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217 results on '"LARSSON, HELENA ELDING"'

1. Looking back at the TEDDY study: lessons and future directions

Author

Lernmark, Åke, Agardh, Daniel, Akolkar, Beena, Gesualdo, Patricia, Hagopian, William A., Haller, Michael J., Hyöty, Heikki, Johnson, Suzanne Bennett, Larsson, Helena Elding, Liu, Edwin, Lynch, Kristian F., McKinney, Eoin F., McIndoe, Richard, Melin, Jessica, Norris, Jill M., Rewers, Marian, Rich, Stephen S., Toppari, Jorma, Triplett, Eric, Vehik, Kendra, Virtanen, Suvi M., Ziegler, Anette-G., Schatz, Desmond A., and Krischer, Jeffrey

Published
2024
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2. Consensus guidance for monitoring individuals with islet autoantibody-positive pre-stage 3 type 1 diabetes

Author

Phillip, Moshe, Achenbach, Peter, Addala, Ananta, Albanese-O’Neill, Anastasia, Battelino, Tadej, Bell, Kirstine J., Besser, Rachel E. J., Bonifacio, Ezio, Colhoun, Helen M., Couper, Jennifer J., Craig, Maria E., Danne, Thomas, de Beaufort, Carine, Dovc, Klemen, Driscoll, Kimberly A., Dutta, Sanjoy, Ebekozien, Osagie, Larsson, Helena Elding, Feiten, Daniel J., Frohnert, Brigitte I., Gabbay, Robert A., Gallagher, Mary P., Greenbaum, Carla J., Griffin, Kurt J., Hagopian, William, Haller, Michael J., Hendrieckx, Christel, Hendriks, Emile, Holt, Richard I. G., Hughes, Lucille, Ismail, Heba M., Jacobsen, Laura M., Johnson, Suzanne B., Kolb, Leslie E., Kordonouri, Olga, Lange, Karin, Lash, Robert W., Lernmark, Åke, Libman, Ingrid, Lundgren, Markus, Maahs, David M., Marcovecchio, M. Loredana, Mathieu, Chantal, Miller, Kellee M., O’Donnell, Holly K., Oron, Tal, Patil, Shivajirao P., Pop-Busui, Rodica, Rewers, Marian J., Rich, Stephen S., Schatz, Desmond A., Schulman-Rosenbaum, Rifka, Simmons, Kimber M., Sims, Emily K., Skyler, Jay S., Smith, Laura B., Speake, Cate, Steck, Andrea K., Thomas, Nicholas P. B., Tonyushkina, Ksenia N., Veijola, Riitta, Wentworth, John M., Wherrett, Diane K., Wood, Jamie R., Ziegler, Anette-Gabriele, and DiMeglio, Linda A.

Published
2024
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3. Correction to: Consensus guidance for monitoring individuals with islet autoantibody‑positive pre‑stage 3 type 1 diabetes

Author

Phillip, Moshe, Achenbach, Peter, Addala, Ananta, Albanese‑O’Neill, Anastasia, Battelino, Tadej, Bell, Kirstine J., Besser, Rachel E. J., Bonifacio, Ezio, Colhoun, Helen M., Couper, Jennifer J., Craig, Maria E., Danne, Thomas, de Beaufort, Carine, Dovc, Klemen, Driscoll, Kimberly A., Dutta, Sanjoy, Ebekozien, Osagie, Larsson, Helena Elding, Feiten, Daniel J., Frohnert, Brigitte I., Gabbay, Robert A., Gallagher, Mary P., Greenbaum, Carla J., Griffin, Kurt J., Hagopian, William, Haller, Michael J., Hendrieckx, Christel, Hendriks, Emile, Holt, Richard I. G., Hughes, Lucille, Ismail, Heba M., Jacobsen, Laura M., Johnson, Suzanne B., Kolb, Leslie E., Kordonouri, Olga, Lange, Karin, Lash, Robert W., Lernmark, Åke, Libman, Ingrid, Lundgren, Markus, Maahs, David M., Marcovecchio, M. Loredana, Mathieu, Chantal, Miller, Kellee M., O’Donnell, Holly K., Oron, Tal, Patil, Shivajirao P., Pop‑Busui, Rodica, Rewers, Marian J., Rich, Stephen S., Schatz, Desmond A., Schulman‑Rosenbaum, Rifka, Simmons, Kimber M., Sims, Emily K., Skyler, Jay S., Smith, Laura B., Speake, Cate, Steck, Andrea K., Thomas, Nicholas P. B., Tonyushkina, Ksenia N., Veijola, Riitta, Wentworth, John M., Wherrett, Diane K., Wood, Jamie R., Ziegler, Anette‑Gabriele, and DiMeglio, Linda A.

Published
2024
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4. Infection episodes and islet autoantibodies in children at increased risk for type 1 diabetes before and during the COVID-19 pandemic

Author

Zeller, Ivo, Weiss, Andreas, Arnolds, Stefanie, Schütte-Borkovec, Katharina, Arabi, Sari, von dem Berge, Thekla, Casteels, Kristina, Hommel, Angela, Kordonouri, Olga, Larsson, Helena Elding, Lundgren, Markus, Rochtus, Anne, Snape, Matthew D., Szypowka, Agnieszka, Vatish, Manu, Winkler, Christiane, Bonifacio, Ezio, and Ziegler, Anette-Gabriele

Published
2024
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6. CVOT Summit Report 2023: new cardiovascular, kidney, and metabolic outcomes

Author

Schnell, Oliver, Barnard-Kelly, Katharine, Battelino, Tadej, Ceriello, Antonio, Larsson, Helena Elding, Fernández-Fernández, Beatriz, Forst, Thomas, Frias, Juan-Pablo, Gavin, III, James R., Giorgino, Francesco, Groop, Per-Henrik, Heerspink, Hiddo J. L., Herzig, Stephan, Hummel, Michael, Huntley, George, Ibrahim, Mahmoud, Itzhak, Baruch, Jacob, Stephan, Ji, Linong, Kosiborod, Mikhail, Lalic, Nebosja, Macieira, Sofia, Malik, Rayaz A., Mankovsky, Boris, Marx, Nikolaus, Mathieu, Chantal, Müller, Timo D., Ray, Kausik, Rodbard, Helena W., Rossing, Peter, Rydén, Lars, Schumm-Draeger, Petra-Maria, Schwarz, Peter, Škrha, Jan, Snoek, Frank, Tacke, Frank, Taylor, Bruce, Jeppesen, Britta Tendal, Tesfaye, Solomon, Topsever, Pinar, Vilsbøll, Tina, Yu, Xuefeng, and Standl, Eberhard

Published
2024
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7. Childhood screening for type 1 diabetes comparing automated multiplex Antibody Detection by Agglutination-PCR (ADAP) with single plex islet autoantibody radiobinding assays

Author

Persson, Martina, Forsander, Gun, Ludvigsson, Johnny, Samuelsson, Ulf, Marcus, Claude, Lind, Alexander, Freyhult, Eva, de Jesus Cortez, Felipe, Ramelius, Anita, Bennet, Rasmus, Robinson, Peter V., Seftel, David, Gebhart, David, Tandel, Devangkumar, Maziarz, Marlena, Larsson, Helena Elding, Lundgren, Markus, Carlsson, Annelie, Nilsson, Anna-Lena, Fex, Malin, Törn, Carina, Agardh, Daniel, Tsai, Cheng-ting, and Lernmark, Åke

Published
2024
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9. Childhood screening for type 1 diabetes comparing automated multiplex Antibody Detection by Agglutination-PCR (ADAP) with single plex islet autoantibody radiobinding assays

Author

Lind, Alexander, primary, Freyhult, Eva, additional, de Jesus Cortez, Felipe, additional, Ramelius, Anita, additional, Bennet, Rasmus, additional, Robinson, Peter V., additional, Seftel, David, additional, Gebhart, David, additional, Tandel, Devangkumar, additional, Maziarz, Marlena, additional, Larsson, Helena Elding, additional, Lundgren, Markus, additional, Carlsson, Annelie, additional, Nilsson, Anna-Lena, additional, Fex, Malin, additional, Törn, Carina, additional, Agardh, Daniel, additional, Tsai, Cheng-ting, additional, Lernmark, Åke, additional, Persson, Martina, additional, Forsander, Gun, additional, Ludvigsson, Johnny, additional, Samuelsson, Ulf, additional, and Marcus, Claude, additional

Published
2024
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11. Assisting the implementation of screening for type 1 diabetes by using artificial intelligence on publicly available data

