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Effects of diabetes definition on global surveillance of diabetes prevalence and diagnosis: a pooled analysis of 96 population-based studies with 331288 participants

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Title: Effects of diabetes definition on global surveillance of diabetes prevalence and diagnosis: a pooled analysis of 96 population-based studies with 331288 participants
Authors: Danaei, Goodarz, Fahimi, Saman, Lu, Yuan, Zhou, Bin, Hajifathalian, Kaveh, Di Cesare, Mariachiara, Lo, Wei-Cheng, Reis-Santos, Barbara, Cowan, Melanie J., Shaw, Jonathan E., Bentham, James, Lin, John K., Bixby, Honor, Magliano, Dianna, Bovet, Pascal, Miranda, J. Jaime, Khang, Young-Ho, Stevens, Gretchen A., Riley, Leanne M., Ali, Mohammed K., Ezzati, Majid, Abdeen, Ziad A., Kadir, Khalid Abdul, Abu-Rmeileh, Niveen M., Acosta-Cazares, Benjamin, Aekplakorn, Wichai, Aguilar-Salinas, Carlos A., Ahmadvand, Alireza, Al Nsour, Mohannad, Alkerwi, Ala'a, Amouyel, Philippe, Andersen, Lars Bo, Anderssen, Sigmund A., Andrade, Dolores S., Anjana, Ranjit Mohan, Aounallah-Skhiri, Hajer, Aris, Tahir, Arlappa, Nimmathota, Arveiler, Dominique, Assah, Felix K., Avdicova, Maria, Balakrishna, Nagalla, Bandosz, Piotr, Barbagallo, Carlo M., Barcelo, Alberto, Batieha, Anwar M., Baur, Louise A., Ben Romdhane, Habiba, Bernabe-Ortiz, Antonio, Bhargava, Santosh K., Bi, Yufang, Bjerregaard, Peter, Bjorkelund, Cecilia, Blake, Margaret, Blokstra, Anneke, Bo, Simona, Boehm, Bernhard O., Boissonnet, Carlos P., Brajkovich, Imperia, Breckenkamp, Juergen, Brewster, Lizzy M., Brian, Garry R., Bruno, Graziella, Bugge, Anna, de Leon, Antonio Cabrera, Can, Gunay, Candido, Ana Paula C., Capuano, Vincenzo, Carvalho, Maria J., Casanueva, Felipe F., Caserta, Carmelo A., Castetbon, Katia, Chamukuttan, Snehalatha, Chaturvedi, Nishi, Chen, Chien-Jen, Chen, Fangfang, Chen, Shuohua, Cheng, Ching-Yu, Chetrit, Angela, Chiou, Shu-Ti, Cho, Yumi, Chudek, Jerzy, Cifkova, Renata, Claessens, Frank, Concin, Hans, Cooper, Cyrus, Cooper, Rachel, Costanzo, Simona, Cottel, Dominique, Cowell, Chris, Crujeiras, Ana B., D'Arrigo, Graziella, Dallongeville, Jean, Dankner, Rachel, Dauchet, Luc, de Gaetano, Giovanni, De Henauw, Stefaan, Deepa, Mohan, Dehghan, Abbas, Dhana, Klodian, Di Castelnuovo, Augusto F., Djalalinia, Shirin, Doua, Kouamelan, Drygas, Wojciech, Du, Yong, Egbagbe, Eruke E., Eggertsen, Robert, El Ati, Jalila, Elosua, Roberto, Erasmus, Rajiv T., Erem, Cihangir, Ergor, Gul, Eriksen, Louise, la Penaa, Jorge Escobedo-De, Fall, Caroline H., Farzadfar, Farshad, Felix-Redondo, Francisco J., Ferguson, Trevor S., Fernandez-Berges, Daniel, Ferrari, Marika, Ferreccio, Catterina, Finn, Joseph D., Foger, Bernhard, Foo, Leng Huat, Fouad, Heba M., Francis, Damian K., Franco, Maria do Carmo, Franco, Oscar H., Frontera, Guillermo, Furusawa, Takuro, Gaciong, Zbigniew, Galbarczyk, Andrzej, Garnett, Sarah P., Gaspoz, Jean-Michel, Gasull, Magda, Gates, Louise, Geleijnse, Johanna M., Ghasemain, Anoosheh, Giampaoli, Simona, Gianfagna, Francesco, Giovannelli, Jonathan, Gross, Marcela Gonzalez, Rivas, Juan P. Gonzalez, Gorbea, Mariano Bonet, Gottrand, Frederic, Grant, Janet F., Grodzicki, Tomasz, Grontved, Anders, Gruden, Grabriella, Gu, Dongfeng, Guan, Ong