work is in the public domain in the USA.)- Published
- 2022
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39. Functional variants in cytochrome b5 type A (CYB5A) are enriched in Southwest American Indian individuals and associate with obesity.
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Day SE, Traurig M, Kumar P, Piaggi P, Koroglu C, Kobes S, Hanson RL, Bogardus C, and Baier LJ
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- Body Mass Index, Gene Frequency, Humans, Polymorphism, Single Nucleotide, American Indian or Alaska Native, Cytochromes b5 genetics, Cytochromes b5 metabolism, Obesity genetics
- Abstract
Objective: This study aimed to identify genetic variants enriched in Southwest American Indian (SWAI) individuals that associate with BMI., Methods: Whole genome sequencing data (n = 296) were used to identify potentially functional variants that are common in SWAI individuals (minor allele frequency ≥10%) but rare in other ethnic groups (minor allele frequency < 0.1%). Enriched variants were tested for association with BMI in 5,870 SWAI individuals. One variant was studied using a luciferase reporter, and haplotypes that included this variant were analyzed for association with various measures of obesity (n = 917-5,870), 24-hour energy expenditure (24-h EE; n = 419), and skeletal muscle biopsy expression data (n = 207)., Results: A 5' untranslated region variant in cytochrome b5 type A (CYB5A), rs548402150, met the enrichment criteria and associated with increased BMI (β = 2%, p = 0.004). Functionally, rs548402150 decreased luciferase expression by 30% (p = 0.003) and correlated with decreased skeletal muscle CYB5A expression (β = -0.5 SD, p = 0.0008). Combining rs548402150 with two splicing quantitative trait loci in CYB5A identified a haplotype carried almost exclusively in SWAI individuals that associated with increased BMI (β = 3%, p = 0.0003) and decreased CYB5A expression, whereas the most common haplotype in all ethnic groups associated with lower BMI and percentage of body fatness, increased 24-h EE, and increased CYB5A expression., Conclusions: Further studies on the effects of CYB5A on 24-h EE and BMI may provide insights into obesity-related physiology., (Published 2022. This article is a U.S. Government work and is in the public domain in the USA. Obesity published by Wiley Periodicals LLC on behalf of The Obesity Society (TOS).)
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- 2022
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40. Exome Sequencing of 21 Bardet-Biedl Syndrome (BBS) Genes to Identify Obesity Variants in 6,851 American Indians.
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Day SE, Muller YL, Koroglu C, Kobes S, Wiedrich K, Mahkee D, Kim HI, Van Hout C, Gosalia N, Ye B, Shuldiner AR, Knowler WC, Hanson RL, Bogardus C, and Baier LJ
- Subjects
- Female, Humans, Male, American Indian or Alaska Native, Bardet-Biedl Syndrome genetics, Exome genetics, Obesity genetics
- Abstract
Objective: In an ongoing effort to identify the genetic variation that contributes to obesity in American Indians, known Bardet-Biedl syndrome (BBS) genes were analyzed for an effect on BMI and leptin signaling., Methods: Potentially deleterious variants (Combined Annotation Dependent Depletion score > 20) in BBS genes were identified in whole-exome sequence data from 6,851 American Indians informative for BMI. Common variants (detected in ≥ 10 individuals) were analyzed for association with BMI; rare variants (detected in < 10 individuals) were analyzed for mean BMI of carriers. Functional assessment of variants' effect on signal transducer and activator of transcription 3 (STAT3) activity was performed in vitro., Results: One common variant, rs59252892 (Thr549Ile) in BBS9, was associated with BMI (P = 0.0008, β = 25% increase per risk allele). Among rare variants for which carriers had severe obesity (mean BMI > 40 kg/m
2 ), four were in BBS9. In vitro analysis of BBS9 found the Ile allele at Thr549Ile had a 20% increase in STAT3 activity compared with the Thr allele (P = 0.01). Western blot analysis showed the Ile allele had a 15% increase in STAT3 phosphorylation (P = 0.006). Comparable functional results were observed with Ser545Gly and Val209Leu but not Leu665Phe and Lys810Glu., Conclusions: Potentially functional variants in BBS genes in American Indians are reported. However, functional evidence supporting a causal role for BBS9 in obesity is inconclusive., (© 2021 Regeneron Pharmaceuticals Inc. Obesity published by Wiley Periodicals LLC on behalf of The Obesity Society. This article has been contributed to by US Government employees and their work is in the public domain in the USA.)- Published
- 2021
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41. Incidence of diabetes in South Asian young adults compared to Pima Indians.
