Determinants of COVID-19 Infection Among Employees of an Italian Financial Institution.

BACKGROUND: Understanding the trend of the severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) is becoming crucial. Previous studies focused on predicting COVID-19 trends, but few papers have considered models for disease estimation and progression based on large real-world data. METHODS: We used de-identified data from 60,938 employees of a major financial institution in Italy with daily COVID-19 status information between 31 March 2020 and 31 August 2021. We consider six statuses: (i) concluded case, (ii) confirmed case, (iii) close contact, (iv) possible-probable contact, (v) possible contact, and (vi) no-COVID-19 or infection. We conducted a logistic regression to assess the odds ratio (OR) of transition to confirmed COVID-19 case at each time point. We also fitted a general model for disease progression via the multi-state transition probability model at each time point, with lags of 7 and 15 days. RESULTS: Employment in a branch versus in a central office was the strongest predictor of case or contact status, while no association was detected with gender or age. The geographic prevalence of possible-probable contacts and close contacts was predictive of the subsequent risk of confirmed cases. The status with the highest probability of becoming a confirmed case was concluded case (12%) in April 2020, possible-probable contact (16%) in November 2020, and close contact (4%) in August 2021. The model based on transition probabilities predicted well the rate of confirmed cases observed 7 or 15 days later. CONCLUSION: Data from industry-based surveillance systems may effectively predict the risk of subsequent infection.

Does Pizza Consumption Favor an Improved Disease Activity in Rheumatoid Arthritis?

To our knowledge, no studies so far have investigated the role of pizza and its ingredients in modulating disease activity in rheumatoid arthritis (RA). We assessed this question via a recent cross-sectional study including 365 participants from Italy, the birthplace of pizza. Multiple robust linear and logistic regression models were fitted with the tertile consumption categories of each available pizza-related food item/group (i.e., pizza, refined grains, mozzarella cheese, and olive oil) as independent variables, and each available RA activity measure (i.e., the Disease Activity Score on 28 joints with C-reactive protein (DAS28-CRP), and the Simplified Disease Activity Index (SDAI)) as the dependent variable. Stratified analyses were carried out according to the disease severity or duration. Participants eating half a pizza >1 time/week (vs. ≤2 times/month) reported beneficial effects on disease activity, with the significant reductions of ~70% (overall analysis), and 80% (the more severe stratum), and the significant beta coefficients of -0.70 for the DAS28-CRP, and -3.6 for the SDAI (overall analysis) and of -1.10 and -5.30 (in long-standing and more severe RA, respectively). Among the pizza-related food items/groups, mozzarella cheese and olive oil showed beneficial effects, especially in the more severe stratum. Future cohort studies are needed to confirm this beneficial effect of pizza and related food items/groups on RA disease activity.

Cross-sectional analysis reveals autoantibody signatures associated with COVID-19 severity.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is associated with increased levels of autoantibodies targeting immunological proteins such as cytokines and chemokines. Reports further indicate that COVID-19 patients may develop a broad spectrum of autoimmune diseases due to reasons not fully understood. Even so, the landscape of autoantibodies induced by SARS-CoV-2 infection remains uncharted territory. To gain more insight, we carried out a comprehensive assessment of autoantibodies known to be linked to diverse autoimmune diseases observed in COVID-19 patients in a cohort of 231 individuals, of which 161 were COVID-19 patients (72 with mild, 61 moderate, and 28 with severe disease) and 70 were healthy controls. Dysregulated IgG and IgA autoantibody signatures, characterized mainly by elevated concentrations, occurred predominantly in patients with moderate or severe COVID-19 infection. Autoantibody levels often accompanied anti-SARS-CoV-2 antibody concentrations while stratifying COVID-19 severity as indicated by random forest and principal component analyses. Furthermore, while young versus elderly COVID-19 patients showed only slight differences in autoantibody levels, elderly patients with severe disease presented higher IgG autoantibody concentrations than young individuals with severe COVID-19. This work maps the intersection of COVID-19 and autoimmunity by demonstrating the dysregulation of multiple autoantibodies triggered during SARS-CoV-2 infection. Thus, this cross-sectional study suggests that SARS-CoV-2 infection induces autoantibody signatures associated with COVID-19 severity and several autoantibodies that can be used as biomarkers of COVID-19 severity, indicating autoantibodies as potential therapeutical targets for these patients.

Olive Oil and Nuts in Rheumatoid Arthritis Disease Activity.

