Kenney Ng1, Vibha Anand2, Harry Stavropoulos3, Riitta Veijola4, Jorma Toppari5,6, Marlena Maziarz7, Markus Lundgren7,8, Kathy Waugh9, Brigitte I Frohnert9, Frank Martin10, Olivia Lou10, William Hagopian11, Peter Achenbach12. 1. IBM Research, Cambridge, MA, USA. kenney.ng@us.ibm.com. 2. IBM Research, Cambridge, MA, USA. 3. IBM Research, Yorktown Heights, NY, USA. 4. Department of Pediatrics, PEDEGO Research Unit, University of Oulu and Oulu University Hospital, Oulu, Finland. 5. Institute of Biomedicine and Centre for Population Health Research, University of Turku, Turku, Finland. 6. Department of Pediatrics, Turku University Hospital, Turku, Finland. 7. Department of Clinical Sciences Malmö, Lund University, Malmö, Sweden. 8. Department of Pediatrics, Kristianstad Hospital, Kristianstad, Sweden. 9. Barbara Davis Center for Diabetes, University of Colorado, Denver, CO, USA. 10. JDRF International, New York, NY, USA. 11. Pacific Northwest Research Institute, Seattle, WA, USA. 12. Institute of Diabetes Research, Helmholtz Zentrum München, German Research Center for Environmental Health, Munich-Neuherberg, Germany. peter.achenbach@helmholtz-muenchen.de.
Abstract
AIMS/HYPOTHESIS: The aim of this study was to explore the utility of islet autoantibody (IAb) levels for the prediction of type 1 diabetes in autoantibody-positive children. METHODS: Prospective cohort studies in Finland, Germany, Sweden and the USA followed 24,662 children at increased genetic or familial risk of developing islet autoimmunity and diabetes. For the 1403 who developed IAbs (523 of whom developed diabetes), levels of autoantibodies against insulin (IAA), glutamic acid decarboxylase (GADA) and insulinoma-associated antigen-2 (IA-2A) were harmonised for analysis. Diabetes prediction models using multivariate logistic regression with inverse probability censored weighting (IPCW) were trained using 10-fold cross-validation. Discriminative power for disease was estimated using the IPCW concordance index (C index) with 95% CI estimated via bootstrap. RESULTS: A baseline model with covariates for data source, sex, diabetes family history, HLA risk group and age at seroconversion with a 10-year follow-up period yielded a C index of 0.61 (95% CI 0.58, 0.63). The performance improved after adding the IAb positivity status for IAA, GADA and IA-2A at seroconversion: C index 0.72 (95% CI 0.71, 0.74). Using the IAb levels instead of positivity indicators resulted in even better performance: C index 0.76 (95% CI 0.74, 0.77). The predictive power was maintained when using the IAb levels alone: C index 0.76 (95% CI 0.75, 0.76). The prediction was better for shorter follow-up periods, with a C index of 0.82 (95% CI 0.81, 0.83) at 2 years, and remained reasonable for longer follow-up periods, with a C index of 0.76 (95% CI 0.75, 0.76) at 11 years. Inclusion of the results of a third IAb test added to the predictive power, and a suitable interval between seroconversion and the third test was approximately 1.5 years, with a C index of 0.78 (95% CI 0.77, 0.78) at 10 years follow-up. CONCLUSIONS/ INTERPRETATION: Consideration of quantitative patterns of IAb levels improved the predictive power for type 1 diabetes in IAb-positive children beyond qualitative IAb positivity status.
AIMS/HYPOTHESIS: The aim of this study was to explore the utility of islet autoantibody (IAb) levels for the prediction of type 1 diabetes in autoantibody-positive children. METHODS: Prospective cohort studies in Finland, Germany, Sweden and the USA followed 24,662 children at increased genetic or familial risk of developing islet autoimmunity and diabetes. For the 1403 who developed IAbs (523 of whom developed diabetes), levels of autoantibodies against insulin (IAA), glutamic acid decarboxylase (GADA) and insulinoma-associated antigen-2 (IA-2A) were harmonised for analysis. Diabetes prediction models using multivariate logistic regression with inverse probability censored weighting (IPCW) were trained using 10-fold cross-validation. Discriminative power for disease was estimated using the IPCW concordance index (C index) with 95% CI estimated via bootstrap. RESULTS: A baseline model with covariates for data source, sex, diabetes family history, HLA risk group and age at seroconversion with a 10-year follow-up period yielded a C index of 0.61 (95% CI 0.58, 0.63). The performance improved after adding the IAb positivity status for IAA, GADA and IA-2A at seroconversion: C index 0.72 (95% CI 0.71, 0.74). Using the IAb levels instead of positivity indicators resulted in even better performance: C index 0.76 (95% CI 0.74, 0.77). The predictive power was maintained when using the IAb levels alone: C index 0.76 (95% CI 0.75, 0.76). The prediction was better for shorter follow-up periods, with a C index of 0.82 (95% CI 0.81, 0.83) at 2 years, and remained reasonable for longer follow-up periods, with a C index of 0.76 (95% CI 0.75, 0.76) at 11 years. Inclusion of the results of a third IAb test added to the predictive power, and a suitable interval between seroconversion and the third test was approximately 1.5 years, with a C index of 0.78 (95% CI 0.77, 0.78) at 10 years follow-up. CONCLUSIONS/ INTERPRETATION: Consideration of quantitative patterns of IAb levels improved the predictive power for type 1 diabetes in IAb-positive children beyond qualitative IAb positivity status.
Authors: V Parikka; K Näntö-Salonen; M Saarinen; T Simell; J Ilonen; H Hyöty; R Veijola; M Knip; O Simell Journal: Diabetologia Date: 2012-03-23 Impact factor: 10.122
Authors: Witold Bauer; Riitta Veijola; Johanna Lempainen; Minna Kiviniemi; Taina Härkönen; Jorma Toppari; Mikael Knip; Attila Gyenesei; Jorma Ilonen Journal: J Clin Endocrinol Metab Date: 2019-10-01 Impact factor: 5.958
Authors: Petra M Pöllänen; Johanna Lempainen; Antti-Pekka Laine; Jorma Toppari; Riitta Veijola; Paula Vähäsalo; Jorma Ilonen; Heli Siljander; Mikael Knip Journal: Diabetologia Date: 2017-03-31 Impact factor: 10.122
Authors: Andrea K Steck; Fran Dong; Kathleen Waugh; Brigitte I Frohnert; Liping Yu; Jill M Norris; Marian J Rewers Journal: J Autoimmun Date: 2016-05-30 Impact factor: 7.094
Authors: Anette G Ziegler; Marian Rewers; Olli Simell; Tuula Simell; Johanna Lempainen; Andrea Steck; Christiane Winkler; Jorma Ilonen; Riitta Veijola; Mikael Knip; Ezio Bonifacio; George S Eisenbarth Journal: JAMA Date: 2013-06-19 Impact factor: 56.272