8,552 results on '"A Meigs"'
Search Results
2. Adaptive selection at G6PD and disparities in diabetes complications
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Breeyear, Joseph H., Hellwege, Jacklyn N., Schroeder, Philip H., House, John S., Poisner, Hannah M., Mitchell, Sabrina L., Charest, Brian, Khakharia, Anjali, Basnet, Til B., Halladay, Christopher W., Reaven, Peter D., Meigs, James B., Rhee, Mary K., Sun, Yang, Lynch, Mary G., Bick, Alexander G., Wilson, Otis D., Hung, Adriana M., Nealon, Cari L., Iyengar, Sudha K., Rotroff, Daniel M., Buse, John B., Leong, Aaron, Mercader, Josep M., Sobrin, Lucia, Brantley, Jr., Milam A., Peachey, Neal S., Motsinger-Reif, Alison A., Wilson, Peter W., Sun, Yan V., Giri, Ayush, Phillips, Lawrence S., and Edwards, Todd L.
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- 2024
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3. Is Our Future a Blue Screen of Death? Tech Commentary
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Meigs, James B.
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High technology industry -- Safety and security measures -- Public opinion ,Risk management -- Methods ,Government regulation ,Company business management ,Risk management ,Market trend/market analysis ,Online service outage - Abstract
Some say the world will end in fire, some say in ice. Personally, my money's on the blue screen of death. On the morning of July 19, millions of users […]
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- 2024
4. Biden's Electric-Vehicle Hustle: Tech Commentary
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Meigs, James B.
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Electric vehicles -- Forecasts and trends -- Laws, regulations and rules ,Automotive emissions -- Laws, regulations and rules ,Government regulation ,Market trend/market analysis ,Ethnic, cultural, racial issues/studies ,Literature/writing ,Philosophy and religion - Abstract
JOE BIDEN NEVER SAID, 'IF YOU LIKE YOUR GAS-POWERED CAR, YOU CAN KEEP your gas-powered car,' in the manner of his former boss dissembling to the American people about doctors [...]
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- 2024
5. A Type 1 Diabetes Polygenic Score Is Not Associated With Prevalent Type 2 Diabetes in Large Population Studies
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Srinivasan, Shylaja, Wu, Peitao, Mercader, Josep M, Udler, Miriam S, Porneala, Bianca C, Bartz, Traci M, Floyd, James S, Sitlani, Colleen, Guo, Xiquing, Haessler, Jeffrey, Kooperberg, Charles, Liu, Jun, Ahmad, Shahzad, van Duijn, Cornelia, Liu, Ching-Ti, Goodarzi, Mark O, Florez, Jose C, Meigs, James B, Rotter, Jerome I, Rich, Stephen S, Dupuis, Josée, and Leong, Aaron
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Biomedical and Clinical Sciences ,Clinical Sciences ,Diabetes ,Pediatric ,Genetics ,Autoimmune Disease ,Human Genome ,Aetiology ,2.1 Biological and endogenous factors ,Metabolic and endocrine ,type 1 diabetes ,type 2 diabetes ,genetics ,polygenic score ,Cardiovascular medicine and haematology - Abstract
ContextBoth type 1 diabetes (T1D) and type 2 diabetes (T2D) have significant genetic contributions to risk and understanding their overlap can offer clinical insight.ObjectiveWe examined whether a T1D polygenic score (PS) was associated with a diagnosis of T2D in the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium.MethodsWe constructed a T1D PS using 79 known single nucleotide polymorphisms associated with T1D risk. We analyzed 13 792 T2D cases and 14 169 controls from CHARGE cohorts to determine the association between the T1D PS and T2D prevalence. We validated findings in an independent sample of 2256 T2D cases and 27 052 controls from the Mass General Brigham Biobank (MGB Biobank). As secondary analyses in 5228 T2D cases from CHARGE, we used multivariable regression models to assess the association of the T1D PS with clinical outcomes associated with T1D.ResultsThe T1D PS was not associated with T2D both in CHARGE (P = .15) and in the MGB Biobank (P = .87). The partitioned human leukocyte antigens only PS was associated with T2D in CHARGE (OR 1.02 per 1 SD increase in PS, 95% CI 1.01-1.03, P = .006) but not in the MGB Biobank. The T1D PS was weakly associated with insulin use (OR 1.007, 95% CI 1.001-1.012, P = .03) in CHARGE T2D cases but not with other outcomes.ConclusionIn large biobank samples, a common variant PS for T1D was not consistently associated with prevalent T2D. However, possible heterogeneity in T2D cannot be ruled out and future studies are needed do subphenotyping.
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- 2023
6. Variant level heritability estimates of type 2 diabetes in African Americans
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Armstrong, Nicole D., Patki, Amit, Srinivasasainagendra, Vinodh, Ge, Tian, Lange, Leslie A., Kottyan, Leah, Namjou, Bahram, Shah, Amy S., Rasmussen-Torvik, Laura J., Jarvik, Gail P., Meigs, James B., Karlson, Elizabeth W., Limdi, Nita A., Irvin, Marguerite R., and Tiwari, Hemant K.
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- 2024
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7. Polygenic scores for longitudinal prediction of incident type 2 diabetes in an ancestrally and medically diverse primary care physician network: a patient cohort study
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Mandla, Ravi, Schroeder, Philip, Porneala, Bianca, Florez, Jose C., Meigs, James B., Mercader, Josep M., and Leong, Aaron
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- 2024
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8. Genetic drivers of heterogeneity in type 2 diabetes pathophysiology
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Suzuki, Ken, Hatzikotoulas, Konstantinos, Southam, Lorraine, Taylor, Henry J., Yin, Xianyong, Lorenz, Kim M., Mandla, Ravi, Huerta-Chagoya, Alicia, Melloni, Giorgio E. M., Kanoni, Stavroula, Rayner, Nigel W., Bocher, Ozvan, Arruda, Ana Luiza, Sonehara, Kyuto, Namba, Shinichi, Lee, Simon S. K., Preuss, Michael H., Petty, Lauren E., Schroeder, Philip, Vanderwerff, Brett, Kals, Mart, Bragg, Fiona, Lin, Kuang, Guo, Xiuqing, Zhang, Weihua, Yao, Jie, Kim, Young Jin, Graff, Mariaelisa, Takeuchi, Fumihiko, Nano, Jana, Lamri, Amel, Nakatochi, Masahiro, Moon, Sanghoon, Scott, Robert A., Cook, James P., Lee, Jung-Jin, Pan, Ian, Taliun, Daniel, Parra, Esteban J., Chai, Jin-Fang, Bielak, Lawrence F., Tabara, Yasuharu, Hai, Yang, Thorleifsson, Gudmar, Grarup, Niels, Sofer, Tamar, Wuttke, Matthias, Sarnowski, Chloé, Gieger, Christian, Nousome, Darryl, Trompet, Stella, Kwak, Soo-Heon, Long, Jirong, Sun, Meng, Tong, Lin, Chen, Wei-Min, Nongmaithem, Suraj S., Noordam, Raymond, Lim, Victor J. Y., Tam, Claudia H. T., Joo, Yoonjung Yoonie, Chen, Chien-Hsiun, Raffield, Laura M., Prins, Bram Peter, Nicolas, Aude, Yanek, Lisa R., Chen, Guanjie, Brody, Jennifer A., Kabagambe, Edmond, An, Ping, Xiang, Anny H., Choi, Hyeok Sun, Cade, Brian E., Tan, Jingyi, Broadaway, K. Alaine, Williamson, Alice, Kamali, Zoha, Cui, Jinrui, Thangam, Manonanthini, Adair, Linda S., Adeyemo, Adebowale, Aguilar-Salinas, Carlos A., Ahluwalia, Tarunveer S., Anand, Sonia S., Bertoni, Alain, Bork-Jensen, Jette, Brandslund, Ivan, Buchanan, Thomas A., Burant, Charles F., Butterworth, Adam S., Canouil, Mickaël, Chan, Juliana C. N., Chang, Li-Ching, Chee, Miao-Li, Chen, Ji, Chen, Shyh-Huei, Chen, Yuan-Tsong, Chen, Zhengming, Chuang, Lee-Ming, Cushman, Mary, Danesh, John, Das, Swapan K., de Silva, H. Janaka, Dedoussis, George, Dimitrov, Latchezar, Doumatey, Ayo P., Du, Shufa, Duan, Qing, Eckardt, Kai-Uwe, Emery, Leslie S., Evans, Daniel S., Evans, Michele K., Fischer, Krista, Floyd, James S., Ford, Ian, Franco, Oscar H., Frayling, Timothy M., Freedman, Barry I., Genter, Pauline, Gerstein, Hertzel C., Giedraitis, Vilmantas, González-Villalpando, Clicerio, González-Villalpando, Maria Elena, Gordon-Larsen, Penny, Gross, Myron, Guare, Lindsay A., Hackinger, Sophie, Hakaste, Liisa, Han, Sohee, Hattersley, Andrew T., Herder, Christian, Horikoshi, Momoko, Howard, Annie-Green, Hsueh, Willa, Huang, Mengna, Huang, Wei, Hung, Yi-Jen, Hwang, Mi Yeong, Hwu, Chii-Min, Ichihara, Sahoko, Ikram, Mohammad Arfan, Ingelsson, Martin, Islam, Md. Tariqul, Isono, Masato, Jang, Hye-Mi, Jasmine, Farzana, Jiang, Guozhi, Jonas, Jost B., Jørgensen, Torben, Kamanu, Frederick K., Kandeel, Fouad R., Kasturiratne, Anuradhani, Katsuya, Tomohiro, Kaur, Varinderpal, Kawaguchi, Takahisa, Keaton, Jacob M., Kho, Abel N., Khor, Chiea-Chuen, Kibriya, Muhammad G., Kim, Duk-Hwan, Kronenberg, Florian, Kuusisto, Johanna, Läll, Kristi, Lange, Leslie A., Lee, Kyung Min, Lee, Myung-Shik, Lee, Nanette R., Leong, Aaron, Li, Liming, Li, Yun, Li-Gao, Ruifang, Ligthart, Symen, Lindgren, Cecilia M., Linneberg, Allan, Liu, Ching-Ti, Liu, Jianjun, Locke, Adam E., Louie, Tin, Luan, Jian’an, Luk, Andrea O., Luo, Xi, Lv, Jun, Lynch, Julie A., Lyssenko, Valeriya, Maeda, Shiro, Mamakou, Vasiliki, Mansuri, Sohail Rafik, Matsuda, Koichi, Meitinger, Thomas, Melander, Olle, Metspalu, Andres, Mo, Huan, Morris, Andrew D., Moura, Filipe A., Nadler, Jerry L., Nalls, Michael A., Nayak, Uma, Ntalla, Ioanna, Okada, Yukinori, Orozco, Lorena, Patel, Sanjay R., Patil, Snehal, Pei, Pei, Pereira, Mark A., Peters, Annette, Pirie, Fraser J., Polikowsky, Hannah G., Porneala, Bianca, Prasad, Gauri, Rasmussen-Torvik, Laura J., Reiner, Alexander P., Roden, Michael, Rohde, Rebecca, Roll, Katheryn, Sabanayagam, Charumathi, Sandow, Kevin, Sankareswaran, Alagu, Sattar, Naveed, Schönherr, Sebastian, Shahriar, Mohammad, Shen, Botong, Shi, Jinxiu, Shin, Dong Mun, Shojima, Nobuhiro, Smith, Jennifer A., So, Wing Yee, Stančáková, Alena, Steinthorsdottir, Valgerdur, Stilp, Adrienne M., Strauch, Konstantin, Taylor, Kent D., Thorand, Barbara, Thorsteinsdottir, Unnur, Tomlinson, Brian, Tran, Tam C., Tsai, Fuu-Jen, Tuomilehto, Jaakko, Tusie-Luna, Teresa, Udler, Miriam S., Valladares-Salgado, Adan, van Dam, Rob M., van Klinken, Jan B., Varma, Rohit, Wacher-Rodarte, Niels, Wheeler, Eleanor, Wickremasinghe, Ananda R., van Dijk, Ko Willems, Witte, Daniel R., Yajnik, Chittaranjan S., Yamamoto, Ken, Yamamoto, Kenichi, Yoon, Kyungheon, Yu, Canqing, Yuan, Jian-Min, Yusuf, Salim, Zawistowski, Matthew, Zhang, Liang, Zheng, Wei, Raffel, Leslie J., Igase, Michiya, Ipp, Eli, Redline, Susan, Cho, Yoon Shin, Lind, Lars, Province, Michael A., Fornage, Myriam, Hanis, Craig L., Ingelsson, Erik, Zonderman, Alan B., Psaty, Bruce M., Wang, Ya-Xing, Rotimi, Charles N., Becker, Diane M., Matsuda, Fumihiko, Liu, Yongmei, Yokota, Mitsuhiro, Kardia, Sharon L. R., Peyser, Patricia A., Pankow, James S., Engert, James C., Bonnefond, Amélie, Froguel, Philippe, Wilson, James G., Sheu, Wayne H. H., Wu, Jer-Yuarn, Hayes, M. Geoffrey, Ma, Ronald C. W., Wong, Tien-Yin, Mook-Kanamori, Dennis O., Tuomi, Tiinamaija, Chandak, Giriraj R., Collins, Francis S., Bharadwaj, Dwaipayan, Paré, Guillaume, Sale, Michèle M., Ahsan, Habibul, Motala, Ayesha A., Shu, Xiao-Ou, Park, Kyong-Soo, Jukema, J. Wouter, Cruz, Miguel, Chen, Yii-Der Ida, Rich, Stephen S., McKean-Cowdin, Roberta, Grallert, Harald, Cheng, Ching-Yu, Ghanbari, Mohsen, Tai, E-Shyong, Dupuis, Josee, Kato, Norihiro, Laakso, Markku, Köttgen, Anna, Koh, Woon-Puay, Bowden, Donald W., Palmer, Colin N. A., Kooner, Jaspal S., Kooperberg, Charles, Liu, Simin, North, Kari E., Saleheen, Danish, Hansen, Torben, Pedersen, Oluf, Wareham, Nicholas J., Lee, Juyoung, Kim, Bong-Jo, Millwood, Iona Y., Walters, Robin G., Stefansson, Kari, Ahlqvist, Emma, Goodarzi, Mark O., Mohlke, Karen L., Langenberg, Claudia, Haiman, Christopher A., Loos, Ruth J. F., Florez, Jose C., Rader, Daniel J., Ritchie, Marylyn D., Zöllner, Sebastian, Mägi, Reedik, Marston, Nicholas A., Ruff, Christian T., van Heel, David A., Finer, Sarah, Denny, Joshua C., Yamauchi, Toshimasa, Kadowaki, Takashi, Chambers, John C., Ng, Maggie C. Y., Sim, Xueling, Below, Jennifer E., Tsao, Philip S., Chang, Kyong-Mi, McCarthy, Mark I., Meigs, James B., Mahajan, Anubha, Spracklen, Cassandra N., Mercader, Josep M., Boehnke, Michael, Rotter, Jerome I., Vujkovic, Marijana, Voight, Benjamin F., Morris, Andrew P., and Zeggini, Eleftheria
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- 2024
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9. Pediatric AKI in the real world: changing outcomes through education and advocacy—a report from the 26th Acute Disease Quality Initiative (ADQI) consensus conference
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Mottes, Theresa, Menon, Shina, Conroy, Andrea, Jetton, Jennifer, Dolan, Kristin, Arikan, Ayse Akcan, Basu, Rajit K., Goldstein, Stuart L., Symons, Jordan M., Alobaidi, Rashid, Askenazi, David J., Bagshaw, Sean M., Barhight, Matthew, Barreto, Erin, Bayrakci, Benan, Ray, II, O. N. Bignall, Bjornstad, Erica, Brophy, Patrick, Charlton, Jennifer, Chanchlani, Rahul, Conroy, Andrea L., Deep, Akash, Devarajan, Prasad, Fuhrman, Dana, Gist, Katja M., Gorga, Stephen M., Greenberg, Jason H., Hasson, Denise, Heydari, Emma, Iyengar, Arpana, Krawczeski, Catherine, Meigs, Leslie, Morgan, Catherine, Morgan, Jolyn, Neumayr, Tara, Ricci, Zaccaria, Selewski, David T., Soranno, Danielle, Stanski, Natalja, Starr, Michelle, Sutherland, Scott M., Symons, Jordan, Tavares, Marcelo, Vega, Molly, Zappitelli, Michael, Ronco, Claudio, Mehta, Ravindra L., Kellum, John, and Ostermann, Marlies