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Narayan KMV, Kondal D, Kobes S, Staimez LR, Mohan D, Gujral UP, Patel SA, Anjana RM, Shivashankar R, Ali MK, Chang HH, Kadir M, Prabhakaran D, Daya N, Selvin E, Tandon N, Hanson R, and Mohan V
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- Adult, Asian People, Female, Humans, Incidence, India epidemiology, Insulin, Male, Potassium Iodide, Prospective Studies, Young Adult, Diabetes Mellitus epidemiology, Indians, North American
- Abstract
Introduction: South Asians (SA) and Pima Indians have high prevalence of diabetes but differ markedly in body size. We hypothesize that young SA will have higher diabetes incidence than Pima Indians at comparable body mass index (BMI) levels., Research Design and Methods: We used prospective cohort data to estimate age-specific, sex, and BMI-specific diabetes incidence in SA aged 20-44 years living in India and Pakistan from the Center for Cardiometabolic Risk Reduction in South Asia Study (n=6676), and compared with Pima Indians, from Pima Indian Study (n=1852)., Results: At baseline, SA were considerably less obese than Pima Indians (BMI (kg/m
2 ): 24.4 vs 33.8; waist circumference (cm): 82.5 vs 107.0). Age-standardized diabetes incidence (cases/1000 person-years, 95% CI) was lower in SA than in Pima Indians (men: 14.2, 12.2-16.2 vs 37.3, 31.8-42.8; women: 14.8, 13.0-16.5 vs 46.1, 41.2-51.1). Risk of incident diabetes among 20-24-year-old Pima men and women was six times (relative risk (RR), 95% CI: 6.04, 3.30 to 12.0) and seven times (RR, 95% CI: 7.64, 3.73 to 18.2) higher as compared with SA men and women, respectively. In those with BMI <25 kg/m2 , however, the risk of diabetes was over five times in SA men than in Pima Indian men. Among those with BMI ≥30 kg/m2 , diabetes incidence in SA men was nearly as high as in Pima men. SA and Pima Indians had similar magnitude of association between age, sex, BMI, and insulin secretion with diabetes. The effect of family history was larger in SA, whereas that of insulin resistance was larger in Pima Indians CONCLUSIONS: In the background of relatively low insulin resistance, higher diabetes incidence in SA is driven by poor insulin secretion in SA men. The findings call for research to improve insulin secretion in early natural history of diabetes., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)- Published
- 2021
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42. Whole genome sequence analyses of eGFR in 23,732 people representing multiple ancestries in the NHLBI trans-omics for precision medicine (TOPMed) consortium.
