FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2316404959> ?p ?o ?g. }

• W2316404959 endingPage "760" @default.
• W2316404959 startingPage "759" @default.
• W2316404959 abstract "The HLA system consists of a group of cell surface proteins encoded for by genes located on the short arm of chromosome 6. Since the discovery of this system in the 1950s, it has become clear that HLA molecules play an important role in controlling immune responses generated by the interaction of these proteins with foreign antigens. Humans have three class 1 HLA antigens (A, B, and C) that are present on all nucleated cells and three categories of class 2 HLA antigens (DR, DQ, and DP) present only on antigen-presenting cells and lymphocytes. It is well recognized that mismatches between the HLA antigens of a recipient and donor form a major barrier to successful outcomes after kidney transplantation, increasing the risk of rejection and graft loss. Techniques for HLA typing have evolved from simple serologic methods to advanced molecular techniques (1,2) as it has become increasingly clear that serologically indistinguishable HLA molecules may have functionally distinct allelic products. After the first International Histocompatibility Workshop in 1964, HLA matching became the cornerstone of deceased donor organ allocation policies (3,4). Although HLA mismatching remains an independent risk factor for poor allograft outcomes in the modern era (5), the quantitative contribution of HLA matching in modern allocation algorithms has decreased progressively over time. In the United States, matching of HLA-A, -B, -C, and -DR antigens was included in early allocation algorithms. With time and experience, it became evident that mismatches of class 1 HLA antigens were less detrimental to allograft outcomes than mismatches of class 2 antigens. In the current United Network For Organ Sharing point system for kidney allocation, only HLA-DR matching is included. Until recently, the effect of HLA-DQ or -DP matching or mismatching on transplant outcomes has been less certain, in part because accurate identification of these class 2 molecules has relied more heavily on DNA sequencing than the more traditionally studied HLA antigens. In this issue of the Clinical Journal of the American Society of Nephrology, Lim et al. (6) use data from the Australian and New Zealand Dialysis and Transplant Registry to analyze the effect of HLA-DQ mismatching on the outcomes of primary kidney transplants performed between 2004 and 2012. Of the 6107 patients receiving primary transplants during this period, 87% were excluded from the analysis because of missing HLA-DQ molecular typing, leaving only 788 patients available for the study. Patients were classified as having either zero HLA-DQ mismatches or one or two HLA-DQ mismatches. The studied outcomes were acute rejection, graft loss, and graft function measured as eGFR. In univariate analyses, compared with patients with zero HLA-DQ mismatches, those with one or two HLA-DQ mismatches exhibited significantly higher rates of acute rejection classified as any rejection episode (16% versus 25%; P<0.01), late rejection (>6 months post-transplant; 2% versus 6%; P=0.03), or antibody–mediated acute rejection (4% versus 8%; P=0.01). Clinical variables included in the multivariate analyses included donor age and type (living versus deceased) and recipient age, sex, cause of ESRD, waiting time, smoking history, presence of coronary artery disease, percentage of panel-reactive antibodies, use of induction therapy, total ischemic time, and type of initial immunosuppression. There were no statistically significant associations between HLA-DQ mismatching and graft loss or graft function using either univariate or multivariate analyses. One reason why the effects of HLA-DQ matching have been underemphasized in the past has been the assumption that HLA-DQ matching closely parallels HLA-DR matching because of linkage disequilibrium, reflecting the proximity of the two genes on chromosome 6. However, in the study by Lim et al. (6), concordance between HLA-DQ and HLA-DR was observed more commonly in patients who had a DQ-DR mismatch (of the 467 patients with one or two HLA-DQ mismatches, 94% also had HLA-DR mismatches) than in patients who had a zero HLA-DQ mismatch (of 321 patients with zero HLA-DQ mismatches, 54% exhibited HLA-DR mismatches). In multivariate analyses, the effect of HLA-DQ mismatching on any acute rejection episode or late acute rejection was independent of mismatching of HLA-A, -B, and -DR antigens. However, when acute antibody–mediated rejection was the dependent variable, there was a statistically significant interaction between HLA-DR mismatching and HLA-DQ mismatching, with the risk of antibody-mediated rejection observed at a higher rate only in HLA-DQ mismatched patients who also had an HLA-DR mismatch. Results of this analysis from the large and robust Australia and New Zealand Dialysis and Transplant Registry help to confirm preliminary findings from other studies suggesting that HLA-DQ mismatching may influence kidney transplant outcomes. Several studies have shown that the development of de novo anti–DQ donor–specific antibodies (DSAs) is associated with antibody-mediated rejection, transplant glomerulopathy, and early graft loss (7–10). More recently, HLA-DQ mismatching at the epitope level has been strongly associated with the development of anti-DQ DSAs (11–13), especially in the setting of minimizing immunosuppression (13). The study by Lim et al. (6) is, of course, weakened somewhat by limitations inherent in a retrospective analysis of registry data. A particular weakness is the lack of data on de novo DSAs that would have allowed a correlation between HLA-DQ mismatching, the emergence of de novo anti–DQ DSAs, and increased rates of acute rejection. Selection bias is also a concern, because the analyses were limited to patients transplanted at centers capable of performing molecular HLA typing, thus excluding a large majority of patients in the registry. Finally, despite the large size of the overall database, the numbers of patients with HLA-DQ mismatches were probably too small for the investigators to determine whether there are significant differences between one and two HLA-DQ mismatches. Results of the study, nevertheless, confirm the importance of HLA-DQ mismatching as a predictor of acute rejection independent of multiple relevant clinical covariates and also, independent of mismatching of HLA-A, HLA-B, and possibly, HLA-DR antigens (6). Whether these results will have any effect on current allocation algorithms remains to be determined. The contribution of HLA matching to the United States allocation algorithm has been superseded by factors, such as time on dialysis, humoral sensitization, and donor clinical characteristics—all motivated to increase the utilization of deceased donor organs and increase overall longevity of transplanted kidneys (14). Thus, it seems unlikely that HLA-DQ matching will be added to the current allocation scheme, because the number of points awarded for HLA-DR matching is already small. However, the increasing recognition of either preformed or de novo anti–HLA-DQ DSAs and their role in acute and chronic immune injury to allografts suggest that additional research is warranted to further evaluate HLA-DQ mismatching, especially at the epitope level, as a tool for assessing the risk of acute rejection and other outcomes in kidney transplant recipients. Disclosures None." @default.