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- 2024
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10. Selection, optimization and validation of ten chronic disease polygenic risk scores for clinical implementation in diverse US populations
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Lennon, Niall J., Kottyan, Leah C., Kachulis, Christopher, Abul-Husn, Noura S., Arias, Josh, Belbin, Gillian, Below, Jennifer E., Berndt, Sonja I., Chung, Wendy K., Cimino, James J., Clayton, Ellen Wright, Connolly, John J., Crosslin, David R., Dikilitas, Ozan, Velez Edwards, Digna R., Feng, QiPing, Fisher, Marissa, Freimuth, Robert R., Ge, Tian, Glessner, Joseph T., Gordon, Adam S., Patterson, Candace, Hakonarson, Hakon, Harden, Maegan, Harr, Margaret, Hirschhorn, Joel N., Hoggart, Clive, Hsu, Li, Irvin, Marguerite R., Jarvik, Gail P., Karlson, Elizabeth W., Khan, Atlas, Khera, Amit, Kiryluk, Krzysztof, Kullo, Iftikhar, Larkin, Katie, Limdi, Nita, Linder, Jodell E., Loos, Ruth J. F., Luo, Yuan, Malolepsza, Edyta, Manolio, Teri A., Martin, Lisa J., McCarthy, Li, McNally, Elizabeth M., Meigs, James B., Mersha, Tesfaye B., Mosley, Jonathan D., Musick, Anjene, Namjou, Bahram, Pai, Nihal, Pesce, Lorenzo L., Peters, Ulrike, Peterson, Josh F., Prows, Cynthia A., Puckelwartz, Megan J., Rehm, Heidi L., Roden, Dan M., Rosenthal, Elisabeth A., Rowley, Robb, Sawicki, Konrad Teodor, Schaid, Daniel J., Smit, Roelof A. J., Smith, Johanna L., Smoller, Jordan W., Thomas, Minta, Tiwari, Hemant, Toledo, Diana M., Vaitinadin, Nataraja Sarma, Veenstra, David, Walunas, Theresa L., Wang, Zhe, Wei, Wei-Qi, Weng, Chunhua, Wiesner, Georgia L., Yin, Xianyong, and Kenny, Eimear E.
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- 2024
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11. Algorithm Literacy as a Subset of Media and Information Literacy: Competences and Design Considerations
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Divina Frau-Meigs
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media and information literacy ,algorithm literacy ,artificial intelligence ,competence framework ,course design ,information ,Electronic computers. Computer science ,QA75.5-76.95 - Abstract
Algorithms, indispensable to understand Artificial Intelligence (AI), are omnipresent in social media, but users’ understanding of these computational processes and the way they impact their consumption of information is often limited. There is a need for Media and Information Literacy (MIL) research investigating (a) how MIL can support algorithm literacy (AL) as a subset of competences and with what working definition, (b) what competences users need in order to evaluate algorithms critically and interact with them effectively, and (c) how to design learner-centred interventions that foster increased user understanding of algorithms and better response to disinformation spread by such processes. Based on Crossover project research, this paper looks at four scenarios used by journalists, developers and MIL experts that mirror users’ daily interactions with social media. The results suggest several steps towards integrating AL within MIL goals, while providing a concrete definition of algorithm literacy that is experience-based. The competences and design considerations are organised in a conceptual framework thematically derived from the experimentation. This contribution can support AI developers and MIL educators in their co-design of algorithm-literacy interventions and guide future research on AL as part of a set of nested AI literacies within MIL.
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- 2024
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12. Variant level heritability estimates of type 2 diabetes in African Americans
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Nicole D. Armstrong, Amit Patki, Vinodh Srinivasasainagendra, Tian Ge, Leslie A. Lange, Leah Kottyan, Bahram Namjou, Amy S. Shah, Laura J. Rasmussen-Torvik, Gail P. Jarvik, James B. Meigs, Elizabeth W. Karlson, Nita A. Limdi, Marguerite R. Irvin, and Hemant K. Tiwari
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Heritable quantitative trait ,Type 2 diabetes mellitus ,Genomics ,Genetic polymorphisms ,Disparities ,Medicine ,Science - Abstract
Abstract Type 2 diabetes (T2D) is caused by both genetic and environmental factors and is associated with an increased risk of cardiorenal complications and mortality. Though disproportionately affected by the condition, African Americans (AA) are largely underrepresented in genetic studies of T2D, and few estimates of heritability have been calculated in this race group. Using genome-wide association study (GWAS) data paired with phenotypic data from ~ 19,300 AA participants of the Reasons for Geographic and Racial Differences in Stroke (REGARDS) study, Genetics of Hypertension Associated Treatments (GenHAT) study, and the Electronic Medical Records and Genomics (eMERGE) network, we estimated narrow-sense heritability using two methods: Linkage-Disequilibrium Adjusted Kinships (LDAK) and Genome-Wide Complex Trait Analysis (GCTA). Study-level heritability estimates adjusting for age, sex, and genetic ancestry ranged from 18% to 34% across both methods. Overall, the current study narrows the expected range for T2D heritability in this race group compared to prior estimates, while providing new insight into the genetic basis of T2D in AAs for ongoing genetic discovery efforts.
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- 2024
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13. Genome-wide association study and functional characterization identifies candidate genes for insulin-stimulated glucose uptake
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Williamson, Alice, Norris, Dougall M, Yin, Xianyong, Broadaway, K Alaine, Moxley, Anne H, Vadlamudi, Swarooparani, Wilson, Emma P, Jackson, Anne U, Ahuja, Vasudha, Andersen, Mette K, Arzumanyan, Zorayr, Bonnycastle, Lori L, Bornstein, Stefan R, Bretschneider, Maxi P, Buchanan, Thomas A, Chang, Yi-Cheng, Chuang, Lee-Ming, Chung, Ren-Hua, Clausen, Tine D, Damm, Peter, Delgado, Graciela E, de Mello, Vanessa D, Dupuis, Josée, Dwivedi, Om P, Erdos, Michael R, Silva, Lilian Fernandes, Frayling, Timothy M, Gieger, Christian, Goodarzi, Mark O, Guo, Xiuqing, Gustafsson, Stefan, Hakaste, Liisa, Hammar, Ulf, Hatem, Gad, Herrmann, Sandra, Højlund, Kurt, Horn, Katrin, Hsueh, Willa A, Hung, Yi-Jen, Hwu, Chii-Min, Jonsson, Anna, Kårhus, Line L, Kleber, Marcus E, Kovacs, Peter, Lakka, Timo A, Lauzon, Marie, Lee, I-Te, Lindgren, Cecilia M, Lindström, Jaana, Linneberg, Allan, Liu, Ching-Ti, Luan, Jian’an, Aly, Dina Mansour, Mathiesen, Elisabeth, Moissl, Angela P, Morris, Andrew P, Narisu, Narisu, Perakakis, Nikolaos, Peters, Annette, Prasad, Rashmi B, Rodionov, Roman N, Roll, Kathryn, Rundsten, Carsten F, Sarnowski, Chloé, Savonen, Kai, Scholz, Markus, Sharma, Sapna, Stinson, Sara E, Suleman, Sufyan, Tan, Jingyi, Taylor, Kent D, Uusitupa, Matti, Vistisen, Dorte, Witte, Daniel R, Walther, Romy, Wu, Peitao, Xiang, Anny H, Zethelius, Björn, Ahlqvist, Emma, Bergman, Richard N, Chen, Yii-Der Ida, Collins, Francis S, Fall, Tove, Florez, Jose C, Fritsche, Andreas, Grallert, Harald, Groop, Leif, Hansen, Torben, Koistinen, Heikki A, Komulainen, Pirjo, Laakso, Markku, Lind, Lars, Loeffler, Markus, März, Winfried, Meigs, James B, Raffel, Leslie J, Rauramaa, Rainer, Rotter, Jerome I, Schwarz, Peter EH, and Stumvoll, Michael
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Biochemistry and Cell Biology ,Genetics ,Biological Sciences ,Diabetes ,Clinical Research ,Human Genome ,Prevention ,Nutrition ,2.1 Biological and endogenous factors ,Aetiology ,5.1 Pharmaceuticals ,Development of treatments and therapeutic interventions ,Metabolic and endocrine ,Humans ,Insulin ,Genome-Wide Association Study ,Insulin Resistance ,Diabetes Mellitus ,Type 2 ,Glucose ,Blood Glucose ,Meta-Analysis of Glucose and Insulin-related Traits Consortium ,Medical and Health Sciences ,Developmental Biology ,Agricultural biotechnology ,Bioinformatics and computational biology - Abstract
Distinct tissue-specific mechanisms mediate insulin action in fasting and postprandial states. Previous genetic studies have largely focused on insulin resistance in the fasting state, where hepatic insulin action dominates. Here we studied genetic variants influencing insulin levels measured 2 h after a glucose challenge in >55,000 participants from three ancestry groups. We identified ten new loci (P
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- 2023
14. Investigating Gene-Diet Interactions Impacting the Association Between Macronutrient Intake and Glycemic Traits.
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Westerman, Kenneth E, Walker, Maura E, Gaynor, Sheila M, Wessel, Jennifer, DiCorpo, Daniel, Ma, Jiantao, Alonso, Alvaro, Aslibekyan, Stella, Baldridge, Abigail S, Bertoni, Alain G, Biggs, Mary L, Brody, Jennifer A, Chen, Yii-Der Ida, Dupuis, Joseé, Goodarzi, Mark O, Guo, Xiuqing, Hasbani, Natalie R, Heath, Adam, Hidalgo, Bertha, Irvin, Marguerite R, Johnson, W Craig, Kalyani, Rita R, Lange, Leslie, Lemaitre, Rozenn N, Liu, Ching-Ti, Liu, Simin, Moon, Jee-Young, Nassir, Rami, Pankow, James S, Pettinger, Mary, Raffield, Laura M, Rasmussen-Torvik, Laura J, Selvin, Elizabeth, Senn, Mackenzie K, Shadyab, Aladdin H, Smith, Albert V, Smith, Nicholas L, Steffen, Lyn, Talegakwar, Sameera, Taylor, Kent D, de Vries, Paul S, Wilson, James G, Wood, Alexis C, Yanek, Lisa R, Yao, Jie, Zheng, Yinan, Boerwinkle, Eric, Morrison, Alanna C, Fornage, Miriam, Russell, Tracy P, Psaty, Bruce M, Levy, Daniel, Heard-Costa, Nancy L, Ramachandran, Vasan S, Mathias, Rasika A, Arnett, Donna K, Kaplan, Robert, North, Kari E, Correa, Adolfo, Carson, April, Rotter, Jerome I, Rich, Stephen S, Manson, JoAnn E, Reiner, Alexander P, Kooperberg, Charles, Florez, Jose C, Meigs, James B, Merino, Jordi, Tobias, Deirdre K, Chen, Han, and Manning, Alisa K
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Biomedical and Clinical Sciences ,Nutrition and Dietetics ,Nutrition ,Diabetes ,Human Genome ,Minority Health ,Genetics ,Cardiovascular ,Health Disparities ,Prevention ,Precision Medicine ,Clinical Research ,Metabolic and endocrine ,Good Health and Well Being ,Humans ,Glycated Hemoglobin ,Diet ,Diabetes Mellitus ,Eating ,Guanine Nucleotide Dissociation Inhibitors ,Genome-Wide Association Study ,Medical and Health Sciences ,Endocrinology & Metabolism ,Biomedical and clinical sciences - Abstract
Few studies have demonstrated reproducible gene-diet interactions (GDIs) impacting metabolic disease risk factors, likely due in part to measurement error in dietary intake estimation and insufficient capture of rare genetic variation. We aimed to identify GDIs across the genetic frequency spectrum impacting the macronutrient-glycemia relationship in genetically and culturally diverse cohorts. We analyzed 33,187 participants free of diabetes from 10 National Heart, Lung, and Blood Institute Trans-Omics for Precision Medicine program cohorts with whole-genome sequencing, self-reported diet, and glycemic trait data. We fit cohort-specific, multivariable-adjusted linear mixed models for the effect of diet, modeled as an isocaloric substitution of carbohydrate for fat, and its interactions with common and rare variants genome-wide. In main effect meta-analyses, participants consuming more carbohydrate had modestly lower glycemic trait values (e.g., for glycated hemoglobin [HbA1c], -0.013% HbA1c/250 kcal substitution). In GDI meta-analyses, a common African ancestry-enriched variant (rs79762542) reached study-wide significance and replicated in the UK Biobank cohort, indicating a negative carbohydrate-HbA1c association among major allele homozygotes only. Simulations revealed that >150,000 samples may be necessary to identify similar macronutrient GDIs under realistic assumptions about effect size and measurement error. These results generate hypotheses for further exploration of modifiable metabolic disease risk in additional cohorts with African ancestry.Article highlightsWe aimed to identify genetic modifiers of the dietary macronutrient-glycemia relationship using whole-genome sequence data from 10 Trans-Omics for Precision Medicine program cohorts. Substitution models indicated a modest reduction in glycemia associated with an increase in dietary carbohydrate at the expense of fat. Genome-wide interaction analysis identified one African ancestry-enriched variant near the FRAS1 gene that may interact with macronutrient intake to influence hemoglobin A1c. Simulation-based power calculations accounting for measurement error suggested that substantially larger sample sizes may be necessary to discover further gene-macronutrient interactions.