Author

Teixeira, Pedro F., Battelino, Tadej, Carlsson, Anneli, Gudbjornsdottir, Soffia, Hannelius, Ulf, von Herrath, Matthias, Knip, Mikael, Korsgren, Olle, Larsson, Helena Elding, Lindqvist, Anton, Ludvigsson, Johnny, Lundgren, Markus, Nowak, Christoph, Pettersson, Paul, Pociot, Flemming, Sundberg, Frida, Akesson, Karin, Lernmark, Ake, Forsander, Gun, Teixeira, Pedro F., Battelino, Tadej, Carlsson, Anneli, Gudbjornsdottir, Soffia, Hannelius, Ulf, von Herrath, Matthias, Knip, Mikael, Korsgren, Olle, Larsson, Helena Elding, Lindqvist, Anton, Ludvigsson, Johnny, Lundgren, Markus, Nowak, Christoph, Pettersson, Paul, Pociot, Flemming, Sundberg, Frida, Akesson, Karin, Lernmark, Ake, and Forsander, Gun

Abstract

The type 1 diabetes community is coalescing around the benefits and advantages of early screening for disease risk. To be accepted by healthcare providers, regulatory authorities and payers, screening programmes need to show that the testing variables allow accurate risk prediction and that individualised risk-informed monitoring plans are established, as well as operational feasibility, cost-effectiveness and acceptance at population level. Artificial intelligence (AI) has the potential to contribute to solving these issues, starting with the identification and stratification of at-risk individuals. ASSET (AI for Sustainable Prevention of Autoimmunity in the Society; www.asset.healthcare) is a public/private consortium that was established to contribute to research around screening for type 1 diabetes and particularly to how AI can drive the implementation of a precision medicine approach to disease prevention. ASSET will additionally focus on issues pertaining to operational implementation of screening. The authors of this article, researchers and clinicians active in the field of type 1 diabetes, met in an open forum to independently debate key issues around screening for type 1 diabetes and to advise ASSET. The potential use of AI in the analysis of longitudinal data from observational cohort studies to inform the design of improved, more individualised screening programmes was also discussed. A key issue was whether AI would allow the research community and industry to capitalise on large publicly available data repositories to design screening programmes that allow the early detection of individuals at high risk and enable clinical evaluation of preventive therapies. Overall, AI has the potential to revolutionise type 1 diabetes screening, in particular to help identify individuals who are at increased risk of disease and aid in the design of appropriate follow-up plans. We hope that this initiative will stimulate further research on this very timely topic.

Published
2024
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12. CVOT Summit Report 2023:new cardiovascular, kidney, and metabolic outcomes

Author

Schnell, Oliver, Barnard-Kelly, Katharine, Battelino, Tadej, Ceriello, Antonio, Larsson, Helena Elding, Fernández-Fernández, Beatriz, Forst, Thomas, Frias, Juan Pablo, Gavin, James R., Giorgino, Francesco, Groop, Per Henrik, Heerspink, Hiddo J.L., Herzig, Stephan, Hummel, Michael, Huntley, George, Ibrahim, Mahmoud, Itzhak, Baruch, Jacob, Stephan, Ji, Linong, Kosiborod, Mikhail, Lalic, Nebosja, Macieira, Sofia, Malik, Rayaz A., Mankovsky, Boris, Marx, Nikolaus, Mathieu, Chantal, Müller, Timo D., Ray, Kausik, Rodbard, Helena W., Rossing, Peter, Rydén, Lars, Schumm-Draeger, Petra Maria, Schwarz, Peter, Škrha, Jan, Snoek, Frank, Tacke, Frank, Taylor, Bruce, Jeppesen, Britta Tendal, Tesfaye, Solomon, Topsever, Pinar, Vilsbøll, Tina, Yu, Xuefeng, Standl, Eberhard, Schnell, Oliver, Barnard-Kelly, Katharine, Battelino, Tadej, Ceriello, Antonio, Larsson, Helena Elding, Fernández-Fernández, Beatriz, Forst, Thomas, Frias, Juan Pablo, Gavin, James R., Giorgino, Francesco, Groop, Per Henrik, Heerspink, Hiddo J.L., Herzig, Stephan, Hummel, Michael, Huntley, George, Ibrahim, Mahmoud, Itzhak, Baruch, Jacob, Stephan, Ji, Linong, Kosiborod, Mikhail, Lalic, Nebosja, Macieira, Sofia, Malik, Rayaz A., Mankovsky, Boris, Marx, Nikolaus, Mathieu, Chantal, Müller, Timo D., Ray, Kausik, Rodbard, Helena W., Rossing, Peter, Rydén, Lars, Schumm-Draeger, Petra Maria, Schwarz, Peter, Škrha, Jan, Snoek, Frank, Tacke, Frank, Taylor, Bruce, Jeppesen, Britta Tendal, Tesfaye, Solomon, Topsever, Pinar, Vilsbøll, Tina, Yu, Xuefeng, and Standl, Eberhard

Abstract

The 9th Cardiovascular Outcome Trial (CVOT) Summit: Congress on Cardiovascular, Kidney, and Metabolic Outcomes was held virtually on November 30-December 1, 2023. This reference congress served as a platform for in-depth discussions and exchange on recently completed outcomes trials including dapagliflozin (DAPA-MI), semaglutide (SELECT and STEP-HFpEF) and bempedoic acid (CLEAR Outcomes), and the advances they represent in reducing the risk of major adverse cardiovascular events (MACE), improving metabolic outcomes, and treating obesity-related heart failure with preserved ejection fraction (HFpEF). A broad audience of endocrinologists, diabetologists, cardiologists, nephrologists and primary care physicians participated in online discussions on guideline updates for the management of cardiovascular disease (CVD) in diabetes, heart failure (HF) and chronic kidney disease (CKD); advances in the management of type 1 diabetes (T1D) and its comorbidities; advances in the management of CKD with SGLT2 inhibitors and non-steroidal mineralocorticoid receptor antagonists (nsMRAs); and advances in the treatment of obesity with GLP-1 and dual GIP/GLP-1 receptor agonists. The association of diabetes and obesity with nonalcoholic steatohepatitis (NASH; metabolic dysfunction-associated steatohepatitis, MASH) and cancer and possible treatments for these complications were also explored. It is generally assumed that treatment of chronic diseases is equally effective for all patients. However, as discussed at the Summit, this assumption may not be true. Therefore, it is important to enroll patients from diverse racial and ethnic groups in clinical trials and to analyze patient-reported outcomes to assess treatment efficacy, and to develop innovative approaches to tailor medications to those who benefit most with minimal side effects. Other keys to a successful management of diabetes and comorbidities, including dementia, entail the use of continuous glucose monitoring (CGM) technolo, The 9th Cardiovascular Outcome Trial (CVOT) Summit: Congress on Cardiovascular, Kidney, and Metabolic Outcomes was held virtually on November 30-December 1, 2023. This reference congress served as a platform for in-depth discussions and exchange on recently completed outcomes trials including dapagliflozin (DAPA-MI), semaglutide (SELECT and STEP-HFpEF) and bempedoic acid (CLEAR Outcomes), and the advances they represent in reducing the risk of major adverse cardiovascular events (MACE), improving metabolic outcomes, and treating obesity-related heart failure with preserved ejection fraction (HFpEF). A broad audience of endocrinologists, diabetologists, cardiologists, nephrologists and primary care physicians participated in online discussions on guideline updates for the management of cardiovascular disease (CVD) in diabetes, heart failure (HF) and chronic kidney disease (CKD); advances in the management of type 1 diabetes (T1D) and its comorbidities; advances in the management of CKD with SGLT2 inhibitors and non-steroidal mineralocorticoid receptor antagonists (nsMRAs); and advances in the treatment of obesity with GLP-1 and dual GIP/GLP-1 receptor agonists. The association of diabetes and obesity with nonalcoholic steatohepatitis (NASH; metabolic dysfunction-associated steatohepatitis, MASH) and cancer and possible treatments for these complications were also explored. It is generally assumed that treatment of chronic diseases is equally effective for all patients. However, as discussed at the Summit, this assumption may not be true. Therefore, it is important to enroll patients from diverse racial and ethnic groups in clinical trials and to analyze patient-reported outcomes to assess treatment efficacy, and to develop innovative approaches to tailor medications to those who benefit most with minimal side effects. Other keys to a successful management of diabetes and comorbidities, including dementia, entail the use of continuous glucose monitoring (CGM) techn

Published
2024

13. The Influence of Pubertal Development on Autoantibody Appearance and Progression to Type 1 Diabetes in the TEDDY Study.

