| 8. |
- Diaz-Gallo, LM, et al.
(författare)
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Understanding interactions between risk factors, and assessing the utility of the additive and multiplicative models through simulations
- 2021
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Ingår i: PloS one. - : Public Library of Science (PLoS). - 1932-6203. ; 16:4, s. e0250282-
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Tidskriftsartikel (refereegranskat)abstract
- Understanding the genetic background of complex diseases requires the expansion of studies beyond univariate associations. Therefore, it is important to use interaction assessments of risk factors in order to discover whether, and how genetic risk variants act together on disease development. The principle of interaction analysis is to explore the magnitude of the combined effect of risk factors on disease causation. In this study, we use simulations to investigate different scenarios of causation to show how the magnitude of the effect of two risk factors interact. We mainly focus on the two most commonly used interaction models, the additive and multiplicative risk scales, since there is often confusion regarding their use and interpretation. Our results show that the combined effect is multiplicative when two risk factors are involved in the same chain of events, an interaction called synergism. Synergism is often described as a deviation from additivity, which is a broader term. Our results also confirm that it is often relevant to estimate additive effect relationships, because they correspond to independent risk factors at low disease prevalence. Importantly, we evaluate the threshold of more than two required risk factors for disease causation, called the multifactorial threshold model. We found a simple mathematical relationship (square root) between the threshold and an additive-to-multiplicative linear effect scale (AMLES), where 0 corresponds to an additive effect and 1 to a multiplicative. We propose AMLES as a metric that could be used to test different effects relationships at the same time, given that it can simultaneously reveal additive, multiplicative and intermediate risk effects relationships. Finally, the utility of our simulation study was demonstrated using real data by analyzing and interpreting gene-gene interaction odds ratios from a rheumatoid arthritis case-control cohort.
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| 9. |
- Elbagir, S, et al.
(författare)
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ANTI-PHOSPHATIDYLSERINE/PROTHROMBIN ANTIBODIES AND VASCULAR EVENTS ASSOCIATE POSITIVELY WITH HLA-DRB1*13 AND NEGATIVELY WITH HLA-DRB1*03 IN SLE
- 2022
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Ingår i: ANNALS OF THE RHEUMATIC DISEASES. - : BMJ. - 0003-4967 .- 1468-2060. ; 81, s. 658-659
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Anti-phosphatidylserine/prothrombin antibodies (anti-PS/PT) associate with thrombotic events (1). HLA-DRB1 alleles contribute to the occurrence of conventional antiphospholipid antibodies (aPL), including anti-beta2glycoprotein-I (beta2GPI) and anti-cardiolipin (CL) (2).ObjectivesWe investigated associations between anti-PS/PT and HLA-DRB1 alleles and thrombosis in patients with SLE. Conventional aPL were included for comparison.MethodsWe included 341 consecutive Swedish SLE patients, with information on general cardiovascular risk factors, including blood lipids, lupus anticoagulant (LAC) and thrombotic events. Anti-PS/PT, anti-beta2GPI and anti-CL of IgA/G/M isotypes were quantified in parallel using particle-based multi-analyte technology. The 99th percentiles among 162 age- and sex-matched populations controls were used as cutoffs. HLA-DRB1 typing