Lupus Science & Medicine最新文献

Background: The interaction between fine particulate matter (PM2.5) and genetic factors can lead to epigenetic modifications, potentially increasing the risk of SLE development. However, the impact of PM2.5 on incident SLE development remains unelucidated. This study investigated the effects of year-to-year variations in PM2.5 exposure on incident SLE risk in Taiwanese adults.

Methods: In this longitudinal study, we followed up 268 254 adults from the Taiwan MJ cohort (2005-2017) for 9.8 years to identify incident SLE cases, ascertained from patients' clinical and laboratory reports. Residential address-specific annual PM2.5 concentrations were obtained from Taiwan Air Quality-Monitoring sites. We employed a time-dependent Cox regression model, considering death as a competing risk, to assess the impact of year-to-year variations of PM2.5 exposure on SLE development.

Results: During 2 628 889 person-years of follow-up, 151 (0.1%) individuals developed new-onset SLE. Participants with higher levels of PM2.5 exposure (per 5 μg/m³ increase) had significantly higher risk of incident SLE (adjusted HR 3.35; 95% CI 2.94 to 3.82). We observed a significant positive linear relation between increasing level of PM2.5 exposure and higher risk of incident SLE in all individual subgroups after stratifying study subjects by age and sex (p<0.001).

Conclusion: PM2.5 exposure emerged as a risk factor for incident SLE. Air pollution mitigation strategies should be considered as a preventive measure for SLE.

Objective: This study aims to evaluate the diagnostic performance of the digital liquid chip method (DLCM) compared with indirect immunofluorescence (IIF) and chemiluminescent immunoassay (CLIA) for anti-double-stranded DNA (dsDNA) antibody detection in SLE.

Methods: The retrospective study consecutively enrolled 1349 patients, including 698 with SLE and 651 with other autoimmune diseases at Peking Union Medical College Hospital. Anti-dsDNA antibodies were detected using IIF (EUROIMMUN, Luebeck, Germany), CLIA (YHLO, Shenzhen, China) and DLCM (Livzon, Zhuhai, China). The sensitivity, specificity and area under the curve (AUC) of each method and combination were compared at the recommended manufacturer cut-offs. The agreement between methods and the association between antibody levels and clinical characteristics including disease activity, complement levels and organ involvement were also evaluated.

Results: All methods exhibited high specificity, while IIF performed best (98.5%), significantly greater than CLIA (96.3%) and DLCM (96.6%) (p < 0.05). CLIA demonstrated the highest sensitivity (48.1%), outperforming IIF (36.0%) and DLCM (41.4%) (p<0.001). Cohen's kappa indicated substantial positive agreement between DLCM and CLIA (κ=0.67), and moderate agreement between IIF and the other methods (κ=0.52-0.55). Combining IIF with DLCM or CLIA improved diagnosis performance, with IIF+CLIA achieving the highest sensitivity (54.0%), accuracy (74.1%) and AUC (0.75). Moreover, anti-dsDNA positivity was strongly associated with lower complement levels (C3: 0.71 vs 0.90 g/L in DLCM+ vs DLCM-, p<0.001) and moderate-severe disease activity (65.0% DLCM positive). DLCM uniquely predicted musculoskeletal involvement (55.3% vs 44.7%, p<0.01). However, the diagnostic performance for renal involvement was limited (sensitivity 46.9%, specificity 56.3%, AUC=0.52).

Conclusions: DLCM demonstrated substantial agreement with CLIA and held potential for SLE diagnosis and monitoring. A multiassay strategy, using a sensitive assay like CLIA or DLCM for initial screening and a highly specific assay like IIF for confirmation, optimises diagnostic performance for anti-dsDNA antibody detection in SLE.

Objectives: To evaluate the Systemic Lupus International Collaborating Clinics-Frailty Index (SLICC-FI) as a predictor of hospitalisations in patients with SLE from a Latin American cohort.

