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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, Elding Larsson, Helena, 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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5. 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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6. Intake of B vitamins and the risk of developing islet autoimmunity and type 1 diabetes in the TEDDY study

Author

Hakola, Leena, Mramba, Lazarus K., Uusitalo, Ulla, Andrén Aronsson, Carin, Hummel, Sandra, Niinistö, Sari, Erlund, Iris, Yang, Jimin, Rewers, Marian J., Akolkar, Beena, McIndoe, Richard A., Rich, Stephen S., Hagopian, William A., Ziegler, Anette, Lernmark, Åke, Toppari, Jorma, Krischer, Jeffrey P., Norris, Jill M., and Virtanen, Suvi M.

Published
2024
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7. Modeling Disease Progression Trajectories from Longitudinal Observational Data

Author

Kwon, Bum Chul, Achenbach, Peter, Dunne, Jessica L., Hagopian, William, Lundgren, Markus, Ng, Kenney, Veijola, Riitta, Frohnert, Brigitte I., Anand, Vibha, and Group, the T1DI Study

Subjects
Computer Science - Machine Learning
Abstract

Analyzing disease progression patterns can provide useful insights into the disease processes of many chronic conditions. These analyses may help inform recruitment for prevention trials or the development and personalization of treatments for those affected. We learn disease progression patterns using Hidden Markov Models (HMM) and distill them into distinct trajectories using visualization methods. We apply it to the domain of Type 1 Diabetes (T1D) using large longitudinal observational data from the T1DI study group. Our method discovers distinct disease progression trajectories that corroborate with recently published findings. In this paper, we describe the iterative process of developing the model. These methods may also be applied to other chronic conditions that evolve over time., Comment: 9 pages, 5 figures, to be published in proceedings of AMIA Annual Symposium 2020

Published
2020

8. Distinct transcriptomic profiles in children prior to the appearance of type 1 diabetes-linked islet autoantibodies and following enterovirus infection

Author

Lin, Jake, Moradi, Elaheh, Salenius, Karoliina, Lehtipuro, Suvi, Häkkinen, Tomi, Laiho, Jutta E., Oikarinen, Sami, Randelin, Sofia, Parikh, Hemang M., Krischer, Jeffrey P., Toppari, Jorma, Lernmark, Åke, Petrosino, Joseph F., Ajami, Nadim J., She, Jin-Xiong, Hagopian, William A., Rewers, Marian J., Lloyd, Richard E., Rautajoki, Kirsi J., Hyöty, Heikki, and Nykter, Matti

Published
2023
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9. Longitudinal Metabolome-Wide Signals Prior to the Appearance of a First Islet Autoantibody in Children Participating in the TEDDY Study

Author

Li, Qian, Parikh, Hemang, Butterworth, Martha D, Lernmark, Åke, Hagopian, William, Rewers, Marian, She, Jin-Xiong, Toppari, Jorma, Ziegler, Anette-G, Akolkar, Beena, Fiehn, Oliver, Fan, Sili, Krischer, Jeffrey P, Barbour, Aaron, Bautista, Kimberly, Baxter, Judith, Felipe-Morales, Daniel, Driscoll, Kimberly, Frohnert, Brigitte I, Stahl, Marisa, Gesualdo, Patricia, Hoffman, Michelle, Karban, Rachel, Liu, Edwin, Norris, Jill, Peacock, Stesha, Shorrosh, Hanan, Steck, Andrea, Stern, Megan, Villegas, Erica, Waugh, Kathleen, Simell, Olli G, Adamsson, Annika, Ahonen, Suvi, Åkerlund, Mari, Hakola, Leena, Hekkala, Anne, Holappa, Henna, Hyöty, Heikki, Ikonen, Anni, Ilonen, Jorma, Jäminki, Sinikka, Jokipuu, Sanna, Karlsson, Leena, Kero, Jukka, Kähönen, Miia, Knip, Mikael, Koivikko, Minna-Liisa, Koskinen, Merja, Koreasalo, Mirva, Kurppa, Kalle, Kytölä, Jarita, Latva-aho, Tiina, Lindfors, Katri, Lönnrot, Maria, Mäntymäki, Elina, Mattila, Markus, Miettinen, Maija, Multasuo, Katja, Mykkänen, Teija, Niininen, Tiina, Niinistö, Sari, Nyblom, Mia, Oikarinen, Sami, Ollikainen, Paula, Othmani, Zhian, Pohjola, Sirpa, Rajala, Petra, Rautanen, Jenna, Riikonen, Anne, Riski, Eija, Pekkola, Miia, Romo, Minna, Ruohonen, Satu, Simell, Satu, Sjöberg, Maija, Stenius, Aino, Tossavainen, Päivi, Vähä-Mäkilä, Mari, Vainionpää, Sini, Varjonen, Eeva, Veijola, Riitta, Viinikangas, Irene, Virtanen, Suvi M, Schatz, Desmond, Hopkins, Diane, Steed, Leigh, Bryant, Jennifer, Silvis, Katherine, Haller, Michael, Gardiner, Melissa, McIndoe, Richard, Sharma, Ashok, Anderson, Stephen W, Jacobsen, Laura, Marks, John, and Towe, PD

