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1. 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. 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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7. Maternal gluten, cereal, and dietary fiber intake during pregnancy and lactation and the risk of islet autoimmunity and type 1 diabetes in the child

Author

Anna, Eurén, Heikki, Hyöty, Kalle, Kurppa, Jutta, Laiho, Olli, Laitinen, Jussi, Lehtonen, Katri, Lindfors, Maria, Lönnrot, Johannes, Malkamäki, Noora, Nurminen, Matti, Nykter, Sami, Oikarinen, Leena, Puustinen, Amirbabak, Sioofy-Khojine, Keijo, Viiri, Daniel, Agardh, Andrén, Aronsson Carin, Markus, Lundgren, Iida, Mäkelä, Martin, Romantschuk, Laura, Soininen, Nicolai A, Lund-Blix, Maria, Magnus, Aino-Kaisa, Rantala, Lars, Stene, Ketil, Størdal, German, Tapia, Laura, Elo, Sini, Junttila, Riitta, Lahesmaa, Johanna, Lempainen, Robert, Moulder, Omid, Rasool, Tomi, Suomi, Jorma, Toppari, Ubaid, Ullah, Riitta, Veijola, Aleksandr, Peet, Kärt, Simre, Vallo, Tillmann, Elena, Bargagli, Francesco, Dotta, Laura, Nigi, Guido, Sebastiani, Leena, Hakola, Hannu, Kiviranta, Panu, Rantakokko, Suvi M, Virtanen, Ondrej, Cinek, Eva, Fronkova, Jaroslav, Havlik, Matthieu, Molinier, Juha, Pajula, Eija, Parmes, Juha, Pärkkä, Petri, Saviranta, Peter, Ylén, Alar, Aints, Anu, Bärenson, Anne, Kirss, Ivo, Laidmäe, Astrid, Oras, Aili, Tagoma, Raivo, Uibo, Tamara, Vorobjova, Loïc, Burr, Stefano, Cattaneo, Hui, Chai-Gao, Peter, Cristofollini, Silvia, Generelli, Samantha, Paoletti, Edith, Ruth, Gabriele, Berg, Wisnu, Wicaksono, Joseph, Petrosino, Rainer, Thiel, Schmidtmann, Daniel, Rosanna, Salo, Lauri, Häme, Alexander, Berler, Korina, Papadopoulou, Hans, Bisgaard, Klaus, Bonnelykke, Sarah, Brandt, Astrid, Sevelsted, Jakob, Stokholm, Jonathan, Thorsen, Mikael, Knip, Aki, Sinkkonen, Marja, Roslund, Hakola, Leena, Lund-Blix, Nicolai A., Takkinen, Hanna-Mari, Tapanainen, Heli, Niinistö, Sari, Korhonen, Tuuli E., Stene, Lars C., Hyöty, Heikki, Toppari, Jorma, Ilonen, Jorma, Knip, Mikael, Veijola, Riitta, and Virtanen, Suvi M.

Published
2024
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11. 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

16. Infections and systemic inflammation are associated with lower plasma concentration of insulin-like growth factor I among Malawian children

Author

Maleta, Kenneth, Fan, Yue-Mei, Luoma, Juho, Ashorn, Ulla, Bendabenda, Jaden, Dewey, Kathryn G, Hyöty, Heikki, Knip, Mikael, Kortekangas, Emma, Lehto, Kirsi-Maarit, Matchado, Andrew, Nkhoma, Minyanga, Nurminen, Noora, Parkkila, Seppo, Purmonen, Sami, Veijola, Riitta, Oikarinen, Sami, and Ashorn, Per

Subjects
Medical Microbiology, Biomedical and Clinical Sciences, Clinical Sciences, Prevention, Emerging Infectious Diseases, Digestive Diseases, Infectious Diseases, Pediatric, Clinical Research, 2.1 Biological and endogenous factors, Aetiology, Reproductive health and childbirth, Infection, Good Health and Well Being, Biomarkers, Child, Preschool, Feces, Finland, Humans, Infant, Inflammation, Insulin-Like Growth Factor I, Malawi, Seasons, childhood growth faltering, stunting, infection, hormonal regulation, pathway analysis, systemic inflammation, Engineering, Medical and Health Sciences, Nutrition & Dietetics, Clinical sciences, Nutrition and dietetics
Abstract

