Citations

Abstract Objectives The objective of this study was to explore the prevalence, characteristics and risk factors of COVID-19 breakthrough infections (BIs) in idiopathic inflammatory myopathies (IIMs) using data from the COVID-19 Vaccination in Autoimmune Diseases (COVAD) study. Methods A validated patient self-reporting e-survey was circulated by the COVAD study group to collect data on COVID-19 infection and vaccination in 2022. BIs were defined as COVID-19 occurring ≥14 days after two vaccine doses. We compared BI characteristics and severity among patients with IIMs, patients with other autoimmune rheumatic and non-rheumatic diseases (AIRD, nrAID), and healthy controls (HCs). Multivariable Cox regression models were used to assess the risk factors for BI, severe BI ,and hospitalizations among patients with IIMs. Results Among the 9449 included responses, BIs occurred in 1447 respondents (15.3%). The median age was 44 years [interquartile range (IQR) 21], 77.4% were female, and 182 BIs (12.9%) occurred among the 1406 patients with IIMs. Multivariable Cox regression among the data for patients with IIMs showed increasing age to be a protective factor for BIs [hazard ratio (HR) = 0.98, 95% CI = 0.97–0.99], and HCQ and SSZ use were risk factors (HR = 1.81, 95% CI = 1.24–2.64, and HR = 3.79, 95% CI = 1.69–8.42, respectively). Glucocorticoid use was a risk factor for a severe BI (HR = 3.61, 95% CI = 1.09–11.8). Non-white ethnicity (HR = 2.61, 95% CI = 1.03–6.59) was a risk factor for hospitalization. Compared with other groups, patients with IIMs required more supplemental oxygen therapy (IIMs = 6.0% vs AIRDs = 1.8%, nrAIDs = 2.2% and HCs = 0.9%), intensive care unit admission (IIMs = 2.2% vs AIRDs = 0.6%, nrAIDs and HCs = 0%), advanced treatment with antiviral or monoclonal antibodies (IIMs = 34.1% vs AIRDs = 25.8%, nrAIDs = 14.6% and HCs = 12.8%) and had more hospitalization (IIMs = 7.7% vs AIRDs = 4.6%, nrAIDs = 1.1% and HCs = 1.5%). Conclusion Patients with IIMs are susceptible to severe COVID-19 BIs. Age and immunosuppressive treatments were related to the risk of BIs.

Abstract Objectives To investigate health-related quality of life in patients with idiopathic inflammatory myopathies (IIMs) compared with those with non-IIM autoimmune rheumatic diseases (AIRDs), non-rheumatic autoimmune diseases (nrAIDs) and without autoimmune diseases (controls) using Patient-Reported Outcome Measurement Information System (PROMIS) instrument data obtained from the second COVID-19 vaccination in autoimmune disease (COVAD-2) e-survey database. Methods Demographics, diagnosis, comorbidities, disease activity, treatments and PROMIS instrument data were analysed. Primary outcomes were PROMIS Global Physical Health (GPH) and Global Mental Health (GMH) scores. Factors affecting GPH and GMH scores in IIMs were identified using multivariable regression analysis. Results We analysed responses from 1582 IIM, 4700 non-IIM AIRD and 545 nrAID patients and 3675 controls gathered through 23 May 2022. The median GPH scores were the lowest in IIM and non-IIM AIRD patients {13 [interquartile range (IQR) 10–15] IIMs vs 13 [11–15] non-IIM AIRDs vs 15 [13–17] nrAIDs vs 17 [15–18] controls, P < 0.001}. The median GMH scores in IIM patients were also significantly lower compared with those without autoimmune diseases [13 (IQR 10–15) IIMs vs 15 (13–17) controls, P < 0.001]. Inclusion body myositis, comorbidities, active disease and glucocorticoid use were the determinants of lower GPH scores, whereas overlap myositis, interstitial lung disease, depression, active disease, lower PROMIS Physical Function 10a and higher PROMIS Fatigue 4a scores were associated with lower GMH scores in IIM patients. Conclusion Both physical and mental health are significantly impaired in IIM patients, particularly in those with comorbidities and increased fatigue, emphasizing the importance of patient-reported experiences and optimized multidisciplinary care to enhance well-being in people with IIMs.