Subjects
Pediatric, Autoimmune Disease, Diabetes, Prevention, Nutrition, Metabolic and endocrine, Alanine, Amino Acids, Branched-Chain, Autoantibodies, Child, Preschool, Dehydroascorbic Acid, Diabetes Mellitus, Type 1, Fatty Acids, Female, Genetic Predisposition to Disease, Glutamate Decarboxylase, Humans, Infant, Infant, Newborn, Insulin Antibodies, Longitudinal Studies, Male, Metabolome, Methionine, Phosphatidylethanolamines, Prodromal Symptoms, Proline, Risk, Triglycerides, gamma-Aminobutyric Acid, TEDDY Study Group, Medical and Health Sciences, Endocrinology & Metabolism
Abstract

Children at increased genetic risk for type 1 diabetes (T1D) after environmental exposures may develop pancreatic islet autoantibodies (IA) at a very young age. Metabolic profile changes over time may imply responses to exposures and signal development of the first IA. Our present research in The Environmental Determinants of Diabetes in the Young (TEDDY) study aimed to identify metabolome-wide signals preceding the first IA against GAD (GADA-first) or against insulin (IAA-first). We profiled metabolomes by mass spectrometry from children's plasma at 3-month intervals after birth until appearance of the first IA. A trajectory analysis discovered each first IA preceded by reduced amino acid proline and branched-chain amino acids (BCAAs), respectively. With independent time point analysis following birth, we discovered dehydroascorbic acid (DHAA) contributing to the risk of each first IA, and γ-aminobutyric acid (GABAs) associated with the first autoantibody against insulin (IAA-first). Methionine and alanine, compounds produced in BCAA metabolism and fatty acids, also preceded IA at different time points. Unsaturated triglycerides and phosphatidylethanolamines decreased in abundance before appearance of either autoantibody. Our findings suggest that IAA-first and GADA-first are heralded by different patterns of DHAA, GABA, multiple amino acids, and fatty acids, which may be important to primary prevention of T1D.

Published
2020

11. Metabolite-related dietary patterns and the development of islet autoimmunity.

Author

Johnson, Randi K, Vanderlinden, Lauren, DeFelice, Brian C, Kechris, Katerina, Uusitalo, Ulla, Fiehn, Oliver, Sontag, Marci, Crume, Tessa, Beyerlein, Andreas, Lernmark, Åke, Toppari, Jorma, Ziegler, Anette-G, She, Jin-Xiong, Hagopian, William, Rewers, Marian, Akolkar, Beena, Krischer, Jeffrey, Virtanen, Suvi M, Norris, Jill M, and TEDDY Study Group

Subjects
TEDDY Study Group
Abstract

The role of diet in type 1 diabetes development is poorly understood. Metabolites, which reflect dietary response, may help elucidate this role. We explored metabolomics and lipidomics differences between 352 cases of islet autoimmunity (IA) and controls in the TEDDY (The Environmental Determinants of Diabetes in the Young) study. We created dietary patterns reflecting pre-IA metabolite differences between groups and examined their association with IA. Secondary outcomes included IA cases positive for multiple autoantibodies (mAb+). The association of 853 plasma metabolites with outcomes was tested at seroconversion to IA, just prior to seroconversion, and during infancy. Key compounds in enriched metabolite sets were used to create dietary patterns reflecting metabolite composition, which were then tested for association with outcomes in the nested case-control subset and the full TEDDY cohort. Unsaturated phosphatidylcholines, sphingomyelins, phosphatidylethanolamines, glucosylceramides, and phospholipid ethers in infancy were inversely associated with mAb+ risk, while dicarboxylic acids were associated with an increased risk. An infancy dietary pattern representing higher levels of unsaturated phosphatidylcholines and phospholipid ethers, and lower sphingomyelins was protective for mAb+ in the nested case-control study only. Characterization of this high-risk infant metabolomics profile may help shape the future of early diagnosis or prevention efforts.