BackgroundInsulin-like growth factor I (IGF-I) is the most important hormonal promoter of linear growth in infants and young children.ObjectivesThe objectives of this study were to compare plasma IGF-I concentration in a low- compared with a high-income country and characterize biological pathways leading to reduced IGF-I concentration in children in a low-income setting.MethodsWe analyzed plasma IGF-I concentration from 716 Malawian and 80 Finnish children at 6-36 mo of age. In the Malawian children, we studied the association between IGF-I concentration and their environmental exposures; nutritional status; systemic and intestinal inflammation; malaria parasitemia and viral, bacterial, and parasitic enteric infections; as well as growth at 18 mo of age. We then conducted a pathway analysis to identify direct and indirect associations between these predictors and IGF-I concentration.ResultsThe mean IGF-I concentrations were similar in Malawi and Finland among 6-mo-old infants. At age 18 mo, the mean ± SD concentration was almost double among the Finns compared with the Malawians [24.2 ± 11.3 compared with 12.5 ± 7.7 ng/mL, age- and sex-adjusted difference in mean (95% CI): 11.8 (9.9, 13.7) ng/mL; P

Published
2021

19. 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

20. Detection of enterovirus RNA in peripheral blood mononuclear cells correlates with the presence of the predisposing allele of the type 1 diabetes risk gene IFIH1 and with disease stage

Author

Sioofy-Khojine, Amir-Babak, Richardson, Sarah J., Locke, Jonathan M., Oikarinen, Sami, Nurminen, Noora, Laine, Antti-Pekka, Downes, Kate, Lempainen, Johanna, Todd, John A., Veijola, Riitta, Ilonen, Jorma, Knip, Mikael, Morgan, Noel G., Hyöty, Heikki, Peakman, Mark, and Eichmann, Martin

Published
2022
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21. Umbilical cord blood DNA methylation in children who later develop type 1 diabetes

Author

Laajala, Essi, Kalim, Ubaid Ullah, Grönroos, Toni, Rasool, Omid, Halla-aho, Viivi, Konki, Mikko, Kattelus, Roosa, Mykkänen, Juha, Nurmio, Mirja, Vähä-Mäkilä, Mari, Kallionpää, Henna, Lietzén, Niina, Ghimire, Bishwa R., Laiho, Asta, Hyöty, Heikki, Elo, Laura L., Ilonen, Jorma, Knip, Mikael, Lund, Riikka J., Orešič, Matej, Veijola, Riitta, Lähdesmäki, Harri, Toppari, Jorma, and Lahesmaa, Riitta

Published
2022
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25. Characteristics of Type 1 Diabetes Among Patients Carrying the Protective HLA‐DQB1*06:02 Allele.

Author

Taka, Antti‐Mathias, Härkönen, Taina, Vähäsalo, Paula, Vatanen, Tommi, Lempainen, Johanna, Veijola, Riitta, Turtinen, Maaret, Ilonen, Jorma, and Knip, Mikael

Abstract

We set out to examine in an observational study characteristics of type 1 diabetes at the time of diagnosis among paediatric patients carrying the protective HLA class II DQB1*06:02 allele. We compared characteristics of type 1 diabetes among 5530 Finnish children aged 0–14 years diagnosed between 2003 and 2018. Seventy‐five children with type 1 diabetes carried the DQB1*06:02 allele. The carriers of DQB1*06:02 allele were compared to all children with type 1 diabetes without this allele and those with a high‐risk genotype. We also analysed, how does the genotype of a high‐risk haplotype paired with DQB1*06:02 affect the phenotype of patients with newly diagnosed type 1 diabetes. Carriers of the DQB1*06:02 allele were diagnosed at an older age than those with any other HLA class II genotype (p = 0.003) or the high‐risk genotype (p < 0.001). After adjusting the results for age and sex, no significant differences in clinical markers were observed. Glutamic acid decarboxylase autoantibody (GADA) levels were higher among carriers of DQB1*06:02 when compared to those with other genotypes (p = 0.033). Having a high‐risk haplotype paired with DQB1*06:02‐positive haplotype was associated with higher levels of islet antigen 2 autoantibodies (IA‐2A) (p < 0.001) and somewhat shorter duration of symptoms (p = 0.043). The association between the protective DQB1*06:02 allele and an older age at diagnosis as well as higher levels of GADA at diagnosis of type 1 diabetes was confirmed. The effects of the DQB1*06:02‐positive haplotype seem to dominate when paired with a high‐risk haplotype. [ABSTRACT FROM AUTHOR]

