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1. 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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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. Genome-wide analysis of oxylipins and oxylipin profiles in a pediatric population

Author

Buckner, Teresa, Johnson, Randi K, Vanderlinden, Lauren A, Carry, Patrick M, Romero, Alex, Onengut-Gumuscu, Suna, Chen, Wei-Min, Kim, Soojeong, Fiehn, Oliver, Frohnert, Brigitte I, Crume, Tessa, Perng, Wei, Kechris, Katerina, Rewers, Marian, and Norris, Jill M

Subjects
Biomedical and Clinical Sciences, Nutrition and Dietetics, Genetics, Human Genome, Prevention, 2.1 Biological and endogenous factors, Aetiology, Inflammatory and immune system, Good Health and Well Being, oxylipin, inflammation, pediatric, genome-wide association study, polyunsaturated fatty acids, lipid mediators, Agricultural Biotechnology, Nutrition and dietetics
Abstract

BackgroundOxylipins are inflammatory biomarkers derived from omega-3 and-6 fatty acids implicated in inflammatory diseases but have not been studied in a genome-wide association study (GWAS). The aim of this study was to identify genetic loci associated with oxylipins and oxylipin profiles to identify biologic pathways and therapeutic targets for oxylipins.MethodsWe conducted a GWAS of plasma oxylipins in 316 participants in the Diabetes Autoimmunity Study in the Young (DAISY). DNA samples were genotyped using the TEDDY-T1D Exome array, and additional variants were imputed using the Trans-Omics for Precision Medicine (TOPMed) multi-ancestry reference panel. Principal components analysis of 36 plasma oxylipins was used to capture oxylipin profiles. PC1 represented linoleic acid (LA)- and alpha-linolenic acid (ALA)-related oxylipins, and PC2 represented arachidonic acid (ARA)-related oxylipins. Oxylipin PC1, PC2, and the top five loading oxylipins from each PC were used as outcomes in the GWAS (genome-wide significance: p

Published
2023

4. An Oxylipin-Related Nutrient Pattern and Risk of Type 1 Diabetes in the Diabetes Autoimmunity Study in the Young (DAISY)

Author

Buckner, Teresa, Johnson, Randi K, Vanderlinden, Lauren A, Carry, Patrick M, Romero, Alex, Onengut-Gumuscu, Suna, Chen, Wei-Min, Fiehn, Oliver, Frohnert, Brigitte I, Crume, Tessa, Perng, Wei, Kechris, Katerina, Rewers, Marian, and Norris, Jill M

Subjects
Biomedical and Clinical Sciences, Nutrition and Dietetics, Pediatric, Nutrition, Prevention, Autoimmune Disease, Clinical Research, Diabetes, Aetiology, 2.1 Biological and endogenous factors, Metabolic and endocrine, Child, Adolescent, Humans, Diabetes Mellitus, Type 1, Oxylipins, Autoimmunity, Islets of Langerhans, Risk Factors, Case-Control Studies, Nutrients, oxylipin, nutrient pattern, type 1 diabetes, reduced rank regression, Food Sciences, Clinical sciences, Nutrition and dietetics, Public health
Abstract

Oxylipins, pro-inflammatory and pro-resolving lipid mediators, are associated with the risk of type 1 diabetes (T1D) and may be influenced by diet. This study aimed to develop a nutrient pattern related to oxylipin profiles and test their associations with the risk of T1D among youth. The nutrient patterns were developed with a reduced rank regression in a nested case-control study (n = 335) within the Diabetes Autoimmunity Study in the Young (DAISY), a longitudinal cohort of children at risk of T1D. The oxylipin profiles (adjusted for genetic predictors) were the response variables. The nutrient patterns were tested in the case-control study (n = 69 T1D cases, 69 controls), then validated in the DAISY cohort using a joint Cox proportional hazards model (n = 1933, including 81 T1D cases). The first nutrient pattern (NP1) was characterized by low beta cryptoxanthin, flavanone, vitamin C, total sugars and iron, and high lycopene, anthocyanidins, linoleic acid and sodium. After adjusting for T1D family history, the HLA genotype, sex and race/ethnicity, NP1 was associated with a lower risk of T1D in the nested case-control study (OR: 0.44, p = 0.0126). NP1 was not associated with the risk of T1D (HR: 0.54, p-value = 0.1829) in the full DAISY cohort. Future studies are needed to confirm the nested case-control findings and investigate the modifiable factors for oxylipins.