was performed using sequence-specific primer PCR.ResultsAnti-PS/PT antibodies associated positively with HLA-DRB1*13 (odds ratio [OR] 2.7, P=0.002), whereas anti-beta2GPI and anti-CL antibodies associated primarily with HLA-DRB1*04 (OR 2.5, P=0.0005; Table 1). These associations remained after adjustment for other significant HLA-DRB1 alleles identified in Table 1 (Figure 1a and b) also for LAC (Figure 1c), and also after adjustment for age and gender (not shown). HLA-DRB1*13, but not DRB1*04, remained as an independent risk factor for thrombosis after adjustment for significant HLA alleles (Figure 1d), and also after adjustment for cardiovascular risk factors in stepwise regression (not shown). Mediation analysis showed that 31.3% of the HLA-DRB1*13-related risk for thrombosis was mediated by anti-PS/PT positivity. HLA-DRB1*03, on the other hand, associated negatively with thrombotic events (Figure 1d) as well as with all aPL (Figure 1a-c). HLA-DRB1*03 had thrombo-protective effect in aPL positive patients (Figure 1d). Additionally, HLA-DRB1*03 positivity was associated with a favourable lipid profile regarding high-density lipoprotein (median 1.4 vs. 1.2 mmol/L, p=0.02) and triglycerides (median 0.9 vs 1.1 mmol/L, p=0.04); whereas no other HLA-DRB1 alleles showed any associations to lipid levels.Table 1.Frequency of individual HLA DRB1 and associations with antibody phenotypes. Odds ratios (OR) and confidence intervals (CI) for being antibody positive given a specific HLA allele and corresponding p values were calculated using Chi2 tests, with significant associations underlined.HLA DRB1HLA-DRB1 n (%) total patientsAnti-PS/PT positive (any isotype) n=48OR (95%CI); PAnti-β2GPI or anti-CL positive (any isotype) n=96OR (95%CI); P*0141 (12.9%)4 (8.3%)0.6 (0.2-1.7); 0.311 (11.4%)0.8 (0.4-1.7); 0.6*03147 (46.5%)13 (27.1%)0.4 (0.2-0.7); 0.00433 (34.4%)0.5 (0.3-0.8); 0.006*0494 (29.7%)18 (37.5%)1.6 (0.8-2.9); 0.241 (42.7%)2.5 (1.5-4.1); 0.0005*0728 (8.9%)6 (12.5%)1.5 (0.6-4); 0.49 (9.4%)1 (0.4-2.4); 0.9*0828 (8.9%)6 (12.5%)1.6 (0.6-4.3); 0.39 (9.4%)1.2 (0.5-2.7); 0.7*099 (2.8%)1 (2.1%)0.7 (0.1-5.5); 0.72 (2.1%)0.6 (0.1-3.0); 0.5*107 (2.2%)0 (0)NA2 (2.1%)0.9 (0.2-4.6); 0.9*1127 (8.5%)6 (12.5%)1.6 (0.6-4.3); 0.38 (8.3%)0.9 (0.4-2.2); 0.8*127 (2.2%)0 (0)NA1 (1%)0.4 (0.05-3.7); 0.4*1379 (25%)21 (43.7%)2.7 (1.4-5.2); 0.00233 (34.3%)2 (1.2-3.4%); 0.01*146 (1.9%)2 (4.2%)3.7 (0.6-23); 0.12 (2.1%)1.4 (0.2-8.9); 0.8*15118 (37.3%)12 (48%)0.5 (0.2-0.9); 0.04527 (28.1%)0.5 (0.3-0.9); 0.01*168 (2.5%)1 (2.1%)0.8 (0.09-6.4); 0.84 (4.2%)2.2 (0.5-9.2); 0.2ConclusionHLA-DRB1*13 confers risk for both anti-PS/PT and thrombotic events in SLE. The association between HLA-DRB1*13 and thrombosis is largely, but not entirely, mediated through anti-PS/PT. Due to the negative association of HLA-DRB1*03 with aPL and the positive association with favourable lipid levels, HLA-DRB1*03 seems to identify a subgroup of SLE patients with reduced vascular risk.References[1]Elbagir S et al. Lupus 2021;30(8):1289.[2]Lundström E et al. Ann Rheum Dis 2013;72:1018.Disclosure of InterestsSahwa Elbagir: None declared, Lina M. Diaz-Gallo: None declared, Giorgia Grosso: None declared, Agneta Zickert: None declared, Iva Gunnarsson: None declared, Michael Mahler Employee of: Dr Mahler is employee of Werfen., Elisabet Svenungsson Speakers bureau: Dr Svennungson has obtained speaker’s fees from Janssen., Grant/research support from: Dr Svennungson has obtained research grant from Merck., Johan Rönnelid Speakers bureau: Dr Rönnelid has given paid lectures for Thermo Fisher Scientific., Consultant of: Dr Rönnelid has been a member of the Scientific Advisory Board for Thermo Fisher Scientific.
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| 10. |
- Leclair, V, et al.