Published
2019

15. Clinical Prediction Models Combining Routine Clinical Measures Have High Accuracy in Identifying Youth-Onset Type 2 Diabetes Defined by Maintained Endogenous Insulin Secretion: The SEARCH for Diabetes in Youth Study.

Author

Jones, Angus G., Shields, Beverley M., Oram, Richard A., Dabelea, Dana M., Hagopian, William A., Sharp, Seth A., Lustigova, Eva, Shah, Amy S., Knupp, Julieanne, Mottl, Amy K., D'Agostino Jr., Ralph B., Williams, Adrienne, Marcovina, Santica M., Pihoker, Catherine, Divers, Jasmin, and Redondo, Maria J.

Subjects
GENETIC risk score, TYPE 2 diabetes, TYPE 1 diabetes, RECEIVER operating characteristic curves, HDL cholesterol
Abstract

OBJECTIVE: With high prevalence of obesity and overlapping features between diabetes subtypes, accurately classifying youth-onset diabetes can be challenging. We aimed to develop prediction models that, using characteristics available at diabetes diagnosis, can identify youth who will retain endogenous insulin secretion at levels consistent with type 2 diabetes (T2D). RESEARCH DESIGN AND METHODS: We studied 2,966 youth with diabetes in the prospective SEARCH for Diabetes in Youth study (diagnosis age ≤19 years) to develop prediction models to identify participants with fasting C-peptide ≥250 pmol/L (≥0.75 ng/mL) after >3 years' (median 74 months) diabetes duration. Models included clinical measures at the baseline visit, at a mean diabetes duration of 11 months (age, BMI, sex, waist circumference, HDL cholesterol), with and without islet autoantibodies (GADA, IA-2A) and a type 1 diabetes genetic risk score (T1DGRS). RESULTS: Models using routine clinical measures with or without autoantibodies and T1DGRS were highly accurate in identifying participants with C-peptide ≥0.75 ng/mL (17% of participants; 2.3% and 53% of those with and without positive autoantibodies) (area under the receiver operating characteristic curve [AUCROC] 0.95–0.98). In internal validation, optimism was very low, with excellent calibration (slope 0.995–0.999). Models retained high performance for predicting retained C-peptide in older youth with obesity (AUCROC 0.88–0.96) and in subgroups defined by self-reported race and ethnicity (AUCROC 0.88–0.97), autoantibody status (AUCROC 0.87–0.96), and clinically diagnosed diabetes types (AUCROC 0.81–0.92). CONCLUSIONS: Prediction models combining routine clinical measures at diabetes diagnosis, with or without islet autoantibodies or T1DGRS, can accurately identify youth with diabetes who maintain endogenous insulin secretion in the range associated with T2D. [ABSTRACT FROM AUTHOR]

Published
2024
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16. Protocol for the Australian Type 1 Diabetes National Screening Pilot: Assessing the feasibility and acceptability of three general population screening models in children.

Author

Bell, Kirstine J., Brodie, Shannon, Couper, Jennifer J., Colman, Peter, Davis, Elizabeth, Deed, Gary, Hagopian, William, Haynes, Aveni, Hendrieckx, Christel, Henry, Amanda, Gordon, Adrienne, Howard, Kirsten, Huynh, Tony, Kerr, Bernadette, Mikler, Kara, Nassar, Natasha, Norris, Sarah, Oram, Richard, Pawlak, Dorota, and Shand, Antonia

Subjects
TYPE 1 diabetes, HUMAN services programs, PILOT projects, MATHEMATICAL models, MEDICAL screening, THEORY
Abstract