Author

Warncke, Katharina, Tamura, Roy, Schatz, Desmond A, Veijola, Riitta, Steck, Andrea K, Akolkar, Beena, Hagopian, William, Krischer, Jeffrey P, Lernmark, Åke, Rewers, Marian J, Toppari, Jorma, McIndoe, Richard, Ziegler, Anette-G, Vehik, Kendra, Haller, Michael J, and Larsson, Helena Elding

Subjects
TYPE 1 diabetes, AUTOANTIBODIES, WEIGHT gain, BODY mass index, INSULIN resistance
Abstract

Context The 2 peaks of type 1 diabetes incidence occur during early childhood and puberty. Objective We sought to better understand the relationship between puberty, islet autoimmunity, and type 1 diabetes. Methods The relationships between puberty, islet autoimmunity, and progression to type 1 diabetes were investigated prospectively in children followed in The Environmental Determinants of Diabetes in the Young (TEDDY) study. Onset of puberty was determined by subject self-assessment of Tanner stages. Associations between speed of pubertal progression, pubertal growth, weight gain, homeostasis model assessment of insulin resistance (HOMA-IR), islet autoimmunity, and progression to type 1 diabetes were assessed. The influence of individual factors was analyzed using Cox proportional hazard ratios. Results Out of 5677 children who were still in the study at age 8 years, 95% reported at least 1 Tanner Stage score and were included in the study. Children at puberty (Tanner Stage ≥2) had a lower risk (HR 0.65, 95% CI 0.45-0.93; P =.019) for incident autoimmunity than prepubertal children (Tanner Stage 1). An increase of body mass index Z-score was associated with a higher risk (HR 2.88, 95% CI 1.61-5.15; P <.001) of incident insulin autoantibodies. In children with multiple autoantibodies, neither HOMA-IR nor rate of progression to Tanner Stage 4 were associated with progression to type 1 diabetes. Conclusion Rapid weight gain during puberty is associated with development of islet autoimmunity. Puberty itself had no significant influence on the appearance of autoantibodies or type 1 diabetes. Further studies are needed to better understand the underlying mechanisms. [ABSTRACT FROM AUTHOR]

Published
2024
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16. Elevations in blood glucose before and after the appearance of islet autoantibodies in children

Author

Warncke, Katharina, Weiss, Andreas, Achenbach, Peter, von dem Berge, Thekla, Berner, Reinhard, Casteels, Kristina, Groele, Lidia, Hatzikotoulas, Konstantinos, Hommel, Angela, Kordonouri, Olga, Larsson, Helena Elding, Lundgren, Markus, Marcus, Benjamin A., Snape, Matthew D., Szypowska, Agnieszka, Todd, John A., Bonifacio, Ezio, and Ziegler, Anette-G.

Subjects
Islands of Langerhans -- Physiological aspects -- Health aspects, Autoimmunity -- Health aspects, Type 1 diabetes -- Risk factors -- Development and progression, Blood sugar -- Measurement -- Health aspects, Pediatric research, Health care industry
Abstract

The etiology of type 1 diabetes has polygenic and environmental determinants that lead to autoimmune responses against pancreatic [beta] cells and promote [beta] cell death. The autoimmunity is considered silent without metabolic consequences until late preclinical stages,and it remains unknown how early in the disease process the pancreatic [beta] cell is compromised. To address this, we investigated preprandial nonfasting and postprandial blood glucose concentrations and islet autoantibody development in 1,050 children with high genetic risk of type 1 diabetes. Pre- and postprandial blood glucose decreased between 4 and 18 months of age and gradually increased until the final measurements at 3.6 years of age. Determinants of blood glucose trajectories in the first year of life included sex, body mass index, glucose-related genetic risk scores, and the type 1 diabetes-susceptible INS gene. Children who developed islet autoantibodies had early elevations in blood glucose concentrations. A sharp and sustained rise in postprandial blood glucose was observed at around 2 months prior to autoantibody seroconversion, with further increases in postprandial and, subsequently, preprandial values after seroconversion. These findings show heterogeneity in blood glucose control in infancy and early childhood and suggest that islet autoimmunity is concurrent or subsequent to insults on the pancreatic islets., Introduction The supply, uptake, and metabolism of glucose are cornerstones of energy production for cell and organ function and survival. Blood glucose levels are regulated by a multitude of hormones [...]

Published
2022
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18. Meta-Immunological Profiling of Children With Type 1 Diabetes Identifies New Biomarkers to Monitor Disease Progression

Author

Galgani, Mario, Nugnes, Rosa, Bruzzese, Dario, Perna, Francesco, De Rosa, Veronica, Procaccini, Claudio, Mozzillo, Enza, Cilio, Corrado M, Larsson, Helena Elding, Lernmark, Åke, La Cava, Antonio, Franzese, Adriana, and Matarese, Giuseppe

Subjects
Diabetes, Autoimmune Disease, Nutrition, Clinical Research, Pediatric, Detection, screening and diagnosis, 2.1 Biological and endogenous factors, 4.1 Discovery and preclinical testing of markers and technologies, Aetiology, Metabolic and endocrine, Autoimmunity, Biomarkers, Child, Diabetes Mellitus, Type 1, Disease Progression, Flow Cytometry, Humans, Insulin-Secreting Cells, Monitoring, Immunologic, Prognosis, T-Lymphocytes, Medical and Health Sciences, Endocrinology & Metabolism
Abstract

Type 1 diabetes is characterized by autoimmune destruction of pancreatic β-cells in genetically susceptible individuals. Triggers of islet autoimmunity, time course, and the precise mechanisms responsible for the progressive β-cell failure are not completely understood. The recent escalation of obesity in affluent countries has been suggested to contribute to the increased incidence of type 1 diabetes. Understanding the link between metabolism and immune tolerance could lead to the identification of new markers for the monitoring of disease onset and progression. We studied several immune cell subsets and factors with high metabolic impact as markers associated with disease progression in high-risk subjects and type 1 diabetic patients at onset and at 12 and 24 months after diagnosis. A multiple correlation matrix among different parameters was evaluated statistically and assessed visually on two-dimensional graphs. Markers to predict residual β-cell function up to 1 year after diagnosis were identified in multivariate logistic regression models. The meta-immunological profile changed significantly over time in patients, and a specific signature that was associated with worsening disease was identified. A multivariate logistic regression model measuring age, BMI, fasting C-peptide, number of circulating CD3(+)CD16(+)CD56(+) cells, and the percentage of CD1c(+)CD19(-)CD14(-)CD303(-) type 1 myeloid dendritic cells at disease onset had a significant predictive value. The identification of a specific meta-immunological profile associated with disease status may contribute to our understanding of the basis of diabetes progression.

Published
2013

19. Effects of Gluten Intake on Risk of Celiac Disease: A Case-Control Study on a Swedish Birth Cohort

Author

Rewers, Marian, Bautista, Kimberly, Baxter, Judith, Bedoy, Ruth, Felipe-Morales, Daniel, Frohnert, Brigitte I., Gesualdo, Patricia, Hoffman, Michelle, Karban, Rachel, Liu, Edwin, Norris, Jill, Samper-Imaz, Adela, Steck, Andrea, Waugh, Kathleen, Wright, Hali, She, Jin-Xiong, Schatz, Desmond, Hopkins, Diane, Steed, Leigh, Thomas, Jamie, Adams, Janey, Silvis, Katherine, Haller, Michael, Gardiner, Melissa, McIndoe, Richard, Sharma, Ashok, Williams, Joshua, Foghis, Gabriela, Anderson, Stephen W., Robinson, Richard, Ziegler, Anette G., Beyerlein, Andreas, Bonifacio, Ezio, Hummel, Michael, Hummel, Sandra, Foterek, Kristina, Kersting, Mathilde, Knopff, Annette, Koletzko, Sibylle, Peplow, Claudia, Roth, Roswith, Stock, Joanna, Strauss, Elisabeth, Warncke, Katharina, Winkler, Christiane, Toppari, Jorma, Simell, Olli G., Adamsson, Annika, Hyöty, Heikki, Ilonen, Jorma, Jokipuu, Sanna, Kallio, Tiina, Kähönen, Miia, Knip, Mikael, Koivu, Annika, Koreasalo, Mirva, Kurppa, Kalle, Lönnrot, Maria, Mäntymäki, Elina, Multasuo, Katja, Mykkänen, Juha, Niininen, Tiina, Nyblom, Mia, Rajala, Petra, Rautanen, Jenna, Riikonen, Anne, Romo, Minna, Simell, Satu, Simell, Tuula, Simell, Ville, Sjöberg, Maija, Stenius, Aino, Särmä, Maria, Vainionpää, Sini, Varjonen, Eeva, Veijola, Riitta, Virtanen, Suvi M., Vähä-Mäkilä, Mari, Åkerlund, Mari, Lernmark, Åke, Agardh, Daniel, Aronsson, Carin Andrén, Ask, Maria, Bremer, Jenny, Carlsson, Ulla-Marie, Cilio, Corrado, Ericson-Hallström, Emelie, Fransson, Lina, Gard, Thomas, Gerardsson, Joanna, Bennet, Rasmus, Hansen, Monica, Hansson, Gertie, Harmby, Cecilia, Hyberg, Susanne, Johansen, Fredrik, Jonasdottir, Berglind, Larsson, Helena Elding, Forss, Sigrid Lenrick, Lundgren, Markus, Månsson-Martinez, Maria, Markan, Maria, Melin, Jessica, Mestan, Zeliha, Rahmati, Kobra, Ramelius, Anita, Rosenquist, Anna, Salami, Falastin, Sibthorpe, Sara, Sjöberg, Birgitta, Swartling, Ulrica, Amboh, Evelyn Tekum, Trulsson, Erika, Törn, Carina, Wallin, Anne, Wimar, Åsa, Åberg, Sofie, Hagopian, William A., Killian, Michael, Crouch, Claire Cowen, Skidmore, Jennifer, Ayres, Stephen, Dunson, Kayleen, Hervey, Rachel, Johnson, Corbin, Lyons, Rachel, Meyer, Arlene, Mulenga, Denise, Scott, Elizabeth, Stabbert, Joshua, Tarr, Alexander, Uland, Morgan, Willis, John, Becker, Dorothy, Franciscus, Margaret, Smith, MaryEllen Dalmagro-Elias, Daftary, Ashi, Klein, Mary Beth, Yates, Chrystal, Krischer, Jeffrey P., Abbondondolo, Michael, Austin-Gonzalez, Sarah, Baethke, Sandra, Brown, Rasheedah, Burkhardt, Brant, Butterworth, Martha, Clasen, Joanna, Cuthbertson, David, Eberhard, Christopher, Fiske, Steven, Garcia, Dena, Garmeson, Jennifer, Gowda, Veena, Heyman, Kathleen, Laras, Francisco Perez, Lee, Hye-Seung, Liu, Shu, Liu, Xiang, Lynch, Kristian, Malloy, Jamie, McCarthy, Cristina, McLeod, Wendy, Meulemans, Steven, Shaffer, Chris, Smith, Laura, Smith, Susan, Sulman, Noah, Tamura, Roy, Uusitalo, Ulla, Vehik, Kendra, Vijayakandipan, Ponni, Wood, Keith, Yang, Jimin, Ballard, Lori, Hadley, David, Akolkar, Beena, Bourcier, Kasia, Briese, Thomas, Johnson, Suzanne Bennett, Triplett, Eric, Andrén Aronsson, Carin, and Norris, Jill M.