Methods: Patients from a single-centre prevalent cohort were included. The SLICC-FI was assessed at baseline. Hospitalisations, their number as well as their duration (in days), were reported during the first 3 years from the baseline visit. Univariable and multivariable negative binomial regressions were performed to determine the association between the baseline SLICC-FI (per 0.05 increase) and hospitalisations during follow-up (number and length), adjusted for possible confounders. An alternative analysis was carried out after excluding the damage-related deficits, rendering a modified SLICC-FI.

Results: Of the 295 patients included, 273 (92.5%) were female, with a mean (SD) age at diagnosis of 34.8 (13.5) years. At baseline, the mean SLICC-FI was 0.18 (0.05) with 86 (29.2%) patients categorised as being frail. The mean number of hospitalisations per patient-year was 0.4 (0.8) and the mean number of days hospitalised during the 3-year period per patient-year was 3.1 (7.6) days. The SLICC-FI predicted a higher number and days of hospitalisations (incidence rate ratio (IRR): 1.671 (95% CI: 1.385 to 2.016) and IRR: 2.018 (95% CI: 1.715 to 2.375), respectively). The modified SLICC-FI also predicted hospitalisations in both number and days.

Conclusion: The SLICC-FI predicted hospitalisations in patients with SLE, independent of other well-known risk factors. Further studies are needed to develop strategies to improve frailty in these patients.

Objectives: To assess brain grey matter alterations in patients with SLE and their correlation with neuropsychological testing using synthetic MRI (SyMRI).

Methods: This prospective study enrolled patients with SLE and age, gender and education-matched healthy controls (HC). Study assessments included brain MRI using SyMRI and neuropsychological tests: Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), Digit Span Test, Self-Rating Anxiety Scale and Self-Rating Depression Scale (SDS). SyMRI post-processing and Automated Anatomical Labeling were used for grey matter mapping. Correlation analysis was performed to assess the relationship between brain grey matter structural alterations and neuropsychological testing.

Results: 77 patients with SLE (57 non-neuropsychiatric SLE (non-NPSLE), 20 NPSLE) and 29 HC participants were enrolled. Patients with SLE showed reduced grey matter volume compared with HC (p<0.05). The NPSLE group exhibited more extensive increases in longitudinal (T1) and transverse (T2) relaxation times in grey matter than the non-NPSLE group (p<0.001). Proton density values were lower in patients with SLE (p<0.001). Lower brain parenchymal volume correlated with higher SLE Disease Activity Index (p<0.05). Lower MMSE/MoCA scores correlated with increased T1/T2 in the left medial cingulate and paracingulate gyri (p<0.05). Higher SDS scores correlated with increased T1/T2 in the left calcarine fissure and surrounding cortex (p<0.05). These changes were also linked to disease markers (C3, C4, immunoglobulin M, erythrocyte sedimentation rate) (p<0.05).

Conclusions: Grey matter alterations in patients with SLE correlate with cognitive impairment, depression and disease activity.

Objective: To report the prevalence of disease activity in individual SLE organ domains, including prevalence stratified by the most common disease activity cut-off score for clinical trial eligibility (SLE Disease Activity Index 2000; SLEDAI-2K ≥6).

Methods: We used data from a multinational longitudinal SLE cohort, prospectively collected between 2013 and 2020. Disease activity was categorised by the SLEDAI-2K into nine organ systems. We calculated proportions of organ-specific disease activity in the overall cohort and stratified by SLEDAI-2K ≥6 or <6, on both a per-patient and per-visit level.

Results: We included 4102 patients (92.0% female, 88.9% Asian) contributing 42 345 eligible visits. Serological disease activity was most prevalent, affecting 75.5% of patients at least once during follow-up, followed by renal (41.6%), cutaneous (36.5%), musculoskeletal (20.1%) and haematological (19.1%) activity. Serositis (3.4%), vasculitis (3.4%), central nervous system activity (3.0%) and fever (2.9%) occurred infrequently. In patient visits with an SLEDAI-2K ≥6 (n=10 031), the most common active manifestations were serological (89.8%), renal (72.9%), cutaneous (26.4%) and musculoskeletal (14.3%). In patient visits with an SLEDAI-2K <6 (n=32 314), renal (7.3%), cutaneous (6.7%), haematological (5.8%) and musculoskeletal (1.3%) disease activity were still present.