Aim: One third of Australian children diagnosed with type 1 diabetes present with life‐threatening diabetic ketoacidosis (DKA) at diagnosis. Screening for early‐stage, presymptomatic type 1 diabetes, with ongoing follow‐up, can substantially reduce this risk (<5% risk). Several screening models are being trialled internationally, without consensus on the optimal approach. This pilot study aims to assess three models for a routine, population‐wide screening programme in Australia. Methods: An implementation science‐guided pilot study to evaluate the feasibility, acceptability and costs of three screening models in children will be conducted between July 2022 and June 2024. These models are as follows: (1) Genetic risk‐stratified screening using newborn heel prick dried bloodspots, followed by autoantibody testing from 11 months of age; (2) genetic risk‐stratified screening of infant (6–12 months) saliva followed by autoantibody testing from 10 months of age; and (3) autoantibody screening using capillary dried bloodspots collected from children aged 2, 6 or 10 years. Cohorts for each model will be recruited from targeted geographic areas across Australia involving ≥2 states per cohort, with a recruitment target of up to 3000 children per cohort (total up to 9000 children). The primary outcome is screening uptake for each cohort. Secondary outcomes include programme feasibility, costs, parental anxiety, risk perception, satisfaction, well‐being and quality of life, and health professional attitudes and satisfaction. Conclusions: This pilot is the first direct comparison of three screening implementation models for general population screening. Findings will provide evidence to inform a potential national screening programme for Australian children. Trial Registration: ACTRN12622000381785. [ABSTRACT FROM AUTHOR]

Published
2024
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19. Telomere length is not a main factor for the development of islet autoimmunity and type 1 diabetes in the TEDDY study

Author

Törn, Carina, Liu, Xiang, Onengut-Gumuscu, Suna, Counts, Kevin M., Moreno, Jose Leonardo, Remedios, Cassandra L., Chen, Wei-Min, LeFaive, Jonathon, Butterworth, Martha D., Akolkar, Beena, Krischer, Jeffrey P., Lernmark, Åke, Rewers, Marian, She, Jin-Xiong, Toppari, Jorma, Ziegler, Anette-Gabriele, Ratan, Aakrosh, Smith, Albert V., Hagopian, William A., Rich, Stephen S., and Parikh, Hemang M.

Published
2022
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21. The influence of pubertal development on autoantibody appearance and progression to type 1 diabetes in the TEDDY study

Author

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

Published
2024
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22. Associations of breastfeeding with childhood autoimmunity, allergies, and overweight: The Environmental Determinants of Diabetes in the Young (TEDDY) study

Author

Hummel, Sandra, Weiß, Andreas, Bonifacio, Ezio, Agardh, Daniel, Akolkar, Beena, Aronsson, Carin A, Hagopian, William A, Koletzko, Sibylle, Krischer, Jeffrey P, Lernmark, Åke, Lynch, Kristian, Norris, Jill M, Rewers, Marian J, She, Jin-Xiong, Toppari, Jorma, Uusitalo, Ulla, Vehik, Kendra, Virtanen, Suvi M, Beyerlein, Andreas, and Ziegler, Anette-G

Published
2021
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24. Anti-interleukin-21 antibody and liraglutide for the preservation of β-cell function in adults with recent-onset type 1 diabetes: a randomised, double-blind, placebo-controlled, phase 2 trial

