Inferences about the causal effect of an exposure on an outcome in MR and its comparison with RCT.

The fundamental conditions for a genetic variant to be an IV are summarized as the following: i. the variant is associated with the exposure, ii. the variant is not associated with the outcome via a confounding pathway, and iii. the variant does not affect the outcome directly, only possibly indirectly via the exposure [12]. Assuming that the RCT is properly blinded and randomized and that the IVs for MR analysis are valid, subgroups should differ systematically in the exposure but not in any other factor except for those causally downstream of the exposure. Therefore, a difference in the average outcome between these subgroups would indicate a causal effect of the exposure on the outcome. Inferring a causal effect of the exposure on the outcome from an association between the IV and the outcome is analogous to inferring an intention-to-treat effect from an association between randomization and the outcome in an RCT [12, 13].</p

Causal relationship of T1DM and 25-OHD on SLE estimated by MVMR.

Causal relationship of T1DM and 25-OHD on SLE estimated by MVMR.</p

Directed acyclic graph of BIMR and MVMR.

(A) BIMR: Gx can be used to estimate the causal effect of exposure X on outcome Y. Gy can be used to estimate the causal effect of outcome Y on exposure X. Genetic variants that are IVs for exposure X(Gx) and that are IVs for outcome Y(Gy) should be completely different. U indicates all confounders, which are assumed to be unknown. (B) MVMR: the total and direct effects of T1DM and 25-OHD level on SLE, based on the following IV assumptions for a genetic variant in MVMR: i. the variant is associated with one or more of the exposures, ii. the variant is not associated with the outcome via a confounding pathway, and iii. the variant does not affect the outcome directly, only possibly indirectly via one or more of the exposures. The direct effect of T1DM on SLE is the effect that T1DM has on SLE not via any other exposure variables, which is equal to βxz; similarly, the direct effect of the 25-OHD level on SLE is equal to βyz. The total effect of T1DM and 25-OHD level on SLE is the effect of T1DM on SLE directly plus the effect of T1DM on SLE via the 25-OHD level, which is equal to βxz + βxyβyz. U indicates all confounders, which are assumed to be unknown.</p

Odds ratios and 95% confidence intervals for the causal effect of T1DM and 25-OHD level on SLE in UVMR and MVMR-Lasso analyses.

The colours of the fitted lines indicate two MR analyses. UVMR, univariable Mendelian randomization; MVMR, multivariable Mendelian randomization; SLE, systemic lupus erythematosus; T1DM, type 1 diabetes; 25-OHD, 25 hydroxyvitamin D; N. SNPs, number of SNPs used in MR; IV, instrumental variables; IVW inverse variance weighted.</p

S1-S7 Tables are included in the file.

BackgroundObservational studies have suggested a relationship between type-1 diabetes mellitus (T1DM) and systemic lupus erythematosus (SLE). In both autoimmunities, 25-hydroxyvitamin D (25-OHD) deficiency is common. However, the causality between T1DM, 25-OHD level and SLE remains largely unknown.MethodsIndependent genetic variants associated with T1DM, 25-OHD level, and SLE from the largest genome-wide association studies were used to conduct two-sample bidirectional Mendelian randomization (BIMR) and two-step Mendelian randomization (MR) analysis to estimate causal relationship between T1DM, 25-OHD level and SLE, and further multivariable Mendelian randomization (MVMR) was used to verify direct causality of T1DM and 25-OHD level on SLE. A series of sensitivity analysis as validation of primary MR results were performed.ResultsConsistent with the results of BIMR, there was strong evidence for a direct causal effect of T1DM on the risk of SLE (ORMVMR-IVW = 1.249, 95% CI = 1.148–1.360, PMVMR-IVW = 1.25×10−5), and 25-OHD level was negatively associated with the risk of SLE (ORMVMR-IVW = 0.305, 95% CI = 0.109–0.857, PMVMR-IVW = 0.031). We also observed a negative causal effect of T1DM on 25-OHD level (ORBIMR-IVW = 0.995, 95% CI = 0.991–0.999, PBIMR-IVW = 0.030) while the causal effect of 25-OHD level on the risk of T1DM did not exist (PBIMR-IVW = 0.106). In BIMR analysis, there was no evidence for causal effects of SLE on the risk of T1DM and 25-OHD level (PBIMR-IVW > 0.05, respectively).ConclusionOur MR analysis suggested that there was a network causal relationship between T1DM, 25-OHD level and SLE. T1DM and 25-OHD level both have causal associations with the risk of SLE, and 25-OHD level could be a mediator in the causality of T1DM and SLE.</div

Causal relationship between SLE, T1DM and 25-OHD level estimated by BIMR.

Causal relationship between SLE, T1DM and 25-OHD level estimated by BIMR.</p

S1-S10 Figs are included in the file.