Published
2023

5. 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

6. The oxylipin profile is associated with development of type 1 diabetes: the Diabetes Autoimmunity Study in the Young (DAISY)

Author

Buckner, Teresa, Vanderlinden, Lauren A, DeFelice, Brian C, Carry, Patrick M, Kechris, Katerina, Dong, Fran, Fiehn, Oliver, Frohnert, Brigitte I, Clare-Salzler, Michael, Rewers, Marian, and Norris, Jill M

Subjects
Nutrition, Prevention, Autoimmune Disease, Clinical Research, Diabetes, Aetiology, 2.1 Biological and endogenous factors, Metabolic and endocrine, Adolescent, Arachidonic Acid, Autoantibodies, Autoimmunity, Case-Control Studies, Child, Child, Preschool, Chromatography, High Pressure Liquid, Diabetes Mellitus, Type 1, Docosahexaenoic Acids, Female, Follow-Up Studies, Glutamate Decarboxylase, HLA-DR3 Antigen, HLA-DR4 Antigen, Humans, Insulin, Linoleic Acid, Male, Oxylipins, Prospective Studies, Receptor-Like Protein Tyrosine Phosphatases, Class 8, Tandem Mass Spectrometry, Inflammation, Lipid mediators, Paediatric, Proinflammatory, Pro-resolving, Type 1 diabetes, Clinical Sciences, Paediatrics and Reproductive Medicine, Public Health and Health Services, Endocrinology & Metabolism
Abstract

Aims/hypothesisOxylipins are lipid mediators derived from polyunsaturated fatty acids. Some oxylipins are proinflammatory (e.g. those derived from arachidonic acid [ARA]), others are pro-resolving of inflammation (e.g. those derived from α-linolenic acid [ALA], docosahexaenoic acid [DHA] and eicosapentaenoic acid [EPA]) and others may be both (e.g. those derived from linoleic acid [LA]). The goal of this study was to examine whether oxylipins are associated with incident type 1 diabetes.MethodsWe conducted a nested case-control analysis in the Diabetes Autoimmunity Study in the Young (DAISY), a prospective cohort study of children at risk of type 1 diabetes. Plasma levels of 14 ARA-derived oxylipins, ten LA-derived oxylipins, six ALA-derived oxylipins, four DHA-derived oxylipins and two EPA-related oxylipins were measured by ultra-HPLC-MS/MS at multiple timepoints related to autoantibody seroconversion in 72 type 1 diabetes cases and 71 control participants, which were frequency matched on age at autoantibody seroconversion (of the case), ethnicity and sample availability. Linear mixed models were used to obtain an age-adjusted mean of each oxylipin prior to type 1 diabetes. Age-adjusted mean oxylipins were tested for association with type 1 diabetes using logistic regression, adjusting for the high risk HLA genotype HLA-DR3/4,DQB1*0302. We also performed principal component analysis of the oxylipins and tested principal components (PCs) for association with type 1 diabetes. Finally, to investigate potential critical timepoints, we examined the association of oxylipins measured before and after autoantibody seroconversion (of the cases) using PCs of the oxylipins at those visits.ResultsThe ARA-related oxylipin 5-HETE was associated with increased type 1 diabetes risk. Five LA-related oxylipins, two ALA-related oxylipins and one DHA-related oxylipin were associated with decreased type 1 diabetes risk. A profile of elevated LA- and ALA-related oxylipins (PC1) was associated with decreased type 1 diabetes risk (OR 0.61; 95% CI 0.40, 0.94). A profile of elevated ARA-related oxylipins (PC2) was associated with increased diabetes risk (OR 1.53; 95% CI 1.03, 2.29). A critical timepoint analysis showed type 1 diabetes was associated with a high ARA-related oxylipin profile at post-autoantibody-seroconversion but not pre-seroconversion.Conclusions/interpretationThe protective association of higher LA- and ALA-related oxylipins demonstrates the importance of both inflammation promotion and resolution in type 1 diabetes. Proinflammatory ARA-related oxylipins may play an important role once the autoimmune process has begun.