Few observational studies investigated the relationship between single food groups and disease activity in rheumatoid arthritis (RA). Within a recent Italian cross-sectional study (365 patients, median age: 58.46 years, 78.63% females), we focused on two food groups, olive oil and nuts, representing vegetable sources of fatty acids. Disease activity was measured with Disease Activity Score on 28 joints based on C-reactive protein (DAS28-CRP) and the Simplified Disease Activity Index (SDAI). Robust linear and logistic regression models included tertile-based consumption categories of each food group and several confounders. Stratified analyses were performed by disease severity or duration. Higher consumption of both food groups exerted a favorable effect on disease activity, significant only for olive oil (Beta: -0.33, p-value: 0.03) in the linear regression on the overall sample. This favorable effect was stronger in the more severe or long-standing forms of RA (p-value for heterogeneity <0.05, especially for disease severity). Significant ORs were as low as ~0.30 for both food groups, strata (i.e., more severe and long-standing RA), and disease activity measures. Mean DAS28-CRP significantly decreased by ~0.70 for olive oil and ~0.55 for nuts in the two strata; mean SDAI significantly decreased by 3.30 or more for olive oil in the two strata. Globally, the beta coefficients doubled, and the ORs halved (in absolute values) for both food groups, reaching significance in 12 of the 16 available models fitted to the more severe or long-standing RA strata. More compromised forms of RA may benefit from increasing consumption of olive oil, olives, and nuts.

Dysregulated autoantibodies targeting vaso- and immunoregulatory receptors in Post COVID Syndrome correlate with symptom severity.

Most patients with Post COVID Syndrome (PCS) present with a plethora of symptoms without clear evidence of organ dysfunction. A subset of them fulfills diagnostic criteria of myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). Symptom severity of ME/CFS correlates with natural regulatory autoantibody (AAB) levels targeting several G-protein coupled receptors (GPCR). In this exploratory study, we analyzed serum AAB levels against vaso- and immunoregulatory receptors, mostly GPCRs, in 80 PCS patients following mild-to-moderate COVID-19, with 40 of them fulfilling diagnostic criteria of ME/CFS. Healthy seronegative (n=38) and asymptomatic post COVID-19 controls (n=40) were also included in the study as control groups. We found lower levels for various AABs in PCS compared to at least one control group, accompanied by alterations in the correlations among AABs. Classification using random forest indicated AABs targeting ADRB2, STAB1, and ADRA2A as the strongest classifiers (AABs stratifying patients according to disease outcomes) of post COVID-19 outcomes. Several AABs correlated with symptom severity in PCS groups. Remarkably, severity of fatigue and vasomotor symptoms were associated with ADRB2 AAB levels in PCS/ME/CFS patients. Our study identified dysregulation of AAB against various receptors involved in the autonomous nervous system (ANS), vaso-, and immunoregulation and their correlation with symptom severity, pointing to their role in the pathogenesis of PCS.

Gaussian graphical models with applications to omics analyses.

Gaussian graphical models (GGMs) provide a framework for modeling conditional dependencies in multivariate data. In this tutorial, we provide an overview of GGM theory and a demonstration of various GGM tools in R. The mathematical foundations of GGMs are introduced with the goal of enabling the researcher to draw practical conclusions by interpreting model results. Background literature is presented, emphasizing methods recently developed for high-dimensional applications such as genomics, proteomics, or metabolomics. The application of these methods is illustrated using a publicly available dataset of gene expression profiles from 578 participants with ovarian cancer in The Cancer Genome Atlas. Stand-alone code for the demonstration is available as an RMarkdown file at https://github.com/katehoffshutta/ggmTutorial.

Effectiveness Associated With Vaccination After COVID-19 Recovery in Preventing Reinfection.

Importance: The benefit of vaccination for preventing reinfection among individuals who have been previously infected with SARS-CoV-2 is largely unknown. Objective: To obtain population-based estimates of the probability of SARS-CoV-2 reinfection and the effectiveness associated with vaccination after recovery from COVID-19. Design, Setting, and Participants: This cohort study used Rhode Island statewide surveillance data from March 1, 2020, to December 9, 2021, on COVID-19 vaccinations, laboratory-confirmed cases, hospitalizations, and fatalities to conduct a population-based, retrospective study during periods when wild type, Alpha, and Delta strains of SARS-CoV-2 were predominant. Participants included Rhode Island residents aged 12 years and older who were previously diagnosed with COVID-19 and unvaccinated at the time of first infection, stratified into 3 subpopulations: long-term congregate care (LTCC) residents, LTCC employees, and the general population (ie, individuals not associated with congregate settings). Data were analyzed from October 2021 to January 2022. Exposures: Completion of the primary vaccination series, defined as 14 days after the second dose of an mRNA vaccine or 1 dose of vector virus vaccine. Main Outcomes and Measures: The main outcome was SARS-CoV-2 reinfection, defined as a laboratory-confirmed positive result on a polymerase chain reaction (PCR) or antigen test at least 90 days after the first laboratory-confirmed positive result on a PCR or antigen test. Results: Overall, 3124 LTCC residents (median [IQR] age, 81 [71-89]; 1675 [53.6%] females), 2877 LTCC employees (median [IQR] age, 41 [30-53]; 2186 [76.0%] females), and 94 516 members of the general population (median [IQR] age, 35 [24-52] years; 45 030 [47.6%] females) met eligibility criteria. Probability of reinfection at 9 months for those who remained unvaccinated after recovery from prior COVID-19 was 13.0% (95% CI, 12.0%-14.0%) among LTCC residents, 10.0% (95% CI, 8.8%-11.5%) among LTCC employees, and 1.9% (95% CI, 1.8%-2.0%) among the general population. Completion of the primary vaccination series after infection was associated with 49% (95% CI, 27%-65%) protection among LTCC residents, 47% (95% CI, 19%-65%) protection among LTCC employees, and 62% (95% CI, 56%-68%) protection in the general population against reinfection, adjusting for potential sociodemographic and clinical confounders and temporal variation in infection rates. Conclusions and Relevance: These findings suggest that risk of SARS-CoV-2 reinfection after recovery from COVID-19 was relatively high among individuals who remained unvaccinated. Vaccination after recovery from COVID-19 was associated with reducing risk of reinfection by approximately half.