Published
2016
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20. Joint modeling of longitudinal autoantibody patterns and progression to type 1 diabetes: results from the TEDDY study

Author

Köhler, Meike, Beyerlein, Andreas, Vehik, Kendra, Greven, Sonja, Umlauf, Nikolaus, Lernmark, Åke, Hagopian, William A., Rewers, Marian, She, Jin-Xiong, Toppari, Jorma, Akolkar, Beena, Krischer, Jeffrey P., Bonifacio, Ezio, Ziegler, Anette-G., Rewers, Marian, Bautista, Kimberly, Baxter, Judith, Bedoy, Ruth, Felipe-Morales, Daniel, Driscoll, Kimberly, Frohnert, Brigitte I., Gesualdo, Patricia, Hoffman, Michelle, Karban, Rachel, Liu, Edwin, Norris, Jill, Samper-Imaz, Adela, Steck, Andrea, Waugh, Kathleen, Wright, Hali, Toppari, Jorma, Simell, Olli G., Adamsson, Annika, Ahonen, Suvi, Hyöty, Heikki, Ilonen, Jorma, Jokipuu, Sanna, Kallio, Tiina, Karlsson, Leena, Kähönen, Miia, Knip, Mikael, Kovanen, Lea, Koreasalo, Mirva, Kurppa, Kalle, Latva-aho, Tiina, Lönnrot, Maria, Mäntymäki, Elina, Multasuo, Katja, Mykkänen, Juha, Niininen, Tiina, Niinistö, Sari, Nyblom, Mia, Rajala, Petra, Rautanen, Jenna, Riikonen, Anne, Riikonen, Mika, Rouhiainen, Jenni, Romo, Minna, Simell, Tuula, Simell, Ville, Sjöberg, Maija, Stenius, Aino, Leppänen, Maria, Vainionpää, Sini, Varjonen, Eeva, Veijola, Riitta, Virtanen, Suvi M., Vähä-Mäkilä, Mari, Åkerlund, Mari, Lindfors, Katri, She, Jin-Xiong, Schatz, Desmond, Hopkins, Diane, Steed, Leigh, Thomas, Jamie, Adams, Janey, Silvis, Katherine, Haller, Michael, Gardiner, Melissa, McIndoe, Richard, Sharma, Ashok, Williams, Joshua, Young, Gabriela, Anderson, Stephen W., Jacobsen, Laura, Ziegler, Anette G., Beyerlein, Andreas, Bonifacio, Ezio, Hummel, Michael, Hummel, Sandra, Foterek, Kristina, Janz, Nicole, Kersting, Mathilde, Knopff, Annette, Koletzko, Sibylle, Peplow, Claudia, Roth, Roswith, Scholz, Marlon, Stock, Joanna, Warncke, Katharina, Wendel, Lorena, Winkler, Christiane, Lernmark, Åke, Agardh, Daniel, Aronsson, Carin Andrén, Ask, Maria, Bremer, Jenny, Carlsson, Ulla-Marie, Cilio, Corrado, Ericson-Hallström, Emelie, Fransson, Lina, Gard, Thomas, Gerardsson, Joanna, Bennet, Rasmus, Hansen, Monica, Hansson, Gertie, Hyberg, Susanne, Johansen, Fredrik, Jonsdottir, Berglind, Larsson, Helena Elding, Lindström, Marielle, Lundgren, Markus, Månsson-Martinez, Maria, Markan, Maria, Melin, Jessica, Mestan, Zeliha, Ottosson, Karin, Rahmati, Kobra, Ramelius, Anita, Salami, Falastin, Sibthorpe, Sara, Sjöberg, Birgitta, Swartling, Ulrica, Amboh, Evelyn Tekum, Törn, Carina, Wallin, Anne, Wimar, Åsa, Åberg, Sofie, Hagopian, William A., Killian, Michael, Crouch, Claire Cowen, Skidmore, Jennifer, Carson, Josephine, Dalzell, Maria, Dunson, Kayleen, Hervey, Rachel, Johnson, Corbin, Lyons, Rachel, Meyer, Arlene, Mulenga, Denise, Tarr, Alexander, Uland, Morgan, Willis, John, Becker, Dorothy, Franciscus, Margaret, Smith, MaryEllen Dalmagro-Elias, Daftary, Ashi, Klein, Mary Beth, Yates, Chrystal, Krischer, Jeffrey P., Abbondondolo, Michael, Austin-Gonzalez, Sarah, Avendano, Maryouri, Baethke, Sandra, Brown, Rasheedah, Burkhardt, Brant, Butterworth, Martha, Clasen, Joanna, Cuthbertson, David, Eberhard, Christopher, Fiske, Steven, Garcia, Dena, Garmeson, Jennifer, Gowda, Veena, Heyman, Kathleen, PerezLaras, Francisco, Lee, Hye-Seung, Liu, Shu, Liu, Xiang, Lynch, Kristian, Malloy, Jamie, McCarthy, Cristina, Meulemans, Steven, Parikh, Hemang, Shaffer, Chris, Smith, Laura, Smith, Susan, Sulman, Noah, Tamura, Roy, Uusitalo, Ulla, Vehik, Kendra, Vijayakandipan, Ponni, Wood, Keith, Yang, Jimin, Ballard, Lori, Hadley, David, McLeod, Wendy, Akolkar, Beena, Yu, Liping, Miao, Dongmei, Bingley, Polly, Williams, Alistair, Chandler, Kyla, Rokni, Saba, Williams, Claire, Wyatt, Rebecca, George, Gifty, Grace, Sian, Erlich, Henry, Mack, Steven J., Ke, Sandra, Mulholland, Niveen, Bourcier, Kasia, Briese, Thomas, Johnson, Suzanne Bennett, Triplett, Eric, TEDDY study group, Ancillary Studies, Diet, Genetics, Human Subjects/Publicity/Publications, Immune Markers, Infectious Agents, Laboratory Implementation, Maternal Studies, Psychosocial, Quality Assurance, Steering, Study Coordinators, Celiac Disease, Clinical Implementation, and Quality Assurance Subcommittee on Data Quality

Published
2017
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22. Building and validating a prediction model for paediatric type 1 diabetes risk using next generation targeted sequencing of class II HLA genes

Author

Zhao, Lue Ping, Carlsson, Annelie, Larsson, Helena Elding, Forsander, Gun, Ivarsson, Sten A., Kockum, Ingrid, Ludvigsson, Johnny, Marcus, Claude, Persson, Martina, Samuelsson, Ulf, Örtqvist, Eva, Pyo, Chul‐Woo, Bolouri, Hamid, Zhao, Michael, Nelson, Wyatt C., Geraghty, Daniel E., and Lernmark, Åke