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- 2023
15. Using tools to fight disinformation in and outside the classrooms
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Corbu, Nicoleta, primary, Frau-Meigs, Divina, additional, Ionescu, Adina, additional, and Azzoug Montané, Jade, additional
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- 2024
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16. MIL theories and the fight against disinformation in practice
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Frau-Meigs, Divina, primary, Corbu, Nicoleta, additional, and Osuna-Acedo, Sara, additional
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- 2024
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17. Reduced fire severity offers near-term buffer to climate-driven declines in conifer resilience across the western United States
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Davis, Kimberley T, Robles, Marcos D, Kemp, Kerry B, Higuera, Philip E, Chapman, Teresa, Metlen, Kerry L, Peeler, Jamie L, Rodman, Kyle C, Woolley, Travis, Addington, Robert N, Buma, Brian J, Cansler, C Alina, Case, Michael J, Collins, Brandon M, Coop, Jonathan D, Dobrowski, Solomon Z, Gill, Nathan S, Haffey, Collin, Harris, Lucas B, Harvey, Brian J, Haugo, Ryan D, Hurteau, Matthew D, Kulakowski, Dominik, Littlefield, Caitlin E, McCauley, Lisa A, Povak, Nicholas, Shive, Kristen L, Smith, Edward, Stevens, Jens T, Stevens-Rumann, Camille S, Taylor, Alan H, Tepley, Alan J, Young, Derek JN, Andrus, Robert A, Battaglia, Mike A, Berkey, Julia K, Busby, Sebastian U, Carlson, Amanda R, Chambers, Marin E, Dodson, Erich Kyle, Donato, Daniel C, Downing, William M, Fornwalt, Paula J, Halofsky, Joshua S, Hoffman, Ashley, Holz, Andrés, Iniguez, Jose M, Krawchuk, Meg A, Kreider, Mark R, Larson, Andrew J, Meigs, Garrett W, Roccaforte, John Paul, Rother, Monica T, Safford, Hugh, Schaedel, Michael, Sibold, Jason S, Singleton, Megan P, Turner, Monica G, Urza, Alexandra K, Clark-Wolf, Kyra D, Yocom, Larissa, Fontaine, Joseph B, and Campbell, John L
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Agricultural ,Veterinary and Food Sciences ,Climate Change Impacts and Adaptation ,Ecological Applications ,Environmental Sciences ,Forestry Sciences ,Regenerative Medicine ,Climate Action ,Fires ,Wildfires ,Climate ,Climate Change ,Tracheophyta ,climate change ,ecological transformation ,post-fire regeneration ,vegetation transition ,wildfire - Abstract
Increasing fire severity and warmer, drier postfire conditions are making forests in the western United States (West) vulnerable to ecological transformation. Yet, the relative importance of and interactions between these drivers of forest change remain unresolved, particularly over upcoming decades. Here, we assess how the interactive impacts of changing climate and wildfire activity influenced conifer regeneration after 334 wildfires, using a dataset of postfire conifer regeneration from 10,230 field plots. Our findings highlight declining regeneration capacity across the West over the past four decades for the eight dominant conifer species studied. Postfire regeneration is sensitive to high-severity fire, which limits seed availability, and postfire climate, which influences seedling establishment. In the near-term, projected differences in recruitment probability between low- and high-severity fire scenarios were larger than projected climate change impacts for most species, suggesting that reductions in fire severity, and resultant impacts on seed availability, could partially offset expected climate-driven declines in postfire regeneration. Across 40 to 42% of the study area, we project postfire conifer regeneration to be likely following low-severity but not high-severity fire under future climate scenarios (2031 to 2050). However, increasingly warm, dry climate conditions are projected to eventually outweigh the influence of fire severity and seed availability. The percent of the study area considered unlikely to experience conifer regeneration, regardless of fire severity, increased from 5% in 1981 to 2000 to 26 to 31% by mid-century, highlighting a limited time window over which management actions that reduce fire severity may effectively support postfire conifer regeneration.
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- 2023
18. Polygenic scores for longitudinal prediction of incident type 2 diabetes in an ancestrally and medically diverse primary care physician network: a patient cohort study
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Ravi Mandla, Philip Schroeder, Bianca Porneala, Jose C. Florez, James B. Meigs, Josep M. Mercader, and Aaron Leong
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Polygenic scores ,Type 2 diabetes ,Primary care ,Electronic health records ,Medicine ,Genetics ,QH426-470 - Abstract
Abstract Background The clinical utility of genetic information for type 2 diabetes (T2D) prediction with polygenic scores (PGS) in ancestrally diverse, real-world US healthcare systems is unclear, especially for those at low clinical phenotypic risk for T2D. Methods We tested the association of PGS with T2D incidence in patients followed within a primary care practice network over 16 years in four hypothetical scenarios that varied by clinical data availability (N = 14,712): (1) age and sex; (2) age, sex, body mass index (BMI), systolic blood pressure, and family history of T2D; (3) all variables in (2) and random glucose; and (4) all variables in (3), HDL, total cholesterol, and triglycerides, combined in a clinical risk score (CRS). To determine whether genetic effects differed by baseline clinical risk, we tested for interaction with the CRS. Results PGS was associated with incident T2D in all models. Adjusting for age and sex only, the Hazard Ratio (HR) per PGS standard deviation (SD) was 1.76 (95% CI 1.68, 1.84) and the HR of top 5% of PGS vs interquartile range (IQR) was 2.80 (2.39, 3.28). Adjusting for the CRS, the HR per SD was 1.48 (1.40, 1.57) and HR of the top 5% of PGS vs IQR was 2.09 (1.72, 2.55). Genetic effects differed by baseline clinical risk ((PGS-CRS interaction p = 0.05; CRS below the median: HR 1.60 (1.43, 1.79); CRS above the median: HR 1.45 (1.35, 1.55)). Conclusions Genetic information can help identify high-risk patients even among those perceived to be low risk in a clinical evaluation.
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- 2024
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19. Mark Zuckerberg Is Just So Very Sorry, You Guys
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Meigs, James B.
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Meta Platforms Inc. -- Officials and employees ,Information services industry -- Officials and employees ,Information services -- Officials and employees ,Company public relations ,Information services industry ,Ethnic, cultural, racial issues/studies ,Literature/writing ,Philosophy and religion - Abstract
Sometimes I almost feel sorry for Mark Zuckerberg. I know, I know. He's the fourth-richest person in the world, and the social-media platforms he controls have blighted the childhoods of [...]
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- 2024
20. Multi-tissue epigenetic analysis identifies distinct associations underlying insulin resistance and Alzheimer’s disease at CPT1A locus
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Sarnowski, Chloé, Huan, Tianxiao, Ma, Yiyi, Joehanes, Roby, Beiser, Alexa, DeCarli, Charles S, Heard-Costa, Nancy L, Levy, Daniel, Lin, Honghuang, Liu, Ching-Ti, Liu, Chunyu, Meigs, James B, Satizabal, Claudia L, Florez, Jose C, Hivert, Marie-France, Dupuis, Josée, De Jager, Philip L, Bennett, David A, Seshadri, Sudha, and Morrison, Alanna C
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Biological Sciences ,Genetics ,Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD) ,Dementia ,Neurodegenerative ,Alzheimer's Disease ,Human Genome ,Aging ,Brain Disorders ,Acquired Cognitive Impairment ,Neurosciences ,2.1 Biological and endogenous factors ,Aetiology ,Metabolic and endocrine ,Neurological ,Humans ,Alzheimer Disease ,Diabetes Mellitus ,Type 2 ,DNA Methylation ,Epigenesis ,Genetic ,Genetic Markers ,Genome-Wide Association Study ,Insulin Resistance ,Epigenetics ,Insulin resistance ,Alzheimer's disease ,FHS ,ROSMAP ,DNA methylation ,Alzheimer’s disease ,Clinical Sciences ,Paediatrics and Reproductive Medicine - Abstract
BackgroundInsulin resistance (IR) is a major risk factor for Alzheimer's disease (AD) dementia. The mechanisms by which IR predisposes to AD are not well-understood. Epigenetic studies may help identify molecular signatures of IR associated with AD, thus improving our understanding of the biological and regulatory mechanisms linking IR and AD.MethodsWe conducted an epigenome-wide association study of IR, quantified using the homeostatic model assessment of IR (HOMA-IR) and adjusted for body mass index, in 3,167 participants from the Framingham Heart Study (FHS) without type 2 diabetes at the time of blood draw used for methylation measurement. We identified DNA methylation markers associated with IR at the genome-wide level accounting for multiple testing (P
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- 2023
21. A framework for detecting noncoding rare-variant associations of large-scale whole-genome sequencing studies
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Li, Zilin, Li, Xihao, Zhou, Hufeng, Gaynor, Sheila M, Selvaraj, Margaret Sunitha, Arapoglou, Theodore, Quick, Corbin, Liu, Yaowu, Chen, Han, Sun, Ryan, Dey, Rounak, Arnett, Donna K, Auer, Paul L, Bielak, Lawrence F, Bis, Joshua C, Blackwell, Thomas W, Blangero, John, Boerwinkle, Eric, Bowden, Donald W, Brody, Jennifer A, Cade, Brian E, Conomos, Matthew P, Correa, Adolfo, Cupples, L Adrienne, Curran, Joanne E, de Vries, Paul S, Duggirala, Ravindranath, Franceschini, Nora, Freedman, Barry I, Göring, Harald HH, Guo, Xiuqing, Kalyani, Rita R, Kooperberg, Charles, Kral, Brian G, Lange, Leslie A, Lin, Bridget M, Manichaikul, Ani, Manning, Alisa K, Martin, Lisa W, Mathias, Rasika A, Meigs, James B, Mitchell, Braxton D, Montasser, May E, Morrison, Alanna C, Naseri, Take, O’Connell, Jeffrey R, Palmer, Nicholette D, Peyser, Patricia A, Psaty, Bruce M, Raffield, Laura M, Redline, Susan, Reiner, Alexander P, Reupena, Muagututi’a Sefuiva, Rice, Kenneth M, Rich, Stephen S, Smith, Jennifer A, Taylor, Kent D, Taub, Margaret A, Vasan, Ramachandran S, Weeks, Daniel E, Wilson, James G, Yanek, Lisa R, Zhao, Wei, Rotter, Jerome I, Willer, Cristen J, Natarajan, Pradeep, Peloso, Gina M, and Lin, Xihong
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Biological Sciences ,Genetics ,Biotechnology ,Human Genome ,Generic health relevance ,Good Health and Well Being ,Humans ,Genome-Wide Association Study ,Whole Genome Sequencing ,Genome ,Phenotype ,Genetic Variation ,NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium ,TOPMed Lipids Working Group ,Technology ,Medical and Health Sciences ,Developmental Biology ,Biological sciences - Abstract
Large-scale whole-genome sequencing studies have enabled analysis of noncoding rare-variant (RV) associations with complex human diseases and traits. Variant-set analysis is a powerful approach to study RV association. However, existing methods have limited ability in analyzing the noncoding genome. We propose a computationally efficient and robust noncoding RV association detection framework, STAARpipeline, to automatically annotate a whole-genome sequencing study and perform flexible noncoding RV association analysis, including gene-centric analysis and fixed window-based and dynamic window-based non-gene-centric analysis by incorporating variant functional annotations. In gene-centric analysis, STAARpipeline uses STAAR to group noncoding variants based on functional categories of genes and incorporate multiple functional annotations. In non-gene-centric analysis, STAARpipeline uses SCANG-STAAR to incorporate dynamic window sizes and multiple functional annotations. We apply STAARpipeline to identify noncoding RV sets associated with four lipid traits in 21,015 discovery samples from the Trans-Omics for Precision Medicine (TOPMed) program and replicate several of them in an additional 9,123 TOPMed samples. We also analyze five non-lipid TOPMed traits.