Author

Ludvik, Bernhard, Prager, Rudolf, Paulweber, Bernhard, Ebenbichler, Christoph F, Keymeulen, B, De Block, C, Grossman, Loren, Houlden, Robyn, Perron, Patrice, Ransom, Thomas, Senior, Peter, Weisnagel, S. John, Woo, Vincent, Dumas, Richard, Thompson, David, Vilsbøll, Tina, Gram, Jeppe, Juhl, Claus Bogh, Hukkanen, Janne, Lahtela, Jorma, Niskanen, Leo, O'Shea, Donal, O'Brien, Timothy, Sreenan, Seamus, Wainstein, Julio, Phillip, Moshe, Knobler, Hilla, Dotta, Francesco, Piatti, Pier Marco, Roberto, Trevisan, Gnasso, Agostino, Gulseth, Hanne, Cooper, John, Pankowska, Ewa, Lukaszewicz, Monika, Wolnik, Bogumił, Manita, Isabel, Marques, Olinda, Roque, Cristina, Príncipe, Rosa Maria, Neves, Celestino, Heitor, Susana, Ruyatkina, L, Dvoryashina, Irina, Vagapova, Gulnar, Belousova, Lidiya, Sergeeva-Kondrachenko, Marina, Peskov, Andrey, Frolova, Elena, Golovach, Albina, Kunitsyna, Marina, Krasnopeeva (Kabachkova), Natalia, Ipatko, Irina, De la Cuesta, Carmen, Tinahones, Francisco José, Rigla, Mercedes, Merino, Juan Francisco, Gómez, Luis Alberto, Fernández, Mercè, Simó, Rafael, Rydén, Mikael, Jendle, Johan, Filipsson, Karin, Eliasson, Björn, Mankovsky, Borys, Lymar, Iurii, Sokolova, Liubov, Myshanych, Galyna, Zlova, Tetiana, Vlasenko, Maryna, Kuskalo -, Petro, Courtney, Hamish, Dayan, Colin, English, Patrick, Heller, Simon, Johnson, Andrew B, Nair, Sunil, Leslie, R. D, Narendran, P, Oliver, Nick, Ramtoola, Shenaz, Shaw, Jim, Viljoen -, Adie, Al-Karadsheh, Amer, Dostou, Jean Marie, Gangi, Sumana, Gottlieb, Peter, Jerkins, Terri, Magnotti, Michael, Marks, Jennifer, Nakhle, Samer, Bonabi, Gholamreza, Myers, Lyle, Pratley, Richard, Hagopian, William, Pettus, Jeremy, von Scholten, Bernt Johan, Wesley, Johnna D, Kreiner, Frederik F, von Herrath, Matthias, Bain, Stephen C, Bode, Bruce, Clausen, Jesper Ole, Coppieters, Ken, Gaysina, Leylya, Gumprecht, Janusz, Hansen, Troels Krarup, Mathieu, Chantal, Morales, Cristobal, Mosenzon, Ofri, Segel, Stine, Tsoukas, George, and Pieber, Thomas R

Published
2021
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26. Islet autoantibody frequency in relatives of children with type 1 diabetes who have a type 2 diabetes diagnosis.

Author

Lewis, Shanice J., Williams, Claire L., Mortimer, Georgina L., Oram, Richard A., Hagopian, William A., Gillespie, Kathleen M., and Long, Anna E.

Subjects
TYPE 1 diabetes, T-test (Statistics), CARRIER proteins, RESEARCH funding, AUTOANTIBODIES, FISHER exact test, DESCRIPTIVE statistics, ENZYMES, INSULIN, AUTOINFLAMMATORY diseases, ISLANDS of Langerhans, TYPE 2 diabetes, AUTOIMMUNE diseases, CHILDREN
Abstract

Aim: This study aimed to evaluate characteristics of autoimmunity in individuals who have a type 2 diagnosis and are relatives of children with type 1 diabetes. Methods: Pre‐diagnosis samples (median 17 months before onset) from relatives who were later diagnosed with type 2 diabetes were measured for autoantibodies to glutamate decarboxylase 65 (GADA), islet antigen‐2 (IA‐2A), zinc transporter 8 (ZnT8A) and insulin (IAA) as well as the type 1 diabetes genetic risk score (GRS2). Associations between islet autoantibodies, insulin treatment and GRS2 were analysed using Fisher's exact and t‐tests. Results: Among 226 relatives (64% men; mean age at sampling 41 years; mean age 54 years at diagnosis), 32 (14%) were islet autoantibody‐positive for at least one autoantibody more than a decade before diagnosis. Approximately half of these (n = 15) were treated with insulin. GADA‐positivity was higher in insulin‐treated relatives than in non‐insulin‐treated relatives (12/18 [67%] vs. 6/18 [33%], p < 0.001). IAA‐positivity was observed in 13/32 (41%) of relatives with autoantibodies. GRS2 scores were increased in autoantibody‐positive relatives (p = 0.032), but there was no clear evidence for a difference according to treatment (p = 0.072). Conclusion: This study highlights the importance of measuring islet autoantibodies, including IAA, in relatives of people with type 1 diabetes to avoid misdiagnosis. [ABSTRACT FROM AUTHOR]

Published
2024
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27. Data-Driven Phenotyping of Presymptomatic Type 1 Diabetes Using Longitudinal Autoantibody Profiles.