Published
2021

9. Plasma protein biomarkers predict the development of persistent autoantibodies and type 1 diabetes 6 months prior to the onset of autoimmunity

Author

Nakayasu, Ernesto S., Bramer, Lisa M., Ansong, Charles, Schepmoes, Athena A., Fillmore, Thomas L., Gritsenko, Marina A., Clauss, Therese R., Gao, Yuqian, Piehowski, Paul D., Stanfill, Bryan A., Engel, Dave W., Orton, Daniel J., Moore, Ronald J., Qian, Wei-Jun, Sechi, Salvatore, Frohnert, Brigitte I., Toppari, Jorma, Ziegler, Anette-G., Lernmark, Åke, Hagopian, William, Akolkar, Beena, Smith, Richard D., Rewers, Marian J., Webb-Robertson, Bobbie-Jo M., and Metz, Thomas O.

Published
2023
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10. DPVis: Visual Analytics with Hidden Markov Models for Disease Progression Pathways

Author

Kwon, Bum Chul, Anand, Vibha, Severson, Kristen A, Ghosh, Soumya, Sun, Zhaonan, Frohnert, Brigitte I, Lundgren, Markus, and Ng, Kenney

Subjects
Computer Science - Machine Learning, Computer Science - Human-Computer Interaction, Statistics - Machine Learning
Abstract

Clinical researchers use disease progression models to understand patient status and characterize progression patterns from longitudinal health records. One approach for disease progression modeling is to describe patient status using a small number of states that represent distinctive distributions over a set of observed measures. Hidden Markov models (HMMs) and its variants are a class of models that both discover these states and make inferences of health states for patients. Despite the advantages of using the algorithms for discovering interesting patterns, it still remains challenging for medical experts to interpret model outputs, understand complex modeling parameters, and clinically make sense of the patterns. To tackle these problems, we conducted a design study with clinical scientists, statisticians, and visualization experts, with the goal to investigate disease progression pathways of chronic diseases, namely type 1 diabetes (T1D), Huntington's disease, Parkinson's disease, and chronic obstructive pulmonary disease (COPD). As a result, we introduce DPVis which seamlessly integrates model parameters and outcomes of HMMs into interpretable and interactive visualizations. In this study, we demonstrate that DPVis is successful in evaluating disease progression models, visually summarizing disease states, interactively exploring disease progression patterns, and building, analyzing, and comparing clinically relevant patient subgroups., Comment: to appear at IEEE Transactions on Visualization and Computer Graphics

Published
2019
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11. 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

13. 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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16. Higher daily physical activity is associated with higher osteocalcin levels in adolescents

Author

Chahla, Saydi E, Frohnert, Brigitte I, Thomas, William, Kelly, Aaron S, Nathan, Brandon M, and Polgreen, Lynda E

Subjects
Prevention, Nutrition, Obesity, Cancer, Metabolic and endocrine, Cardiovascular, Oral and gastrointestinal, adolescents, obesity, osteocalcin, physical activity, Public Health and Health Services
Abstract

BackgroundExercise stimulates bone remodeling and improves insulin sensitivity (Si), even without associated weight loss. Osteocalcin (OCN), a bone-derived protein, is associated with improved Si.PurposeWe examined how daily physical activity is associated with OCN and Si.MethodsPhysical activity was measured through questionnaires completed in Minneapolis from 2010-2012. A physical activity score (PAQsum) was calculated to quantify physical activity (range 1-5). OCN and bone specific alkaline phosphatase (BAP) were measured by ELISA. Si was measured by the insulin modified frequently sampled IV glucose tolerance test.ResultsThe mean PAQsum value was 2.4±0.8 in 47 participants (12-17.9 years old). PAQsum was positively associated with OCN (p= 0.006). Participants with PAQsum2 (p

Published
2015

22. Anxiety and Risk Perception in Parents of Children Identified by Population Screening as High Risk for Type 1 Diabetes.