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Lin BM, Grinde KE, Brody JA, Breeze CE, Raffield LM, Mychaleckyj JC, Thornton TA, Perry JA, Baier LJ, de las Fuentes L, Guo X, Heavner BD, Hanson RL, Hung YJ, Qian H, Hsiung CA, Hwang SJ, Irvin MR, Jain D, Kelly TN, Kobes S, Lange L, Lash JP, Li Y, Liu X, Mi X, Musani SK, Papanicolaou GJ, Parsa A, Reiner AP, Salimi S, Sheu WH, Shuldiner AR, Taylor KD, Smith AV, Smith JA, Tin A, Vaidya D, Wallace RB, Yamamoto K, Sakaue S, Matsuda K, Kamatani Y, Momozawa Y, Yanek LR, Young BA, Zhao W, Okada Y, Abecasis G, Psaty BM, Arnett DK, Boerwinkle E, Cai J, Yii-Der Chen I, Correa A, Cupples LA, He J, Kardia SL, Kooperberg C, Mathias RA, Mitchell BD, Nickerson DA, Turner ST, Vasan RS, Rotter JI, Levy D, Kramer HJ, Köttgen A, Nhlbi Trans-Omics For Precision Medicine TOPMed Consortium, TOPMed Kidney Working Group, Rich SS, Lin DY, Browning SR, and Franceschini N
- Subjects
- Alleles, Gene Frequency, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Male, National Heart, Lung, and Blood Institute (U.S.), Polymorphism, Single Nucleotide, Public Health Surveillance, Quantitative Trait, Heritable, United States epidemiology, Genomics methods, Glomerular Filtration Rate, Precision Medicine methods, Whole Genome Sequencing
- Abstract
Background: Genetic factors that influence kidney traits have been understudied for low frequency and ancestry-specific variants., Methods: We combined whole genome sequencing (WGS) data from 23,732 participants from 10 NHLBI Trans-Omics for Precision Medicine (TOPMed) Program multi-ethnic studies to identify novel loci for estimated glomerular filtration rate (eGFR). Participants included European, African, East Asian, and Hispanic ancestries. We applied linear mixed models using a genetic relationship matrix estimated from the WGS data and adjusted for age, sex, study, and ethnicity., Findings: When testing single variants, we identified three novel loci driven by low frequency variants more commonly observed in non-European ancestry (PRKAA2, rs180996919, minor allele frequency [MAF] 0.04%, P = 6.1 × 10
-11 ; METTL8, rs116951054, MAF 0.09%, P = 4.5 × 10-9 ; and MATK, rs539182790, MAF 0.05%, P = 3.4 × 10-9 ). We also replicated two known loci for common variants (rs2461702, MAF=0.49, P = 1.2 × 10-9 , nearest gene GATM, and rs71147340, MAF=0.34, P = 3.3 × 10-9 , CDK12). Testing aggregated variants within a gene identified the MAF gene. A statistical approach based on local ancestry helped to identify replication samples for ancestry-specific variants., Interpretation: This study highlights challenges in studying variants influencing kidney traits that are low frequency in populations and more common in non-European ancestry., Competing Interests: Declaration of Competing Interest GRA is employed by Regeneron Pharmaceuticals and he owns stock and stock options for Regeneron Pharmaceuticals. BMP serves on the Steering Committee of the Yale Open Data Access Project funded by Johnson & Johnson. BMP reports serving on the Steering Committee of the Yale Open Data Access Project funded by Johnson & Johnson. Y-DIC, LRY, JCM, BDM, JIR, KDT, JPL, EB, JAS, GRA report grants from NIH during the conduct of the study. Remaining authors have nothing to disclose., (Copyright © 2020 The Author(s). Published by Elsevier B.V. All rights reserved.)- Published
- 2021
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43. Assessment of the potential role of natural selection in type 2 diabetes and related traits across human continental ancestry groups: comparison of phenotypic with genotypic divergence.