Author

Ghalwash, Mohamed, Anand, Vibha, Ng, Kenney, Dunne, Jessica L., Lou, Olivia, Lundgren, Markus, Hagopian, William A., Rewers, Marian, Ziegler, Anette-G., Veijola, Riitta, Ziegler, Anette G., Bonifacio, Ezio, Achenbach, Peter, Winkler, Christiane, Frohnert, Brigitte I., Norris, Jill, Steck, Andrea, Waugh, Kathleen, Yu, Liping, and Killian, Michael

Subjects
TYPE 1 diabetes, AUTOANTIBODIES, HIERARCHICAL clustering (Cluster analysis), SURVIVAL analysis (Biometry), SEROCONVERSION
Abstract

OBJECTIVE: To characterize distinct islet autoantibody profiles preceding stage 3 type 1 diabetes RESEARCH DESIGN AND METHODS: The T1DI (Type 1 Diabetes Intelligence) study combined data from 1,845 genetically susceptible prospectively observed children who were positive for at least one islet autoantibody: insulin autoantibody (IAA), GAD antibody (GADA), or islet antigen 2 antibody (IA-2A). Using a novel similarity algorithm that considers an individual's temporal autoantibody profile, age at autoantibody appearance, and variation in the positivity of autoantibody types, we performed an unsupervised hierarchical clustering analysis. Progression rates to diabetes were analyzed via survival analysis. RESULTS: We identified five main clusters of individuals with distinct autoantibody profiles characterized by seroconversion age and sequence of appearance of the three autoantibodies. The highest 5-year risk from first positive autoantibody to type 1 diabetes (69.9%; 95% CI 60.0–79.2) was observed in children who first developed IAA in early life (median age 1.6 years) followed by GADA (1.9 years) and then IA-2A (2.1 years). Their 10-year risk was 89.9% (95% CI 81.9–95.4). A high 5-year risk was also found in children with persistent IAA and GADA (39.1%) and children with persistent GADA and IA-2A (30.9%). A lower 5-year risk (10.5%) was observed in children with a late appearance of persistent GADA (6.1 years). The lowest 5-year diabetes risk (1.6%) was associated with positivity for a single, often reverting, autoantibody. CONCLUSIONS: The novel clustering algorithm identified children with distinct islet autoantibody profiles and progression rates to diabetes. These results are useful for prediction, selection of individuals for prevention trials, and studies investigating various pathways to type 1 diabetes. [ABSTRACT FROM AUTHOR]

Published
2024
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32. Clinical prediction models combining routine clinical measures have high accuracy in identifying youth-onset type 2 diabetes defined by maintained endogenous insulin secretion: The SEARCH for Diabetes in Youth Study

Author

Jones, Angus G., primary, Shields, Beverley M, primary, A Oram, Richard, primary, M Dabelea, Dana, primary, A Hagopian, William, primary, Lustigova, Eva, primary, S Shah, Amy, primary, Knupp, Julieanne, primary, K Mottl, Amy, primary, B. D`Agostino Jr, Ralph, primary, Williams, Adrienne, primary, Pihoker, Catherine, primary, Divers, Jasmin, primary, and Redondo, Maria J, primary

Published
2024
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33. Host autophagy mediates organ wasting and nutrient mobilization for tumor growth

Author

Khezri, Rojyar, Holland, Petter, Schoborg, Todd Andrew, Abramovich, Ifat, Takáts, Szabolcs, Dillard, Caroline, Jain, Ashish, O'Farrell, Fergal, Schultz, Sebastian Wolfgang, Hagopian, William M, Quintana, Eduardo Martin, Ng, Rachel, Katheder, Nadja Sandra, Rahman, Mohammed Mahidur, Teles Reis, José Gerardo, Brech, Andreas, Jasper, Heinrich, Rusan, Nasser M, Jahren, Anne Hope, Gottlieb, Eyal, and Rusten, Tor Erik

Published
2021
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36. Teplizumab (anti-CD3 mAb) treatment preserves C-peptide responses in patients with new-onset type 1 diabetes in a randomized controlled trial: metabolic and immunologic features at baseline identify a subgroup of responders.