Author

O’Donnell, Holly K., Rasmussen, Cristy Geno, Dong, Fran, Simmons, Kimber M., Steck, Andrea K., Frohnert, Brigitte I., Bautista, Kimberly, Rewers, Marian J., Baxter, Judith, ASK Study Group, Felipe-Morales, Daniel, Driscoll, Kimberly, Gesualdo, Patricia, Hoffman, Michelle, Karban, Rachel, Pauley, Meghan, Sepulveda, Flor, Silva, Crystal, Taki, Iman, and Waugh, Kathleen

Subjects
TYPE 1 diabetes, RISK perception, MEDICAL screening, PARENTS, DIABETIC acidosis, WORRY
Abstract

OBJECTIVE: To assess anxiety and risk perception among parents whose children screened positive for islet autoantibodies, indicating elevated risk for type 1 diabetes (T1D). RESEARCH DESIGN AND METHODS: The Autoimmunity Screening for Kids (ASK) study identified 319 children age 1 to 17 years at risk for T1D via screening for islet autoantibodies; 280 children with confirmed islet autoantibodies and their caregivers enrolled in a follow-up education and monitoring program to prevent diabetic ketoacidosis at diagnosis. Parents completed questionnaires at each monitoring visit, including a 6-item version of the State Anxiety Inventory (SAI), to assess anxiety about their child developing T1D, and a single question to assess risk perception. RESULTS: At the first ASK follow-up monitoring visit, mean parental anxiety was elevated above the clinical cutoff of 40 (SAI 46.1 ± 11.2). At the second follow-up monitoring visit (i.e., visit 2), mean anxiety remained elevated but started to trend down. Approximately half (48.9%) of parents reported their child was at increased risk for T1D at the initial follow-up monitoring visit (visit 1). Parents of children with more than one islet autoantibody and a first-degree relative with T1D were more likely to report their child was at increased risk. CONCLUSIONS: Most parents of autoantibody-positive children have high anxiety about their child developing T1D. Information about the risk of developing T1D is difficult to convey, as evidenced by the wide range of risk perception reported in this sample. [ABSTRACT FROM AUTHOR]

Published
2023
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23. Gastrointestinal Infections Modulate the Risk for Insulin Autoantibodies as the First-Appearing Autoantibody in the TEDDY Study.

Author

Lönnrot, Maria, Lynch, Kristian F., Rewers, Marian, Lernmark, Åke, Vehik, Kendra, Akolkar, Beena, Hagopian, William, Krischer, Jeffrey, McIndoe, Rickhard A., Toppari, Jorma, Ziegler, Anette-G., Petrosino, Joseph F., Lloyd, Richard, Hyöty, Heikki, TEDDY Study Group, Bautista, Kimberly, Baxter, Judith, Felipe-Morales, Daniel, Frohnert, Brigitte I., and Stahl, Marisa

Abstract

OBJECTIVE: To investigate gastrointestinal infection episodes (GIEs) in relation to the appearance of islet autoantibodies in The Environmental Determinants of Diabetes in the Young (TEDDY) cohort. RESEARCH DESIGN AND METHODS: GIEs on risk of autoantibodies against either insulin (IAA) or GAD (GADA) as the first-appearing autoantibody were assessed in a 10-year follow-up of 7,867 children. Stool virome was characterized in a nested case-control study. RESULTS: GIE reports (odds ratio [OR] 2.17 [95% CI 1.39–3.39]) as well as Norwalk viruses found in stool (OR 5.69 [1.36–23.7]) at <1 year of age were associated with an increased IAA risk at 2–4 years of age. GIEs reported at age 1 to <2 years correlated with a lower risk of IAA up to 10 years of age (OR 0.48 [0.35–0.68]). GIE reports at any other age were associated with an increase in IAA risk (OR 2.04 for IAA when GIE was observed 12–23 months prior [1.41–2.96]). Impacts on GADA risk were limited to GIEs <6 months prior to autoantibody development in children <4 years of age (OR 2.16 [1.54–3.02]). CONCLUSIONS: Bidirectional associations were observed. GIEs were associated with increased IAA risk when reported before 1 year of age or 12–23 months prior to IAA. Norwalk virus was identified as one possible candidate factor. GIEs reported during the 2nd year of life were associated with a decreased IAA risk. [ABSTRACT FROM AUTHOR]

Published
2023
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24. Refining the Definition of Stage 1 Type 1 Diabetes: An Ontology-Driven Analysis of the Heterogeneity of Multiple Islet Autoimmunity.