Peng, Guerrero, Ramiro, Guessous, Idris, Guimaraes, Andre L., Gutierrez, Laura, Hardy, Rebecca, Kumar, Rachakulla Hari, He, Jiang, Heidemann, Christin, Hihtaniemi, Ilpo Tapani, Ho, Sai Yin, Ho, Suzanne C., Hofman, Albert, Russo, Andrea R. V., Hormiga, Claudia M., Horta, Bernardo L., Houti, Leila, Hussieni, Abdullatif S., Huybrechts, Inge, Hwalla, Nahla, Iacoviello, Licia, Iannone, Anna G., Ibrahim, Mohsen M., Ikeda, Nayu, Ikram, M. Arfan, Irazola, Vilma E., Islam, Muhammad, Iwasaki, Masanori, Jacobs, Jeremy M., Jafar, Tazeen, Jasienska, Grazyna, Jiang, Chao Qiang, Jonas, Jost B., Joshi, Pradeep, Kafatos, Anthony, Kalter-Leibovici, Ofra, Kasaeian, Amir, Katz, Joanne, Kaur, Prabhdeep, Kavousi, Maryam, Kelishadi, Roya, Kengne, Andre P., Kersting, Mathilde, Khader, Yousef Saleh, Kiechl, Stefan, Kim, Jeongseon, Kiyohara, Yutaka, Kolsteren, Patrick, Korrovits, Paul, Koskinen, Seppo, Kratzer, Wolfgang, Kromhout, Daan, Kula, Krzysztof, Kurjata, Pawel, Kyobutungi, Catherine, Lachat, Carl, Laid, Youcef, Lam, Tai Hing, Landrove, Orlando, Lanska, Vera, Lappas, Georg, Laxmaiah, Avula, Leclercq, Catherine, Lee, Jeannette, Lee, Jeonghee, Lehtimaki, Terho, Lekhraj, Rampal, Leon-Munoz, Luz M., Li, Yanping, Lim, Wei-Yen, Lima-Costa, M. Fernanda, Lin, Hsien-Ho, Lin, Xu, Lissner, Lauren, Lorbeer, Roberto, Lozano, Jose Eugenio, Lundqvist, Annamari, Lytsy, Per, Ma, Guansheng, Machado-Coelho, George L. L., Machi, Suka, Maggi, Stefania, Makdisse, Marcia, Rao, Kodavanti Mallikharjuna, Manios, Yannis, Manzato, Enzo, Margozzini, Paula, Marques-Vidal, Pedro, Martorell, Reynaldo, Masoodi, Shariq R., Matsha, Tandi E., Mbanya, Jean Claude N., McFarlane, Shelly R., McGarvey, Stephen T., McLachlan, Stela, McNulty, Breige A., Mediene-Benchekor, Sounnia, Meirhaeghe, Aline, Menezes, Ana Maria B., Merat, Shahin, Meshram, Indrapal I., Mi, Jie, Miquel, Juan Francisco, Mohamed, Mostafa K., Mohammad, Kazem, Mohan, Viswanathan, Yusoff, Muhammad Fadhli Mohd, Moller, Niels C., Molnar, Denes, Mondo, Charles K., Moreno, Luis A., Morgan, Karen, Moschonis, George, Mossakowska, Malgorzata, Mostafa, Aya, Mota, Jorge, Muiesan, Maria L., Muller-Nurasyid, Martina, Mursu, Jaakko, Nagel, Gabriele, Namesna, Jana, Nang, Ei Ei K., Nangia, Vinay B., Navarrete-Munoz, Eva Maria, Ndiaye, Ndeye Coumba, Nervi, Flavio, Nguyen, Nguyen D., Nieto-Martinez, Ramfi S. E., Ning, Guang, Ninomiya, Toshiharu, Noale, Marianna, Noto, Davide, Ochoa-Aviles, Angelica M., Oh, Kyungwon, Onat, Altan, Osmond, Clive, Otero, Johanna A., Palmieri, Luigi, Panda-Jonas, Songhomitra, Panza, Francesco, Parsaeian, Mahboubeh, Peixoto, Sergio Viana, Pereira, Alexandre C., Peters, Annette, Peykari, Niloofar, Pilav, Aida, Pitakaka, Freda, Piwonska, Aleksandra, Piwonski, Jerzy, Plans-Rubio, Pedro, Porta, Miquel, Portegies, Marileen L. P., Poustchi, Hossein, Pradeepa, Rajendra, Price, Jacqueline F., Punab, Margus, Qasrawi, Radwan F., Qorbani, Mostafa, Raitakari, Olli, Rao, Sudha Ramachandra, Ramachandran, Ambady, Ramos, Rafel, Rampal, Sanjay, Rathmann, Wolfgang, Redon, Josep, Reganit, Paul Ferdinand M., Rigo, Fernando, Robinson, Sian M., Robitaille, Cynthia, Rodriguez, Laura A., Rodriguez-Artalejo, Fernando, Rodriguez-Perez, Maria del Cristo, Rojas-Martinez, Rosalba, Romaguera, Dora, Rosengren, Annika, Rubinstein, Adolfo, Rui, Ornelas, Ruiz-Betancourt, Blanca