Author

Herold, Kevan C, Gitelman, Stephen E, Ehlers, Mario R, Gottlieb, Peter A, Greenbaum, Carla J, Hagopian, William, Boyle, Karen D, Keyes-Elstein, Lynette, Aggarwal, Sudeepta, Phippard, Deborah, Sayre, Peter H, McNamara, James, Bluestone, Jeffrey A, and AbATE Study Team

Subjects
AbATE Study Team, Humans, Diabetes Mellitus, Type 1, C-Peptide, Adolescent, Adult, Child, Female, Male, Antibodies, Monoclonal, Humanized, Clinical Trials and Supportive Activities, Autoimmune Disease, Pediatric, Clinical Research, Diabetes, Metabolic and endocrine, Endocrinology & Metabolism, Medical and Health Sciences
Abstract

Trials of immune therapies in new-onset type 1 diabetes (T1D) have shown success, but not all subjects respond, and the duration of response is limited. Our aim was to determine whether two courses of teplizumab, an Fc receptor-nonbinding anti-CD3 monoclonal antibody, reduces the decline in C-peptide levels in patients with T1D 2 years after disease onset. We also set out to identify characteristics of responders. We treated 52 subjects with new-onset T1D with teplizumab for 2 weeks at diagnosis and after 1 year in an open-label, randomized, controlled trial. In the intent to treat analysis of the primary end point, patients treated with teplizumab had a reduced decline in C-peptide at 2 years (mean -0.28 nmol/L [95% CI -0.36 to -0.20]) versus control (mean -0.46 nmol/L [95% CI -0.57 to -0.35]; P = 0.002), a 75% improvement. The most common adverse events were rash, transient upper respiratory infections, headache, and nausea. In a post hoc analysis we characterized clinical responders and found that metabolic (HbA1c and insulin use) and immunologic features distinguished this group from those who did not respond to teplizumab. We conclude that teplizumab treatment preserves insulin production and reduces the use of exogenous insulin in some patients with new-onset T1D. Metabolic and immunologic features at baseline can identify a subgroup with robust responses to immune therapy.

Published
2013

40. Clinical prediction models combining routine clinical measures identify participants with youth-onset diabetes who maintain insulin secretion in the range associated with type 2 diabetes: The SEARCH for Diabetes in Youth Study

Author

Jones, Angus G, primary, Shields, Beverley, additional, Oram, Richard A, additional, Dabelea, Dana M, additional, Hagopian, William A, additional, Lustigova, Eva, additional, Shah, Amy S, additional, Knupp, Julieanne, additional, Mottl, Amy K, additional, D`Agostino, Ralph B, additional, Williams, Adrienne, additional, Marcovina, Santica M, additional, Pihoker, Catherine, additional, Divers, Jasmin, additional, and Redondo, Maria J, additional

Published
2023
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41. Gastrointestinal infections modulate the risk for insulin autoantibodies as the first-appearing autoantibody in the TEDDY Study

Author

Lönnrot, Maria, primary, F. Lynch, Kristian, primary, Rewers, Marian, primary, Lernmark, Åke, primary, Vehik, Kendra, primary, Akolkar, Beena, primary, Hagopian, William, primary, Krischer, Jeffrey, primary, A. McIndoe, Rickhard, primary, Toppari, Jorma, primary, Ziegler, Anette G., primary, Petrosino, Joseph F., primary, Lloyd, Richard, primary, and Hyöty, Heikki, primary

Published
2023
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42. HLA genotype and probiotics modify the association between timing of solid food introduction and islet autoimmunity in the TEDDY Study

Author

Hagopian, William, primary, Uusitalo, Ulla, primary, Mramba, Lazarus K., primary, Aronsson, Carin Andrén, primary, Vehik, Kendra, primary, Yang, Jimin, primary, Hummel, Sandra, primary, Lernmark, Åke, primary, Rewers, Marian, primary, McIndoe, Richard, primary, Toppari, Jorma, primary, Ziegler, Anette G., primary, Akolkar, Beena, primary, Krischer, Jeffrey P., primary, Virtanen, Suvi M., primary, and Norris, Jill M., primary

Published
2023
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46. Diabetes Study of Children of Diverse Ethnicity and Race: Study design.