Author

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

Subjects
*TYPE 1 diabetes, *AUTOIMMUNITY, *ISLANDS, *AUTOANTIBODIES, *HETEROGENEITY
Abstract

OBJECTIVE: To estimate the risk of progression to stage 3 type 1 diabetes based on varying definitions of multiple islet autoantibody positivity (mIA). RESEARCH DESIGN AND METHODS: Type 1 Diabetes Intelligence (T1DI) is a combined prospective data set of children from Finland, Germany, Sweden, and the U.S. who have an increased genetic risk for type 1 diabetes. Analysis included 16,709 infants-toddlers enrolled by age 2.5 years and comparison between groups using Kaplan-Meier survival analysis. RESULTS: Of 865 (5%) children with mIA, 537 (62%) progressed to type 1 diabetes. The 15-year cumulative incidence of diabetes varied from the most stringent definition (mIA/Persistent/2: two or more islet autoantibodies positive at the same visit with two or more antibodies persistent at next visit; 88% [95% CI 85–92%]) to the least stringent (mIA/Any: positivity for two islet autoantibodies without co-occurring positivity or persistence; 18% [5–40%]). Progression in mIA/Persistent/2 was significantly higher than all other groups (P < 0.0001). Intermediate stringency definitions showed intermediate risk and were significantly different than mIA/Any (P < 0.05); however, differences waned over the 2-year follow-up among those who did not subsequently reach higher stringency. Among mIA/Persistent/2 individuals with three autoantibodies, loss of one autoantibody by the 2-year follow-up was associated with accelerated progression. Age was significantly associated with time from seroconversion to mIA/Persistent/2 status and mIA to stage 3 type 1 diabetes. CONCLUSIONS: The 15-year risk of progression to type 1 diabetes risk varies markedly from 18 to 88% based on the stringency of mIA definition. While initial categorization identifies highest-risk individuals, short-term follow-up over 2 years may help stratify evolving risk, especially for those with less stringent definitions of mIA. [ABSTRACT FROM AUTHOR]

Published
2023
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25. Genome-wide analysis of oxylipins and oxylipin profiles in a pediatric population

Author

Buckner, Teresa, primary, Johnson, Randi K., additional, Vanderlinden, Lauren A., additional, Carry, Patrick M., additional, Romero, Alex, additional, Onengut-Gumuscu, Suna, additional, Chen, Wei-Min, additional, Kim, Soojeong, additional, Fiehn, Oliver, additional, Frohnert, Brigitte I., additional, Crume, Tessa, additional, Perng, Wei, additional, Kechris, Katerina, additional, Rewers, Marian, additional, and Norris, Jill M., additional

Published
2023
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27. Prevalence of SARS-CoV-2 Antibodies Among Healthy Children From Colorado From 2020 to 2021: A Brief Report

Author

Frost, Holly M., primary, Geno Rasmussen, Cristy, additional, Shorrosh, Hanan, additional, Pyle, Laura, additional, Bautista, Kimberly, additional, Frohnert, Brigitte I., additional, Stahl, Marisa, additional, Simmons, Kimber, additional, Steck, Andrea K., additional, Jia, Xiaofan, additional, Yu, Liping, additional, and Rewers, Marian, additional

Published
2023
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28. Islet autoantibody screening in at-risk adolescents to predict type 1 diabetes until young adulthood: a prospective cohort study

Author

Ghalwash, Mohamed, primary, Anand, Vibha, additional, Lou, Olivia, additional, Martin, Frank, additional, Rewers, Marian, additional, Ziegler, Anette-G, additional, Toppari, Jorma, additional, Hagopian, William A, additional, Veijola, Riitta, additional, Achenbach, Peter, additional, Bonifacio, Ezio, additional, Crouch, Claire, additional, Dunne, Jessica, additional, Elding Larsson, Helena, additional, Frohnert, Brigitte I, additional, Hu, Jianying, additional, Hyöty, Heikki, additional, Ilonen, Jorma, additional, Jönsson, Josefin, additional, Killian, Michael, additional, Knip, Mikael, additional, Koski, Eileen, additional, Lernmark, Åke, additional, Li, Ying, additional, Li, Zhiguo, additional, Liu, Bin, additional, Lundgren, Markus, additional, Malhotra, Ashwani, additional, Maziarz, Marlena, additional, Meyer, Jocelyn, additional, Moore, Shelley, additional, Ng, Kenney, additional, Norris, Jill, additional, Roy, Shreya, additional, Spiliopoulos, Lampros, additional, Steck, Andrea, additional, Stavropoulos, Harry, additional, Waugh, Kathleen, additional, Winkler, Christiane, additional, and Yu, Liping, additional