Published
2017
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24. Multiplex agglutination-PCR (ADAP) autoantibody assays compared to radiobinding autoantibodies in type 1 diabetes and celiac disease

Author

Lind, Alexander, primary, de Jesus Cortez, Felipe, additional, Ramelius, Anita, additional, Bennet, Rasmus, additional, Robinson, Peter V., additional, Seftel, David, additional, Gebhart, David, additional, Tandel, Devangkumar, additional, Maziarz, Marlena, additional, Agardh, Daniel, additional, Larsson, Helena Elding, additional, Lundgren, Markus, additional, Tsai, Cheng-ting, additional, and Lernmark, Åke, additional

Published
2022
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25. Month of birth and the risk of developing type 1 diabetes among children in the Swedish national Better Diabetes Diagnosis Study

Author

Hedlund, Emma, Ludvigsson, Johnny, Larsson, Helena Elding, Forsander, Gun, Ivarsson, Sten, Marcus, Claude, Samuelsson, Ulf, Persson, Martina, Carlsson, Annelie, Hedlund, Emma, Ludvigsson, Johnny, Larsson, Helena Elding, Forsander, Gun, Ivarsson, Sten, Marcus, Claude, Samuelsson, Ulf, Persson, Martina, and Carlsson, Annelie

Abstract

Aim Previous studies have reported an association between month of birth and incidence of type 1 diabetes. Using population-based data, including almost all newly diagnosed children with type 1 diabetes in Sweden, we tested whether month of birth influences the risk of type 1 diabetes. Methods For 8761 children diagnosed with type 1 diabetes between May 2005 and December 2016 in the Better Diabetes Diagnosis study, month of birth, sex and age were compared. Human leucocyte antigen (HLA) genotype and autoantibodies at diagnosis were analysed for a subset of the cohort (n = 3647). Comparisons with the general population used data from Statistics Sweden. Results We found no association between month of birth or season and the incidence of type 1 diabetes in the cohort as a whole. However, boys diagnosed before 5 years were more often born in May (p = 0.004). We found no correlation between month of birth and HLA or antibodies. Conclusion In this large nationwide study, the impact of month of birth on type 1 diabetes diagnosis was weak, except for boys diagnosed before 5 years of age, who were more likely born in May. This may suggest different triggers for different subgroups of patients with type 1 diabetes., Funding Agencies: Barndiabetesfonden (Swedish Child Diabetes Foundation); Region Skånes research and development fund

Published
2022
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26. Additional file 1 of Heterogeneity of beta-cell function in subjects with multiple islet autoantibodies in the TEDDY family prevention study - TEFA

Author

Martinez, Maria M��nsson, Spiliopoulos, Lampros, Salami, Falastin, Agardh, Daniel, Toppari, Jorma, Lernmark, ��ke, Kero, Jukka, Veijola, Riitta, Tossavainen, P��ivi, Palmu, Sauli, Lundgren, Markus, Borg, Henrik, Katsarou, Anastasia, Larsson, Helena Elding, Knip, Mikael, Maziarz, Marlena, and T��rn, Carina

Subjects
Data_FILES
Abstract

Additional file 1.

Published
2022
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27. Heterogeneity of DKA Incidence and Age-Specific Clinical Characteristics in Children Diagnosed With Type 1 Diabetes in the TEDDY Study

Author

Jacobsen, Laura M., primary, Vehik, Kendra, primary, Veijola, Riitta, primary, Warncke, Katharina, primary, Toppari, Jorma, primary, Steck, Andrea K., primary, Gesualdo, Patricia, primary, Akolkar, Beena, primary, Lundgren, Markus, primary, Hagopian, William A., primary, She, Jin-Xiong, primary, Rewers, Marian, primary, Ziegler, Anette G., primary, Krischer, Jeffrey P., primary, Larsson, Helena Elding, primary, Haller, Michael J., primary, and Group, the TEDDY Study, primary

Published
2022
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28. HbA1c as a time predictive biomarker for an additional islet autoantibody and type 1 diabetes in seroconverted TEDDY children.

Author

Salami, Falastin, Tamura, Roy, You, Lu, Lernmark, Åke, Larsson, Helena Elding, Lundgren, Markus, Krischer, Jeffrey, Ziegler, Anette‐Gabriele, Toppari, Jorma, Veijola, Riitta, Rewers, Marian, Haller, Michael J., Hagopian, William, Akolkar, Beena, and Törn, Carina

Subjects
GLYCOSYLATED hemoglobin, BIOMARKERS, AUTOANTIBODIES, DISEASE progression, SOCIAL determinants of health, CONFIDENCE intervals, TYPE 1 diabetes, SEROCONVERSION, RISK assessment, DESCRIPTIVE statistics, LONGITUDINAL method, DISEASE risk factors
Abstract

Objective: Increased level of glycated hemoglobin (HbA1c) is associated with type 1 diabetes onset that in turn is preceded by one to several autoantibodies against the pancreatic islet beta cell autoantigens; insulin (IA), glutamic acid decarboxylase (GAD), islet antigen‐2 (IA‐2) and zinc transporter 8 (ZnT8). The risk for type 1 diabetes diagnosis increases by autoantibody number. Biomarkers predicting the development of a second or a subsequent autoantibody and type 1 diabetes are needed to predict disease stages and improve secondary prevention trials. This study aimed to investigate whether HbA1c possibly predicts the progression from first to a subsequent autoantibody or type 1 diabetes in healthy children participating in the Environmental Determinants of Diabetes in the Young (TEDDY) study. Research Design and Methods: A joint model was designed to assess the association of longitudinal HbA1c levels with the development of first (insulin or GAD autoantibodies) to a second, second to third, third to fourth autoantibody or type 1 diabetes in healthy children prospectively followed from birth until 15 years of age. Results: It was found that increased levels of HbA1c were associated with a higher risk of type 1 diabetes (HR 1.82, 95% CI [1.57–2.10], p < 0.001) regardless of first appearing autoantibody, autoantibody number or type. A decrease in HbA1c levels was associated with the development of IA‐2A as a second autoantibody following GADA (HR 0.85, 95% CI [0.75, 0.97], p = 0.017) and a fourth autoantibody following GADA, IAA and ZnT8A (HR 0.90, 95% CI [0.82, 0.99], p = 0.036). HbA1c trajectory analyses showed a significant increase of HbA1c over time (p < 0.001) and that the increase is more rapid as the number of autoantibodies increased from one to three (p < 0.001). Conclusion: In conclusion, increased HbA1c is a reliable time predictive marker for type 1 diabetes onset. The increased rate of increase of HbA1c from first to third autoantibody and the decrease in HbA1c predicting the development of IA‐2A are novel findings proving the link between HbA1c and the appearance of autoantibodies. [ABSTRACT FROM AUTHOR]

Published
2022
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29. Erratum. Consensus Guidance for Monitoring Individuals With Islet Autoantibody–Positive Pre-Stage 3 Type 1 Diabetes. Diabetes Care 2024;47:1276–1298.

Author

Phillip, Moshe, Achenbach, Peter, Addala, Ananta, Albanese-O'Neill, Anastasia, Battelino, Tadej, Bell, Kirstine J., Besser, Rachel E.J., Bonifacio, Ezio, Colhoun, Helen M., Couper, Jennifer J., Craig, Maria E., Danne, Thomas, Beaufort, Carine de, Dovc, Klemen, Driscoll, Kimberly A., Dutta, Sanjoy, Ebekozien, Osagie, Larsson, Helena Elding, Feiten, Daniel J., and Frohnert, Brigitte I.

Subjects
TYPE 1 diabetes, DIABETES, PEDIATRICS
Abstract

The correction notice in Diabetes Care addresses an error in the affiliation information for author Linda A. DiMeglio, clarifying that she is affiliated with the Department of Pediatrics at Indiana University School of Medicine in Indianapolis, IN. The online version of the article has been updated to reflect this correction. The article, "Consensus Guidance for Monitoring Individuals With Islet Autoantibody–Positive Pre-Stage 3 Type 1 Diabetes," includes contributions from a wide range of authors in the field. [Extracted from the article]

Published
2024
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32. The association of physical activity to oral glucose tolerance test outcomes in multiple autoantibody positive children: The TEDDY Study.

