PubMed · 15635432
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Anna Maria Papini. 2005. Simple test for multiple sclerosis.. https://doi.org/10.1038/nm0105-13
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CONTEXT: Biomarkers that can accurately predict risk of type 1 diabetes (T1D) in genetically predisposed children can facilitate interventions to delay or prevent the disease. OBJECTIVE: This work aimed to determine if a combination of genetic, immunologic, and metabolic features, measured at infancy, can be used to predict the likelihood that a child will develop T1D by age 6 years. METHODS: Newborns with human leukocyte antigen (HLA) typing were enrolled in the prospective birth cohort of The Environmental Determinants of Diabetes in the Young (TEDDY). TEDDY ascertained children in Finland, Germany, Sweden, and the United States. TEDDY children were either from the general population or from families with T1D with an HLA genotype associated with T1D specific to TEDDY eligibility criteria. From the TEDDY cohort there were 702 children will all data sources measured at ages 3, 6, and 9 months, 11.4% of whom progressed to T1D by age 6 years. The main outcome measure was a diagnosis of T1D as diagnosed by American Diabetes Association criteria. RESULTS: Machine learning-based feature selection yielded classifiers based on disparate demographic, immunologic, genetic, and metabolite features. The accuracy of the model using all available data evaluated by the area under a receiver operating characteristic curve is 0.84. Reducing to only 3- and 9-month measurements did not reduce the area under the curve significantly. Metabolomics had the largest value when evaluating the accuracy at a low false-positive rate. CONCLUSION: The metabolite features identified as important for progression to T1D by age 6 years point to altered sugar metabolism in infancy. Integrating this information with classic risk factors improves prediction of the progression to T1D in early childhood.
BACKGROUND AND OBJECTIVES: Autoantibodies targeting the acetylcholine receptor (AChR), found in patients with myasthenia gravis (MG), mediate pathology through 3 mechanisms: complement-directed tissue damage, blocking of the acetylcholine binding site, and internalization of the AChR. Clinical assays, used to diagnose and monitor patients, measure only autoantibody binding. Consequently, they are limited in providing association with disease burden, understanding of mechanistic heterogeneity, and monitoring therapeutic response. The objective of this study was to develop a cell-based assay that measures AChR autoantibody-mediated complement membrane attack complex (MAC) formation. METHODS: An HEK293T cell line-modified using CRISPR/Cas9 genome editing to disrupt expression of the complement regulator genes (CD46, CD55, and CD59)-was used to measure AChR autoantibody-mediated MAC formation through flow cytometry. RESULTS: Serum samples (n = 155) from 96 clinically confirmed AChR MG patients, representing a wide range of disease burden and autoantibody titer, were tested along with 32 healthy donor (HD) samples. AChR autoantibodies were detected in 139 of the 155 (89.7%) MG samples through a cell-based assay. Of the 139 AChR-positive samples, autoantibody-mediated MAC formation was detected in 83 (59.7%), whereas MAC formation was undetectable in the HD group or AChR-positive samples with low autoantibody levels. MAC formation was positively associated with autoantibody binding in most patient samples; ratios (mean fluorescence intensity) of MAC formation to AChR autoantibody binding ranged between 0.27 and 48, with a median of 0.79 and an interquartile range of 0.43 (0.58-1.1). However, the distribution of ratios was asymmetric and included extreme values; 16 samples were beyond the 10-90 percentile, with high MAC to low AChR autoantibody binding ratio or the reverse. Correlation between MAC formation and clinical disease scores suggested a modest positive association (rho = 0.34, p = 0.0023), which included a subset of outliers that did not follow this pattern. MAC formation did not associate with exposure to immunotherapy, thymectomy, or MG subtypes defined by age-of-onset. DISCUSSION: A novel assay for evaluating AChR autoantibody-mediated complement activity was developed. A subset of patients that lacks association between MAC formation and autoantibody binding or disease burden was identified. The assay may provide a better understanding of the heterogeneous autoantibody molecular pathology and identify patients expected to benefit from complement inhibitor therapy.
This study aimed to identify potential immunological markers for predicting type 1 diabetes in patients with gestational diabetes mellitus (GDM) and any immunological impairment in their newborn. In 62 GDM patients and 74 women with normal glucose tolerance (NGT), and their babies, we assessed total lymphocytes, T lymphocyte subsets CD3 and CD8 expressing T cell receptor (TCR) alpha/beta or gamma/delta, CD16 and CD19, pancreatic autoantibodies and cytokines (IL-5, IL-2, soluble receptor IL-2). At delivery, umbilical cord blood samples were taken for lymphocyte subpopulations and cytokine measurements. GDM mothers had higher levels of total lymphocytes, CD8 expressing TCR gamma/delta, and lower levels of CD3 expressing TCR alpha/beta than NGT controls. Insulin-treated GDM mothers had lower CD4 and CD4/CD8 ratios, and higher CD8 and IL-5 than diet-treated GDM or controls. Five women were positive for pancreatic autoantibodies, with lower CD4 (p<0.01) and CD4/CD8 ratios (p<0.05), and higher CD8 (p<0.03) and CD19 than GDM and control mothers negative for autoantibodies. GDM newborn had higher CD8 gamma/delta and lower CD16 than NGT babies. There were no significant differences in TNF-alpha concentrations in the cord blood obtained from the GDM and NGT newborn. In conclusion, GDM women and their newborn have lymphocyte subset impairments, which are more important in patients positive for autoantibodies and/or treated with insulin.