Published
2024
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26. Temporal Alterations in CD8+ T Cells During the Progression From Stage 1 to Stage 3 Type 1 Diabetes.

Author

Schroderus, Anna-Mari, Pitkänen, Viola, Ekman, Ilse, Stevens, Daniella, Rytkönen-Nissinen, Marja, Rintamäki, Reeta, Pihlajamäki, Jussi, Knip, Mikael, Veijola, Riitta, Toppari, Jorma, Ilonen, Jorma, Lempainen, Johanna, and Kinnunen, Tuure

Subjects
TYPE 1 diabetes, IMMUNOLOGIC memory, T cells, TREATMENT effectiveness, MULTIOMICS
Abstract

CD8+ T cells are perceived to play a major role in the pathogenesis of type 1 diabetes (T1D). In this study, we characterized the function and phenotype of circulating CD8+ memory T cells in samples from individuals at different stages of T1D progression using flow cytometry and single-cell multiomics. We observed two distinct CD8+ T-cell signatures during progression of T1D within the highly differentiated CD27−CD8+ memory T-cell subset. A proinflammatory signature, with an increased frequency of IFN-γ+TNF-α+ CD27−CD8+ memory T cells, was observed in children with newly diagnosed T1D (stage 3) and correlated with the level of dysglycemia at diagnosis. In contrast, a coinhibitory signature, with an increased frequency of KLRG1+TIGIT+ CD27−CD8+ memory T cells, was observed in islet autoantibody-positive children who later progressed to T1D (stage 1). No alterations within CD27−CD8+ memory T cells were observed in adults with established T1D or in children during the initial seroconversion to islet autoantibody positivity. Single-cell multiomics analyses suggested that CD27−CD8+ T cells expressing the IFNG+TNF+ proinflammatory signature may be distinct from those expressing the KLRG1+TIGIT+ coinhibitory signature at the single-cell level. Collectively, our findings suggest that distinct blood CD8+ T-cell signatures could be employed as potential biomarkers of T1D progression. Article Highlights: Blood CD8+ T-cell signatures have recently been associated with a slower progression of type 1 diabetes (T1D) after diagnosis and with clinical response to immunotherapy. We investigated blood CD8+ T-cell signatures in individuals at different stages of T1D progression. We observed two distinct CD8+ T-cell signatures at different stages of T1D: a proinflammatory signature in children with newly diagnosed T1D (stage 3) and a coinhibitory signature in autoantibody-positive children who later progressed to T1D (stage 1). CD8+ T-cell signatures could potentially be utilized as biomarkers for evaluating the risk of T1D progression. [ABSTRACT FROM AUTHOR]

Published
2024
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27. Early‐pregnancy BMI, maternal gestational weight gain, and asthma and allergic diseases in children.

Author

Ojwang', Vincent, Nwaru, Bright I., Hanna‐Mari, Takkinen, Heli, Tapanainen, Kaila, Minna, Ahonen, Suvi, Niemelä, Onni, Haapala, Anna‐Maija, Ilonen, Jorma, Toppari, Jorma, Hyöty, Heikki, Veijola, Riitta, Knip, Mikael, and Virtanen, Suvi M.