Author

Johnson, Suzanne Bennett, Tamura, Roy, McIver, Kerry L., Pate, Russell R., Driscoll, Kimberly A., Melin, Jessica, Larsson, Helena Elding, Haller, Michael J., and Yang, Jimin

Subjects
AUTOANTIBODIES, GLYCOSYLATED hemoglobin, DISEASE progression, TYPE 1 diabetes, BLOOD sugar, PHYSICAL activity, ACCELEROMETRY, GLUCOSE tolerance tests, GLUCOSE, C-peptide
Abstract

Objective: To examine the association of physical activity (PA), measured by accelerometry, to hemoglobin AIC (HbA1c) and oral glucose tolerance test (OGTT) outcomes in children who were multiple persistent confirmed autoantibody positive for type 1 diabetes (T1D). Methods: The Environmental Determinants of Diabetes in the Young (TEDDY) multinational study followed children from birth. Children ≥3 years of age who were multiple persistent confirmed autoantibody positive were monitored by OGTTs every 6 months. TEDDY children's PA was measured by accelerometry beginning at 5 years of age. We examined the relationship between moderate plus vigorous (mod + vig) PA, HbA1c, and OGTT in 209 multiple autoantibody children who had both OGTT and PA measurements. Results: Mod + vig PA was associated with both glucose and C‐peptide measures (fasting, 120‐min, and AUC); higher mod + vig PA was associated with a better OGTT response primarily in children with longer duration of multiple autoantibody positivity. Mod + vig PA also interacted with child age; lower mod + vig PA was associated with a greater increase in C‐peptide response across age. Mod + vig PA was not related to fasting insulin, HOMA‐IR or HbA1c. Conclusions: The OGTT is the gold standard for diabetes diagnosis and is used to monitor those at high risk for T1D. We found higher levels of mod + vig PA were associated with better OGTT outcomes in children ≥5 years of age who have been multiple autoantibody positive for longer periods of time. Physical activity should be the focus of future efforts to better understand the determinants of disease progression in high‐risk children. [ABSTRACT FROM AUTHOR]

Published
2022
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33. Heterogeneity of Beta-cell Function in Subjects With Multiple Islet Autoantibodies in the TEDDY Family Prevention Study - TEFA

Author

Martinez, Maria Cecilia Månsson, primary, Spiliopoulos, Lampros, additional, Salami, Falastin, additional, Agardh, Daniel, additional, Toppari, Jorma, additional, Lernmark, Åke, additional, Kero, Jukka, additional, Veijola, Riitta, additional, Tossavainen, Päivi, additional, Palmu, Sauli, additional, Lundgren, Markus, additional, Borg, Henrik, additional, Katsarou, Anastasia, additional, Larsson, Helena Elding, additional, Knip, Mikael, additional, Maziarz, Marlena, additional, and Törn, Carina, additional

Published
2021
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34. The KAG motif of HLA-DRB1 (β71, β74, β86) predicts seroconversion and development of type 1 diabetes

Author

Zhao, Lue Ping, primary, Papadopoulos, George K, additional, Lybrand, Terry P., additional, Moustakas, Antonis K., additional, Bondinas, George P., additional, Carlsson, Annelie, additional, Larsson, Helena Elding, additional, Ludvigsson, Johnny, additional, Marcus, Claude, additional, Persson, Martina, additional, Samuelsson, Ulf, additional, Wang, Ruihan, additional, Pyo, Chul-Woo, additional, Nelson, Wyatt C., additional, Geraghty, Daniel E., additional, Rich, Stephen S., additional, and Lernmark, Åke, additional

Published
2021
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35. Celiac disease can be predicted by high levels of tissue transglutaminase antibodies in children and adolescents with type 1 diabetes

Author

Bybrant, Mara Cerqueiro, Uden, Elin, Frederiksen, Filippa, Gustafsson, Anna L., Arvidsson, Carl-Goran, Fureman, Anna-Lena, Forsander, Gun, Larsson, Helena Elding, Ivarsson, Sten A., Lindgren, Marie, Ludvigsson, Johnny, Marcus, Claude, Lycka, Auste Pundziute, Persson, Martina, Samuelsson, Ulf, Sarnblad, Stefan, Akesson, Karin, Ortqvist, Eva, Carlsson, Annelie, Bybrant, Mara Cerqueiro, Uden, Elin, Frederiksen, Filippa, Gustafsson, Anna L., Arvidsson, Carl-Goran, Fureman, Anna-Lena, Forsander, Gun, Larsson, Helena Elding, Ivarsson, Sten A., Lindgren, Marie, Ludvigsson, Johnny, Marcus, Claude, Lycka, Auste Pundziute, Persson, Martina, Samuelsson, Ulf, Sarnblad, Stefan, Akesson, Karin, Ortqvist, Eva, and Carlsson, Annelie

Abstract

Objectives Children with type 1 diabetes (T1D) are not included in guidelines regarding diagnosis criteria for celiac disease (CD) without a diagnostic biopsy, due to lack of data. We explored whether tissue transglutaminase antibodies (anti-tTG) that were >= 10 times the upper limit of normal (10x ULN) predicted CD in T1D. Methods Data from the Swedish prospective Better Diabetes Diagnosis study was used, and 2035 children and adolescents with T1D diagnosed between 2005-2010 were included. Of these, 32 had been diagnosed with CD before T1D. The children without CD were repeatedly screened for CD using anti-tTG antibodies of immunoglobulin type A. In addition, their human leukocyte antigen (HLA) were genotyped. All children with positive anti-tTG were advised to undergo biopsy. Biopsies were performed on 119 children and graded using the Marsh-Oberhuber classification. Results All of the 60 children with anti-tTG >= 10x ULN had CD verified by biopsies. The degree of mucosal damage correlated with anti-tTG levels. Among 2003 screened children, 6.9% had positive anti-tTG and 5.6% were confirmed CD. The overall CD prevalence, when including the 32 children with CD before T1D, was 7.0% (145/2035). All but one of the children diagnosed with CD had HLA-DQ2 and/or DQ8. Conclusions As all screened children and adolescents with T1D with tissue transglutaminase antibodies above 10 times the positive value 10x ULN had CD, we propose that the guidelines for diagnosing CD in screened children, when biopsies can be omitted, should also apply to children and adolescents with T1D as a noninvasive method., Funding Agencies|Barndiabetesfonden; Skane County Councils Research and Development Foundation

Published
2021
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36. The KAG motif of HLA-DRB1 (beta 71, beta 74, beta 86) predicts seroconversion and development of type 1 diabetes

Author

Zhao, Lue Ping, Papadopoulos, George K., Lybrand, Terry P., Moustakas, Antonis K., Bondinas, George P., Carlsson, Annelie, Larsson, Helena Elding, Ludvigsson, Johnny, Marcus, Claude, Persson, Martina, Samuelsson, Ulf, Wang, Ruihan, Pyo, Chul-Woo, Nelson, Wyatt C., Geraghty, Daniel E., Rich, Stephen S., Lernmark, Åke, Zhao, Lue Ping, Papadopoulos, George K., Lybrand, Terry P., Moustakas, Antonis K., Bondinas, George P., Carlsson, Annelie, Larsson, Helena Elding, Ludvigsson, Johnny, Marcus, Claude, Persson, Martina, Samuelsson, Ulf, Wang, Ruihan, Pyo, Chul-Woo, Nelson, Wyatt C., Geraghty, Daniel E., Rich, Stephen S., and Lernmark, Åke

Abstract

Background: HLA-DR4, a common antigen of HLA-DRB1, has multiple subtypes that are strongly associated with risk of type 1 diabetes (T1D); however, some are risk neutral or resistant. The pathobiological mechanism of HLA-DR4 subtypes remains to be elucidated. Methods: We used a population-based case-control study of T1D (962 patients and 636 controls) to decipher genetic associations of HLA-DR4 subtypes and specific residues with susceptibility to T1D. Using a birth cohort of 7865 children with periodically measured islet autoantibodies (GADA, IAA or IA-2A), we proposed to validate discovered genetic associations with a totally different study design and time-to-seroconversions prior to clinical onset of T1D. A novel analytic strategy hierarchically organized the HLA-DRB1 alleles by sequence similarity and identified critical amino acid residues by minimizing local genomic architecture and higher-order interactions. Findings: Three amino acid residues of HLA-DRB1 (beta 71, beta 74, beta 86) were found to be predictive of T1D risk in the population-based study. The " KAG" motif, corresponding to HLA-DRB1x04:01, was most strongly associated with T1D risk ([O]dds [R]atio=3.64, p = 3.19 x 10(-64)). Three less frequent motifs ("EAV", OR = 2.55, p = 0.025; "RAG", OR = 1.93, p = 0.043; and "RAV", OR = 1.56, p = 0.003) were associated with T1D risk, while two motifs ("REG" and "REV") were equally protective (OR = 0.11, p = 4.23 x 10(-4)). In an independent birth cohort of HLA-DR3 and HLA-DR4 subjects, those having the "KAG" motif had increased risk for time-to-seroconversion (Hazard Ratio = 1.74, p = 6.51 x 10(-14)) after adjusting potential confounders. Interpretations: DNA sequence variation in HLA-DRB1 at positions beta 71, beta 74, and beta 86 are non-conservative (beta 74 A -> E, beta 71 E vs K vs R and beta 86 G vs V). They result in substantial differences in peptide antigen anchor pocket preferences at p1, p4 and potentially neighboring regions such as pocket