Author

Redondo, Maria J., Harrall, Kylie K., Glueck, Deborah H., Tosur, Mustafa, Uysal, Serife, Muir, Andrew, Atkinson, Elizabeth G., Shapiro, Melanie R., Yu, Liping, Winter, William E., Weedon, Michael, Brusko, Todd M., Oram, Richard, Vehik, Kendra, Hagopian, William, Atkinson, Mark A., and Dabelea, Dana

Subjects
DIABETES in children, GENETIC risk score, RACE, TYPE 1 diabetes, EXPERIMENTAL design, TYPE 2 diabetes
Abstract

Aims: Determining diabetes type in children has become increasingly difficult due to an overlap in typical characteristics between type 1 diabetes (T1D) and type 2 diabetes (T2D). The Diabetes Study in Children of Diverse Ethnicity and Race (DISCOVER) programme is a National Institutes of Health (NIH)‐supported multicenter, prospective, observational study that enrols children and adolescents with non‐secondary diabetes. The primary aim of the study was to develop improved models to differentiate between T1D and T2D in diverse youth. Materials and Methods: The proposed models will evaluate the utility of three existing T1D genetic risk scores in combination with data on islet autoantibodies and other parameters typically available at the time of diabetes onset. Low non‐fasting serum C‐peptide (<0.6 nmol/L) between 3 and 10 years after diabetes diagnosis will be considered a biomarker for T1D as it reflects the loss of insulin secretion ability. Participating centres are enrolling youth (<19 years old) either with established diabetes (duration 3–10 years) for a cross‐sectional evaluation or with recent onset diabetes (duration 3 weeks–15 months) for the longitudinal observation with annual visits for 3 years. Cross‐sectional data will be used to develop models. Longitudinal data will be used to externally validate the best‐fitting model. Results: The results are expected to improve the ability to classify diabetes type in a large and growing subset of children who have an unclear form of diabetes at diagnosis. Conclusions: Accurate and timely classification of diabetes type will help establish the correct clinical management early in the course of the disease. [ABSTRACT FROM AUTHOR]

Published
2024
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47. 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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48. Type 2 Diabetes: Evidence for Linkage on Chromosome 20 in 716 Finnish Affected Sib Pairs

Author

Ghosh, Soumitra, Watanabe, Richard M., Hauser, Elizabeth R., Valle, Timo, Magnuson, Victoria L., Erdos, Michael R., Langefeld, Carl D., Balow,, James, Ally, Delphine S., Kohtamaki, Kimmo, Chines, Peter, Birznieks, Gunther, Kaleta, Hong-Shi, Musick, Anjene, Te, Catherine, Tannenbaum, Joyce, Eldridge, William, Shapiro, Shane, Martin, Colin, Witt, Alyson, So, Alistair, Chang, Jennie, Shurtleff, Ben, Porter, Rachel, Kudelko, Kristina, Unni, Arun, Segal, Leonid, Sharaf, Ravi, Blaschak-Harvan, Jillian, Eriksson, Johan, Tenkula, Tuula, Vidgren, Gabriele, Ehnholm, Christian, Tuomilehto-Wolf, Eva, Hagopian, William, Buchanan, Thomas A., Tuomilehto, Jaakko, Bergman, Richard N., Collins, Francis S., and Boehnke, Michael

Published
1999

49. The human gut microbiome in early-onset type 1 diabetes from the TEDDY study

Author

Vatanen, Tommi, Franzosa, Eric A., Schwager, Randall, Tripathi, Surya, Arthur, Timothy D., Vehik, Kendra, Lernmark, Åke, Hagopian, William A., Rewers, Marian J., She, Jin-Xiong, Toppari, Jorma, Ziegler, Anette-G., Akolkar, Beena, Krischer, Jeffrey P., Stewart, Christopher J., Ajami, Nadim J., Petrosino, Joseph F., Gevers, Dirk, Lähdesmäki, Harri, Vlamakis, Hera, Huttenhower, Curtis, and Xavier, Ramnik J.

Published
2018
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50. Temporal development of the gut microbiome in early childhood from the TEDDY study

Author

Stewart, Christopher J., Ajami, Nadim J., O’Brien, Jacqueline L., Hutchinson, Diane S., Smith, Daniel P., Wong, Matthew C., Ross, Matthew C., Lloyd, Richard E., Doddapaneni, HarshaVardhan, Metcalf, Ginger A., Muzny, Donna, Gibbs, Richard A., Vatanen, Tommi, Huttenhower, Curtis, Xavier, Ramnik J., Rewers, Marian, Hagopian, William, Toppari, Jorma, Ziegler, Anette-G., She, Jin-Xiong, Akolkar, Beena, Lernmark, Ake, Hyoty, Heikki, Vehik, Kendra, Krischer, Jeffrey P., and Petrosino, Joseph F.

Published
2018
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