Subjects
ATOPIC dermatitis, JUVENILE diseases, WEIGHT gain, ALLERGIES, ALLERGIC rhinitis
Abstract

Background: Association of early pregnancy body mass index (BMI) and maternal gestational weight gain (GWG), and asthma and allergic disease in children is unclear. Methods: We analyzed data from 3176 mother–child pairs in a prospective birth cohort study. Maternal anthropometric measurements in the first and last antenatal clinic visits were obtained through post‐delivery questionnaires to calculate early pregnancy BMI and maternal GWG. Asthma and allergic diseases in children by the age of 5 years was assessed using a validated questionnaire. Furthermore, serum samples were analyzed for IgE antibodies to eight allergens. We applied Cox proportional hazards and logistic regression analyses to estimate the association of early pregnancy BMI and maternal GWG (as continuous variables and categorized into quarters), and asthma, atopic eczema, atopic sensitization, and allergic rhinitis in children. Results: Neither early pregnancy BMI nor maternal GWG was associated with asthma and allergic disease in children when analyzed as continuous variables. However, compared to the first quarter of GWG (a rate <0.32 kg/week), mothers in the third quarter (rate 0.42–0.52 kg/week) had children with significantly higher odds of developing atopic eczema (adjusted OR 1.49, 95% CI [1.13–1.96]) by 5 years of age. Conclusion: Association of early pregnancy BMI and maternal GWG, and asthma and allergic disease in children, is inconsistent. High maternal GWG may be associated with increased odds of atopic eczema. [ABSTRACT FROM AUTHOR]

Published
2024
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28. 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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29. 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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39. Circulating metabolic signatures of rapid and slow progression to type 1 diabetes in islet autoantibody-positive children

Author

Lamichhane, Santosh, primary, Sen, Partho, additional, Dickens, Alex M., additional, Kråkström, Matilda, additional, Ilonen, Jorma, additional, Lempainen, Johanna, additional, Hyöty, Heikki, additional, Lahesmaa, Riitta, additional, Veijola, Riitta, additional, Toppari, Jorma, additional, Hyötyläinen, Tuulia, additional, Knip, Mikael, additional, and Orešič, Matej, additional

Published
2023
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43. Evaluation of plasma IL-21 as a potential biomarker for type 1 diabetes progression

Author

Schroderus, Anna-Mari, primary, Poorbaugh, Josh, additional, McElyea, Samantha, additional, Beasley, Stephanie, additional, Zhang, Lin, additional, Näntö-Salonen, Kirsti, additional, Rintamäki, Reeta, additional, Pihlajamäki, Jussi, additional, Knip, Mikael, additional, Veijola, Riitta, additional, Toppari, Jorma, additional, Ilonen, Jorma, additional, Benschop, Robert J., additional, and Kinnunen, Tuure, additional

Published
2023
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44. Gene expression signature predicts rate of type 1 diabetes progression