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Hanson RL, Van Hout CV, Hsueh WC, Shuldiner AR, Kobes S, Sinha M, Baier LJ, and Knowler WC
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- C-Peptide metabolism, Cross-Sectional Studies, Diabetes Mellitus, Type 2 genetics, Genotype, Glucose Tolerance Test, Glycated Hemoglobin metabolism, Humans, Insulin Resistance physiology, Blood Glucose metabolism, Diabetes Mellitus, Type 2 metabolism, Insulin metabolism, Obesity metabolism
- Abstract
Aims/hypothesis: Prevalence of type 2 diabetes differs among human ancestry groups, and many hypotheses invoke differential natural selection to account for these differences. We sought to assess the potential role of differential natural selection across major continental ancestry groups for diabetes and related traits, by comparison of genetic and phenotypic differences., Methods: This was a cross-sectional comparison among 734 individuals from an urban sample (none of whom was more closely related to another than third-degree relatives), including 83 African Americans, 523 American Indians and 128 European Americans. Participants were not recruited based on diabetes status or other traits. BMI was calculated, and diabetes was diagnosed by a 75 g oral glucose tolerance test. In those with normal glucose tolerance (n = 434), fasting insulin and 30 min post-load insulin, adjusted for 30 min glucose, were taken as measures of insulin resistance and secretion, respectively. Whole exome sequencing was performed, resulting in 97,388 common (minor allele frequency ≥ 5%) variants; the coancestry coefficient (F
ST ) was calculated across all markers as a measure of genetic divergence among ancestry groups. The phenotypic divergence index (PST ) was also calculated from the phenotypic differences and heritability (which was estimated from genetic relatedness calculated empirically across all markers in 761 American Indian participants prior to the exclusion of close relatives). Under evolutionary neutrality, the expectation is PST = FST , while for traits under differential selection PST is expected to be significantly greater than FST. A bootstrap procedure was used to test the hypothesis PST = FST. RESULTS: With adjustment for age and sex, prevalence of type 2 diabetes was 34.0% in American Indians, 12.4% in African Americans and 10.4% in European Americans (p = 2.9 × 10-10 for difference among groups). Mean BMI was 36.3, 33.4 and 33.0 kg/m2 , respectively (p = 1.9 × 10-7 ). Mean fasting insulin was 63.8, 48.4 and 45.2 pmol/l (p = 9.2 × 10-5 ), while mean 30 min insulin was 559.8, 553.5 and 358.8 pmol/l, respectively (p = 5.7 × 10-8 ). FST across all markers was 0.130, while PST for liability to diabetes, adjusted for age and sex, was 0.149 (p = 0.35 for difference with FST ). PST was 0.094 for BMI (p = 0.54), 0.095 for fasting insulin (p = 0.54) and 0.216 (p = 0.18) for 30 min insulin. For type 2 diabetes and BMI, the maximum divergence between populations was observed between American Indians and European Americans (PST-MAX = 0.22, p = 0.37, and PST-MAX = 0.14, p = 0.61), which suggests that a relatively modest 22% or 14% of the genetic variance, respectively, can potentially be explained by differential selection (assuming the absence of neutral drift)., Conclusions/interpretation: These analyses suggest that while type 2 diabetes and related traits differ significantly among continental ancestry groups, the differences are consistent with neutral expectations based on heritability and genetic distances. While these analyses do not exclude a modest role for natural selection, they do not support the hypothesis that differential natural selection is necessary to explain the phenotypic differences among these ancestry groups. Graphical abstract.- Published
- 2020
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44. Exome Sequencing Identifies A Nonsense Variant in DAO Associated With Reduced Energy Expenditure in American Indians.
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Piaggi P, Köroğlu Ç, Nair AK, Sutherland J, Muller YL, Kumar P, Hsueh WC, Kobes S, Shuldiner AR, Kim HI, Gosalia N, Van Hout CV, Jones M, Knowler WC, Krakoff J, Hanson RL, Bogardus C, and Baier LJ
- Subjects
- Adolescent, Adult, Alleles, Exome, Female, Gene Frequency, Glucose Tolerance Test, Humans, Male, Middle Aged, Obesity genetics, Exome Sequencing, Young Adult, Codon, Nonsense, D-Amino-Acid Oxidase genetics, Energy Metabolism genetics, American Indian or Alaska Native genetics
- Abstract
Background: Obesity and energy expenditure (EE) are heritable and genetic variants influencing EE may contribute to the development of obesity. We sought to identify genetic variants that affect EE in American Indians, an ethnic group with high prevalence of obesity., Methods: Whole-exome sequencing was performed in 373 healthy Pima Indians informative for 24-hour EE during energy balance. Genetic association analyses of all high-quality exonic variants (≥5 carriers) was performed, and those predicted to be damaging were prioritized., Results: Rs752074397 introduces a premature stop codon (Cys264Ter) in DAO and demonstrated the strongest association for 24-hour EE, where the Ter allele associated with substantially lower 24-hour EE (mean lower by 268 kcal/d) and sleeping EE (by 135 kcal/d). The Ter allele has a frequency = 0.5% in Pima Indians, whereas is extremely rare in most other ethnic groups (frequency < 0.01%). In vitro functional analysis showed reduced protein levels for the truncated form of DAO consistent with increased protein degradation. DAO encodes D-amino acid oxidase, which is involved in dopamine synthesis which might explain its role in modulating EE., Conclusion: Our results indicate that a nonsense mutation in DAO may influence EE in American Indians. Identification of variants that influence energy metabolism may lead to new pathways to treat human obesity., Clinical Trial Registration Number: NCT00340132., (Published by Oxford University Press on behalf of the Endocrine Society 2020.)