Published
2021
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37. Effects of Gluten Intake on Risk of Celiac Disease: A Case-Control Study on a Swedish Birth Cohort

Author

Aronsson, Carin Andrén, Lee, Hye-Seung, Koletzko, Sibylle, Uusitalo, Ulla, Yang, Jimin, Virtanen, Suvi M., Liu, Edwin, Lernmark, Åke, Norris, Jill M., Agardh, Daniel, Rewers, Marian, Bautista, Kimberly, Baxter, Judith, Bedoy, Ruth, Felipe-Morales, Daniel, Frohnert, Brigitte I., Gesualdo, Patricia, Hoffman, Michelle, Karban, Rachel, Liu, Edwin, Norris, Jill, Samper-Imaz, Adela, Steck, Andrea, Waugh, Kathleen, Wright, Hali, She, Jin-Xiong, Schatz, Desmond, Hopkins, Diane, Steed, Leigh, Thomas, Jamie, Adams, Janey, Silvis, Katherine, Haller, Michael, Gardiner, Melissa, McIndoe, Richard, Sharma, Ashok, Williams, Joshua, Foghis, Gabriela, Anderson, Stephen W., Robinson, Richard, Ziegler, Anette G., Beyerlein, Andreas, Bonifacio, Ezio, Hummel, Michael, Hummel, Sandra, Foterek, Kristina, Kersting, Mathilde, Knopff, Annette, Koletzko, Sibylle, Peplow, Claudia, Roth, Roswith, Stock, Joanna, Strauss, Elisabeth, Warncke, Katharina, Winkler, Christiane, Toppari, Jorma, Simell, Olli G., Adamsson, Annika, Hyöty, Heikki, Ilonen, Jorma, Jokipuu, Sanna, Kallio, Tiina, Kähönen, Miia, Knip, Mikael, Koivu, Annika, Koreasalo, Mirva, Kurppa, Kalle, Lönnrot, Maria, Mäntymäki, Elina, Multasuo, Katja, Mykkänen, Juha, Niininen, Tiina, Nyblom, Mia, Rajala, Petra, Rautanen, Jenna, Riikonen, Anne, Romo, Minna, Simell, Satu, Simell, Tuula, Simell, Ville, Sjöberg, Maija, Stenius, Aino, Särmä, Maria, Vainionpää, Sini, Varjonen, Eeva, Veijola, Riitta, Virtanen, Suvi M., Vähä-Mäkilä, Mari, Åkerlund, Mari, Lernmark, Åke, Agardh, Daniel, Ask, Maria, Bremer, Jenny, Carlsson, Ulla-Marie, Cilio, Corrado, Ericson-Hallström, Emelie, Fransson, Lina, Gard, Thomas, Gerardsson, Joanna, Bennet, Rasmus, Hansen, Monica, Hansson, Gertie, Harmby, Cecilia, Hyberg, Susanne, Johansen, Fredrik, Jonasdottir, Berglind, Larsson, Helena Elding, Forss, Sigrid Lenrick, Lundgren, Markus, Månsson-Martinez, Maria, Markan, Maria, Melin, Jessica, Mestan, Zeliha, Rahmati, Kobra, Ramelius, Anita, Rosenquist, Anna, Salami, Falastin, Sibthorpe, Sara, Sjöberg, Birgitta, Swartling, Ulrica, Amboh, Evelyn Tekum, Trulsson, Erika, Törn, Carina, Wallin, Anne, Wimar, Åsa, Åberg, Sofie, Hagopian, William A., Killian, Michael, Crouch, Claire Cowen, Skidmore, Jennifer, Ayres, Stephen, Dunson, Kayleen, Hervey, Rachel, Johnson, Corbin, Lyons, Rachel, Meyer, Arlene, Mulenga, Denise, Scott, Elizabeth, Stabbert, Joshua, Tarr, Alexander, Uland, Morgan, Willis, John, Becker, Dorothy, Franciscus, Margaret, Smith, MaryEllen Dalmagro-Elias, Daftary, Ashi, Klein, Mary Beth, Yates, Chrystal, Krischer, Jeffrey P., Abbondondolo, Michael, Austin-Gonzalez, Sarah, Baethke, Sandra, Brown, Rasheedah, Burkhardt, Brant, Butterworth, Martha, Clasen, Joanna, Cuthbertson, David, Eberhard, Christopher, Fiske, Steven, Garcia, Dena, Garmeson, Jennifer, Gowda, Veena, Heyman, Kathleen, Laras, Francisco Perez, Lee, Hye-Seung, Liu, Shu, Liu, Xiang, Lynch, Kristian, Malloy, Jamie, McCarthy, Cristina, McLeod, Wendy, Meulemans, Steven, Shaffer, Chris, Smith, Laura, Smith, Susan, Sulman, Noah, Tamura, Roy, Uusitalo, Ulla, Vehik, Kendra, Vijayakandipan, Ponni, Wood, Keith, Yang, Jimin, Ballard, Lori, Hadley, David, Akolkar, Beena, Bourcier, Kasia, Briese, Thomas, Johnson, Suzanne Bennett, and Triplett, Eric

Published
2016
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40. Screening for autoantibody targets in post-vaccination narcolepsy using proteome arrays

Author

Lind, Alexander, Eriksson, Daniel, Akel, Omar, Ramelius, Anita, Palm, Lars, Lernmark, Ake, Kämpe, Olle, Larsson, Helena Elding, Landegren, Nils, Lind, Alexander, Eriksson, Daniel, Akel, Omar, Ramelius, Anita, Palm, Lars, Lernmark, Ake, Kämpe, Olle, Larsson, Helena Elding, and Landegren, Nils

Abstract

Narcolepsy type 1 (NT1) is a chronic sleep disorder caused by a specific loss of hypocretin‐producing neurons. The incidence of NT1 increased in Sweden, Finland and Norway following Pandemrix®‐vaccination, initiated to prevent the 2009 influenza pandemic. The pathogenesis of NT1 is poorly understood, and causal links to vaccination are yet to be clarified. The strong association with Human leukocyte antigen (HLA) DQB1*06:02 suggests an autoimmune pathogenesis, but proposed autoantigens remain controversial. We used a two‐step approach to identify autoantigens in patients that acquired NT1 after Pandemrix®‐vaccination. Using arrays of more than 9000 full‐length human proteins, we screened the sera of 10 patients and 24 healthy subjects for autoantibodies. Identified candidate antigens were expressed in vitro to enable validation studies with radiobinding assays (RBA). The validation cohort included NT1 patients (n = 39), their first‐degree relatives (FDR) (n = 66), population controls (n = 188), and disease controls representing multiple sclerosis (n = 100) and FDR to type 1 diabetes patients (n = 41). Reactivity towards previously suggested NT1 autoantigen candidates including Tribbles homolog 2, Prostaglandin D2 receptor, Hypocretin receptor 2 and α‐MSH/proopiomelanocortin was not replicated in the protein array screen. By comparing case to control signals, three novel candidate autoantigens were identified in the protein array screen; LOC401464, PARP3 and FAM63B. However, the RBA did not confirm elevated reactivity towards either of these proteins. In summary, three putative autoantigens in NT1 were identified by protein array screening. Autoantibodies against these candidates could not be verified with independent methods. Further studies are warranted to identify hypothetical autoantigens related to the pathogenesis of Pandemrix®‐induced NT1.