Sandra, Rutkowski, Marcin, Sabanayagam, Charumathi, Sachdev, Harshpal S., Saidi, Olfa, Sakarya, Sibel, Salanave, Benoit, Salonen, Jukka T., Salvetti, Massimo, Sanchez-Abanto, Jose, Nunes, Renata, Santos, Rute, Sardinha, Luis B., Scazufca, Marcia, Schargrodsky, Herman, Scheidt-Nave, Christa, Shibuya, Kenji, Shin, Youchan, Shiri, Rahman, Siantar, Rosalynn, Sibai, Abla M., Simon, Mary, Simons, Judith, Simons, Leon A., Sjostrom, Michael, Slowikowska-Hilczer, Jolanta, Slusarczyk, Przemyslaw, Smeeth, Liam, Snijder, Marieke B., Solfrizzi, Vincenzo, Sonestedt, Emily, Soumare, Aicha, Staessen, Jan A., Steene-Johannessen, Jostein, Stehle, Peter, Stein, Aryeh D., Stessman, Jochanan, Stockl, Doris, Stokwiszewski, Jakub, Strufaldi, Maria Wany, Sun, Chien-An, Sundstrom, Johan, Suriyawongpaisal, Paibul, Sy, Rody G., Tai, E. Shyong, Tarawneh, Mohammed, Tarqui-Mamani, Carolina B., Thijs, Lutgarde, Tolstrup, Janne S., Topbas, Murat, Torrent, Maties, Traissac, Pierre, Trinh, Oanh T. H., Tulloch-Reid, Marshall K., Tuomainen, Tomi-Pekka, Turley, Maria L., Tzourio, Christophe, Ueda, Peter, Ukoli, Flora M., Ulmer, Hanno, Valdivia, Gonzalo, Van Valkengoed, Irene G. M., Vanderschueren, Dirk, Vanuzzo, Diego, Vega, Tomas, Velasquez-Melendez, Gustavo, Veronesi, Giovanni, Verschuren, Monique, Vioque, Jesus, Virtanen, Jyrki, Visvikis-Siest, Sophie, Viswanathan, Bharathi, Vollenweider, Peter, Voutilainen, Sari, Wade, Alisha N., Wagner, Aline, Walton, Janette, Mohamud, Wan Nazaimoon Wan, Wang, Ming-Dong, Wang, Ya Xing, Wannamethee, S. Goya, Weerasekera, Deepa, Whincup, Peter H., Widhalm, Kurt, Wiecek, Andrzej, Wilks, Rainford J., Willeit, Johann, Wojtyniak, Bogdan, Wong, Tien Yin, Woo, Jean, Woodward, Mark, Giwercman, Aleksander, Wu, Frederick C., Wu, Shou Ling, Xu, Haiquan, Yang, Xiaoguang, Ye, Xingwang, Yoshihara, Akihiro, Younger-Coleman, Novie O., Zambon, Sabina, Zargar, Abdul Hamid, Zdrojewski, Tomasz, Zhao, Wenhua, Zheng, Yingfeng
Contributors: Lund University, Faculty of Medicine, Department of Clinical Sciences, Malmö, Diabetes - Cardiovascular Disease, Lunds universitet, Medicinska fakulteten, Institutionen för kliniska vetenskaper, Malmö, Diabetes - kardiovaskulär sjukdom, Originator, Lund University, Faculty of Medicine, Department of Translational Medicine, Reproductive medicine, Malmö, Lunds universitet, Medicinska fakulteten, Institutionen för translationell medicin, Reproduktionsmedicin, Malmö, Originator, Lund University, Profile areas and other strong research environments, Strategic research areas (SRA), EXODIAB: Excellence of Diabetes Research in Sweden, Lunds universitet, Profilområden och andra starka forskningsmiljöer, Strategiska forskningsområden (SFO), EXODIAB: Excellence of Diabetes Research in Sweden, Originator, Lund University, Profile areas and other strong research environments, Strategic research areas (SRA), EpiHealth: Epidemiology for Health, Lunds universitet, Profilområden och andra starka forskningsmiljöer, Strategiska forskningsområden (SFO), EpiHealth: Epidemiology for Health, Originator
Source: The Lancet Diabetes & Endocrinology. 3(8):624-637
Subject Terms: Medical and Health Sciences, Clinical Medicine, Endocrinology and Diabetes, Medicin och hälsovetenskap, Klinisk medicin, Endokrinologi och diabetes