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- 2020
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45. Weight tracking in childhood and adolescence and type 2 diabetes risk.
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Olaiya MT, Knowler WC, Sinha M, Kobes S, Nelson RG, Baier LJ, Muller YL, and Hanson RL
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- Adolescent, Arizona epidemiology, Child, Child, Preschool, Cohort Studies, Female, Humans, Incidence, Longitudinal Studies, Male, Body-Weight Trajectory, Diabetes Mellitus, Type 2 epidemiology, American Indian or Alaska Native statistics & numerical data
- Abstract
Aims/hypothesis: The aim of this work was to examine the associations of average weight and weight velocity in three growth periods from birth through adolescence with type 2 diabetes incidence., Methods: Child participants were selected from a 43 year longitudinal study of American Indians to represent three growth periods: pre-adolescence (birth to ~8 years); early adolescence (~8 to ~13 years); and late adolescence (~13 to ~18 years). Age-, sex- and height-standardised weight z score mean and weight z score velocity (change/year) were computed for each period. Participants were followed for up to 25 years from the end of each growth period until they developed diabetes. Associations of weight z score mean or weight z score velocity with diabetes incidence were determined with sex-, birth date- and maternal diabetes-adjusted Poisson regression models., Results: Among 2100 participants representing the pre-adolescence growth period, 1558 representing the early adolescence period and 1418 representing the late adolescence period, there were 290, 315 and 380 incident diabetes cases, respectively. During the first 10 years of follow-up, the diabetes incidence rate ratio (95% CI) was 1.72 (1.40, 2.11)/SD of log
10 weight z score mean in pre-adolescence, 2.09 (1.68, 2.60)/SD of log10 weight z score mean in early adolescence and 1.85 (1.58, 2.17)/SD of log10 weight z score mean in late adolescence. The diabetes incidence rate ratio (95% CI) was 1.79 (1.49, 2.17)/SD of log10 weight z score velocity in pre-adolescence, 1.13 (0.91, 1.41)/SD of log10 weight z score velocity in early adolescence and 1.29 (1.09, 1.51)/SD of log10 weight z score velocity in late adolescence. There were strong correlations in the weight z score means and weak correlations in the weight z score velocities between successive periods., Conclusions/interpretation: Higher weight and accelerated weight gain in all growth periods associate with increased type 2 diabetes risk. Importantly, higher weight and greater weight velocity during pre-adolescence jointly associate with the highest type 2 diabetes risk. Graphical abstract.- Published
- 2020
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46. Low Serum Insulinlike Growth Factor II Levels Correlate with High BMI in American Indian Adults.