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- 2022
22. A MUC5B Gene Polymorphism, rs35705950-T, Confers Protective Effects Against COVID-19 Hospitalization but Not Severe Disease or Mortality
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Verma, Anurag, Minnier, Jessica, Wan, Emily S, Huffman, Jennifer E, Gao, Lina, Joseph, Jacob, Ho, Yuk-Lam, Wu, Wen-Chih, Cho, Kelly, Gorman, Bryan R, Rajeevan, Nallakkandi, Pyarajan, Saiju, Garcon, Helene, Meigs, James B, Sun, Yan V, Reaven, Peter D, McGeary, John E, Suzuki, Ayako, Gelernter, Joel, Lynch, Julie A, Petersen, Jeffrey M, Zekavat, Seyedeh Maryam, Natarajan, Pradeep, Dalal, Sharvari, Jhala, Darshana N, Arjomandi, Mehrdad, Gatsby, Elise, Lynch, Kristine E, Bonomo, Robert A, Freiberg, Matthew, Pathak, Gita A, Zhou, Jin J, Donskey, Curtis J, Madduri, Ravi K, Wells, Quinn S, Huang, Rose DL, Polimanti, Renato, Chang, Kyong-Mi, Liao, Katherine P, Tsao, Philip S, Wilson, Peter WF, Hung, Adriana M, O’Donnell, Christopher J, Gaziano, John M, Hauger, Richard L, Iyengar, Sudha K, Luoh, Shiuh-Wen, and Initiative, the Million Veteran Program COVID-19 Science
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Lung ,Rare Diseases ,Genetics ,Autoimmune Disease ,Aetiology ,2.1 Biological and endogenous factors ,Respiratory ,Good Health and Well Being ,Humans ,COVID-19 ,Mucin-5B ,Polymorphism ,Genetic ,Idiopathic Pulmonary Fibrosis ,Genotype ,Hospitalization ,Genetic Predisposition to Disease ,coronavirus disease 2019 ,severe acute respiratory syndrome coronavirus 2 ,idiopathic pulmonary fibrosis ,electronic health records ,genetic association ,Million Veteran Program COVID-19 Science Initiative ,Medical and Health Sciences ,Respiratory System - Abstract
Rationale: A common MUC5B gene polymorphism, rs35705950-T, is associated with idiopathic pulmonary fibrosis (IPF), but its role in severe acute respiratory syndrome coronavirus 2 infection and disease severity is unclear. Objectives: To assess whether rs35705950-T confers differential risk for clinical outcomes associated with coronavirus disease (COVID-19) infection among participants in the Million Veteran Program (MVP). Methods: The MUC5B rs35705950-T allele was directly genotyped among MVP participants; clinical events and comorbidities were extracted from the electronic health records. Associations between the incidence or severity of COVID-19 and rs35705950-T were analyzed within each ancestry group in the MVP followed by transancestry meta-analysis. Replication and joint meta-analysis were conducted using summary statistics from the COVID-19 Host Genetics Initiative (HGI). Sensitivity analyses with adjustment for additional covariates (body mass index, Charlson comorbidity index, smoking, asbestosis, rheumatoid arthritis with interstitial lung disease, and IPF) and associations with post-COVID-19 pneumonia were performed in MVP subjects. Measurements and Main Results: The rs35705950-T allele was associated with fewer COVID-19 hospitalizations in transancestry meta-analyses within the MVP (Ncases = 4,325; Ncontrols = 507,640; OR = 0.89 [0.82-0.97]; P = 6.86 × 10-3) and joint meta-analyses with the HGI (Ncases = 13,320; Ncontrols = 1,508,841; OR, 0.90 [0.86-0.95]; P = 8.99 × 10-5). The rs35705950-T allele was not associated with reduced COVID-19 positivity in transancestry meta-analysis within the MVP (Ncases = 19,168/Ncontrols = 492,854; OR, 0.98 [0.95-1.01]; P = 0.06) but was nominally significant (P
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- 2022
23. A combined polygenic score of 21,293 rare and 22 common variants improves diabetes diagnosis based on hemoglobin A1C levels
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Dornbos, Peter, Koesterer, Ryan, Ruttenburg, Andrew, Nguyen, Trang, Cole, Joanne B, Leong, Aaron, Meigs, James B, Florez, Jose C, Rotter, Jerome I, Udler, Miriam S, and Flannick, Jason
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Biological Sciences ,Genetics ,Precision Medicine ,Diabetes ,2.1 Biological and endogenous factors ,Metabolic and endocrine ,Good Health and Well Being ,Humans ,Multifactorial Inheritance ,Glycated Hemoglobin ,Diabetes Mellitus ,Type 2 ,Genome-Wide Association Study ,AMP-T2D-GENES Consortium ,Medical and Health Sciences ,Developmental Biology ,Agricultural biotechnology ,Bioinformatics and computational biology - Abstract
Polygenic scores (PGSs) combine the effects of common genetic variants1,2 to predict risk or treatment strategies for complex diseases3-7. Adding rare variation to PGSs has largely unknown benefits and is methodically challenging. Here, we developed a method for constructing rare variant PGSs and applied it to calculate genetically modified hemoglobin A1C thresholds for type 2 diabetes (T2D) diagnosis7-10. The resultant rare variant PGS is highly polygenic (21,293 variants across 154 genes), depends on ultra-rare variants (72.7% observed in fewer than three people) and identifies significantly more undiagnosed T2D cases than expected by chance (odds ratio = 2.71; P = 1.51 × 10-6). A PGS combining common and rare variants is expected to identify 4.9 million misdiagnosed T2D cases in the United States-nearly 1.5-fold more than the common variant PGS alone. These results provide a method for constructing complex trait PGSs from rare variants and suggest that rare variants will augment common variants in precision medicine approaches for common disease.
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- 2022
24. Nontargeted and Targeted Metabolomic Profiling Reveals Novel Metabolite Biomarkers of Incident Diabetes in African Americans
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Chen, Zsu-Zsu, Pacheco, Julian Avila, Gao, Yan, Deng, Shuliang, Peterson, Bennet, Shi, Xu, Zheng, Shuning, Tahir, Usman A, Katz, Daniel H, Cruz, Daniel E, Ngo, Debby, Benson, Mark D, Robbins, Jeremy M, Guo, Xiuqing, del Rocio Sevilla Gonzalez, Magdalena, Manning, Alisa, Correa, Adolfo, Meigs, James B, Taylor, Kent D, Rich, Stephen S, Goodarzi, Mark O, Rotter, Jerome I, Wilson, James G, Clish, Clary B, and Gerszten, Robert E
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Medical Biochemistry and Metabolomics ,Biomedical and Clinical Sciences ,Diabetes ,Clinical Research ,Prevention ,Metabolic and endocrine ,Humans ,Blood Glucose ,Black or African American ,Metabolomics ,Biomarkers ,Diabetes Mellitus ,Medical and Health Sciences ,Endocrinology & Metabolism ,Biomedical and clinical sciences - Abstract
Nontargeted metabolomics methods have increased potential to identify new disease biomarkers, but assessments of the additive information provided in large human cohorts by these less biased techniques are limited. To diversify our knowledge of diabetes-associated metabolites, we leveraged a method that measures 305 targeted or "known" and 2,342 nontargeted or "unknown" compounds in fasting plasma samples from 2,750 participants (315 incident cases) in the Jackson Heart Study (JHS)-a community cohort of self-identified African Americans-who are underrepresented in omics studies. We found 307 unique compounds (82 known) associated with diabetes after adjusting for age and sex at a false discovery rate of
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- 2022
25. Characterisation of the scrape-off layer in JET-ILW deuterium and helium low-confinement mode plasmas
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D. Rees, M. Groth, S. Aleiferis, S. Brezinsek, M. Brix, I. Jepu, K.D. Lawson, A.G. Meigs, S. Menmuir, K. Kirov, P. Lomas, C. Lowry, B. Thomas, A. Widdowson, P. Carvalho, and E. Delabie
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Helium plasma ,Detachment ,JET ,Tokamak ,Divertor ,SOL ,Nuclear engineering. Atomic power ,TK9001-9401 - Abstract
Langmuir probe measurements in neutral beam injection (NBI) heated, low-confinement mode plasmas in JET ITER-like wall showed that the current to the divertor targets, Idiv, in helium (He) plasmas was up to 70% lower on the low-field side (LFS) than in otherwise identical deuterium (D) plasmas. The edge plasma density at which the rollover of Idiv occurred i.e. the onset of detachment, was 10% higher in He plasmas on both the LFS and high-field side (HFS). The density of Idiv rollover increases by 25% for He when the NBI power increases 1MW to 5MW. The total radiated power was similar in He and D plasmas for densities below the Idiv rollover. At densities above the Idiv rollover density, the total radiated power and power from within the separatrix are higher in He, reducing the power across the separatrix and subsequently Idiv,LFS. In He plasmas, the peak radiated power was observed within the confined region above the X-point in tomographic reconstructions from bolometry.
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- 2024
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26. Cardiovascular Risk Estimation Is Suboptimal in People With HIV
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Virginia A. Triant, Asya Lyass, Leo B. Hurley, Leila H. Borowsky, Rachel Q. Ehrbar, Wei He, David Cheng, Janet Lo, Daniel B. Klein, James B. Meigs, Steven K. Grinspoon, Jorge Plutzky, Michael J. Silverberg, Michael LaValley, Joseph M. Massaro, and Ralph B. D'Agostino
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aging ,cardiovascular ,HIV ,risk prediction ,Diseases of the circulatory (Cardiovascular) system ,RC666-701 - Abstract
Background Established cardiovascular disease (CVD) risk prediction functions may not accurately predict CVD risk in people with HIV. We assessed the performance of 3 CVD risk prediction functions in 2 HIV cohorts. Methods and Results CVD risk scores were calculated in the Mass General Brigham and Kaiser Permanente Northern California HIV cohorts, using the American College of Cardiology/American Heart Association atherosclerotic CVD function, the FHS (Framingham Heart Study) hard coronary heart disease function and the Framingham Heart Study hard CVD function. Outcomes were myocardial infarction or coronary death for FHS hard coronary heart disease function; and myocardial infarction, stroke, or coronary death for American College of Cardiology/American Heart Association and FHS hard CVD function. We calculated regression coefficients and assessed discrimination and calibration by sex; predicted to observed risk of outcome was also compared. In the combined cohort of 9412, 158 (1.7%) had a coronary heart disease event, and 309 (3.3%) had a CVD event. Among women, CVD risk was generally underestimated by all 3 risk functions. Among men, CVD risk was underestimated by the American College of Cardiology/American Heart Association and FHS hard CVD function, but overestimated by the FHS hard coronary heart disease function. Calibration was poor for women using the FHS hard CVD function and for men using all functions. Discrimination in all functions was good for women (c‐statistics ranging from 0.78 to 0.90) and moderate for men (c‐statistics ranging from 0.71 to 0.72). Conclusions Established CVD risk prediction functions generally underestimate risk in people with HIV. Differences in model performance by sex underscore the need for both HIV‐specific and sex‐specific functions. Development of CVD risk prediction models tailored to HIV will enhance care for aging people with HIV.
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- 2024
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27. 'They Forgot to Be Afraid'
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Meigs, James B.
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Israel. Security Agency -- Reports ,Espionage, Israeli -- Reports ,Ethnic, cultural, racial issues/studies ,Literature/writing ,Philosophy and religion - Abstract
IN THE EARLY-MORNING hours of October 7, Shin Bet, the IDF's security service, began detecting hints of activity across the border with Gaza. According to a report in Haaretz, it [...]
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- 2023
28. Understanding the complex genetic architecture connecting rheumatoid arthritis, osteoporosis and inflammation: discovering causal pathways
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Kasher, Melody, Williams, Frances MK, Freidin, Maxim B, Malkin, Ida, Cherny, Stacey S, Benjamin, Emelia, Chasman, Daniel I, Dehghan, Abbas, Ahluwalia, Tarunveer Singh, Meigs, James, Tracy, Russell, Alizadeh, Behrooz Z, Ligthart, Symen, Bis, Josh, Eiriksdottir, Gudny, Pankratz, Nathan, Gross, Myron, Rainer, Alex, Snieder, Harold, Wilson, James G, Psaty, Bruce M, Dupuis, Josee, Prins, Bram, Vaso, Urmo, Stathopoulou, Maria, Franke, Lude, Lehtimaki, Terho, Koenig, Wolfgang, Jamshidi, Yalda, Siest, Sophie, Abbasi, Ali, Uitterlinden, Andre G, Abdollahi, Mohammadreza, Schnabel, Renate, Schick, Ursula M, Nolte, Ilja M, Kraja, Aldi, Hsu, Yi-Hsiang, Tylee, Daniel S, Zwicker, Alyson, Uher, Rudolf, Davey-Smith, George, Morrison, Alanna C, Hicks, Andrew, van Duijn, Cornelia M, Ward-Caviness, Cavin, Boerwinkle, Eric, Rotter, J, Rice, Ken, Lange, Leslie, Perola, Markus, de Geus, Eco, Morris, Andrew P, Makela, Kari Matti, Stacey, David, Eriksson, Johan, Frayling, Tim M, Slagboom, Eline P, and Livshits, Gregory
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Aging ,Autoimmune Disease ,Arthritis ,Osteoporosis ,Human Genome ,Heart Disease ,Cardiovascular ,Rheumatoid Arthritis ,Genetics ,2.1 Biological and endogenous factors ,Aetiology ,Inflammatory and immune system ,Arthritis ,Rheumatoid ,C-Reactive Protein ,Genome-Wide Association Study ,Humans ,Inflammation ,Mendelian Randomization Analysis ,Polymorphism ,Single Nucleotide ,CHARGE Inflammation Working Group ,Biological Sciences ,Medical and Health Sciences ,Genetics & Heredity - Abstract
Rheumatoid arthritis (RA) and osteoporosis (OP) are two comorbid complex inflammatory conditions with evidence of shared genetic background and causal relationships. We aimed to clarify the genetic architecture underlying RA and various OP phenotypes while additionally considering an inflammatory component, C-reactive protein (CRP). Genome-wide association study summary statistics were acquired from the GEnetic Factors for OSteoporosis Consortium, Cohorts for Heart and Aging Research Consortium and UK Biobank. Mendelian randomization (MR) was used to detect the presence of causal relationships. Colocalization analysis was performed to determine shared genetic variants between CRP and OP phenotypes. Analysis of pleiotropy between traits owing to shared causal single nucleotide polymorphisms (SNPs) was performed using PL eiotropic A nalysis under CO mposite null hypothesis (PLACO). MR analysis was suggestive of horizontal pleiotropy between RA and OP traits. RA was a significant causal risk factor for CRP (β = 0.027, 95% confidence interval = 0.016-0.038). There was no evidence of CRP→OP causal relationship, but horizontal pleiotropy was apparent. Colocalization established shared genomic regions between CRP and OP, including GCKR and SERPINA1 genes. Pleiotropy arising from shared causal SNPs revealed through the colocalization analysis was all confirmed by PLACO. These genes were found to be involved in the same molecular function 'protein binding' (GO:0005515) associated with RA, OP and CRP. We identified three major components explaining the epidemiological relationship among RA, OP and inflammation: (1) Pleiotropy explains a portion of the shared genetic relationship between RA and OP, albeit polygenically; (2) RA contributes to CRP elevation and (3) CRP, which is influenced by RA, demonstrated pleiotropy with OP.
