ORCID Profile
0000-0003-3322-4089
Current Organisations
Universiti Tunku Abdul Rahman
,
Wye College
,
National Institute of Arthritis and Musculoskeletal and Skin Diseases
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
Related Links
Publications
HLA-DRB1*11and variants of the MHC class II locus are strong risk factors for systemic juvenile idiopathic arthritis
Publisher: Proceedings of the National Academy of Sciences
Date: 23-11-2015
Abstract: To determine whether genetic variation within the MHC locus influences the risk of developing systemic juvenile idiopathic arthritis (sJIA), we examined a dense set of MHC region single nucleotide polymorphisms, classic HLA alleles, and the in idual amino acids of HLA molecules in nine independent sJIA case-control populations. Association testing revealed that genetic variants within the MHC class II gene cluster significantly influenced sJIA risk in every study population. The strongest risk factor for sJIA was HLA-DRB1*11 , which conferred at least a two-fold increase in disease risk in each population studied. These data implicate the interaction of antigen presenting cells with T cells in the pathogenesis of sJIA.
Severe delayed hypersensitivity reactions to IL-1 and IL-6 inhibitors link to common HLA-DRB1*15 alleles
Publisher: BMJ
Date: 17-11-2021
DOI: 10.1136/ANNRHEUMDIS-2021-220578
Abstract: Drug reaction with eosinophilia and systemic symptoms (DRESS) is a severe, delayed hypersensitivity reaction (DHR). We observed DRESS to inhibitors of interleukin 1 (IL-1) or IL-6 in a small group of patients with Still’s disease with atypical lung disease. We sought to characterise features of patients with Still’s disease with DRESS compared with drug-tolerant Still’s controls. We analysed human leucocyte antigen (HLA) alleles for association to inhibitor-related DHR, including in a small Kawasaki disease (KD) cohort. In a case/control study, we collected a multicentre series of patients with Still’s disease with features of inhibitor-related DRESS (n=66) and drug-tolerant Still’s controls (n=65). We retrospectively analysed clinical data from all Still’s subjects and typed 94/131 for HLA. European Still’s-DRESS cases were ancestry matched to International Childhood Arthritis Genetics Consortium paediatric Still’s cases (n=550) and compared for HLA allele frequencies. HLA association also was analysed using Still’s-DRESS cases (n=64) compared with drug-tolerant Still’s controls (n=30). KD subjects (n=19) were similarly studied. Still’s-DRESS features included eosinophilia (89%), AST-ALT elevation (75%) and non-evanescent rash (95% 88% involving face). Macrophage activation syndrome during treatment was frequent in Still’s-DRESS (64%) versus drug-tolerant Still’s (3% p=1.2×10 −14 ). We found striking enrichment for HLA-DRB1*15 haplotypes in Still’s-DRESS cases versus INCHARGE Still’s controls (p=7.5×10 -13 ) and versus self-identified, ancestry-matched Still’s controls (p=6.3×10 −10 ). In the KD cohort, DRB1*15:01 was present only in those with suspected anakinra reactions. DRESS-type reactions occur among patients treated with IL-1/IL-6 inhibitors and strongly associate with common HLA-DRB1*15 haplotypes. Consideration of preprescription HLA typing and vigilance for serious reactions to these drugs are warranted.
Genetic architecture distinguishes systemic juvenile idiopathic arthritis from other forms of juvenile idiopathic arthritis: clinical and therapeutic implications
Publisher: BMJ
Date: 07-12-2016
DOI: 10.1136/ANNRHEUMDIS-2016-210324
Abstract: Juvenile idiopathic arthritis (JIA) is a heterogeneous group of conditions unified by the presence of chronic childhood arthritis without an identifiable cause. Systemic JIA (sJIA) is a rare form of JIA characterised by systemic inflammation. sJIA is distinguished from other forms of JIA by unique clinical features and treatment responses that are similar to autoinflammatory diseases. However, approximately half of children with sJIA develop destructive, long-standing arthritis that appears similar to other forms of JIA. Using genomic approaches, we sought to gain novel insights into the pathophysiology of sJIA and its relationship with other forms of JIA. We performed a genome-wide association study of 770 children with sJIA collected in nine countries by the International Childhood Arthritis Genetics Consortium. Single nucleotide polymorphisms were tested for association with sJIA. Weighted genetic risk scores were used to compare the genetic architecture of sJIA with other JIA subtypes. The major histocompatibility complex locus and a locus on chromosome 1 each showed association with sJIA exceeding the threshold for genome-wide significance, while 23 other novel loci were suggestive of association with sJIA. Using a combination of genetic and statistical approaches, we found no evidence of shared genetic architecture between sJIA and other common JIA subtypes. The lack of shared genetic risk factors between sJIA and other JIA subtypes supports the hypothesis that sJIA is a unique disease process and argues for a different classification framework. Research to improve sJIA therapy should target its unique genetics and specific pathophysiological pathways.
Development of an effective clustering algorithm for older fallers
Publisher: Public Library of Science (PLoS)
Date: 28-11-2022
DOI: 10.1371/JOURNAL.PONE.0277966
Abstract: Falls are common and often lead to serious physical and psychological consequences for older persons. The occurrence of falls are usually attributed to the interaction between multiple risk factors. The clinical evaluation of falls risks is time-consuming as a result, hence limiting its availability. The purpose of this study was, therefore, to develop a clustering-based algorithm to determine falls risk. Data from the Malaysian Elders Longitudinal Research (MELoR), comprising 1411 subjects aged ≥55 years, were utilized. The proposed algorithm was developed through the stages of: data pre-processing, feature identification and extraction with either t-Distributed Stochastic Neighbour Embedding (t-SNE) or principal component analysis (PCA)), clustering (K-means clustering, Hierarchical clustering, and Fuzzy C-means clustering) and characteristics interpretation with statistical analysis. A total of 1279 subjects and 9 variables were selected for clustering after the data pre-possessing stage. Using feature extraction with the t-SNE and the K-means clustering algorithm, subjects were clustered into low, intermediate A, intermediate B and high fall risk groups which corresponded with fall occurrence of 13%, 19%, 21% and 31% respectively. Slower gait, poorer balance, weaker muscle strength, presence of cardiovascular disorder, poorer cognitive performance, and advancing age were the key variables identified. The proposed fall risk clustering algorithm grouped the subjects according to features. Such a tool could serve as a case identification or clinical decision support tool for clinical practice to enhance access to falls prevention efforts.
IL1RN Variation Influences Both Disease Susceptibility and Response to Recombinant Human Interleukin‐1 Receptor Antagonist Therapy in Systemic Juvenile Idiopathic Arthritis
Publisher: Wiley
Date: 28-06-2018
DOI: 10.1002/ART.40498
Related Organisations
Wye College
Location: United Kingdom of Great Britain and Northern Ireland
National Institute Of Arthritis And Musculoskeletal And Skin Diseases
Location: United States of America
Related Funding Activities
No related grants have been discovered for Michael Joseph Ombrello.