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Muller YL, Hanson RL, Mahkee D, Piaggi P, Kobes S, Hsueh WC, Knowler WC, Bogardus C, and Baier LJ
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- Adult, Aged, Cross-Sectional Studies, Female, Genotype, Humans, Indians, North American, Longitudinal Studies, Male, Middle Aged, Young Adult, Body Mass Index, Insulin-Like Growth Factor I metabolism
- Abstract
Objective: Insulinlike growth factor II (IGF-II) regulates metabolism and growth. In humans, both positive and negative relationships have been reported between serum IGF-II levels and obesity. This study assessed the relationship between serum IGF-II levels and BMI and determined whether IGF-II levels predict weight gain., Methods: Serum samples were available from 911 American Indians with a recorded BMI. IGF-II was measured using enzyme-linked immunosorbent assay., Results: Serum IGF-II levels were negatively correlated with BMI (r = -0.17, P = 4.4 × 10
-7 , adjusted for age, sex, and storage time). The strongest correlation was in participants aged ≥ 30 years (r = -0.28, P = 3.4 × 10-8 , N = 349), a modest correlation was in participants aged 20 to 29 years (r = -0.15, P = 7.6 × 10-3 , N = 322), and participants aged 15 to 19 years had no correlation (r = 0.05, P = 0.48, N = 240). IGF-II levels did not predict weight gain. However, among individuals who had genotypes for 64 established obesity variants (age ≥ 20 years, N = 671), a genetic risk score for high BMI was associated with lower IGF-II (β = -0.08 SD of IGF-II per SD of the genetic risk score, P = 0.025)., Conclusions: There is a negative relationship between IGF-II levels and BMI, in which the correlation is stronger at older ages. The association between genetic risk for BMI and IGF-II levels suggests that this correlation may be due to an effect of obesity on IGF-II., (Published 2020. This article is a U.S. Government work and is in the public domain in the USA.)- Published
- 2020
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47. Glycemia affects glomerular filtration rate in people with type 2 diabetes.
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Weil EJ, Kobes S, Jones LI, and Hanson RL
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- Age Factors, Arizona ethnology, Creatinine blood, Diabetes Mellitus, Type 2 ethnology, Diabetic Nephropathies ethnology, Female, Glucose Tolerance Test, Hemoglobin A analysis, Humans, Kidney physiopathology, Linear Models, Longitudinal Studies, Male, Middle Aged, ROC Curve, Racial Groups, Regression Analysis, Sex Factors, Blood Glucose analysis, Diabetes Mellitus, Type 2 blood, Diabetes Mellitus, Type 2 physiopathology, Fasting blood, Glomerular Filtration Rate physiology, Indians, North American
- Abstract
Background: In type 2 diabetes (T2DM), the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation for estimated glomerular filtration rate (eGFR) systematically underestimates the measured adjusted glomerular filtration rate (aGFR) when aGFR is high. We studied the extent to which glycemic variables associate with kidney function, and developed equations including these variables that estimate aGFR in people with T2DM., Methods: Diabetic Pima people had aGFR measured from iothalamate clearance divided by body surface area. eGFRs < 60 ml/min/1.73m
2 were excluded. Multivariate linear regression identified variables correlated with kidney function. We constructed equations for approximating aGFR. Correlation analysis and 10-fold cross-validation were used to compare the CKD-EPI equation and the new approximating equations to the measured aGFR. Ability to detect hyperfiltration, defined as aGFR > 120 ml/min/1.73m2 , was compared by analysis of receiver-operating (ROC) curves., Results: aGFR was measured 2798 times in 269 individuals. HbA1c, fasting plasma glucose (FPG), age, and serum creatinine (SCR) were significantly associated with aGFR. The best equations for approximating aGFR used HbA1c and FPG in addition to age and SCR. They approximate aGFR in this cohort of obese people with T2DM more precisely than the CKD-EPI equation. Analysis of ROC curves show that these equations detect hyperfiltration better than does the CKD-EPI equation., Conclusions: HbA1c, FPG, age, and SCR yielded the best equations for estimating aGFR in these subjects. The new equations identify hyperfiltration better than the CKD-EPI equation in this cohort and may inform clinical decisions regarding hyperfiltration in individuals with T2DM.- Published
- 2019
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48. A trans-ancestral meta-analysis of genome-wide association studies reveals loci associated with childhood obesity.