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- 2022
29. Reconciling species conservation and ecosystem resilience: Northern spotted owl habitat sustainability in a fire-dependent forest landscape
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Halofsky, Joshua S., Donato, Daniel C., Singleton, Peter H., Churchill, Derek J., Meigs, Garrett W., Gaines, William L., Kane, Jonathan T., Kane, Van R., Munzing, Danielle, and Hessburg, Paul F.
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- 2024
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30. Second international consensus report on gaps and opportunities for the clinical translation of precision diabetes medicine
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Tobias, Deirdre K., Merino, Jordi, Ahmad, Abrar, Aiken, Catherine, Benham, Jamie L., Bodhini, Dhanasekaran, Clark, Amy L., Colclough, Kevin, Corcoy, Rosa, Cromer, Sara J., Duan, Daisy, Felton, Jamie L., Francis, Ellen C., Gillard, Pieter, Gingras, Véronique, Gaillard, Romy, Haider, Eram, Hughes, Alice, Ikle, Jennifer M., Jacobsen, Laura M., Kahkoska, Anna R., Kettunen, Jarno L. T., Kreienkamp, Raymond J., Lim, Lee-Ling, Männistö, Jonna M. E., Massey, Robert, Mclennan, Niamh-Maire, Miller, Rachel G., Morieri, Mario Luca, Most, Jasper, Naylor, Rochelle N., Ozkan, Bige, Patel, Kashyap Amratlal, Pilla, Scott J., Prystupa, Katsiaryna, Raghavan, Sridharan, Rooney, Mary R., Schön, Martin, Semnani-Azad, Zhila, Sevilla-Gonzalez, Magdalena, Svalastoga, Pernille, Takele, Wubet Worku, Tam, Claudia Ha-ting, Thuesen, Anne Cathrine B., Tosur, Mustafa, Wallace, Amelia S., Wang, Caroline C., Wong, Jessie J., Yamamoto, Jennifer M., Young, Katherine, Amouyal, Chloé, Andersen, Mette K., Bonham, Maxine P., Chen, Mingling, Cheng, Feifei, Chikowore, Tinashe, Chivers, Sian C., Clemmensen, Christoffer, Dabelea, Dana, Dawed, Adem Y., Deutsch, Aaron J., Dickens, Laura T., DiMeglio, Linda A., Dudenhöffer-Pfeifer, Monika, Evans-Molina, Carmella, Fernández-Balsells, María Mercè, Fitipaldi, Hugo, Fitzpatrick, Stephanie L., Gitelman, Stephen E., Goodarzi, Mark O., Grieger, Jessica A., Guasch-Ferré, Marta, Habibi, Nahal, Hansen, Torben, Huang, Chuiguo, Harris-Kawano, Arianna, Ismail, Heba M., Hoag, Benjamin, Johnson, Randi K., Jones, Angus G., Koivula, Robert W., Leong, Aaron, Leung, Gloria K. W., Libman, Ingrid M., Liu, Kai, Long, S. Alice, Lowe, Jr, William L., Morton, Robert W., Motala, Ayesha A., Onengut-Gumuscu, Suna, Pankow, James S., Pathirana, Maleesa, Pazmino, Sofia, Perez, Dianna, Petrie, John R., Powe, Camille E., Quinteros, Alejandra, Jain, Rashmi, Ray, Debashree, Ried-Larsen, Mathias, Saeed, Zeb, Santhakumar, Vanessa, Kanbour, Sarah, Sarkar, Sudipa, Monaco, Gabriela S. F., Scholtens, Denise M., Selvin, Elizabeth, Sheu, Wayne Huey-Herng, Speake, Cate, Stanislawski, Maggie A., Steenackers, Nele, Steck, Andrea K., Stefan, Norbert, Støy, Julie, Taylor, Rachael, Tye, Sok Cin, Ukke, Gebresilasea Gendisha, Urazbayeva, Marzhan, Van der Schueren, Bart, Vatier, Camille, Wentworth, John M., Hannah, Wesley, White, Sara L., Yu, Gechang, Zhang, Yingchai, Zhou, Shao J., Beltrand, Jacques, Polak, Michel, Aukrust, Ingvild, de Franco, Elisa, Flanagan, Sarah E., Maloney, Kristin A., McGovern, Andrew, Molnes, Janne, Nakabuye, Mariam, Njølstad, Pål Rasmus, Pomares-Millan, Hugo, Provenzano, Michele, Saint-Martin, Cécile, Zhang, Cuilin, Zhu, Yeyi, Auh, Sungyoung, de Souza, Russell, Fawcett, Andrea J., Gruber, Chandra, Mekonnen, Eskedar Getie, Mixter, Emily, Sherifali, Diana, Eckel, Robert H., Nolan, John J., Philipson, Louis H., Brown, Rebecca J., Billings, Liana K., Boyle, Kristen, Costacou, Tina, Dennis, John M., Florez, Jose C., Gloyn, Anna L., Gomez, Maria F., Gottlieb, Peter A., Greeley, Siri Atma W., Griffin, Kurt, Hattersley, Andrew T., Hirsch, Irl B., Hivert, Marie-France, Hood, Korey K., Josefson, Jami L., Kwak, Soo Heon, Laffel, Lori M., Lim, Siew S., Loos, Ruth J. F., Ma, Ronald C. W., Mathieu, Chantal, Mathioudakis, Nestoras, Meigs, James B., Misra, Shivani, Mohan, Viswanathan, Murphy, Rinki, Oram, Richard, Owen, Katharine R., Ozanne, Susan E., Pearson, Ewan R., Perng, Wei, Pollin, Toni I., Pop-Busui, Rodica, Pratley, Richard E., Redman, Leanne M., Redondo, Maria J., Reynolds, Rebecca M., Semple, Robert K., Sherr, Jennifer L., Sims, Emily K., Sweeting, Arianne, Tuomi, Tiinamaija, Udler, Miriam S., Vesco, Kimberly K., Vilsbøll, Tina, Wagner, Robert, Rich, Stephen S., and Franks, Paul W.
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- 2023
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31. GWAS of random glucose in 476,326 individuals provide insights into diabetes pathophysiology, complications and treatment stratification
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Lagou, Vasiliki, Jiang, Longda, Ulrich, Anna, Zudina, Liudmila, González, Karla Sofia Gutiérrez, Balkhiyarova, Zhanna, Faggian, Alessia, Maina, Jared G., Chen, Shiqian, Todorov, Petar V., Sharapov, Sodbo, David, Alessia, Marullo, Letizia, Mägi, Reedik, Rujan, Roxana-Maria, Ahlqvist, Emma, Thorleifsson, Gudmar, Gao, Ηe, Εvangelou, Εvangelos, Benyamin, Beben, Scott, Robert A., Isaacs, Aaron, Zhao, Jing Hua, Willems, Sara M., Johnson, Toby, Gieger, Christian, Grallert, Harald, Meisinger, Christa, Müller-Nurasyid, Martina, Strawbridge, Rona J., Goel, Anuj, Rybin, Denis, Albrecht, Eva, Jackson, Anne U., Stringham, Heather M., Corrêa, Jr., Ivan R., Farber-Eger, Eric, Steinthorsdottir, Valgerdur, Uitterlinden, André G., Munroe, Patricia B., Brown, Morris J., Schmidberger, Julian, Holmen, Oddgeir, Thorand, Barbara, Hveem, Kristian, Wilsgaard, Tom, Mohlke, Karen L., Wang, Zhe, Shmeliov, Aleksey, den Hoed, Marcel, Loos, Ruth J. F., Kratzer, Wolfgang, Haenle, Mark, Koenig, Wolfgang, Boehm, Bernhard O., Tan, Tricia M., Tomas, Alejandra, Salem, Victoria, Barroso, Inês, Tuomilehto, Jaakko, Boehnke, Michael, Florez, Jose C., Hamsten, Anders, Watkins, Hugh, Njølstad, Inger, Wichmann, H.-Erich, Caulfield, Mark J., Khaw, Kay-Tee, van Duijn, Cornelia M., Hofman, Albert, Wareham, Nicholas J., Langenberg, Claudia, Whitfield, John B., Martin, Nicholas G., Montgomery, Grant, Scapoli, Chiara, Tzoulaki, Ioanna, Elliott, Paul, Thorsteinsdottir, Unnur, Stefansson, Kari, Brittain, Evan L., McCarthy, Mark I., Froguel, Philippe, Sexton, Patrick M., Wootten, Denise, Groop, Leif, Dupuis, Josée, Meigs, James B., Deganutti, Giuseppe, Demirkan, Ayse, Pers, Tune H., Reynolds, Christopher A., Aulchenko, Yurii S., Kaakinen, Marika A., Jones, Ben, and Prokopenko, Inga
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- 2023
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32. On a General Method for Resolving Integrals of Multiple Spherical Bessel Functions Against Power Laws into Distributions
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Meigs, Kiersten and Slepian, Zachary
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Mathematics - Classical Analysis and ODEs ,Astrophysics - Cosmology and Nongalactic Astrophysics - Abstract
We here present a method of performing integrals of products of spherical Bessel functions (SBFs) weighted by a power-law. Our method, which begins with double-SBF integrals, exploits a differential operator $\hat{D}$ defined via Bessel's differential equation. Application of this operator raises the power-law in steps of two. We also here display a suitable base integral expression to which this operator can be applied for both even and odd cases. We test our method by showing that it reproduces previously-known solutions. Importantly, it also goes beyond them, offering solutions in terms of singular distributions, Heaviside functions, and Gauss's hypergeometric,$\;_2{\rm F}_1$ for $all$ double-SBF integrals with positive semi-definite integer power-law weight. We then show how our method for double-SBF integrals enables evaluating $arbitrary$ triple-SBF overlap integrals, going beyond the cases currently in the literature. This in turn enables reduction of arbitrary quadruple, quintuple, and sextuple-SBF integrals and beyond into tractable forms., Comment: 20 pages, 1 figure, submitted
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- 2021
33. Fraudband for Everyone! Tech Commentary
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Meigs, James B.
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Online services -- Government finance ,Fraud -- Forecasts and trends ,Subsidies -- Investigations ,Company legal issue ,Cable television/data services ,Online services ,Market trend/market analysis ,Ethnic, cultural, racial issues/studies ,Literature/writing ,Philosophy and religion - Abstract
Everybody loves infrastructure. It's one of the few things Democrats and Republicans agree on these days. When Joe Biden came into office touting plans to spend more money than any [...]
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- 2024
34. The Likely Lab Leak and the Covid Cassandra
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Meigs, James B.
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Epidemics -- Investigations -- United States -- China ,Public health administration -- Political aspects ,Company legal issue ,Company distribution practices ,Ethnic, cultural, racial issues/studies ,Literature/writing ,Philosophy and religion - Abstract
I THOUGHT I WAS done with writing about Covid-19. But Covid-19 isn't done with me--or with any of us. I'm writing this precisely four years after Chinese health officials first [...]