The optimal detection strategies for effective convalescent immunity after SARS-CoV-2 infection and vaccination remain unclear. The objective of this study was to characterize convalescent immunity targeting the SARS-CoV-2 spike protein using a multiparametric approach. At the beginning of the pandemic, we recruited 30 unvaccinated convalescent donors who had previously been infected with COVID-19 and 7 unexposed asymptomatic controls. Peripheral blood mononuclear cells (PBMCs) were obtained from leukapheresis cones. The humoral immune response was assessed by measuring serum anti-SARS-CoV-2 spike S1 subunit IgG via semiquantitative ELISA, and T-cell immunity against S1 and S2 subunits were studied via IFN-γ enzyme-linked immunosorbent spot (ELISpot) and flow cytometric (FC) activation-induced marker (AIM) assays and the assessment of cytotoxic CD8+ T-cell function (in the subset of HLA-A2-positive patients). No single immunoassay was sufficient in identifying anti-spike convalescent immunity among all patients. There was no consistent correlation between adaptive humoral and cellular anti-spike responses. Our data indicate that the magnitude of anti-spike convalescent humoral and cellular immunity is highly heterogeneous and highlights the need for using multiple assays to comprehensively measure SARS-CoV-2 convalescent immunity. These observations might have implications for COVID-19 surveillance, and the determination of optimal vaccination strategies for emerging variants. Further studies are needed to determine the optimal assessment of adaptive humoral and cellular immunity following SARS-CoV-2 infection, especially in the context of emerging variants and unclear vaccination schedules.

Abstract Background Tuberculosis (TB) is an infectious disease that induces a complex response from the host immune system. Most carriers of the bacteria experience the asymptomatic, non-infectious phase, latent tuberculosis infection (LTBI), but have the potential to reactivate to active, contagious TB infection. Current diagnostics for TB are not sufficient for LTBI and are focused on using one biomarker, interferon-gamma (IFN-g). To build better diagnostics for the complex disease, multi-biomarker approaches to profile the immune system, coupled with machine learning algorithms, are necessary to diagnose LTBI and determine patients' risks of reactivation to active TB. We aim to use a multiplexed silicon photonic microring resonator sensing platform to profile thirteen biomarkers in patients with LTBI infection and use machine learning data analysis to identify biomarkers relevant for diagnosing LTBI and predicting reactivation risk. Methods We employed microring resonators coupled with a sandwich-style detection format to simultaneously detect thirteen cytokines and chemokines in plasma in under 40 minutes. Chip-integrated silicon photonic microrings are small in size and easily multiplexed with up to sixteen different capture agents. Biomolecular binding events are measured by monitoring the resonant wavelength within the sensing cavity, which shifts as binding events occur. Quantitation of each biomarker is possible with calibration curves that correlate standard protein concentrations to wavelength shifts. The multiplexed assay has been optimized for each individual biomarker, tested for cross-reactivity, and calibrated in the plasma matrix of interest. The LODs vary, depending on biomarker and matrix dilution, but range from 4.9 pg/mL to 691 pg/mL. The patient plasma samples are left over from the TB screening IFN-g release assay, QuantiFERON-TB Gold Plus (QFT), putting this method within the current clinical workflow. We use precision normalization approaches from the four QFT stimulations to account for immunologic differences among the subjects. The biomarker concentrations are evaluated using a random forest machine learning model to identify which biomarkers are important for identifying patients with LTBI and predicting their reactivation risk. Results We used this sensor method to profile 42 patients, 24 LTBI negative controls and 18 LTBI positive controls, with 13 being considered at high risk of reactivation. Using a precision normalization approach, a combination of nine normalized conditions using five of the thirteen biomarkers (CCL4, CCL8, IP-10, IL-2, IL-17) discriminated between LTBI positive and negative subjects with a ROC AUC of 0.90. Additionally, eight normalized conditions using four of the same biomarkers discriminated between high and low-risk subjects with an AUC of 0.83. Currently, we have expanded the sample cohort to include an additional 72 subjects, 25 LTBI positive and 47 LTBI negative. Preliminary analysis shows CCL8, IL-2, and IP-10 alone are able to distinguish between LTBI status (P values <0.01) using raw target concentrations prior to precision normalization. Conclusion Overall, we show the importance of multi-biomarker approaches to develop diagnostic and prognostic tools for a complex disease, such as TB. Further work to use the initial machine learning algorithm with the expanded cohort to validate biomarker importance is underway.