Published
2023
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29. 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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32. 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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34. Islet Autoantibody Levels Differentiate Progression Trajectories in Individuals With Presymptomatic Type 1 Diabetes

Author

Kwon, Bum Chul, primary, Achenbach, Peter, additional, Anand, Vibha, additional, Frohnert, Brigitte I., additional, Hagopian, William, additional, Hu, Jianying, additional, Koski, Eileen, additional, Lernmark, Åke, additional, Lou, Olivia, additional, Martin, Frank, additional, Ng, Kenney, additional, Toppari, Jorma, additional, and Veijola, Riitta, additional

Published
2022
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38. 351-OR: Investigation of Novel Vitamin D Metabolites and Risk of Islet Autoimmunity and Progression to Type 1 Diabetes

Author

KIM, SOOJEONG, primary, ZWICK, NICOLE L., additional, CARRY, PATRICK M., additional, DEFELICE, BRIAN C., additional, JOHNSON, RANDI K., additional, DONG, FRAN, additional, BUCKNER, TERESA, additional, VANDERLINDEN, LAUREN A., additional, FROHNERT, BRIGITTE I., additional, KECHRIS, KATERINA, additional, FIEHN, OLIVER, additional, REWERS, MARIAN, additional, and NORRIS, JILL M., additional

Published
2022
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39. Integration of Infant Metabolite, Genetic, and Islet Autoimmunity Signatures to Predict Type 1 Diabetes by Age 6 Years

Author

Webb-Robertson, Bobbie-Jo M, primary, Nakayasu, Ernesto S, additional, Frohnert, Brigitte I, additional, Bramer, Lisa M, additional, Akers, Sarah M, additional, Norris, Jill M, additional, Vehik, Kendra, additional, Ziegler, Anette-G, additional, Metz, Thomas O, additional, Rich, Stephen S, additional, and Rewers, Marian J, additional

Published
2022
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45. Rising Hemoglobin A1c in the Nondiabetic Range Predicts Progression of Type 1 Diabetes As Well As Oral Glucose Tolerance Tests.

Author

Vehik, Kendra, Boulware, David, Killian, Michael, Rewers, Marian, McIndoe, Richard, Toppari, Jorma, Lernmark, Åke, Akolkar, Beena, Ziegler, Anette-G., Rodriguez, Henry, Schatz, Desmond A., Krischer, Jeffrey P., Hagopian, William, The TEDDY Study Group Colorado Clinical Center, Barbour, Aaron, Bautista, Kimberly, Baxter, Judith, Felipe-Morales, Daniel, Frohnert, Brigitte I., and Stahl, Marisa

Subjects
BLOOD sugar analysis, AUTOANTIBODIES, RESEARCH, RESEARCH methodology, TYPE 1 diabetes, EVALUATION research, COMPARATIVE studies, GLUCOSE tolerance tests, LONGITUDINAL method
Abstract

Objective: Biomarkers predicting risk of type 1 diabetes (stage 3) among children with islet autoantibodies are greatly needed to prevent diabetic ketoacidosis and facilitate prevention therapies.Research Design and Methods: Children in the prospective The Environmental Determinants of Diabetes in the Young (TEDDY) study (n = 707) with confirmed diabetes-associated autoantibodies (GAD antibody, IA-2A, and/or insulin autoantibody) and two or more HbA1c measurements were followed to diabetes or median age 11.1 years. Once confirmed autoantibody positive, HbA1c was measured quarterly. Cox models and receiver operative characteristic curve analyses revealed the prognostic utility for risk of stage 3 on a relative HbA1c increase from the baseline visit or an oral glucose tolerance test (OGTT) 2-h plasma glucose (2-hPG). This HbA1c approach was then validated in the Type 1 Diabetes TrialNet Pathway to Prevention Study (TrialNet) (n = 1,190).Results: A 10% relative HbA1c increase from baseline best marked the increased risk of stage 3 in TEDDY (74% sensitive; 88% specific). Significant predictors of risk for HbA1c change were age and HbA1c at the baseline test, genetic sex, maximum number of autoantibodies, and maximum rate of HbA1c increase by time of change. The multivariable model featuring a HbA1c ≥10% increase and these additional factors revealed increased risk of stage 3 in TEDDY (hazard ratio [HR] 12.74, 95% CI 8.7-18.6, P < 0.0001) and TrialNet (HR 5.09, 95% CI 3.3-7.9, P < 0.0001). Furthermore, the composite model using HbA1c ≥10% increase performed similarly to an OGTT 2-hPG composite model (TEDDY area under the curve [AUC] 0.88 and 0.85, respectively) and to the HbA1c model in TrialNet (AUC 0.82).Conclusions: An increase of ≥10% in HbA1c from baseline is as informative as OGTT 2-hPG in predicting risk of stage 3 in youth with genetic risk and diabetes-associated autoantibodies. [ABSTRACT FROM AUTHOR]