• W2316404959 created "2016-06-24" @default.
• W2316404959 creator A5042510448 @default.
• W2316404959 creator A5074863938 @default.
• W2316404959 date "2016-03-31" @default.
• W2316404959 modified "2023-09-23" @default.
• W2316404959 title "HLA-DQ Mismatching: Mounting Evidence for a Role in Kidney Transplant Rejection" @default.
• W2316404959 cites W1499906725 @default.
• W2316404959 cites W194371544 @default.
• W2316404959 cites W1960048041 @default.
• W2316404959 cites W1977363554 @default.
• W2316404959 cites W2017631689 @default.
• W2316404959 cites W2019709619 @default.
• W2316404959 cites W2042021216 @default.
• W2316404959 cites W2103073605 @default.
• W2316404959 cites W2139972161 @default.
• W2316404959 cites W2160607034 @default.
• W2316404959 cites W2326384578 @default.
• W2316404959 cites W2464759444 @default.
• W2316404959 cites W97181640 @default.
• W2316404959 doi "https://doi.org/10.2215/cjn.02970316" @default.
• W2316404959 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/4858473" @default.
• W2316404959 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/27034401" @default.
• W2316404959 hasPublicationYear "2016" @default.
• W2316404959 type Work @default.
• W2316404959 sameAs 2316404959 @default.
• W2316404959 citedByCount "5" @default.
• W2316404959 countsByYear W23164049592018 @default.
• W2316404959 countsByYear W23164049592020 @default.
• W2316404959 countsByYear W23164049592023 @default.
• W2316404959 crossrefType "journal-article" @default.
• W2316404959 hasAuthorship W2316404959A5042510448 @default.
• W2316404959 hasAuthorship W2316404959A5074863938 @default.
• W2316404959 hasBestOaLocation W23164049591 @default.
• W2316404959 hasConcept C126322002 @default.
• W2316404959 hasConcept C147483822 @default.
• W2316404959 hasConcept C188280979 @default.
• W2316404959 hasConcept C203014093 @default.
• W2316404959 hasConcept C2780091579 @default.
• W2316404959 hasConcept C2780179540 @default.
• W2316404959 hasConcept C2780303639 @default.
• W2316404959 hasConcept C2909478873 @default.
• W2316404959 hasConcept C2909675724 @default.
• W2316404959 hasConcept C2911091166 @default.
• W2316404959 hasConcept C2994498544 @default.
• W2316404959 hasConcept C3019230761 @default.
• W2316404959 hasConcept C71924100 @default.
• W2316404959 hasConcept C8891405 @default.
• W2316404959 hasConceptScore W2316404959C126322002 @default.
• W2316404959 hasConceptScore W2316404959C147483822 @default.
• W2316404959 hasConceptScore W2316404959C188280979 @default.
• W2316404959 hasConceptScore W2316404959C203014093 @default.
• W2316404959 hasConceptScore W2316404959C2780091579 @default.
• W2316404959 hasConceptScore W2316404959C2780179540 @default.
• W2316404959 hasConceptScore W2316404959C2780303639 @default.
• W2316404959 hasConceptScore W2316404959C2909478873 @default.
• W2316404959 hasConceptScore W2316404959C2909675724 @default.
• W2316404959 hasConceptScore W2316404959C2911091166 @default.
• W2316404959 hasConceptScore W2316404959C2994498544 @default.
• W2316404959 hasConceptScore W2316404959C3019230761 @default.
• W2316404959 hasConceptScore W2316404959C71924100 @default.
• W2316404959 hasConceptScore W2316404959C8891405 @default.
• W2316404959 hasIssue "5" @default.
• W2316404959 hasLocation W23164049591 @default.
• W2316404959 hasLocation W23164049592 @default.
• W2316404959 hasLocation W23164049593 @default.
• W2316404959 hasLocation W23164049594 @default.
• W2316404959 hasOpenAccess W2316404959 @default.
• W2316404959 hasPrimaryLocation W23164049591 @default.
• W2316404959 hasRelatedWork W2001253159 @default.
• W2316404959 hasRelatedWork W2143535117 @default.
• W2316404959 hasRelatedWork W2336664987 @default.
• W2316404959 hasRelatedWork W2377772842 @default.
• W2316404959 hasRelatedWork W2620117546 @default.
• W2316404959 hasRelatedWork W2975303308 @default.
• W2316404959 hasRelatedWork W3030273286 @default.
• W2316404959 hasRelatedWork W3185112495 @default.
• W2316404959 hasRelatedWork W4313296042 @default.
• W2316404959 hasRelatedWork W88414019 @default.
• W2316404959 hasVolume "11" @default.
• W2316404959 isParatext "false" @default.
• W2316404959 isRetracted "false" @default.
• W2316404959 magId "2316404959" @default.
• W2316404959 workType "article" @default.