Abstract Optimal detection strategies for effective convalescent immunity after SARS-CoV-2 infection and vaccination remain unclear. The objective of this study was to characterize convalescent immunity targeting the SARS-CoV-2 spike protein using a multiparametric approach. At the beginning of the pandemic, between April 23, 2020, to May 11, 2020, we recruited 30 COVID-19 unvaccinated convalescent donors and 7 unexposed asymptomatic donors. Peripheral blood mononuclear cells (PBMCs) were obtained from leukapheresis cones. The humoral immune response was assessed by measuring serum anti-SARS-CoV-2 spike S1 subunit IgG semiquantitative ELISA and T cell immunity against S1 and S2 subunits were studied by IFN-γ Enzyme-Linked Immune absorbent Spot (ELISpot), flow cytometric (FC) activation-induced marker (AIM) assays and the assessment of cytotoxic CD8+ T-cell function (in the subset of HLA-A2 positive patients). No single immunoassay was sufficient in identifying anti-spike convalescent immunity among all patients. There was no consistent correlation between adaptive humoral and cellular anti-spike responses. Our data indicate that the magnitude of anti-spike convalescent humoral and cellular immunity is highly heterogeneous and highlights the need for using multiple assays to comprehensively measure SARS-CoV-2 convalescent immunity. These observations might have implications for COVID-19 surveillance, and optimal vaccination strategies for emerging variants. Further studies are needed to determine the optimal assessment of adaptive humoral and cellular immunity following SARS-CoV-2 infection, especially in the context of emerging variants and unclear vaccination schedules.

Abstract Background Biomarkers that predict the patients most likely to develop progressive NTM-LD are urgently needed. We hypothesize flow cytometric (FC) detection of T cell markers after ex vivo antigen challenge in PBMC can differentiate patients with progressive vs. nonprogressive NTM-LD. We also measured the serum anti-glycopeptidolipid core IgA antibody by commercial ELISA kit and compared with FC immunoprofiling results. Methods PBMCs were isolated from patients with NTM-LD and stimulated with purified protein derivative (PPD), mycobacteria-specific peptide pools (MTB300, RD1-peptides). We have performed ELISA and FC T-cell phenotypic assays to measure diagnostic accuracy of these assays by ROC and non-parametric statistics analysis. Results A total of 29 NTM-LD patients were studied and classified into 15 non-progressive and 14 progressive NTM-LD based on radiological and clinical evaluation. Patients with progressive NTM-LD showed a statistically significantly higher frequency of PPD-specific CD3+/CD8+ IFN-γ+HLA-DR+ subsets and high diagnostic accuracy compared with non-progressors: Sensitivity = 80% specificity = 71.4% with the ROC of 0.72. There were no other statistically significant differences in other antigen-stimulated T-cell subsets but a trend was observed with HLA-DR+TNF-α+, CD25+CD134+ and CD25+PDL1+ T-cell subsets in progressors. The anti-GPL IgA levels did not show a significant difference and might not be useful in distinguishing progressive from non-progressive NTM-LD. Conclusion Our study findings suggest that PPD-specific CD8+ IFN-γ+HLA-DR+ can distinguish progressive and nonprogressive NTM-LD patients, which can identify those patients at risk of disease progression.

Abstract Objectives We investigated coronavirus disease 2019 (COVID-19) vaccine safety in pregnant and breastfeeding women with autoimmune diseases (AID) in the COVID-19 Vaccination in Autoimmune Diseases (COVAD) study. Methods Delayed-onset (>7 days) vaccine-related adverse events (AE), disease flares and AID-related treatment modifications were analysed upon diagnosis of AID vs healthy controls (HC) and the pregnancy/breastfeeding status at the time of at least one dose of vaccine. Results Among the 9201 participants to the self-administered online survey, 6787 (73.8%) were women. Forty pregnant and 52 breastfeeding patients with AID were identified, of whom the majority had received at least one dose of COVID-19 vaccine (100% and 96.2%, respectively). AE were reported significantly more frequently in pregnant than in non-pregnant patients (overall AE 45% vs 26%, P = 0.01; minor AE 40% vs 25.9%, P = 0.03; major AE 17.5% vs 4.6%, P < 0.01), but no difference was found in comparison with pregnant HC. No difference was observed between breastfeeding patients and HC with respect to AE. Post-vaccination disease flares were reported by 17.5% of pregnant and 20% of breastfeeding patients, and by 18.3% of age- and disease-matched non-pregnant and non-breastfeeding patients (n = 262). All pregnant/breastfeeding patients who experienced a disease flare were managed with glucocorticoids; 28.6% and 20% of them required initiation or change in immunosuppressants, respectively. Conclusion This study provides reassuring insights into the safety of COVID-19 vaccines administered to women with AID during the gestational and post-partum periods, helping overcome hesitant attitudes, as the benefits for the mother and for the fetus by passive immunization appear to outweigh potential risks.