- Author
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Bradfield JP, Vogelezang S, Felix JF, Chesi A, Helgeland Ø, Horikoshi M, Karhunen V, Lowry E, Cousminer DL, Ahluwalia TS, Thiering E, Boh ET, Zafarmand MH, Vilor-Tejedor N, Wang CA, Joro R, Chen Z, Gauderman WJ, Pitkänen N, Parra EJ, Fernandez-Rhodes L, Alyass A, Monnereau C, Curtin JA, Have CT, McCormack SE, Hollensted M, Frithioff-Bøjsøe C, Valladares-Salgado A, Peralta-Romero J, Teo YY, Standl M, Leinonen JT, Holm JC, Peters T, Vioque J, Vrijheid M, Simpson A, Custovic A, Vaudel M, Canouil M, Lindi V, Atalay M, Kähönen M, Raitakari OT, van Schaik BDC, Berkowitz RI, Cole SA, Voruganti VS, Wang Y, Highland HM, Comuzzie AG, Butte NF, Justice AE, Gahagan S, Blanco E, Lehtimäki T, Lakka TA, Hebebrand J, Bonnefond A, Grarup N, Froguel P, Lyytikäinen LP, Cruz M, Kobes S, Hanson RL, Zemel BS, Hinney A, Teo KK, Meyre D, North KE, Gilliland FD, Bisgaard H, Bustamante M, Bonnelykke K, Pennell CE, Rivadeneira F, Uitterlinden AG, Baier LJ, Vrijkotte TGM, Heinrich J, Sørensen TIA, Saw SM, Pedersen O, Hansen T, Eriksson J, Widén E, McCarthy MI, Njølstad PR, Power C, Hyppönen E, Sebert S, Brown CD, Järvelin MR, Timpson NJ, Johansson S, Hakonarson H, Jaddoe VWV, and Grant SFA
- Subjects
- Bayes Theorem, Case-Control Studies, Child, Female, Genetic Loci, Genetic Predisposition to Disease, Humans, Male, Chromosome Mapping methods, Genome-Wide Association Study methods, Pediatric Obesity genetics, Polymorphism, Single Nucleotide, Wilms Tumor genetics
- Abstract
Although hundreds of genome-wide association studies-implicated loci have been reported for adult obesity-related traits, less is known about the genetics specific for early-onset obesity and with only a few studies conducted in non-European populations to date. Searching for additional genetic variants associated with childhood obesity, we performed a trans-ancestral meta-analysis of 30 studies consisting of up to 13 005 cases (≥95th percentile of body mass index (BMI) achieved 2-18 years old) and 15 599 controls (consistently <50th percentile of BMI) of European, African, North/South American and East Asian ancestry. Suggestive loci were taken forward for replication in a sample of 1888 cases and 4689 controls from seven cohorts of European and North/South American ancestry. In addition to observing 18 previously implicated BMI or obesity loci, for both early and late onset, we uncovered one completely novel locus in this trans-ancestral analysis (nearest gene, METTL15). The variant was nominally associated with only the European subgroup analysis but had a consistent direction of effect in other ethnicities. We then utilized trans-ancestral Bayesian analysis to narrow down the location of the probable causal variant at each genome-wide significant signal. Of all the fine-mapped loci, we were able to narrow down the causative variant at four known loci to fewer than 10 single nucleotide polymorphisms (SNPs) (FAIM2, GNPDA2, MC4R and SEC16B loci). In conclusion, an ethnically diverse setting has enabled us to both identify an additional pediatric obesity locus and further fine-map existing loci., (© The Author(s) 2019. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)
- Published
- 2019
- Full Text
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49. Birthweight and early-onset type 2 diabetes in American Indians: differential effects in adolescents and young adults and additive effects of genotype, BMI and maternal diabetes.