Author

Suomi, Tomi, primary, Starskaia, Inna, additional, Kalim, Ubaid Ullah, additional, Rasool, Omid, additional, Jaakkola, Maria K., additional, Grönroos, Toni, additional, Välikangas, Tommi, additional, Brorsson, Caroline, additional, Mazzoni, Gianluca, additional, Bruggraber, Sylvaine, additional, Overbergh, Lut, additional, Dunger, David, additional, Peakman, Mark, additional, Chmura, Piotr, additional, Brunak, Søren, additional, Schulte, Anke M., additional, Mathieu, Chantal, additional, Knip, Mikael, additional, Lahesmaa, Riitta, additional, Elo, Laura L., additional, Gillard, Pieter, additional, Casteels, Kristina, additional, Overbergh, Lutgart, additional, Wallace, Chris, additional, Evans, Mark, additional, Thankamony, Ajay, additional, Hendriks, Emile, additional, Marcoveccchio, Loredana, additional, Tree, Timothy, additional, Morgan, Noel G., additional, Richardson, Sarah, additional, Todd, John A., additional, Wicker, Linda, additional, Mander, Adrian, additional, Dayan, Colin, additional, Alhadj Ali, Mohammad, additional, Pieber, Thomas, additional, Eizirik, Decio L., additional, Cnop, Myriam, additional, Pociot, Flemming, additional, Johannesen, Jesper, additional, Rossing, Peter, additional, Quigley, Cristina Legido, additional, Mallone, Roberto, additional, Scharfmann, Raphael, additional, Boitard, Christian, additional, Otonkoski, Timo, additional, Veijola, Riitta, additional, Oresic, Matej, additional, Toppari, Jorma, additional, Danne, Thomas, additional, Ziegler, Anette G., additional, Achenbach, Peter, additional, Rodriguez-Calvo, Teresa, additional, Solimena, Michele, additional, Bonifacio, Ezio E., additional, Speier, Stephan, additional, Holl, Reinhard, additional, Dotta, Francesco, additional, Chiarelli, Francesco, additional, Marchetti, Piero, additional, Bosi, Emanuele, additional, Cianfarani, Stefano, additional, Ciampalini, Paolo, additional, De Beaufort, Carine, additional, Dahl-Jørgensen, Knut, additional, Skrivarhaug, Torild, additional, Joner, Geir, additional, Krogvold, Lars, additional, Jarosz-Chobot, Przemka, additional, Battelino, Tadej, additional, Thorens, Bernard, additional, Gotthardt, Martin, additional, Roep, Bart O., additional, Nikolic, Tanja, additional, Zaldumbide, Arnaud, additional, Lernmark, Ake, additional, Lundgren, Marcus, additional, Costacalde, Guillaume, additional, Strube, Thorsten, additional, Nitsche, Almut, additional, Vela, Jose, additional, Von Herrath, Matthias, additional, Wesley, Johnna, additional, Napolitano-Rosen, Antonella, additional, Thomas, Melissa, additional, Schloot, Nanette, additional, Goldfine, Allison, additional, Waldron-Lynch, Frank, additional, Kompa, Jill, additional, Vedala, Aruna, additional, Hartmann, Nicole, additional, Nicolas, Gwenaelle, additional, van Rampelbergh, Jean, additional, Bovy, Nicolas, additional, Dutta, Sanjoy, additional, Soderberg, Jeannette, additional, Ahmed, Simi, additional, Martin, Frank, additional, Latres, Esther, additional, Agiostratidou, Gina, additional, Koralova, Anne, additional, Willemsen, Ruben, additional, Smith, Anne, additional, Anand, Binu, additional, Datta, Vipan, additional, Puthi, Vijith, additional, Zac-Varghese, Sagen, additional, Dias, Renuka, additional, Sundaram, Premkumar, additional, Vaidya, Bijay, additional, Patterson, Catherine, additional, Owen, Katharine, additional, Piel, Barbara, additional, Heller, Simon, additional, Randell, Tabitha, additional, Gazis, Tasso, additional, Reismen, Elise Bismuth, additional, Carel, Jean-Claude, additional, Riveline, Jean-Pierre, additional, Gautier, Jean-Francoise, additional, Andreelli, Fabrizion, additional, Travert, Florence, additional, Cosson, Emmanuel, additional, Penfornis, Alfred, additional, Petit, Catherine, additional, Feve, Bruno, additional, Lucidarme, Nadine, additional, Beressi, Jean-Paul, additional, Ajzenman, Catherina, additional, Radu, Alina, additional, Greteau-Hamoumou, Stephanie, additional, Bibal, Cecile, additional, Meissner, Thomas, additional, Heidtmann, Bettina, additional, Toni, Sonia, additional, Rami-Merhar, Birgit, additional, Eeckhout, Bart, additional, Peene, Bernard, additional, Vantongerloo, N., additional, Maes, Toon, additional, and Gommers, Leen, additional

Published
2023
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45. Increased Frequency of the HLA-DRB1*04:04-DQA1*03-DQB1*03:02 Haplotype Among HLA-DQB1*06:02–Positive Children With Type 1 Diabetes.