Description: Background Diabetes has been defined on the basis of different biomarkers, including fasting plasma glucose (FPG), 2-h plasma glucose in an oral glucose tolerance test (2hOGTT), and HbA(1c). We assessed the effect of different diagnostic definitions on both the population prevalence of diabetes and the classification of previously undiagnosed individuals as having diabetes versus not having diabetes in a pooled analysis of data from population-based health examination surveys in different regions. Methods We used data from 96 population-based health examination surveys that had measured at least two of the biomarkers used for defining diabetes. Diabetes was defined using HbA(1c) (HbA(1c) >= 6 . 5% or history of diabetes diagnosis or using insulin or oral hypoglycaemic drugs) compared with either FPG only or FPG-or-2hOGTT definitions (FPG >= 7 . 0 mmol/L or 2hOGTT >= 11 . 1 mmol/L or history of diabetes or using insulin or oral hypoglycaemic drugs). We calculated diabetes prevalence, taking into account complex survey design and survey sample weights. We compared the prevalences of diabetes using different definitions graphically and by regression analyses. We calculated sensitivity and specificity of diabetes diagnosis based on HbA1c compared with diagnosis based on glucose among previously undiagnosed individuals (ie, excluding those with history of diabetes or using insulin or oral hypoglycaemic drugs). We calculated sensitivity and specificity in each survey, and then pooled results using a random-effects model. We assessed the sources of heterogeneity of sensitivity by meta-regressions for study characteristics selected a priori. Findings Population prevalence of diabetes based on FPG- or-2hOGTT was correlated with prevalence based on FPG alone (r= 0 . 98), but was higher by 2-6 percentage points at different prevalence levels. Prevalence based on HbA(1c) was lower than prevalence based on FPG in 42 . 8% of age-sex-survey groups and higher in another 41 . 6%; in the other 15 . 6%, the two definitions providedsimilar prevalence estimates. The variation across studies in the relation between glucose-based and HbA(1c)-based prevalences was partly related to participants' age, followed by natural logarithm of per person gross domestic product, the year of survey, mean BMI, and whether the survey population was national, subnational, or from specific communities. Diabetes defined as HbA(1c) 6 . 5% or more had a pooled sensitivity of 52 . 8% (95% CI 51 . 3-54 . 3%) and a pooled specificity of 99 . 74% (99 . 71-99 . 78%) compared with FPG 7 . 0 mmol/L or more for diagnosing previously undiagnosed participants; sensitivity compared with diabetes defined based on FPG-or-2hOGTT was 30 . 5% (28 . 7-32 . 3%). None of the preselected study-level characteristics explained the heterogeneity in the sensitivity of HbA(1c) versus FPG. Interpretation Different biomarkers and definitions for diabetes can provide different estimates of population prevalence of diabetes, and differentially identify people without previous diagnosis as having diabetes. Using an HbA(1c)-based definition alone in health surveys will not identify a substantial proportion of previously undiagnosed people who would be considered as having diabetes using a glucose-based test.