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Olaiya MT, Wedekind LE, Hanson RL, Sinha M, Kobes S, Nelson RG, Baier LJ, and Knowler WC
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- Adolescent, Body Mass Index, Child, Child, Preschool, Diabetes, Gestational epidemiology, Female, Genotype, Humans, Male, Pregnancy, Prospective Studies, Risk Factors, Birth Weight physiology, Diabetes Mellitus, Type 2 epidemiology
- Abstract
Aims/hypothesis: The aim of this work was to estimate the impact of birthweight on early-onset (age <40 years) type 2 diabetes., Methods: A longitudinal study of American Indians, aged ≥5 years, was conducted from 1965 to 2007. Participants who had a recorded birthweight were followed until they developed diabetes or their last examination before the age of 40 years, whichever came first. Age- and sex-adjusted diabetes incidence rates were computed and Poisson regression was used to model the effect of birthweight on diabetes incidence, adjusted for sex, BMI, a type 2 diabetes susceptibility genetic risk score (GRS) and maternal covariates., Results: Among 3039 participants, there were 652 incident diabetes cases over a median follow-up of 14.3 years. Diabetes incidence increased with age and was greater in the lowest and highest quintiles of birthweight. Adjusted for covariates, the effect of birthweight on diabetes varied over time, with a non-linear effect at 10-19 years (p < 0.001) and a negative linear effect at older age intervals (20-29 years, p < 0.001; 30-39 years, p = 0.003). Higher GRS, greater BMI and maternal diabetes had additive but not interactive effects on the association between birthweight and diabetes incidence., Conclusions/interpretation: In this high-risk population, both low and high birthweights were associated with increased type 2 diabetes risk in adolescence (age 10-19 years) but only low birthweight was associated with increased risk in young adulthood (20-39 years). Higher type 2 diabetes GRS, greater BMI and maternal diabetes added to the risk of early-onset diabetes.
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- 2019
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50. Assessing the Role of 98 Established Loci for BMI in American Indians.
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Muller YL, Hanson RL, Piaggi P, Chen P, Wiessner G, Okani C, Skelton G, Kobes S, Hsueh WC, Knowler WC, Bogardus C, and Baier LJ
- Subjects
- Adult, Body Mass Index, Female, Genetic Predisposition to Disease, Humans, Longitudinal Studies, Male, Risk Factors, Genome-Wide Association Study methods, Indians, North American genetics, Polymorphism, Single Nucleotide genetics
- Abstract
Objective: Meta-analyses of genome-wide association studies in Europeans have identified > 98 loci for BMI. Transferability of these established associations in Pima Indians was analyzed., Methods: Among 98 lead single nucleotide polymorphisms (SNPs), 82 had minor allele frequency ≥ 0.01 in Pima Indians and were analyzed for association with the maximum BMI in adulthood (n = 3,491) and BMI z score in childhood (n = 1,958). Common tag SNPs across 98 loci were also analyzed for additional signals., Results: Among the lead SNPs, 13 (TMEM18, TCF7L2, MRPS33P4, PRKD1, ZFP64, FTO, TAL1, CALCR, GNPDA2, CREB1, LMX1B, ADCY9, NLRC3) were associated with BMI (P ≤ 0.05) in Pima adults. A multi-allelic genetic risk score (GRS), which summed the risk alleles for 82 lead SNPs, showed a significant trend for a positive relationship between GRS and BMI in adulthood (beta = 0.48% per risk allele; P = 1.6 × 10
-9 ) and BMI z score in childhood (beta = 0.024 SD; P = 1.7 × 10-7 ). GRS was significantly associated with BMI across all age groups ≥ 5 years, except for those ≥ 50 years. The strongest association was seen in adolescence (age 14-16 years; P = 1.84 × 10-9 )., Conclusions: In aggregate, European-derived lead SNPs had a notable effect on BMI in Pima Indians. Polygenic obesity in this population manifests strongly in childhood and adolescence and persists throughout much of adult life., (© 2019 The Obesity Society.)- Published
- 2019
- Full Text
- View/download PDF
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