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- 2024
35. A multiancestry genome-wide association study of unexplained chronic ALT elevation as a proxy for nonalcoholic fatty liver disease with histological and radiological validation
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Vujkovic, Marijana, Ramdas, Shweta, Lorenz, Kim M, Guo, Xiuqing, Darlay, Rebecca, Cordell, Heather J, He, Jing, Gindin, Yevgeniy, Chung, Chuhan, Myers, Robert P, Schneider, Carolin V, Park, Joseph, Lee, Kyung Min, Serper, Marina, Carr, Rotonya M, Kaplan, David E, Haas, Mary E, MacLean, Matthew T, Witschey, Walter R, Zhu, Xiang, Tcheandjieu, Catherine, Kember, Rachel L, Kranzler, Henry R, Verma, Anurag, Giri, Ayush, Klarin, Derek M, Sun, Yan V, Huang, Jie, Huffman, Jennifer E, Creasy, Kate Townsend, Hand, Nicholas J, Liu, Ching-Ti, Long, Michelle T, Yao, Jie, Budoff, Matthew, Tan, Jingyi, Li, Xiaohui, Lin, Henry J, Chen, Yii-Der Ida, Taylor, Kent D, Chang, Ruey-Kang, Krauss, Ronald M, Vilarinho, Silvia, Brancale, Joseph, Nielsen, Jonas B, Locke, Adam E, Jones, Marcus B, Verweij, Niek, Baras, Aris, Reddy, K Rajender, Neuschwander-Tetri, Brent A, Schwimmer, Jeffrey B, Sanyal, Arun J, Chalasani, Naga, Ryan, Kathleen A, Mitchell, Braxton D, Gill, Dipender, Wells, Andrew D, Manduchi, Elisabetta, Saiman, Yedidya, Mahmud, Nadim, Miller, Donald R, Reaven, Peter D, Phillips, Lawrence S, Muralidhar, Sumitra, DuVall, Scott L, Lee, Jennifer S, Assimes, Themistocles L, Pyarajan, Saiju, Cho, Kelly, Edwards, Todd L, Damrauer, Scott M, Wilson, Peter W, Gaziano, J Michael, O’Donnell, Christopher J, Khera, Amit V, Grant, Struan FA, Brown, Christopher D, Tsao, Philip S, Saleheen, Danish, Lotta, Luca A, Bastarache, Lisa, Anstee, Quentin M, Daly, Ann K, Meigs, James B, Rotter, Jerome I, Lynch, Julie A, Rader, Daniel J, Voight, Benjamin F, and Chang, Kyong-Mi
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Genetics ,Liver Disease ,Human Genome ,Digestive Diseases ,Chronic Liver Disease and Cirrhosis ,Aetiology ,2.1 Biological and endogenous factors ,Alanine Transaminase ,Alpha-Ketoglutarate-Dependent Dioxygenase FTO ,Genome-Wide Association Study ,Humans ,Intracellular Signaling Peptides and Proteins ,Lipase ,Membrane Proteins ,Non-alcoholic Fatty Liver Disease ,Polymorphism ,Single Nucleotide ,Protein Serine-Threonine Kinases ,Regeneron Genetics Center ,Geisinger-Regeneron DiscovEHR Collaboration ,EPoS Consortium ,VA Million Veteran Program ,Biological Sciences ,Medical and Health Sciences ,Developmental Biology - Abstract
Nonalcoholic fatty liver disease (NAFLD) is a growing cause of chronic liver disease. Using a proxy NAFLD definition of chronic elevation of alanine aminotransferase (cALT) levels without other liver diseases, we performed a multiancestry genome-wide association study (GWAS) in the Million Veteran Program (MVP) including 90,408 cALT cases and 128,187 controls. Seventy-seven loci exceeded genome-wide significance, including 25 without prior NAFLD or alanine aminotransferase associations, with one additional locus identified in European American-only and two in African American-only analyses (P
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- 2022
36. Host and gut microbial tryptophan metabolism and type 2 diabetes: an integrative analysis of host genetics, diet, gut microbiome and circulating metabolites in cohort studies
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Qi, Qibin, Li, Jun, Yu, Bing, Moon, Jee-Young, Chai, Jin C, Merino, Jordi, Hu, Jie, Ruiz-Canela, Miguel, Rebholz, Casey, Wang, Zheng, Usyk, Mykhaylo, Chen, Guo-Chong, Porneala, Bianca C, Wang, Wenshuang, Nguyen, Ngoc Quynh, Feofanova, Elena V, Grove, Megan L, Wang, Thomas J, Gerszten, Robert E, Dupuis, Josée, Salas-Salvadó, Jordi, Bao, Wei, Perkins, David L, Daviglus, Martha L, Thyagarajan, Bharat, Cai, Jianwen, Wang, Tao, Manson, JoAnn E, Martínez-González, Miguel A, Selvin, Elizabeth, Rexrode, Kathryn M, Clish, Clary B, Hu, Frank B, Meigs, James B, Knight, Rob, Burk, Robert D, Boerwinkle, Eric, and Kaplan, Robert C
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Obesity ,Human Genome ,Prevention ,Genetics ,Nutrition ,Diabetes ,2.2 Factors relating to the physical environment ,Aetiology ,Metabolic and endocrine ,Oral and gastrointestinal ,Bacteria ,Cohort Studies ,Diabetes Mellitus ,Type 2 ,Diet ,Gastrointestinal Microbiome ,Humans ,Kynurenine ,Lactase ,Tryptophan ,diabetes mellitus ,dietary factors ,genetics ,Clinical Sciences ,Paediatrics and Reproductive Medicine ,Gastroenterology & Hepatology - Abstract
ObjectiveTryptophan can be catabolised to various metabolites through host kynurenine and microbial indole pathways. We aimed to examine relationships of host and microbial tryptophan metabolites with incident type 2 diabetes (T2D), host genetics, diet and gut microbiota.MethodWe analysed associations between circulating levels of 11 tryptophan metabolites and incident T2D in 9180 participants of diverse racial/ethnic backgrounds from five cohorts. We examined host genome-wide variants, dietary intake and gut microbiome associated with these metabolites.ResultsTryptophan, four kynurenine-pathway metabolites (kynurenine, kynurenate, xanthurenate and quinolinate) and indolelactate were positively associated with T2D risk, while indolepropionate was inversely associated with T2D risk. We identified multiple host genetic variants, dietary factors, gut bacteria and their potential interplay associated with these T2D-relaetd metabolites. Intakes of fibre-rich foods, but not protein/tryptophan-rich foods, were the dietary factors most strongly associated with tryptophan metabolites. The fibre-indolepropionate association was partially explained by indolepropionate-associated gut bacteria, mostly fibre-using Firmicutes. We identified a novel association between a host functional LCT variant (determining lactase persistence) and serum indolepropionate, which might be related to a host gene-diet interaction on gut Bifidobacterium, a probiotic bacterium significantly associated with indolepropionate independent of other fibre-related bacteria. Higher milk intake was associated with higher levels of gut Bifidobacterium and serum indolepropionate only among genetically lactase non-persistent individuals.ConclusionHigher milk intake among lactase non-persistent individuals, and higher fibre intake were associated with a favourable profile of circulating tryptophan metabolites for T2D, potentially through the host-microbial cross-talk shifting tryptophan metabolism toward gut microbial indolepropionate production.
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- 2022
37. The mechanisms of catalysis and ligand binding for the SARS-CoV-2 NSP3 macrodomain from neutron and x-ray diffraction at room temperature
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Correy, Galen J, Kneller, Daniel W, Phillips, Gwyndalyn, Pant, Swati, Russi, Silvia, Cohen, Aina E, Meigs, George, Holton, James M, Gahbauer, Stefan, Thompson, Michael C, Ashworth, Alan, Coates, Leighton, Kovalevsky, Andrey, Meilleur, Flora, and Fraser, James S
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Inorganic Chemistry ,Biochemistry and Cell Biology ,Chemical Sciences ,Biological Sciences ,Vaccine Related ,Infectious Diseases ,Emerging Infectious Diseases ,Pneumonia & Influenza ,Prevention ,Lung ,Pneumonia ,Development of treatments and therapeutic interventions ,5.1 Pharmaceuticals - Abstract
The nonstructural protein 3 (NSP3) macrodomain of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (Mac1) removes adenosine diphosphate (ADP) ribosylation posttranslational modifications, playing a key role in the immune evasion capabilities of the virus responsible for the coronavirus disease 2019 pandemic. Here, we determined neutron and x-ray crystal structures of the SARS-CoV-2 NSP3 macrodomain using multiple crystal forms, temperatures, and pHs, across the apo and ADP-ribose-bound states. We characterize extensive solvation in the Mac1 active site and visualize how water networks reorganize upon binding of ADP-ribose and non-native ligands, inspiring strategies for displacing waters to increase the potency of Mac1 inhibitors. Determining the precise orientations of active site water molecules and the protonation states of key catalytic site residues by neutron crystallography suggests a catalytic mechanism for coronavirus macrodomains distinct from the substrate-assisted mechanism proposed for human MacroD2. These data provoke a reevaluation of macrodomain catalytic mechanisms and will guide the optimization of Mac1 inhibitors.
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- 2022
38. Characterisation of the scrape-off layer in JET-ILW deuterium and helium low-confinement mode plasmas
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Rees, D., Groth, M., Aleiferis, S., Brezinsek, S., Brix, M., Jepu, I., Lawson, K.D., Meigs, A.G., Menmuir, S., Kirov, K., Lomas, P., Lowry, C., Thomas, B., Widdowson, A., Carvalho, P., and Delabie, E.
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- 2024
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39. A genetic association study of circulating coagulation factor VIII and von Willebrand factor levels
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Abe, Namiko, Abecasis, Gonçalo, Aguet, Francois, Albert, Christine, Almasy, Laura, Alonso, Alvaro, Ament, Seth, Anderson, Peter, Anugu, Pramod, Applebaum-Bowden, Deborah, Ardlie, Kristin, Arking, Dan, Arnett, Donna K, Ashley-Koch, Allison, Aslibekyan, Stella, Assimes, Tim, Auer, Paul, Avramopoulos, Dimitrios, Ayas, Najib, Balasubramanian, Adithya, Barnard, John, Barnes, Kathleen, Barr, R. Graham, Barron-Casella, Emily, Barwick, Lucas, Beaty, Terri, Beck, Gerald, Becker, Diane, Becker, Lewis, Beer, Rebecca, Beitelshees, Amber, Benjamin, Emelia, Benos, Takis, Bezerra, Marcos, Bielak, Larry, Bis, Joshua, Blackwell, Thomas, Blangero, John, Blue, Nathan, Boerwinkle, Eric, Bowden, Donald W., Bowler, Russell, Brody, Jennifer, Broeckel, Ulrich, Broome, Jai, Brown, Deborah, Bunting, Karen, Burchard, Esteban, Bustamante, Carlos, Buth, Erin, Cade, Brian, Cardwell, Jonathan, Carey, Vincent, Carrier, Julie, Carson, April P., Carty, Cara, Casaburi, Richard, Casas Romero, Juan P, Casella, James, Castaldi, Peter, Chaffin, Mark, Chang, Christy, Chang, Yi-Cheng, Chasman, Daniel, Chavan, Sameer, Chen, Bo-Juen, Chen, Wei-Min, Ida Chen, Yii-Der, Cho, Michael, Choi, Seung Hoan, Chuang, Lee-Ming, Chung, Mina, Chung, Ren-Hua, Clish, Clary, Comhair, Suzy, Conomos, Matthew, Cornell, Elaine, Correa, Adolfo, Crandall, Carolyn, Crapo, James, Cupples, L. Adrienne, Curran, Joanne, Curtis, Jeffrey, Custer, Brian, Damcott, Coleen, Darbar, Dawood, David, Sean, Davis, Colleen, Daya, Michelle, de Andrade, Mariza, de las Fuentes, Lisa, de Vries, Paul, DeBaun, Michael, Deka, Ranjan, DeMeo, Dawn, Devine, Scott, Dinh, Huyen, Doddapaneni, Harsha, Duan, Qing, Dugan-Perez, Shannon, Duggirala, Ravi, Durda, Jon Peter, Dutcher, Susan K., Eaton, Charles, Ekunwe, Lynette, El Boueiz, Adel, Ellinor, Patrick, Emery, Leslie, Erzurum, Serpil, Farber, Charles, Farek, Jesse, Fingerlin, Tasha, Flickinger, Matthew, Fornage, Myriam, Franceschini, Nora, Frazar, Chris, Fu, Mao, Fullerton, Stephanie M., Fulton, Lucinda, Gabriel, Stacey, Gan, Weiniu, Gao, Shanshan, Gao, Yan, Gass, Margery, Geiger, Heather, Gelb, Bruce, Geraci, Mark, Germer, Soren, Gerszten, Robert, Ghosh, Auyon, Gibbs, Richard, Gignoux, Chris, Gladwin, Mark, Glahn, David, Gogarten, Stephanie, Gong, Da-Wei, Goring, Harald, Graw, Sharon, Gray, Kathryn J., Grine, Daniel, Gross, Colin, Gu, C. Charles, Guan, Yue, Guo, Xiuqing, Gupta, Namrata, Haessler, Jeff, Hall, Michael, Han, Yi, Hanly, Patrick, Harris, Daniel, Hawley, Nicola L., He, Jiang, Heavner, Ben, Heckbert, Susan, Hernandez, Ryan, Herrington, David, Hersh, Craig, Hidalgo, Bertha, Hixson, James, Hobbs, Brian, Hokanson, John, Hong, Elliott, Hoth, Karin, Hsiung, Chao (Agnes), Hu, Jianhong, Hung, Yi-Jen, Huston, Haley, Hwu, Chii Min, Irvin, Marguerite Ryan, Jackson, Rebecca, Jain, Deepti, Jaquish, Cashell, Johnsen, Jill, Johnson, Andrew, Johnson, Craig, Johnston, Rich, Jones, Kimberly, Kang, Hyun Min, Kaplan, Robert, Kardia, Sharon, Kelly, Shannon, Kenny, Eimear, Kessler, Michael, Khan, Alyna, Khan, Ziad, Kim, Wonji, Kimoff, John, Kinney, Greg, Konkle, Barbara, Kooperberg, Charles, Kramer, Holly, Lange, Christoph, Lange, Ethan, Lange, Leslie, Laurie, Cathy, Laurie, Cecelia, LeBoff, Meryl, Lee, Jiwon, Lee, Sandra, Lee, Wen-Jane, LeFaive, Jonathon, Levine, David, Levy, Dan, Lewis, Joshua, Li, Xiaohui, Li, Yun, Lin, Henry, Lin, Honghuang, Lin, Xihong, Liu, Simin, Liu, Yongmei, Liu, Yu, Loos, Ruth J. F., Lubitz, Steven, Lunetta, Kathryn, Luo, James, Magalang, Ulysses, Mahaney, Michael, Make, Barry, Manichaikul, Ani, Manning, Alisa, Manson, JoAnn, Martin, Lisa, Marton, Melissa, Mathai, Susan, Mathias, Rasika, May, Susanne, McArdle, Patrick, McDonald, Merry-Lynn, McFarland, Sean, McGarvey, Stephen, McGoldrick, Daniel, McHugh, Caitlin, McNeil, Becky, Mei, Hao, Meigs, James, Menon, Vipin, Mestroni, Luisa, Metcalf, Ginger, Meyers, Deborah A, Mignot, Emmanuel, Mikulla, Julie, Min, Nancy, Minear, Mollie, Minster, Ryan L, Mitchell, Braxton D., Moll, Matt, Momin, Zeineen, Montasser, May E., Montgomery, Courtney, Muzny, Donna, Mychaleckyj, Josyf