Access URL: https://doi.org/10.1016/S2213-8587(15)00129-1
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  Data: Effects of diabetes definition on global surveillance of diabetes prevalence and diagnosis: a pooled analysis of 96 population-based studies with 331288 participants
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– Name: Author
  Label: Contributors
  Group: Au
  Data: Lund University, Faculty of Medicine, Department of Clinical Sciences, Malmö, Diabetes - Cardiovascular Disease, Lunds universitet, Medicinska fakulteten, Institutionen för kliniska vetenskaper, Malmö, Diabetes - kardiovaskulär sjukdom, Originator<br />Lund University, Faculty of Medicine, Department of Translational Medicine, Reproductive medicine, Malmö, Lunds universitet, Medicinska fakulteten, Institutionen för translationell medicin, Reproduktionsmedicin, Malmö, Originator<br />Lund University, Profile areas and other strong research environments, Strategic research areas (SRA), EXODIAB: Excellence of Diabetes Research in Sweden, Lunds universitet, Profilområden och andra starka forskningsmiljöer, Strategiska forskningsområden (SFO), EXODIAB: Excellence of Diabetes Research in Sweden, Originator<br />Lund University, Profile areas and other strong research environments, Strategic research areas (SRA), EpiHealth: Epidemiology for Health, Lunds universitet, Profilområden och andra starka forskningsmiljöer, Strategiska forskningsområden (SFO), EpiHealth: Epidemiology for Health, Originator
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <i>The Lancet Diabetes & Endocrinology</i>. 3(8):624-637
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Medical+and+Health+Sciences%22">Medical and Health Sciences</searchLink><br /><searchLink fieldCode="DE" term="%22Clinical+Medicine%22">Clinical Medicine</searchLink><br /><searchLink fieldCode="DE" term="%22Endocrinology+and+Diabetes%22">Endocrinology and Diabetes</searchLink><br /><searchLink fieldCode="DE" term="%22Medicin+och+hälsovetenskap%22">Medicin och hälsovetenskap</searchLink><br /><searchLink fieldCode="DE" term="%22Klinisk+medicin%22">Klinisk medicin</searchLink><br /><searchLink fieldCode="DE" term="%22Endokrinologi+och+diabetes%22">Endokrinologi och diabetes</searchLink>
– Name: Abstract
  Label: Description
  Group: Ab
  Data: Background Diabetes has been defined on the basis of different biomarkers, including fasting plasma glucose (FPG), 2-h plasma glucose in an oral glucose tolerance test (2hOGTT), and HbA(1c). We assessed the effect of different diagnostic definitions on both the population prevalence of diabetes and the classification of previously undiagnosed individuals as having diabetes versus not having diabetes in a pooled analysis of data from population-based health examination surveys in different regions. Methods We used data from 96 population-based health examination surveys that had measured at least two of the biomarkers used for defining diabetes. Diabetes was defined using HbA(1c) (HbA(1c) >= 6 . 5% or history of diabetes diagnosis or using insulin or oral hypoglycaemic drugs) compared with either FPG only or FPG-or-2hOGTT definitions (FPG >= 7 . 0 mmol/L or 2hOGTT >= 11 . 1 mmol/L or history of diabetes or using insulin or oral hypoglycaemic drugs). We calculated diabetes prevalence, taking into account complex survey design and survey sample weights. We compared the prevalences of diabetes using different definitions graphically and by regression analyses. We calculated sensitivity and specificity of diabetes diagnosis based on HbA1c compared with diagnosis based on glucose among previously undiagnosed individuals (ie, excluding those with history of diabetes or using insulin or oral hypoglycaemic drugs). We calculated sensitivity and specificity in each survey, and then pooled results using a random-effects model. We assessed the sources of heterogeneity of sensitivity by meta-regressions for study characteristics selected a priori. Findings Population prevalence of diabetes based on FPG- or-2hOGTT was correlated with prevalence based on FPG alone (r= 0 . 98), but was higher by 2-6 percentage points at different prevalence levels. Prevalence based on HbA(1c) was lower than prevalence based on FPG in 42 . 8% of age-sex-survey groups and higher in another 41 . 6%; in the other 15 . 6%, the two definitions providedsimilar prevalence estimates. The variation across studies in the relation between glucose-based and HbA(1c)-based prevalences was partly related to participants' age, followed by natural logarithm of per person gross domestic product, the year of survey, mean BMI, and whether the survey population was national, subnational, or from specific communities. Diabetes defined as HbA(1c) 6 . 5% or more had a pooled sensitivity of 52 . 8% (95% CI 51 . 3-54 . 3%) and a pooled specificity of 99 . 74% (99 . 71-99 . 78%) compared with FPG 7 . 0 mmol/L or more for diagnosing previously undiagnosed participants; sensitivity compared with diabetes defined based on FPG-or-2hOGTT was 30 . 5% (28 . 7-32 . 3%). None of the preselected study-level characteristics explained the heterogeneity in the sensitivity of HbA(1c) versus FPG. Interpretation Different biomarkers and definitions for diabetes can provide different estimates of population prevalence of diabetes, and differentially identify people without previous diagnosis as having diabetes. Using an HbA(1c)-based definition alone in health surveys will not identify a substantial proportion of previously undiagnosed people who would be considered as having diabetes using a glucose-based test.