(författare)
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HLA-DRB1 ASSOCIATIONS WITH AUTOANTIBODY-DEFINED SUBGROUPS IN IDIOPATHIC INFLAMMATORY MYOPATHIES (IIM)
- 2022
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Ingår i: ANNALS OF THE RHEUMATIC DISEASES. - : BMJ. - 0003-4967 .- 1468-2060. ; 81, s. 104-105
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- There is a gap between how IIM patients are classified in practice and current validated classification criteria1. Also, different associations with genetic variations in HLA can inform about different T-cell mechanisms involved in disease pathogenesis.ObjectivesWe aimed to systematically study associations between HLA-DRB1 alleles, clinical manifestations, and autoantibody-defined IIM subgroups.MethodsWe included 1348 IIM patients from five European countries. An unsupervised cluster analysis was performed using 14 autoantibodies: anti-Jo1, -PL7, -PL12, -EJ, -OJ, -SRP, -U1RNP, -Ro52, -Mi2, -TIF1γ, -MDA5, -PMScl, -SAE1, and -NXP2 to identify patients’ subgroups. Logistic regressions were used to estimate the associations between HLA-DRB1 alleles, clinical manifestations and the identified subgroups.ResultsEight subgroups were defined by the autoantibody status (Table 1). Three of the subgroups (1, 2 and 6) have overlapping autoantibodies, while four are almost monospecific (3,4,5 and 7), and one (8) has patients negative for tested autoantibodies. Figure 1 represents the significant associations between HLA-DRB1 alleles and the eight subgroups. Heliotrope rash and Gottron’s sign were significantly more frequent in subgroups 3 (OR:2.2 95%CI:[1.1-4.8], OR:2.6 95%CI:[1.3-5.9], respectively), 4 (OR:12 95%CI:[3.6-75], OR:7.8 95%CI:[2.8-33], respectively) and 7 (OR:22 95%CI:[4.5-385], OR:10 95%CI:[3.1-65], respectively), and Raynaud’s phenomenon was significantly more frequent in subgroup 6 (OR:3.3 95%CI:[1.2-11]).Table 1.Autoantibody-defined subgroups using an unsupervised cluster analysis.Subgroups/ MedoidsVariables1 Ro522 U1RNP3 PMScl4 Mi25 Jo16 Jo1/Ro527 TIF18 None*Alln (%)137 (10)183 (14)107 (8)65 (5)119 (9)140 (10)78 (6)519 (39)1348 (100)Female (%)93 (68)116 (63)79 (74)45 (69)76 (64)96 (69)64 (82)313 (60)882 (65)Age at diagnosis, median (IQR)56 (16)51.5 (23)51 (25)57 (22.5)47.5 (23.25)52 (19.5)53.5 (21.75)58 (22)55 (23)AutoantibodiesAnti-Jo106 (3)01 (2)119 (100)140 (100)00266 (20)Anti-PL77 (5)13 (7)00000020 (1.5)Anti-PL125 (4)3 (2)1 (1)01 (1)00010 (0.7)Anti-EJ2 (2)00000002 (0.1)Anti-OJ07 (4)0000007 (0.5)Anti-TIF110 (7)2 (1)2 (2)00078 (100)092 (7)Anti-Mi21 (1)1 (1)1 (1)65 (100)02 (1)0070 (5)Anti-SAE18 (6)23 (13)00000031 (2)Anti-NXP21 (1)23 (13)1 (1)0000025 (2)Anti-MDA59 (7)10 (6)1 (1)1 (2)01 (1)0022 (2)Anti-SRP8 (6)32 (18)00000040 (3)Anti-Ro52137 (100)16 (9)000140 (100)00293 (22)Anti-PMScl11 (8)1 (1)107 (100)00000119 (9)Anti-U1RNP079 (43)0003 (2)0082 (6)*IIM patients negative for the tested autoantibodies.Figure 1.Forest plot of significant associations of HLA. *DRB1 alleles with autoantibody-defined subgroups. Scandinavia includes patients from Denmark, Norway, and Sweden.ConclusionOur study reveals that certain subgroups of IIM patients are characterized by overlap of myositis -specific and -associated autoantibodies, which in turn are associated with different HLA-DRB1 alleles including potential novel associations. These results point to different disease mechanisms in the subgroups, as well as suggest that IIM classification could be improved by integrating broader serological and genetic data.References[1]Parker MJS, Oldroyd A, Roberts ME, et al. The performance of the European League Against Rheumatism/American College of Rheumatology idiopathic inflammatory myopathies classification criteria in an expert-defined 10 year incident cohort. Rheumatology (Oxford). 2019;58(3):468-475.AcknowledgementsWe thank all the patients who participated in the study.Disclosure of InterestsValerie Leclair: None declared, Angeles Shunashy Galindo-Feria: None declared, Simon Rothwell: None declared, Olga Kryštůfková: None declared, Heřman Mann: None declared, Louise Pyndt Diederichsen: None declared, helena andersson: None declared, Martin Klein: None declared, Sarah Tansley: None declared, Neil McHugh: None declared, Janine Lamb: None declared, Jiří Vencovský Speakers bureau: Abbvie, Biogen, Boehringer, Eli Lilly, Gilead, MSD, Novartis, Pfizer, Roche, Sanofi, UCB, Werfen, Consultant of: Abbvie, Argenx, Boehringer, Eli Lilly, Gilead, Octapharma, Pfizer, UCB, Grant/research support from: Abbvie, Hector Chinoy: None declared, Marie Holmqvist: None declared, Leonid Padyukov: None declared, Ingrid E. Lundberg Shareholder of: Roche and Novartis, Consultant of: Corbus Pharmaceuticals Inc, Astra Zeneca, Bristol Myer´s Squibb, Corbus Pharmaceutical, EMD Serono Research & Development Institute, Argenx, Octapharma, Kezaar, Orphazyme, and Janssen, Grant/research support from: Astra Zeneca, Lina M. Diaz-Gallo: None declared
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