Riitta Veijola19
Heikki Hyöty14
Ville Simell10
19Riitta Veijola
14Heikki Hyöty
10Ville Simell
Learn More
The risk determinants of type 1 diabetes, initiators of autoimmune response, mechanisms regulating progress toward beta cell failure, and factors determining time of presentation of clinical diabetes are poorly understood. We investigated changes in the serum metabolome prospectively in children who later progressed to type 1 diabetes. Serum metabolite(More)
Recent evidence from serum metabolomics indicates that specific metabolic disturbances precede β-cell autoimmunity in humans and can be used to identify those children who subsequently progress to type 1 diabetes. The mechanisms behind these disturbances are unknown. Here we show the specificity of the pre-autoimmune metabolic changes, as indicated by their(More)
  • Christopher T. Brown, Austin G. Davis-Richardson, Adriana Giongo, Kelsey A. Gano, David B. Crabb, Nabanita Mukherjee +13 others
  • 2011
Recent studies have suggested a bacterial role in the development of autoimmune disorders including type 1 diabetes (T1D). Over 30 billion nucleotide bases of Illumina shotgun metagenomic data were analyzed from stool samples collected from four pairs of matched T1D case-control subjects collected at the time of the development of T1D associated(More)
  • Momoko Horikoshi, Hanieh Yaghootkar, Dennis O. Mook-Kanamori, Ulla Sovio, H. Rob Taal, Branwen J. Hennig +142 others
  • 2013
Birth weight within the normal range is associated with a variety of adult-onset diseases, but the mechanisms behind these associations are poorly understood. Previous genome-wide association studies of birth weight identified a variant in the ADCY5 gene associated both with birth weight and type 2 diabetes(More)
Previous studies have identified a correlation, either positive or negative, between specific stool bacteria strains and certain autoimmune diseases. These conflicting data may relate to sample collection. The aim of this work was to evaluate the influence of the collection parameters of time and temperature on bacterial community composition. Samples were(More)
Little is known about the human intra-individual metabolic profile changes over an extended period of time. Here, we introduce a novel concept suggesting that children even at a very young age can be categorized in terms of metabolic state as they advance in development. The hidden Markov models were used as a method for discovering the underlying(More)
  • Bright I Nwaru, Maijaliisa Erkkola, Suvi Ahonen, Minna Kaila, Anna-Maija Haapala, Carina Kronberg-Kippilä +6 others
  • 2010
OBJECTIVE The goal was to examine the relationship between age at the introduction of solid foods during the first year of life and allergic sensitization in 5-year-old children. METHODS We analyzed data from the Finnish Type 1 Diabetes Prediction and Prevention nutrition study, a prospective, birth cohort study. We studied 994 children with HLA-conferred(More)
Human bocavirus 1 (HBoV1) DNA is frequently detected in the upper airways of young children with respiratory symptoms. Because of its persistence and frequent co-detection with other viruses, however, its etiologic role has remained controversial. During 2009-2011, using HBoV1 IgM, IgG, and IgG-avidity enzyme immunoassays and quantitative PCR, we examined(More)
  • Sami Oikarinen, Mika Martiskainen, Sisko Tauriainen, Heini Huhtala, Jorma Ilonen, Riitta Veijola +3 others
  • 2011
OBJECTIVE To assess whether the detection of enterovirus RNA in blood predicts the development of clinical type 1 diabetes in a prospective birth cohort study. Further, to study the role of enteroviruses in both the initiation of the process and the progression to type 1 diabetes. RESEARCH DESIGN AND METHODS This was a nested case-control study where all(More)
  • Heli T.A. Siljander, Satu Simell, Anne Hekkala, Jyrki Lähde, Tuula Simell, Paula Vähäsalo +4 others
  • 2009
OBJECTIVE As data on the predictive characteristics of diabetes-associated autoantibodies for type 1 diabetes in the general population are scarce, we assessed the predictive performance of islet cell autoantibodies (ICAs) in combination with autoantibodies against insulin (IAAs), autoantibodies against GAD, and/or islet antigen 2 for type 1 diabetes in(More)