Published
2020
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41. Motifs of Three HLA-DQ Amino Acid Residues (alpha 44, beta 57, beta 135) Capture Full Association With the Risk of Type 1 Diabetes in DQ2 and DQ8 Children

Author

Zhao, Lue Ping, Papadopoulos, George K., Kwok, William W., Moustakas, Antonis K., Bondinas, George P., Larsson, Helena Elding, Ludvigsson, Johnny, Marcus, Claude, Samuelsson, Ulf, Wang, Ruihan, Pyo, Chul-Woo, Nelson, Wyatt C., Geraghty, Daniel E., Lernmark, Ake, Zhao, Lue Ping, Papadopoulos, George K., Kwok, William W., Moustakas, Antonis K., Bondinas, George P., Larsson, Helena Elding, Ludvigsson, Johnny, Marcus, Claude, Samuelsson, Ulf, Wang, Ruihan, Pyo, Chul-Woo, Nelson, Wyatt C., Geraghty, Daniel E., and Lernmark, Ake

Abstract

HLA-DQA1 and -DQB1 are strongly associated with type 1 diabetes (T1D), and DQ8.1 and DQ2.5 are major risk haplotypes. Next-generation targeted sequencing of HLA-DQA1 and -DQB1 in Swedish newly diagnosed 1- to 18 year-old patients (n= 962) and control subjects (n= 636) was used to construct abbreviated DQ haplotypes, converted into amino acid (AA) residues, and assessed for their associations with T1D. A hierarchically organized haplotype (HOH) association analysis allowed 45 unique DQ haplotypes to be categorized into seven clusters. The DQ8/9 cluster included two DQ8.1 risk and the DQ9 resistant haplotypes, and the DQ2 cluster included the DQ2.5 risk and DQ2.2 resistant haplotypes. Within each cluster, HOH found residues alpha 44Q (odds ratio [OR] 3.29,P= 2.38 * 10(-85)) and beta 57A (OR 3.44,P= 3.80 * 10(-84)) to be associated with T1D in the DQ8/9 cluster representing all ten residues (alpha 22, alpha 23, alpha 44, alpha 49, alpha 51, alpha 53, alpha 54, alpha 73, alpha 184, beta 57) due to complete linkage disequilibrium (LD) of alpha 44 with eight such residues. Within the DQ2 cluster and due to LD, HOH analysis found alpha 44C and beta 135D to share the risk for T1D (OR 2.10,P= 1.96 * 10(-20)). The motif "QAD" of alpha 44, beta 57, and beta 135 captured the T1D risk association of DQ8.1 (OR 3.44,P= 3.80 * 10(-84)), and the corresponding motif "CAD" captured the risk association of DQ2.5 (OR 2.10,P= 1.96 * 10(-20)). Two risk associations were related to GAD65 autoantibody (GADA) and IA-2 autoantibody (IA-2A) but in opposite directions. CAD was positively associated with GADA (OR 1.56,P= 6.35 * 10(-8)) but negatively with IA-2A (OR 0.59,P= 6.55 * 10(-11)). QAD was negatively associated with GADA (OR 0.88;P= 3.70 * 10(-3)) but positively with IA-2A (OR 1.64;P= 2.40 * 10(-14)), despite a single difference at alpha 44. The residues are found in and around anchor pockets 1 and 9, as potential T-cell receptor contacts, in the areas for CD4 binding and putative homodi, Funding Agencies|National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), National Institutes of HealthUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of Diabetes & Digestive & Kidney Diseases (NIDDK) [1R01DK117276]; European Foundation for the Study of Diabetes; Swedish Child Diabetes Foundation (Barndiabetesfonden); NIDDK, National Institutes of HealthUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of Diabetes & Digestive & Kidney Diseases (NIDDK) [DK63861, DK26190]; Swedish Research CouncilSwedish Research Council; Skane County Council for Research and Development; Swedish Association of Local Authorities and Regions (SKL)

Published
2020
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45. Heterogeneity of DKA Incidence and Age-Specific Clinical Characteristics in Children Diagnosed With Type 1 Diabetes in the TEDDY Study.

Author

Jacobsen, Laura M., Vehik, Kendra, Veijola, Riitta, Warncke, Katharina, Toppari, Jorma, Steck, Andrea K., Gesualdo, Patricia, Akolkar, Beena, Lundgren, Markus, Hagopian, William A., She, Jin-Xiong, Rewers, Marian, Ziegler, Anette-G., Krischer, Jeffrey P., Larsson, Helena Elding, Haller, Michael J., Barbour, Aaron, Bautista, Kimberly, Baxter, Judith, and Felipe-Morales, Daniel

Subjects
TYPE 1 diabetes, TYPE 2 diabetes, GLUCOSE tolerance tests, DIABETIC acidosis, HETEROGENEITY, AGE groups, RESEARCH, AGE distribution, DISEASE incidence, EVALUATION research, INSULIN, COMPARATIVE studies, RESEARCH funding, DISEASE complications
Abstract

Objective: The Environmental Determinants of Diabetes in the Young (TEDDY) study is uniquely capable of investigating age-specific differences associated with type 1 diabetes. Because age is a primary driver of heterogeneity in type 1 diabetes, we sought to characterize by age metabolic derangements prior to diagnosis and clinical features associated with diabetic ketoacidosis (DKA).Research Design and Methods: The 379 TEDDY children who developed type 1 diabetes were grouped by age at onset (0-4, 5-9, and 10-14 years; n = 142, 151, and 86, respectively) with comparisons of autoantibody profiles, HLAs, family history of diabetes, presence of DKA, symptomatology at onset, and adherence to TEDDY protocol. Time-varying analysis compared those with oral glucose tolerance test data with TEDDY children who did not progress to diabetes.Results: Increasing fasting glucose (hazard ratio [HR] 1.09 [95% CI 1.04-1.14]; P = 0.0003), stimulated glucose (HR 1.50 [1.42-1.59]; P < 0.0001), fasting insulin (HR 0.89 [0.83-0.95]; P = 0.0009), and glucose-to-insulin ratio (HR 1.29 [1.16-1.43]; P < 0.0001) were associated with risk of progression to type 1 diabetes. Younger children had fewer autoantibodies with more symptoms at diagnosis. Twenty-three children (6.1%) had DKA at onset, only 1 (0.97%) of 103 with and 22 (8.0%) of 276 children without a first-degree relative (FDR) with type 1 diabetes (P = 0.008). Children with DKA were more likely to be nonadherent to study protocol (P = 0.047), with longer duration between their last TEDDY evaluation and diagnosis (median 10.2 vs. 2.0 months without DKA; P < 0.001).Conclusions: DKA at onset in TEDDY is uncommon, especially for FDRs. For those without familial risk, metabolic monitoring continues to provide a primary benefit of reduced DKA but requires regular follow-up. Clinical and laboratory features vary by age at onset, adding to the heterogeneity of type 1 diabetes. [ABSTRACT FROM AUTHOR]

Published
2022
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47. Next generation HLA sequence analysis uncovers seven HLA-DQ amino acid residues and six motifs resistant to childhood type 1 diabetes

Author

Admin, Ada, primary, Zhao, Lue Ping, primary, Papadopoulos, George K, primary, Kwok, William W., primary, Moustakas, Antonis K., primary, Bondinas, George P., primary, Carlsson, Annelie, primary, Larsson, Helena Elding, primary, Ludvigsson, Johnny, primary, Marcus, Claude, primary, Samuelsson, Ulf, primary, Wang, Ruihan, primary, Pyo, Chul-Woo, primary, Nelson, Wyatt C., primary, Geraghty, Daniel E., primary, and Lernmark, Åke, primary

Published
2020
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48. 348-OR: Metabolic Phenotype of Autoantibody Positive (AbPos) Long-Term Nonprogressors (LTNPs) to Type 1 Diabetes (T1D)

Author

EVANS-MOLINA, CARMELLA, primary, SAEED, ZEB I., additional, SOSENKO, JAY, additional, ATKINSON, MARK A., additional, HEROLD, KEVAN C., additional, ISMAIL, HEBA M., additional, LARSSON, HELENA ELDING, additional, LUNDGREN, MARKUS, additional, MORAN, ANTOINETTE, additional, MOORE, DANIEL J., additional, NATHAN, BRANDON M., additional, PALMER, JERRY P., additional, SIMS, EMILY K., additional, STECK, ANDREA, additional, WHERRETT, DIANE K., additional, and REDONDO, MARIA J., additional

Published
2020
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49. Motifs of three HLA-DQ amino acid residues (α44, β57, β135) capture full association with the risk of type 1 diabetes in DQ2 and DQ8 children

Author

Admin, Ada, primary, Zhao, Lue Ping, primary, Papadopoulos, George K, primary, Kwok, William W., primary, Moustakas, Antonis K., primary, Bondinas, George P., primary, Larsson, Helena Elding, primary, Ludvigsson, Johnny, primary, Marcus, Claude, primary, Samuelsson, Ulf, primary, Wang, Ruihan, primary, Pyo, Chul-Woo, primary, Nelson, Wyatt C., primary, Geraghty, Daniel E., primary, and Lernmark, Åke, primary

Published
2020
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50. Motifs of Three HLA-DQ Amino Acid Residues (α44, β57, β135) Capture Full Association With the Risk of Type 1 Diabetes in DQ2 and DQ8 Children

Author

Zhao, Lue Ping, primary, Papadopoulos, George K., additional, Kwok, William W., additional, Moustakas, Antonis K., additional, Bondinas, George P., additional, Larsson, Helena Elding, additional, Ludvigsson, Johnny, additional, Marcus, Claude, additional, Samuelsson, Ulf, additional, Wang, Ruihan, additional, Pyo, Chul-Woo, additional, Nelson, Wyatt C., additional, Geraghty, Daniel E., additional, and Lernmark, Åke, additional

Published
2020
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