C, Nadkarni, Girish, Naik, Rakhi, Naseri, Take, Natarajan, Pradeep, Nekhai, Sergei, Nelson, Sarah C., Neltner, Bonnie, Nessner, Caitlin, Nickerson, Deborah, Nkechinyere, Osuji, North, Kari, O'Connell, Jeff, O'Connor, Tim, Ochs-Balcom, Heather, Okwuonu, Geoffrey, Pack, Allan, Paik, David T., Palmer, Nicholette, Pankow, James, Papanicolaou, George, Parker, Cora, Peloso, Gina, Peralta, Juan Manuel, Perez, Marco, Perry, James, Peters, Ulrike, Peyser, Patricia, Phillips, Lawrence S, Pleiness, Jacob, Pollin, Toni, Post, Wendy, Becker, Julia Powers, Boorgula, Meher Preethi, Preuss, Michael, Psaty, Bruce, Qasba, Pankaj, Qiao, Dandi, Qin, Zhaohui, Rafaels, Nicholas, Raffield, Laura, Rajendran, Mahitha, Ramachandran, Vasan S., Rao, D. C., Rasmussen-Torvik, Laura, Ratan, Aakrosh, Redline, Susan, Reed, Robert, Reeves, Catherine, Regan, Elizabeth, Reiner, Alex, Reupena, Muagututi‘a Sefuiva, Rice, Ken, Rich, Stephen, Robillard, Rebecca, Robine, Nicolas, Roden, Dan, Roselli, Carolina, Rotter, Jerome, Ruczinski, Ingo, Runnels, Alexi, Russell, Pamela, Ruuska, Sarah, Ryan, Kathleen, Sabino, Ester Cerdeira, Saleheen, Danish, Salimi, Shabnam, Salvi, Sejal, Salzberg, Steven, Sandow, Kevin, Sankaran, Vijay G., Santibanez, Jireh, Schwander, Karen, Schwartz, David, Sciurba, Frank, Seidman, Christine, Seidman, Jonathan, Sériès, Frédéric, Sheehan, Vivien, Sherman, Stephanie L., Shetty, Amol, Shetty, Aniket, Hui-Heng Sheu, Wayne, Shoemaker, M. Benjamin, Silver, Brian, Silverman, Edwin, Skomro, Robert, Smith, Albert Vernon, Smith, Jennifer, Smith, Josh, Smith, Nicholas, Smith, Tanja, Smoller, Sylvia, Snively, Beverly, Snyder, Michael, Sofer, Tamar, Sotoodehnia, Nona, Stilp, Adrienne M., Storm, Garrett, Streeten, Elizabeth, Su, Jessica Lasky, Sung, Yun Ju, Sylvia, Jody, Szpiro, Adam, Taliun, Daniel, Tang, Hua, Taub, Margaret, Taylor, Kent D., Taylor, Matthew, Taylor, Simeon, Telen, Marilyn, Thornton, Timothy A., Threlkeld, Machiko, Tinker, Lesley, Tirschwell, David, Tishkoff, Sarah, Tiwari, Hemant, Tong, Catherine, Tracy, Russell, Tsai, Michael, Vaidya, Dhananjay, Van Den Berg, David, VandeHaar, Peter, Vrieze, Scott, Walker, Tarik, Wallace, Robert, Walts, Avram, Wang, Fei Fei, Wang, Heming, Wang, Jiongming, Watson, Karol, Watt, Jennifer, Weeks, Daniel E., Weinstock, Joshua, Weir, Bruce, Weiss, Scott T, Weng, Lu-Chen, Wessel, Jennifer, Willer, Cristen, Williams, Kayleen, Williams, L. Keoki, Wilson, Carla, Wilson, James, Winterkorn, Lara, Wong, Quenna, Wu, Joseph, Xu, Huichun, Yanek, Lisa, Yang, Ivana, Yu, Ketian, Zekavat, Seyedeh Maryam, Zhang, Yingze, Zhao, Snow Xueyan, Zhao, Wei, Zhu, Xiaofeng, Ziv, Elad, Zody, Michael, Zoellner, Sebastian, Lindstrom, Sara, Wang, Lu, Smith, Erin N., Gordon, William, van Hylckama Vlieg, Astrid, Brody, Jennifer A., Pattee, Jack W., Haessler, Jeffrey, Brumpton, Ben M., Chasman, Daniel I., Suchon, Pierre, Chen, Ming-Huei, Turman, Constance, Germain, Marine, Wiggins, Kerri L., MacDonald, James, Braekkan, Sigrid K., Armasu, Sebastian M., Pankratz, Nathan, Jackson, Rabecca D., Nielsen, Jonas B., Giulianini, Franco, Puurunen, Marja K., Ibrahim, Manal, Heckbert, Susan R., Bammler, Theo K., Frazer, Kelly A., McCauley, Bryan M., Taylor, Kent, Pankow, James S., Reiner, Alexander P., Gabrielsen, Maiken E., Deleuze, Jean-François, O'Donnell, Chris J., Kim, Jihye, McKnight, Barbara, Kraft, Peter, Hansen, John-Bjarne, Rosendaal, Frits R., Heit, John A., Psaty, Bruce M., Tang, Weihong, Hveem, Kristian, Ridker, Paul M., Morange, Pierre-Emmanuel, Johnson, Andrew D., Kabrhel, Christopher, AlexandreTrégouët, David, Smith, Nicholas L., de Vries, Paul S., Reventun, Paula, Brown, Michael R., Heath, Adam S., Huffman, Jennifer E., Le, Ngoc-Quynh, Bebo, Allison, Temprano-Sagrera, Gerard, Raffield, Laura M., Ozel, Ayse Bilge, Thibord, Florian, Lewis, Joshua P., Rodriguez, Benjamin A. T., Polasek, Ozren, Yanek, Lisa R., Carrasquilla, German D., Marioni, Riccardo E., Kleber, Marcus E., Trégouët, David-Alexandre, Yao, Jie, Li-Gao, Ruifang, Joshi, Peter K., Trompet, Stella, Martinez-Perez, Angel, Ghanbari, Mohsen, Howard, Tom E., Reiner, Alex P., Arvanitis, Marios, Ryan, Kathleen A., Bartz, Traci M., Rudan, Igor, Faraday, Nauder, Linneberg, Allan, Davies, Gail, Delgado, Graciela E., Klaric, Lucija, Noordam, Raymond, van Rooij, Frank, Curran, Joanne E., Wheeler, Marsha M., Osburn, William O., O'Connell, Jeffrey R., Beswick, Andrew, Kolcic, Ivana, Souto, Juan Carlos, Becker, Lewis C., Hansen, Torben, Doyle, Margaret F., Harris, Sarah E., Moissl, Angela P., Rich, Stephen S., Campbell, Harry, Stott, David J., Soria, Jose Manuel, de Maat, Moniek P. M., Brody, Lawrence C., Auer, Paul L., Ben-Shlomo, Yoav, Hayward, Caroline, Mathias, Rasika A., Kilpeläinen, Tuomas O., Lange, Leslie A., Cox, Simon R., März, Winfried, Rotter, Jerome I., Mook-Kanamori, Dennis O., Wilson, James F., van der Harst, Pim, Jukema, J. Wouter, Ikram, M. Arfan, Desch, Karl C., Sabater-Lleal, Maria, Lowenstein, Charles J., and Morrison, Alanna C.
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- 2024
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40. The Fauci Conspiracy: Tech Commentary
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Meigs, James B.
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Epidemics -- Investigations -- Origin ,Conspiracy -- Public opinion ,Company legal issue ,Ethnic, cultural, racial issues/studies ,Literature/writing ,Philosophy and religion - Abstract
We told you so. There's probably a more polite way to say that, but who cares? We are long past the time for politeness regarding the derelictions of our leadership [...]
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- 2024
41. The Age of the Spurious Upgrade: Tech Commentary
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Meigs, James B.
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Technology and civilization -- Forecasts and trends ,Wi-Fi -- Usage -- Social aspects ,Internet access -- Social aspects -- Forecasts and trends ,Internet access ,Market trend/market analysis ,Ethnic, cultural, racial issues/studies ,Literature/writing ,Philosophy and religion - Abstract
In his 2009 book, Shop Class as Soulcraft, philosopher-mechanic Matthew B. Crawford described the uniquely modern frustration that a certain kind of person feels when confronted with one of those [...]
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- 2024
42. Precision subclassification of type 2 diabetes: a systematic review
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Misra, Shivani, Wagner, Robert, Ozkan, Bige, Schön, Martin, Sevilla-Gonzalez, Magdalena, Prystupa, Katsiaryna, Wang, Caroline C., Kreienkamp, Raymond J., Cromer, Sara J., Rooney, Mary R., Duan, Daisy, Thuesen, Anne Cathrine Baun, Wallace, Amelia S., Leong, Aaron, Deutsch, Aaron J., Andersen, Mette K., Billings, Liana K., Eckel, Robert H., Sheu, Wayne Huey-Herng, Hansen, Torben, Stefan, Norbert, Goodarzi, Mark O., Ray, Debashree, Selvin, Elizabeth, Florez, Jose C., Meigs, James B., and Udler, Miriam S.
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- 2023
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43. Changeover between helium and hydrogen fueled plasmas in JET and WEST
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T. Wauters, R. Bisson, E. Delabie, D. Douai, A. Gallo, J. Gaspar, I. Jepu, Y. Kovtun, E. Pawelec, D. Matveev, A. Meigs, S. Brezinsek, I. Coffey, T. Dittmar, N. Fedorczak, J. Gunn, A. Hakola, P. Jacquet, K. Kirov, E. Lerche, J. Likonen, E. Litherland-Smith, T. Loarer, P. Lomas, C. Lowry, M. Maslov, I. Monakhov, J. Morales, C. Noble, R. Nouailletas, B. Pégourié, C. Perez von Thun, R.A. Pitts, C. Reux, F. Rimini, H. Sheikh, S. Silburn, H. Sun, D. Taylor, E. Tsitrone, S. Vartanian, E. Wang, and A. Widdowson
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JET ,WEST ,Changeover ,Helium ,Hydrogen ,ICWC ,Nuclear engineering. Atomic power ,TK9001-9401 - Abstract
The pre-fusion power operation (PFPO) phase of ITER, as described in the ITER research plan with Staged Approach,22 ITER Organization, “ITER Research Plan within the Staged Approach,” ITR-18–003, Aug. 2018. includes both hydrogen (H) and helium (He) plasma operations. In preparation for PFPO, both WEST and JET ran He plasma campaigns to study plasma-wall interactions in a tungsten environment. The campaigns included a back-and-forth transition between H or deuterium (D) and He plasma operation allowing the assessment of the achievable plasma content as well as the accessible wall reservoirs for respective species. The WEST changeovers included tokamak pulses with a fixed divertor configuration. The JET changeovers applied ion cyclotron wall conditioning (ICWC) and tokamak pulses including limiter phases and four different divertor configurations. Glow discharge conditioning (GDC) was applied to complete the changeovers. The results are characterized by subdivertor optical and mass spectrometric gas analyzers and spatially resolved optical emission spectroscopy. A He content of 96–97 % after H operations is achieved by tens of ICWC pulses (JET) and several dedicated diverted plasmas (WEST and JET), while a fivefold is estimated to be required for the back transition. Effective pumping of the wall released species is a key parameter for a fast changeover. Upon applying higher heating power, the relative content of the fueled plasma species decreases. The JET gas balance analysis indicates that He operation may increase H retention. WEST divertor spectroscopy indicates a larger He inventory near the inner divertor strike line. He GDC has a clear effect on the He recycling light at the WEST divertor while D GDC did not reduce the long lasting He content observed in D pulses after the JET He campaign.
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- 2024
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44. Type 2 Diabetes Partitioned Polygenic Scores Associate With Disease Outcomes in 454,193 Individuals Across 13 Cohorts
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DiCorpo, Daniel, LeClair, Jessica, Cole, Joanne B, Sarnowski, Chloé, Ahmadizar, Fariba, Bielak, Lawrence F, Blokstra, Anneke, Bottinger, Erwin P, Chaker, Layal, Chen, Yii-Der I, Chen, Ye, de Vries, Paul S, Faquih, Tariq, Ghanbari, Mohsen, Gudmundsdottir, Valborg, Guo, Xiuqing, Hasbani, Natalie R, Ibi, Dorina, Ikram, M Arfan, Kavousi, Maryam, Leonard, Hampton L, Leong, Aaron, Mercader, Josep M, Morrison, Alanna C, Nadkarni, Girish N, Nalls, Mike A, Noordam, Raymond, Preuss, Michael, Smith, Jennifer A, Trompet, Stella, Vissink, Petra, Yao, Jie, Zhao, Wei, Boerwinkle, Eric, Goodarzi, Mark O, Gudnason, Vilmundur, Jukema, J Wouter, Kardia, Sharon LR, Loos, Ruth JF, Liu, Ching-Ti, Manning, Alisa K, Mook-Kanamori, Dennis, Pankow, James S, Picavet, H Susan J, Sattar, Naveed, Simonsick, Eleanor M, Verschuren, WM Monique, van Dijk, Ko Willems, Florez, Jose C, Rotter, Jerome I, Meigs, James B, Dupuis, Josée, and Udler, Miriam S
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Epidemiology ,Biomedical and Clinical Sciences ,Health Sciences ,Genetics ,Obesity ,Heart Disease - Coronary Heart Disease ,Liver Disease ,Heart Disease ,Cardiovascular ,Digestive Diseases ,Diabetes ,Nutrition ,Prevention ,Aetiology ,2.1 Biological and endogenous factors ,Metabolic and endocrine ,Good Health and Well Being ,Alleles ,Cross-Sectional Studies ,Diabetes Mellitus ,Type 2 ,Genetic Loci ,Humans ,Pharmaceutical Preparations ,Medical and Health Sciences ,Endocrinology & Metabolism ,Biomedical and clinical sciences ,Health sciences - Abstract
ObjectiveType 2 diabetes (T2D) has heterogeneous patient clinical characteristics and outcomes. In previous work, we investigated the genetic basis of this heterogeneity by clustering 94 T2D genetic loci using their associations with 47 diabetes-related traits and identified five clusters, termed β-cell, proinsulin, obesity, lipodystrophy, and liver/lipid. The relationship between these clusters and individual-level metabolic disease outcomes has not been assessed.Research design and methodsHere we constructed individual-level partitioned polygenic scores (pPS) for these five clusters in 12 studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium and the UK Biobank (n = 454,193) and tested for cross-sectional association with T2D-related outcomes, including blood pressure, renal function, insulin use, age at T2D diagnosis, and coronary artery disease (CAD).ResultsDespite all clusters containing T2D risk-increasing alleles, they had differential associations with metabolic outcomes. Increased obesity and lipodystrophy cluster pPS, which had opposite directions of association with measures of adiposity, were both significantly associated with increased blood pressure and hypertension. The lipodystrophy and liver/lipid cluster pPS were each associated with CAD, with increasing and decreasing effects, respectively. An increased liver/lipid cluster pPS was also significantly associated with reduced renal function. The liver/lipid cluster includes known loci linked to liver lipid metabolism (e.g., GCKR, PNPLA3, and TM6SF2), and these findings suggest that cardiovascular disease risk and renal function may be impacted by these loci through their shared disease pathway.ConclusionsOur findings support that genetically driven pathways leading to T2D also predispose differentially to clinical outcomes.