Published
2022
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46. Predictors of the Initiation of Islet Autoimmunity and Progression to Multiple Autoantibodies and Clinical Diabetes: The TEDDY Study.

Author

Krischer, Jeffrey P., Liu, Xiang, Lernmark, Åke, Hagopian, William A., Rewers, Marian J., She, Jin-Xiong, Toppari, Jorma, Ziegler, Anette-G., Akolkar, Beena, TEDDY Study Group, Rewers, Marian, Barbour, Aaron, Bautista, Kimberly, Baxter, Judith, Felipe-Morales, Daniel, Frohnert, Brigitte I., Stahl, Marisa, Gesualdo, Patricia, Hoffman, Michelle, and Karban, Rachel

Subjects
DISEASE progression, AUTOANTIBODIES, RESEARCH, HLA-B27 antigen, TYPE 1 diabetes, EVALUATION research, ISLANDS of Langerhans, INSULIN, COMPARATIVE studies, DISEASE susceptibility, GENOTYPES, IMMUNITY, RESEARCH funding, ESTERASES
Abstract

Objective: To distinguish among predictors of seroconversion, progression to multiple autoantibodies and from multiple autoantibodies to type 1 diabetes in young children.Research Design and Methods: Genetically high-risk newborns (n = 8,502) were followed for a median of 11.2 years (interquartile range 9.3-12.6); 835 (9.8%) developed islet autoantibodies and 283 (3.3%) were diagnosed with type 1 diabetes. Predictors were examined using Cox proportional hazards models.Results: Predictors of seroconversion and progression differed, depending on the type of first appearing autoantibody. Male sex, Finnish residence, having a sibling with type 1 diabetes, the HLA DR4 allele, probiotic use before age 28 days, and single nucleotide polymorphism (SNP) rs689_A (INS) predicted seroconversion to IAA-first (having islet autoantibody to insulin as the first appearing autoantibody). Increased weight at 12 months and SNPs rs12708716_G (CLEC16A) and rs2292239_T (ERBB3) predicted GADA-first (autoantibody to GAD as the first appearing). For those having a father with type 1 diabetes, the SNPs rs2476601_A (PTPN22) and rs3184504_T (SH2B3) predicted both. Younger age at seroconversion predicted progression from single to multiple autoantibodies as well as progression to diabetes, except for those presenting with GADA-first. Family history of type 1 diabetes and the HLA DR4 allele predicted progression to multiple autoantibodies but not diabetes. Sex did not predict progression to multiple autoantibodies, but males progressed more slowly than females from multiple autoantibodies to diabetes. SKAP2 and MIR3681HG SNPs are newly reported to be significantly associated with progression from multiple autoantibodies to type 1 diabetes.Conclusions: Predictors of IAA-first versus GADA-first autoimmunity differ from each other and from the predictors of progression to diabetes. [ABSTRACT FROM AUTHOR]

Published
2022
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48. Genetic scores to stratify risk of developing multiple islet autoantibodies and type 1 diabetes: A prospective study in children

Author

Bonifacio, Ezio, Beyerlein, Andreas, Hippich, Markus, Winkler, Christiane, Vehik, Kendra, Weedon, Michael N., Laimighofer, Michael, Hattersley, Andrew T., Krumsiek, Jan, Frohnert, Brigitte I., Steck, Andrea K., Hagopian, William A., Krischer, Jeffrey P., Lernmark, Åke, Rewers, Marian J., She, Jin-Xiong, Toppari, Jorma, Akolkar, Beena, Oram, Richard A., Rich, Stephen S., and Ziegler, Anette-G.