Author

Ilonen, Jorma, Kiviniemi, Minna, El-Amir, Mostafa I., Nygård, Lucas, Härkönen, Taina, Lempainen, Johanna, Knip, Mikael, Finnish Pediatric Diabetes Register, Groop, Per-Henrik, Otonkoski, Timo, Veijola, Riitta, Abram, Alar, Aito, Henrikka, Arkhipov, Ivan, Blanco-Sequeiros, Elina, Bondestam, Jonas, Granholm, Markus, Haapalehto-Ikonen, Maarit, Horn, Torsten, and Huopio, Hanna

Subjects
TYPE 1 diabetes, HAPLOTYPES, DIABETES in children, GENE frequency, AUTOANTIBODIES
Abstract

HLA-DR/DQ haplotypes largely define genetic susceptibility to type 1 diabetes (T1D). The DQB1*06:02-positive haplotype (DR15-DQ602) common in individuals of European ancestry is very rare among children with T1D. Among 4,490 children with T1D in the Finnish Pediatric Diabetes Register, 57 (1.3%) case patients with DQB1*06:02 were identified, in comparison with 26.1% of affected family-based association control participants. There were no differences between DQB1*06:02-positive and -negative children with T1D regarding sex, age, islet autoantibody distribution, or autoantibody levels, but significant differences were seen in the frequency of second class II HLA haplotypes. The most prevalent haplotype present with DQB1*06:02 was DRB1*04:04-DQA1*03-DQB1*03:02, which was found in 27 (47.4%) of 57 children, compared with only 797 (18.0%) of 4,433 among DQB1*06:02-negative case patients (P < 0.001 by χ2 test). The other common risk-associated haplotypes, DRB1*04:01-DQA1*03-DQB1*03:02 and (DR3)-DQA1*05-DQB1*02, were less prevalent in DQB1*06:02-positive versus DQB1*06:02-negative children (P < 0.001). HLA-B allele frequencies did not differ by DQB1*06:02 haplotype between children with T1D and control participants or by DRB1*04:04-DQA1*03-DQB1*03:02 haplotype between DQB1*06:02-positive and -negative children with T1D. The increased frequency of the DRB1*04:04 allele among DQB1*06:02-positive case patients may indicate a preferential ability of the DR404 molecule to present islet antigen epitopes despite competition by DQ602. Article Highlights: The common HLA-DQB1*06:02 allele is strongly protective against type 1 diabetes (T1D), although few DQB1*06:02-positive individuals have been found. We compared the distribution of the second class II haplotypes in DQB1*06:02-positive children with T1D with the distribution of haplotypes found in in other children with T1D. HLA-DRB1*04:04-DQA1*03-DQB1*03:02 was the most common second haplotype in DQB1*06:02-positive children with T1D, whereas DRB1*04:01-DQA1*03-DQB1*03:02 was most frequent in other children with T1D. This finding suggests that the islet-specific epitopes presented by DRB1*04:04 can more efficiently avoid binding to DQB1*06:02 than epitopes presented by other risk-associated haplotypes. [ABSTRACT FROM AUTHOR]

Published
2024
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48. HLA‐DQ‐conferred risk for type 1 diabetes does not alter neutralizing antibody response to a widely used enterovirus vaccine, the poliovirus vaccine

Author

Sioofy‐Khojine, Amir‐Babak, primary, Lehtonen, Jussi P., additional, Nurminen, Noora, additional, Laiho, Jutta E., additional, Toppari, Jorma, additional, Veijola, Riitta, additional, Lempainen, Johanna, additional, Ilonen, Jorma, additional, Knip, Mikael, additional, and Hyöty, Heikki, additional

Published
2023
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50. Longitudinal consumption of fruits and vegetables and risk of asthma by 5 years of age

Author

Metsälä, Johanna, primary, Vuorinen, Anna‐Leena, additional, Takkinen, Hanna‐Mari, additional, Peltonen, Essi J., additional, Ahonen, Suvi, additional, Åkerlund, Mari, additional, Tapanainen, Heli, additional, Mattila, Markus, additional, Toppari, Jorma, additional, Ilonen, Jorma, additional, Veijola, Riitta, additional, Haahtela, Tari, additional, Knip, Mikael, additional, Kaila, Minna, additional, and Virtanen, Suvi M., additional

Published
2023
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