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- 2022
45. Multi-tissue epigenetic analysis identifies distinct associations underlying insulin resistance and Alzheimer’s disease at CPT1A locus
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Chloé Sarnowski, Tianxiao Huan, Yiyi Ma, Roby Joehanes, Alexa Beiser, Charles S. DeCarli, Nancy L. Heard-Costa, Daniel Levy, Honghuang Lin, Ching-Ti Liu, Chunyu Liu, James B. Meigs, Claudia L. Satizabal, Jose C. Florez, Marie-France Hivert, Josée Dupuis, Philip L. De Jager, David A. Bennett, Sudha Seshadri, and Alanna C. Morrison
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Epigenetics ,Insulin resistance ,Alzheimer’s disease ,FHS ,ROSMAP ,DNA methylation ,Medicine ,Genetics ,QH426-470 - Abstract
Abstract Background Insulin resistance (IR) is a major risk factor for Alzheimer’s disease (AD) dementia. The mechanisms by which IR predisposes to AD are not well-understood. Epigenetic studies may help identify molecular signatures of IR associated with AD, thus improving our understanding of the biological and regulatory mechanisms linking IR and AD. Methods We conducted an epigenome-wide association study of IR, quantified using the homeostatic model assessment of IR (HOMA-IR) and adjusted for body mass index, in 3,167 participants from the Framingham Heart Study (FHS) without type 2 diabetes at the time of blood draw used for methylation measurement. We identified DNA methylation markers associated with IR at the genome-wide level accounting for multiple testing (P
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- 2023
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46. Precision subclassification of type 2 diabetes: a systematic review
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Shivani Misra, Robert Wagner, Bige Ozkan, Martin Schön, Magdalena Sevilla-Gonzalez, Katsiaryna Prystupa, Caroline C. Wang, Raymond J. Kreienkamp, Sara J. Cromer, Mary R. Rooney, Daisy Duan, Anne Cathrine Baun Thuesen, Amelia S. Wallace, Aaron Leong, Aaron J. Deutsch, Mette K. Andersen, Liana K. Billings, Robert H. Eckel, Wayne Huey-Herng Sheu, Torben Hansen, Norbert Stefan, Mark O. Goodarzi, Debashree Ray, Elizabeth Selvin, Jose C. Florez, ADA/EASD PMDI, James B. Meigs, and Miriam S. Udler
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Medicine - Abstract
Abstract Background Heterogeneity in type 2 diabetes presentation and progression suggests that precision medicine interventions could improve clinical outcomes. We undertook a systematic review to determine whether strategies to subclassify type 2 diabetes were associated with high quality evidence, reproducible results and improved outcomes for patients. Methods We searched PubMed and Embase for publications that used ‘simple subclassification’ approaches using simple categorisation of clinical characteristics, or ‘complex subclassification’ approaches which used machine learning or ‘omics approaches in people with established type 2 diabetes. We excluded other diabetes subtypes and those predicting incident type 2 diabetes. We assessed quality, reproducibility and clinical relevance of extracted full-text articles and qualitatively synthesised a summary of subclassification approaches. Results Here we show data from 51 studies that demonstrate many simple stratification approaches, but none have been replicated and many are not associated with meaningful clinical outcomes. Complex stratification was reviewed in 62 studies and produced reproducible subtypes of type 2 diabetes that are associated with outcomes. Both approaches require a higher grade of evidence but support the premise that type 2 diabetes can be subclassified into clinically meaningful subtypes. Conclusion Critical next steps toward clinical implementation are to test whether subtypes exist in more diverse ancestries and whether tailoring interventions to subtypes will improve outcomes.
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- 2023
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47. The power of TOPMed imputation for the discovery of Latino-enriched rare variants associated with type 2 diabetes
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Huerta-Chagoya, Alicia, Schroeder, Philip, Mandla, Ravi, Deutsch, Aaron J., Zhu, Wanying, Petty, Lauren, Yi, Xiaoyan, Cole, Joanne B., Udler, Miriam S., Dornbos, Peter, Porneala, Bianca, DiCorpo, Daniel, Liu, Ching-Ti, Li, Josephine H., Szczerbiński, Lukasz, Kaur, Varinderpal, Kim, Joohyun, Lu, Yingchang, Martin, Alicia, Eizirik, Decio L., Marchetti, Piero, Marselli, Lorella, Chen, Ling, Srinivasan, Shylaja, Todd, Jennifer, Flannick, Jason, Gubitosi-Klug, Rose, Levitsky, Lynne, Shah, Rachana, Kelsey, Megan, Burke, Brian, Dabelea, Dana M., Divers, Jasmin, Marcovina, Santica, Stalbow, Lauren, Loos, Ruth J. F., Darst, Burcu F., Kooperberg, Charles, Raffield, Laura M., Haiman, Christopher, Sun, Quan, McCormick, Joseph B., Fisher-Hoch, Susan P., Ordoñez, Maria L., Meigs, James, Baier, Leslie J., González-Villalpando, Clicerio, González-Villalpando, Maria Elena, Orozco, Lorena, García-García, Lourdes, Moreno-Estrada, Andrés, Aguilar-Salinas, Carlos A., Tusié, Teresa, Dupuis, Josée, Ng, Maggie C. Y., Manning, Alisa, Highland, Heather M., Cnop, Miriam, Hanson, Robert, Below, Jennifer, Florez, Jose C., Leong, Aaron, and Mercader, Josep M.
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- 2023
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48. Changeover between helium and hydrogen fueled plasmas in JET and WEST
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Wauters, T., Bisson, R., Delabie, E., Douai, D., Gallo, A., Gaspar, J., Jepu, I., Kovtun, Y., Pawelec, E., Matveev, D., Meigs, A., Brezinsek, S., Coffey, I., Dittmar, T., Fedorczak, N., Gunn, J., Hakola, A., Jacquet, P., Kirov, K., Lerche, E., Likonen, J., Litherland-Smith, E., Loarer, T., Lomas, P., Lowry, C., Maslov, M., Monakhov, I., Morales, J., Noble, C., Nouailletas, R., Pégourié, B., Perez von Thun, C., Pitts, R.A., Reux, C., Rimini, F., Sheikh, H., Silburn, S., Sun, H., Taylor, D., Tsitrone, E., Vartanian, S., Wang, E., and Widdowson, A.
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- 2024
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49. Obesity Partially Mediates the Diabetogenic Effect of Lowering LDL Cholesterol.
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Wu, Peitao, Moon, Jee-Young, Daghlas, Iyas, Franco, Giulianini, Porneala, Bianca, Ahmadizar, Fariba, Richardson, Tom, Isaksen, Jonas, Hindy, Georgy, Yao, Jie, Sitlani, Colleen, Raffield, Laura, Yanek, Lisa, Feitosa, Mary, Cuadrat, Rafael, Qi, Qibin, Arfan Ikram, M, Ellervik, Christina, Ericson, Ulrika, Goodarzi, Mark, Brody, Jennifer, Lange, Leslie, Mercader, Josep, Vaidya, Dhananjay, An, Ping, Schulze, Matthias, Masana, Lluis, Ghanbari, Mohsen, Olesen, Morten, Cai, Jianwen, Guo, Xiuqing, Floyd, James, Jäger, Susanne, Province, Michael, Kalyani, Rita, Psaty, Bruce, Orho-Melander, Marju, Ridker, Paul, Kanters, Jørgen, Uitterlinden, Andre, Davey Smith, George, Gill, Dipender, Kaplan, Robert, Kavousi, Maryam, Raghavan, Sridharan, Chasman, Daniel, Rotter, Jerome, Meigs, James, Florez, Jose, Dupuis, Josée, Liu, Ching-Ti, and Merino, Jordi
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Cholesterol ,LDL ,Diabetes Mellitus ,Type 2 ,Genome-Wide Association Study ,Humans ,Mendelian Randomization Analysis ,Obesity ,Risk Factors - Abstract
OBJECTIVE: LDL cholesterol (LDLc)-lowering drugs modestly increase body weight and type 2 diabetes risk, but the extent to which the diabetogenic effect of lowering LDLc is mediated through increased BMI is unknown. RESEARCH DESIGN AND METHODS: We conducted summary-level univariable and multivariable Mendelian randomization (MR) analyses in 921,908 participants to investigate the effect of lowering LDLc on type 2 diabetes risk and the proportion of this effect mediated through BMI. We used data from 92,532 participants from 14 observational studies to replicate findings in individual-level MR analyses. RESULTS: A 1-SD decrease in genetically predicted LDLc was associated with increased type 2 diabetes odds (odds ratio [OR] 1.12 [95% CI 1.01, 1.24]) and BMI (β = 0.07 SD units [95% CI 0.02, 0.12]) in univariable MR analyses. The multivariable MR analysis showed evidence of an indirect effect of lowering LDLc on type 2 diabetes through BMI (OR 1.04 [95% CI 1.01, 1.08]) with a proportion mediated of 38% of the total effect (P = 0.03). Total and indirect effect estimates were similar across a number of sensitivity analyses. Individual-level MR analyses confirmed the indirect effect of lowering LDLc on type 2 diabetes through BMI with an estimated proportion mediated of 8% (P = 0.04). CONCLUSIONS: These findings suggest that the diabetogenic effect attributed to lowering LDLc is partially mediated through increased BMI. Our results could help advance understanding of adipose tissue and lipids in type 2 diabetes pathophysiology and inform strategies to reduce diabetes risk among individuals taking LDLc-lowering medications.
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- 2022
50. Whole genome sequence association analysis of fasting glucose and fasting insulin levels in diverse cohorts from the NHLBI TOPMed program
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DiCorpo, Daniel, Gaynor, Sheila M, Russell, Emily M, Westerman, Kenneth E, Raffield, Laura M, Majarian, Timothy D, Wu, Peitao, Sarnowski, Chloé, Highland, Heather M, Jackson, Anne, Hasbani, Natalie R, de Vries, Paul S, Brody, Jennifer A, Hidalgo, Bertha, Guo, Xiuqing, Perry, James A, O’Connell, Jeffrey R, Lent, Samantha, Montasser, May E, Cade, Brian E, Jain, Deepti, Wang, Heming, D’Oliveira Albanus, Ricardo, Varshney, Arushi, Yanek, Lisa R, Lange, Leslie, Palmer, Nicholette D, Almeida, Marcio, Peralta, Juan M, Aslibekyan, Stella, Baldridge, Abigail S, Bertoni, Alain G, Bielak, Lawrence F, Chen, Chung-Shiuan, Chen, Yii-Der Ida, Choi, Won Jung, Goodarzi, Mark O, Floyd, James S, Irvin, Marguerite R, Kalyani, Rita R, Kelly, Tanika N, Lee, Seonwook, Liu, Ching-Ti, Loesch, Douglas, Manson, JoAnn E, Minster, Ryan L, Naseri, Take, Pankow, James S, Rasmussen-Torvik, Laura J, Reiner, Alexander P, Reupena, Muagututi’a Sefuiva, Selvin, Elizabeth, Smith, Jennifer A, Weeks, Daniel E, Xu, Huichun, Yao, Jie, Zhao, Wei, Parker, Stephen, Alonso, Alvaro, Arnett, Donna K, Blangero, John, Boerwinkle, Eric, Correa, Adolfo, Cupples, L Adrienne, Curran, Joanne E, Duggirala, Ravindranath, He, Jiang, Heckbert, Susan R, Kardia, Sharon LR, Kim, Ryan W, Kooperberg, Charles, Liu, Simin, Mathias, Rasika A, McGarvey, Stephen T, Mitchell, Braxton D, Morrison, Alanna C, Peyser, Patricia A, Psaty, Bruce M, Redline, Susan, Shuldiner, Alan R, Taylor, Kent D, Vasan, Ramachandran S, Viaud-Martinez, Karine A, Florez, Jose C, Wilson, James G, Sladek, Robert, Rich, Stephen S, Rotter, Jerome I, Lin, Xihong, Dupuis, Josée, Meigs, James B, Wessel, Jennifer, and Manning, Alisa K
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Biological Sciences ,Genetics ,Human Genome ,Biotechnology ,Diabetes ,2.1 Biological and endogenous factors ,Aetiology ,Metabolic and endocrine ,Good Health and Well Being ,Diabetes Mellitus ,Type 2 ,Fasting ,Glucose ,Humans ,Insulin ,National Heart ,Lung ,and Blood Institute (U.S.) ,Nerve Tissue Proteins ,Polymorphism ,Single Nucleotide ,Precision Medicine ,Receptors ,Immunologic ,United States ,Biological sciences ,Biomedical and clinical sciences - Abstract
The genetic determinants of fasting glucose (FG) and fasting insulin (FI) have been studied mostly through genome arrays, resulting in over 100 associated variants. We extended this work with high-coverage whole genome sequencing analyses from fifteen cohorts in NHLBI's Trans-Omics for Precision Medicine (TOPMed) program. Over 23,000 non-diabetic individuals from five race-ethnicities/populations (African, Asian, European, Hispanic and Samoan) were included. Eight variants were significantly associated with FG or FI across previously identified regions MTNR1B, G6PC2, GCK, GCKR and FOXA2. We additionally characterize suggestive associations with FG or FI near previously identified SLC30A8, TCF7L2, and ADCY5 regions as well as APOB, PTPRT, and ROBO1. Functional annotation resources including the Diabetes Epigenome Atlas were compiled for each signal (chromatin states, annotation principal components, and others) to elucidate variant-to-function hypotheses. We provide a catalog of nucleotide-resolution genomic variation spanning intergenic and intronic regions creating a foundation for future sequencing-based investigations of glycemic traits.
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- 2022
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