TB remains a leading cause of morbidity and mortality worldwide. However, most infected immunocompetent individuals are asymptomatic and only 5–10% of these will eventually develop active TB during their lifetime (typically within 2 years after exposure). Therefore, rapid diagnosis and efficient management of asymptomatic infected individuals who are at the highest risk of progression and transmission remain major clinical and public health challenges. In recent years, there has been important scientific progress in our understanding of the spectrum of asymptomatic Mycobacterium tuberculosis (Mtb) infections that not only includes the dynamic state of latent TB infection (LTBI), but also the preclinical state of incipient and subclinical TB. The latter is possibly as prevalent as symptomatically active TB and potentially contributes to global Mtb transmission in various settings. We summarize the latest developments and current challenges of the existing testing tools for LTBI and describe promising biomarkers and diagnostics for the spectrum of asymptomatic TB. Following the negative results of a recent clinical trial for a biomarker-guided preventive therapy approach, we also suggest some treatment options for incipient TB.

Abstract Introduction Interstitial lung disease (ILD) is a heterogenous group of parenchymal lung disorders with multiple etiologies. ILD often affects the quality of life and survival of patients. OSA is commonly diagnosed among ILD patients with reported prevalences of 50-90%, and a prevalence of moderate to severe OSA as high as 65%. There is also concern that patients with ILD may be less tolerant of PAP therapies. We aim to identify differences in PAP treatment response and adherence in patients with ILD and OSA compared with a group of matched OSA controls. Methods A retrospective cohort analysis was conducted on patients ≥ 18 years-old that were evaluated at Mayo Clinic Center for Sleep Medicine between July 2012 to June 2022. Inclusion criteria were patients with a diagnosis of ILD per ATS criteria also diagnosed with OSA by HSAT or PSG. Control OSA patients were matched to ILD/OSA patients by age, BMI, and AHI ranges. Exclusion criteria included comorbid conditions of asthma or COPD. Patients with early non-adherence to CPAP or those who didn't have sleep follow up were also excluded. Follow-up sleep visits were assessed to determine CPAP adherence, change in PAP pressure, addition of oxygen, and change in Epworth sleepiness scale. Results We compared 51 patients with ILD/OSA and 51 OSA matched-controls. Twenty-five ILD/OSA patients were diagnosed by PSG and 26 by HSAT. Median treatment follow-up for the ILD group was 7 months (IQR of 3 to 18 months). Baseline characteristics, including oxygen desaturation index (ODI), oxygen saturation, time spent below 89% during overnight oximetry, and baseline Epworth scale, were similar between ILD/OSA and non-ILD OSA patients. Importantly, there was no statistically significant difference in PAP adherence, median PAP levels, PAP pressure change, residual AHI, or change in Epworth after treatment Conclusion There was no significant difference in baseline OSA characteristics, treatment response, and PAP adherence in ILD patients with OSA compared with matched non-ILD OSA controls. Outcome assessment may be limited by sample size and the shorter follow-up period. Future studies should include longer-term follow-up with a larger ILD cohort to assess the impact of ILD progression on PAP adherence over time. Support (if any)

Abstract Objectives Disease flares in the post–coronavirus disease 2019 (COVID-19) vaccination period represent a prominent concern, though risk factors are poorly understood. We studied these flares among patients with idiopathic inflammatory myopathies (IIMs) and other autoimmune rheumatic diseases (AIRDs). Methods The COVAD-1 and -2 global surveys were circulated in early 2021 and 2022, respectively, and we captured demographics, comorbidities, AIRDs details, COVID-19 infection history and vaccination details. Flares of IIMs were defined as (a) patient self-reported, (b) immunosuppression (IS) denoted, (c) clinical sign directed and (d) with >7.9-point minimal clinically significant improvement difference worsening of Patient-Reported Outcomes Measurement Information System (PROMIS) PROMISPF10a score. Risk factors of flares were analysed using regression models. Results Of 15 165 total respondents, 1278 IIMs (age 63 years, 70.3% female, 80.8% Caucasians) and 3453 AIRDs were included. Flares of IIM were seen in 9.6%, 12.7%, 8.7% and 19.6% patients by definitions (a) to (d), respectively, with a median time to flare of 71.5 (10.7–235) days, similar to AIRDs. Patients with active IIMs pre-vaccination (OR 1.2; 95% CI 1.03, 1.6, P = 0.025) were prone to flares, while those receiving rituximab (OR 0.3; 95% CI 0.1, 0.7, P = 0.010) and AZA (OR 0.3, 95% CI 0.1, 0.8, P = 0.016) were at lower risk. Female gender and comorbidities predisposed to flares requiring changes in IS. Asthma (OR 1.62; 95% CI 1.05, 2.50, P = 0.028) and higher pain visual analogue score (OR 1.19; 95% CI 1.11, 1.27, P < 0.001) were associated with disparity between self-reported and IS-denoted flares. Conclusion A diagnosis of IIMs confers an equal risk of flares in the post–COVID-19 vaccination period to AIRDs, with active disease, female gender and comorbidities conferring a higher risk. Disparity between patient- and physician-reported outcomes represents a future avenue for exploration.