– Name: URL
  Label: Access URL
  Group: URL
  Data: <link linkTarget="URL" linkTerm="https://doi.org/10.1016/S2213-8587(15)00129-1" linkWindow="_blank">https://doi.org/10.1016/S2213-8587(15)00129-1</link>
PLink https://erproxy.cvtisr.sk/sfx/access?url=https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsswe&AN=edsswe.oai.portal.research.lu.se.publications.bd358363.f845.4b1f.a95c.f2a9cd7d3906
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      – Type: doi
        Value: 10.1016/S2213-8587(15)00129-1
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 14
        StartPage: 624
    Subjects:
      – SubjectFull: Medical and Health Sciences
        Type: general
      – SubjectFull: Clinical Medicine
        Type: general
      – SubjectFull: Endocrinology and Diabetes
        Type: general
      – SubjectFull: Medicin och hälsovetenskap
        Type: general
      – SubjectFull: Klinisk medicin
        Type: general
      – SubjectFull: Endokrinologi och diabetes
        Type: general
    Titles:
      – TitleFull: Effects of diabetes definition on global surveillance of diabetes prevalence and diagnosis: a pooled analysis of 96 population-based studies with 331288 participants
        Type: main
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      – PersonEntity:
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      – PersonEntity:
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      – PersonEntity:
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      – PersonEntity:
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      – PersonEntity:
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      – PersonEntity:
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      – PersonEntity:
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      – PersonEntity:
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      – PersonEntity:
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      – PersonEntity:
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      – PersonEntity:
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      – PersonEntity:
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      – PersonEntity:
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      – PersonEntity:
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      – PersonEntity:
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      – PersonEntity:
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      – PersonEntity:
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      – PersonEntity:
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      – PersonEntity:
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      – PersonEntity:
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      – PersonEntity:
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      – PersonEntity:
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      – PersonEntity:
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      – PersonEntity:
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      – PersonEntity:
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      – PersonEntity:
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      – PersonEntity:
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      – PersonEntity:
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      – PersonEntity:
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      – PersonEntity:
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      – PersonEntity:
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      – PersonEntity:
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      – PersonEntity:
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      – PersonEntity:
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      – PersonEntity:
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      – PersonEntity:
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      – PersonEntity:
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      – PersonEntity:
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      – PersonEntity:
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      – PersonEntity:
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      – PersonEntity:
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      – PersonEntity:
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      – PersonEntity:
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      – PersonEntity:
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      – PersonEntity:
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      – PersonEntity:
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      – PersonEntity:
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      – PersonEntity:
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      – PersonEntity:
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      – PersonEntity:
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      – PersonEntity:
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      – PersonEntity:
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      – PersonEntity:
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      – PersonEntity:
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      – PersonEntity:
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      – PersonEntity:
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      – PersonEntity:
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      – PersonEntity:
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      – PersonEntity:
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      – PersonEntity:
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      – PersonEntity:
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      – PersonEntity:
          Name:
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      – PersonEntity:
          Name:
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      – PersonEntity:
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      – PersonEntity:
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      – PersonEntity:
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      – PersonEntity:
          Name:
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      – PersonEntity:
          Name:
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      – PersonEntity:
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      – PersonEntity:
          Name:
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      – PersonEntity:
          Name:
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      – PersonEntity:
          Name:
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      – PersonEntity:
          Name:
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      – PersonEntity:
          Name:
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      – PersonEntity:
          Name:
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      – PersonEntity:
          Name:
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      – PersonEntity:
          Name:
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      – PersonEntity:
          Name:
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      – PersonEntity:
          Name:
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      – PersonEntity:
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      – PersonEntity:
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      – PersonEntity:
          Name:
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      – PersonEntity:
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      – PersonEntity:
          Name:
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      – PersonEntity:
          Name:
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      – PersonEntity:
          Name:
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      – PersonEntity:
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            – Type: volume
              Value: 3
            – Type: issue
              Value: 8
          Titles:
            – TitleFull: The Lancet Diabetes & Endocrinology
              Type: main
ResultId 1