Conclusion: Serological, renal, cutaneous, musculoskeletal and haematological manifestations predominate in patients with active SLE; other organs are affected infrequently. Trial outcome measures could focus on measuring change in these systems and omit detailed analysis of rare events. Conversely, some patients with active disease in common domains would be ineligible for clinical trials based on an SLEDAI-2K <6. Use of organ-specific activity measures and inclusion criteria may overcome this limitation.

Objective: Patients with SLE have a well-known increased risk of major comorbidities, although they are also very heterogeneous in terms of the prevalence of comorbid conditions. The relationships of such comorbidities with the outcomes and the severity of index diseases are less known. We aimed to evaluate the interactions between comorbid conditions, in a large multicentre SLE cohort, and their impact on severity and outcomes, using a cluster analysis.

Methods: Data on 14 cumulative comorbidities were derived from patients with SLE (American College of Rheumatology (ACR)-97 criteria) who had been included in the retrospective phase of the RELESSER (Spanish Society of Rheumatology National Register of SLE). The Severity Katz Index and the SLICC/ACR Damage Index were calculated. Unsupervised cluster analysis was performed to better characterise the relationships between comorbidities in a large multicentre cohort of patients with SLE. For intercluster differences testing, analysis of variance and Tukey tests were used to compare continuous numerical variables; a Kruskal-Wallis test to discrete variables and the χ² (or Fisher's exact test) were used for categorical ones.

Results: A total of 3658 patients with SLE were included. Men accounted for 9.6% of patients. The mean (SD) age was 45.9 years, and 93% were Caucasian. Four clusters, with markedly different comorbidity profiles and outcomes, were identified: in cluster 2 (n=516), patients were grouped around depression (100% of the cases); in cluster 3 (n=418) around serious infections (100%); and in cluster 4 (n=388) around cardiovascular events (also 100%). However, in cluster 1, the largest one (n=2336), no patient had any of the three defining comorbidities of the other clusters, and this cluster was associated with the best outcomes.

Conclusions: Cluster analysis identifies well-differentiated subsets of patients with SLE in terms of their comorbidities. The most relevant comorbidities in SLE tend to aggregate in the most severe patient subsets.

Background: Mycophenolic acid (MPA) is recommended for the treatment of lupus nephritis (LN). However, the high pharmacokinetic (PK) variability of MPA contributes to its suboptimal efficacy and an increased incidence of adverse reactions. Rare data reported the impacts of genetic and clinical characteristics on MPA clearance in the paediatric patients with LN.

Methods: Paediatric patients with LN receiving mycophenolate mofetil (MMF) were prospectively enrolled. MPA PK parameters were calculated on reaching steady state (defined as at least 7 days), based on plasma concentrations measured before and after administration at intervals of 0.5, 1.5, 2.5, 4, 6, 9 and 12 hours post-MMF treatment. Genetic variants associated with the MPA PK process were identified. The population PKs (PPKs) model was constructed using Phoenix NLME software and validated internally as well as externally.

Results: A total of 51 patients were included in the study, resulting in the acquisition of 146 area under the concentration-time curve (AUC) values. PK analysis revealed that the mean AUC value was 31.05 μg×hour/mL. The mean clearance value was 11.10 L/hour. We screened 29 single nucleotide polymorphisms across 13 candidate genes and identified that eight genetic variants within the UGT1A9, ABCC2 and CES1 genes significantly impacted the AUC of MPA. Furthermore, our data were adequately represented by a two-compartment model incorporating lag time and linear elimination kinetics. However, when combined with clinical variables, only body weight emerged as a critical covariate significantly associated with MPA peripheral volume of distribution. External validation involving nine patients demonstrated strong predictive performance.

Conclusion: Body weight emerges as the primary covariate over pharmacogenetic variants in PPK modelling of MPA in paediatric LN. We suggest that an individualised initial dose and adjustment based on body weight can be given in the paediatric population.