BackgroundObservational studies have suggested a relationship between type-1 diabetes mellitus (T1DM) and systemic lupus erythematosus (SLE). In both autoimmunities, 25-hydroxyvitamin D (25-OHD) deficiency is common. However, the causality between T1DM, 25-OHD level and SLE remains largely unknown.MethodsIndependent genetic variants associated with T1DM, 25-OHD level, and SLE from the largest genome-wide association studies were used to conduct two-sample bidirectional Mendelian randomization (BIMR) and two-step Mendelian randomization (MR) analysis to estimate causal relationship between T1DM, 25-OHD level and SLE, and further multivariable Mendelian randomization (MVMR) was used to verify direct causality of T1DM and 25-OHD level on SLE. A series of sensitivity analysis as validation of primary MR results were performed.ResultsConsistent with the results of BIMR, there was strong evidence for a direct causal effect of T1DM on the risk of SLE (ORMVMR-IVW = 1.249, 95% CI = 1.148–1.360, PMVMR-IVW = 1.25×10−5), and 25-OHD level was negatively associated with the risk of SLE (ORMVMR-IVW = 0.305, 95% CI = 0.109–0.857, PMVMR-IVW = 0.031). We also observed a negative causal effect of T1DM on 25-OHD level (ORBIMR-IVW = 0.995, 95% CI = 0.991–0.999, PBIMR-IVW = 0.030) while the causal effect of 25-OHD level on the risk of T1DM did not exist (PBIMR-IVW = 0.106). In BIMR analysis, there was no evidence for causal effects of SLE on the risk of T1DM and 25-OHD level (PBIMR-IVW > 0.05, respectively).ConclusionOur MR analysis suggested that there was a network causal relationship between T1DM, 25-OHD level and SLE. T1DM and 25-OHD level both have causal associations with the risk of SLE, and 25-OHD level could be a mediator in the causality of T1DM and SLE.</div

Association of γδ T Cell Compartment Size to Disease Activity and Response to Therapy in SLE

<div><p>Objective</p><p>Although γδT cells are widely recognized as pivotal elements in immune-mediated diseases, their role in the pathogenesis of SLE and therapeutic outcome remains under explored. The current study aims to characterize the γδT cell compartment in SLE and correlate its status to disease severity and response to therapy.</p><p>Methods</p><p>Human peripheral blood-derived γδ T cells were isolated from 14 healthy volunteers and 22 SLE patients (before and after 4 and 12 weeks following the onset of glucocorticoids (GC), mycophenolatemofetil (MMF) orhydroxychloroquine (HCQ) treatment). The γδ T cells were characterized using flow cytometry. In addition, serum concentration of IFN-γ, TNF-α, IL-2, IL-4, IL-6, IL-10 and IL-17A was determined by cytometric bead array (CBA).</p><p>Results</p><p>The SLEDAI scores dropped significantly following therapy in a subset of patients (responders–R) but not in some (non- responders–NR). Peripheral blood γδ T cells in general, and γ9⁺δ T cells and TNF-α/IL-17-secreting CD4^-CD8^-γδ T cell subsets in particular, were decreased in SLE compared to healthy controls. The numbers of the γδ T cell subsets reached levels similar to those of healthy controls following therapy in R but not in NR. Serum IL-6, IL-10 and IL-17 but not IFN-γ and TNF-α were significantly increased in SLE compared to the healthy controls and exhibited differential changes following therapy. In addition, inverse correlation was observed between SLEDAI scores and γδ T cell compartments, especially with TNF-α⁺γδT cells, TNF-α⁺γ9+δT cells and IL17⁺CD4^-CD8^-γδT cells subsets. Differential correlation patterns were also observed between serum cytokine levels and various γδ T cell compartments.</p><p>Conclusions</p><p>A strong association exists between γδ T cell compartments and SLE pathogenesis, disease severity and response to therapy.</p></div

Demographic and clinical parameters of study participants.

<p>Demographic and clinical parameters of study participants.</p

Characterization of different subsets of CD3⁺γδTCR⁺ cells in SLE patients.

<p>A. The panels in this section show the gating strategy employed for the analysis of γδ T cells and subsets. Peripheral venous blood derived leukocytes were stained with different fluorescent antibodies and after lysis of red blood cells, the remaining cells were gated on living lymphocytes and further gated on CD3⁺γδTCR⁺ cells and CD3⁺γδTCR⁺CD4^-CD8^- cells, and then further gated on IFN-γ⁺, TNF-α⁺, IL17⁺ and CD27⁺ cells, respectively. The frequencies of different subsets of γδT cells were analyzed by flow cytometry. B—I. Flow cytometry results represented as the scatter dot plots of γδ T cells and subsets in healthy controls (HC) or in SLE patients before and after therapy (treatment time is indicated). SLE patients are further grouped as responders (R) and non-responders (NR) based on response to therapy.</p