Subjects
Pancreatic beta cells -- Genetic aspects -- Health aspects, Genotypes -- Identification and classification, Autoantibodies -- Genetic aspects -- Health aspects, Type 1 diabetes -- Genetic aspects -- Risk factors, Biological sciences
Abstract

Background Around 0.3% of newborns will develop autoimmunity to pancreatic beta cells in childhood and subsequently develop type 1 diabetes before adulthood. Primary prevention of type 1 diabetes will require early intervention in genetically at-risk infants. The objective of this study was to determine to what extent genetic scores (two previous genetic scores and a merged genetic score) can improve the prediction of type 1 diabetes. Methods and findings The Environmental Determinants of Diabetes in the Young (TEDDY) study followed genetically at-risk children at 3- to 6-monthly intervals from birth for the development of islet autoantibodies and type 1 diabetes. Infants were enrolled between 1 September 2004 and 28 February 2010 and monitored until 31 May 2016. The risk (positive predictive value) for developing multiple islet autoantibodies (pre-symptomatic type 1 diabetes) and type 1 diabetes was determined in 4,543 children who had no first-degree relatives with type 1 diabetes and either a heterozygous HLA DR3 and DR4-DQ8 risk genotype or a homozygous DR4-DQ8 genotype, and in 3,498 of these children in whom genetic scores were calculated from 41 single nucleotide polymorphisms. In the children with the HLA risk genotypes, risk for developing multiple islet autoantibodies was 5.8% (95% CI 5.0%-6.6%) by age 6 years, and risk for diabetes by age 10 years was 3.7% (95% CI 3.0%-4.4%). Risk for developing multiple islet autoantibodies was 11.0% (95% CI 8.7%-13.3%) in children with a merged genetic score of >14.4 (upper quartile; n = 907) compared to 4.1% (95% CI 3.3%-4.9%, P 14.4 compared with 2.7% (95% CI 1.9%-3.6%) in children with a score of [less than or equal to]14.4 (P 14.4. Scores were higher in European versus US children (P = 0.003). In children with a merged score of >14.4, risk for multiple islet autoantibodies was similar and consistently >10% in Europe and in the US; risk was greater in males than in females (P = 0.01). Limitations of the study include that the genetic scores were originally developed from case-control studies of clinical diabetes in individuals of mainly European decent. It is, therefore, possible that it may not be suitable to all populations. Conclusions A type 1 diabetes genetic score identified infants without family history of type 1 diabetes who had a greater than 10% risk for pre-symptomatic type 1 diabetes, and a nearly 2-fold higher risk than children identified by high-risk HLA genotypes alone. This finding extends the possibilities for enrolling children into type 1 diabetes primary prevention trials., Author(s): Ezio Bonifacio 1, Andreas Beyerlein 2,3,4, Markus Hippich 2,3,4, Christiane Winkler 2,3,4, Kendra Vehik 5, Michael N. Weedon 6, Michael Laimighofer 7, Andrew T. Hattersley 6, Jan Krumsiek 7, [...]

Published
2018
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49. Islet Autoimmunity and HLA Markers of Presymptomatic and Clinical Type 1 Diabetes: Joint Analyses of Prospective Cohort Studies in Finland, Germany, Sweden, and the U.S.

Author

Anand, Vibha, primary, Li, Ying, primary, Liu, Bin, primary, Ghalwash, Mohamed, primary, Koski, Eileen, primary, Ng, Kenney, primary, Dunne, Jessica L., primary, Jönsson, Josefine, primary, Winkler, Christiane, primary, Knip, Mikael, primary, Toppari, Jorma, primary, Ilonen, Jorma, primary, Killian, Michael B., primary, Frohnert, Brigitte I., primary, Lundgren, Markus, primary, Ziegler, Anette-Gabriele, primary, Hagopian, William, primary, Veijola, Riitta, primary, and Rewers, Marian, primary

Published
2021
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50. 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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