Introduction: In the type 1 and 2 SLE model, inflammation mediates type 1 manifestations, but its role in type 2 manifestations (eg, fatigue, myalgias, mood disturbance, cognitive dysfunction) is less clear. Therapeutic hydroxychloroquine (HCQ) levels reduce type 1 activity, but their relationship with type 2 activity is unknown. Exploring this relationship may illuminate type 2 SLE pathophysiology.

Methods: We measured whole blood HCQ levels using liquid chromatography-mass spectrometry, categorising them as underexposure (<200 ng/mL), subtherapeutic (200 to <750 ng/mL) or therapeutic (≥750 ng/mL). We measured type 1 SLE activity using the type 1 Physician Global Assessment (PGA) and Systemic Lupus Erythematosus Disease Activity Index and type 2 SLE activity using the type 2 PGA and patient-reported polysymptomatic distress scores. Patients were categorised into minimal (low type 1 and type 2), type 1 (high type 1 and low type 2), type 2 (low type 1 and high type 2) and mixed activity (high type 1 and type 2) groups. We analysed relationships between HCQ levels and type 1 and type 2 SLE activities.

Results: Among 154 patients (median age 43, 90% women, 63% Black race, 7% Hispanic ethnicity) across 297 visits, HCQ levels were underexposed at 41 (14%) visits, subtherapeutic at 76 (26%) and therapeutic at 180 (61%) visits. Patients had minimal activity at 102 visits (34%), type 1 activity at 33 (11%), type 2 activity at 85 (29%) and mixed activity at 77 (26%) visits.Underexposed HCQ levels were independently associated with higher type 1 (OR 2.33, 95% CI 1.23 to 4.44) and type 2 activities (OR 1.80, 95% CI 1.07 to 3.04). Mixed activity most strongly associated with Underexposed HCQ levels (OR 3.4-10.3, p<0.05).

Conclusions: Low HCQ levels are associated with increased type 1 and type 2 SLE activities, particularly for the mixed activity group, suggesting that immunologic activity may contribute to type 2 symptoms in some patients.

Objectives: This study aimed to investigate serum glutathione reductase (GR) levels in patients with SLE and to assess its association with disease activity.

Methods: The retrospective study collected clinical data, including serum GR, complement (C) 3 and C4 levels, among patients with SLE. The SLE Disease Activity Index 2000 (SLEDAI 2000) and SLE Disease Activity Score (SLE-DAS) were calculated, and C3 and C4 were used as controls to assess the importance of serum GR levels in evaluating SLE disease activity.

Results: Serum GR levels were significantly higher in patients with SLE (n=142) than in healthy controls (n=100). Serum GR levels were positively correlated with SLEDAI 2000 (ρ=0.335) and SLE-DAS (ρ=0.454) values in patients with SLE. Further, C3 and C4 were negatively correlated with SLEDAI 2000 (ρ = -0.544 and -0.418) and with SLE-DAS (ρ = -0.290 and -0.242). Fisher's Z test showed that GR was inferior to C3; however, similar to C4 in the correlation with SLEDAI 2000, whereas GR was comparable to C3 but superior to C4 in the correlation with SLE-DAS. The identification of moderate-to-severe disease activity based on SLEDAI 2000 of >6 revealed a receiver operating characteristic curve area under the curve (AUC) for GR of 0.700 (95% CI: 0.617 to 0.774), which was comparable to the AUC for C3 (0.784, 95% CI: 0.707 to 0.848) and C4 (0.697, 95% CI: 0.615 to 0.771); in determining moderate-to-severe disease activity as defined by SLE-DAS of >7.64, GR (0.767, 95% CI: 0.689 to 0.834) was equal to C3 (0.661, 95% CI: 0.576 to 0.738) but superior to C4 (0.617, 95% CI: 0.532 to 0.698).

Conclusion: Serum GR levels are positively correlated with SLE disease activity and exhibit clinical value in identifying moderate-to-severe disease activity in SLE.