Prevalence of latent and overt polyautoimmunity in type 1 diabetes: A systematic review and meta-analysis.

BACKGROUND: Patients afflicted by type 1 diabetes (T1D) exhibit polyautoimmunity (PolyA). However, the frequency and distribution of PolyA in T1D is still unknown. OBJECTIVE: We conducted a systematic review and meta-analysis to define the prevalence of latent and overt PolyA in individuals with T1D. METHODS: Following PRISMA guidelines, a comprehensive search across medical databases identified studies on latent and overt PolyA in T1D. Two researchers independently screened, extracted data, and assessed study quality. A random effects model was utilized to calculate the pooled prevalence, along with its corresponding 95 % confidence interval (CI), for latent PolyA and overt PolyA. Meta-regression analysis was conducted to study the effect of study designs, age, sex, and duration of disease on pooled prevalence. RESULTS: A total of 158 articles, encompassing a diverse composition of study designs were scrutinized. The analysis included 270,890 individuals with a confirmed diagnosis of T1D. The gender was evenly distributed (50.30 % male). Notably, our analysis unveiled an overt PolyA prevalence rate of 8.50 % (95 % CI, 6.77 to 10.62), with North America having the highest rates (14.50 %, 95 % CI, 7.58 to 24.89). This PolyA profile was further characterized by a substantial incidence of concurrent autoimmune thyroid disease (7.44 %, 95 % CI, 5.65 to 9.74). Moreover, we identified a notable prevalence of latent PolyA in the T1D population, quantified at 14.45 % (95 % CI, 11.17 to 18.49) being most frequent in Asia (23.29 %, 95 % CI, 16.29 to 32.15) and Oceania (21.53 %, 95 % CI, 16.48 to 27.62). Remarkably, this latent PolyA phenomenon primarily featured an array of autoantibodies, including rheumatoid factor, followed by Ro52, thyroid peroxidase antibodies, and thyroglobulin antibodies. Duration of the disease was associated with a highest frequency of latent (ß: 0.0456, P-value: 0.0140) and overt PolyA (ß: 0.0373, P-value: 0.0152). No difference in the pooled prevalence by study design was observed. CONCLUSION: This meta-analysis constitutes a substantial advancement in the realm of early detection of PolyA in the context of T1D. Individuals with T1D should regularly undergo assessments to identify potential concurrent autoimmune diseases, especially as they age.

Performance of the Systemic Lupus Erythematosus Risk Probability Index (SLERPI) in a cohort of Colombian population.

OBJECTIVE: To evaluate the performance of the Systemic Lupus Erythematosus Risk Probability Index (SLERPI) in Colombian patients with systemic lupus erythematosus (SLE). METHODS: The Colombian cohort included 435 SLE patients and 430 controls with other autoimmune diseases (ADs). Clinical and serological data were collected, and SLE was indicated by SLERPI scores > 7. The American College of Rheumatology (ACR)-1997, Systemic Lupus International Collaborating Clinics (SLICC)-2012, and European League Against Rheumatism (EULAR)/ACR-2019 criteria were used as reference standards. The impact of overt polyautoimmunity (PolyA) on SLERPI performance was assessed. Additionally, multivariate lineal regression analysis was performed to evaluate the contribution of SLERPI features to the overall SLERPI score. RESULTS: SLE patients had higher SLERPI scores (P < 0.0001), with almost 90% meeting "definite" lupus criteria. Main factors influencing SLERPI included immunological disorder (ß:44.75, P < 0.0001), malar/maculopapular rash (ß:18.43, P < 0.0001), and anti-nuclear antibody positivity (ß:15.65, P < 0.0001). In contrast, subacute cutaneous lupus erythematosus/discoid lupus erythematosus (ß:2.40, P > 0.05) and interstitial lung disease (ß:-21.58, P > 0.05) were not significant factors to the overall SLERPI score. SLERPI demonstrated high sensitivity for SLE, both for the overall SLE group and for those without overt PolyA (95.4% and 94.6%, respectively), but had relatively low specificity (92.8% and 93.7%, respectively). The model showed high sensitivity for hematological lupus (98.8%) and lupus nephritis (96.0%), but low sensitivity for neuropsychiatric lupus (93.2%). Compared to the ACR-1997, SLICC-2012 and EULAR/ACR-2019 criteria, SLERPI yielded the highest sensitivity and lowest specificity. CONCLUSION: SLERPI efficiently identified SLE patients in a Colombian cohort, showing high sensitivity but low specificity. The model effectively distinguishes SLE patients, even in the presence of concurrent overt PolyA. Key Points •SLERPI has a high sensitivity, but low specificity compared to ACR-1997, SLICC-2012 and EULAR/ACR-2019 criteria in the Colombian population. •Within the SLERPI score, immunological disorder, malar/maculopapular rash, and anti-nuclear antibody positivity are the strongest predictors of SLE. •SLERPI model can efficiently distinguish patients with SLE, regardless of concomitant overt PolyA. •SLERPI demonstrates high sensitivity in identifying hematological and nephritic subphenotypes of SLE.