The network interplay of interferon and Toll-like receptor signaling pathways in the anti-Candida immune response.

Fungal infections represent a major global health problem affecting over a billion people that kills more than 1.5 million annually. In this study, we employed an integrative approach to reveal the landscape of the human immune responses to Candida spp. through meta-analysis of microarray, bulk, and single-cell RNA sequencing (scRNA-seq) data for the blood transcriptome. We identified across these different studies a consistent interconnected network interplay of signaling molecules involved in both Toll-like receptor (TLR) and interferon (IFN) signaling cascades that is activated in response to different Candida species (C. albicans, C. auris, C. glabrata, C. parapsilosis, and C. tropicalis). Among these molecules are several types I IFN, indicating an overlap with antiviral immune responses. scRNA-seq data confirmed that genes commonly identified by the three transcriptomic methods show cell type-specific expression patterns in various innate and adaptive immune cells. These findings shed new light on the anti-Candida immune response, providing putative molecular pathways for therapeutic intervention.

Reproducibility of A Posteriori Dietary Patterns across Time and Studies: A Scoping Review.

Few studies have considered if a posteriori dietary patterns (DPs) are generalizable across different centers or studies, or if they are consistently seen over time. To date, no systematic search of the literature on these topics has been carried out. A scoping review was conducted through a systematic search on the PubMed database. In the current review, we included the 34 articles examining the extent to which a posteriori DPs were consistently seen: 1) across centers from the same study or across different studies potentially representing different populations or countries (here indicated as cross-study reproducibility) and 2) over longer time periods (i.e., ≥2 y) (here indicated as stability over time). Selected articles (published in 1981-2019, 32% from 2010 onwards) were based on observational studies, mostly from Europe and North America. Five articles were based on children and/or adolescents and 14 articles included adults (2 men; 12 women, of whom 3 were pregnant women). A posteriori DPs were mostly derived (32 articles) with principal component or factor analyses. Among the 9 articles assessing DP reproducibility across studies (number of centers/studies: 2-27; median: 3), 5 provided a formal assessment using statistical methods (4 index-based approaches of different complexity, 1 statistical model). A median of 4 DPs was reproduced across centers/studies (range: 1-7). Among the 25 articles assessing DP stability over time (number of time-occasions: 2-6; median: 3), 19 provided a formal assessment with statistical methods (17 index-based and/or test-based approaches, 1 statistical model, 1 with both strategies). The number and composition of DPs remained mostly stable over time. Based on the limited evidence collected, most identified DPs showed good reproducibility across studies and stability over time. However, when present within the single studies, the criteria for the formal assessment of cross-study reproducibility or stability over time were generally very basic.

Reproducibility and Validity of A Posteriori Dietary Patterns: A Systematic Review.

The effective use of dietary patterns (DPs) remains limited. There is a need to assess their consistency over multiple administrations of the same dietary source, different dietary sources, or across different studies. Similarly, their generalizability should be based on a previous assessment of DP construct validity. However, to date, no systematic reviews of reproducibility and validity of a posteriori DPs have been carried out. In addition, several methodological questions related to their identification are still open and prevent a fair comparison of epidemiological results on DPs and disease. A systematic review of the literature on the PubMed database was conducted. We identified 218 articles, 64 of which met the inclusion criteria. Of these, the 38 articles dealing with reproducibility and relative and construct validity of DPs were included. These articles (published in 1999-2017, 53% from 2010 onwards) were based on observational studies conducted worldwide. The 14 articles that assessed DP reproducibility across different statistical solutions examined different research questions. Included were: the number of food groups or subjects; input variable format (as well as adjustment for energy intake); algorithms and the number of DPs to retain in cluster analysis; rotation method; and score calculation in factor analysis. However, we identified at most 3 articles per research question on DP reproducibility across statistical solutions. From another 15 articles, reproducibility of DPs over shorter (≤1 y) time periods was generally good and higher than DP relative validity (as measured across different dietary sources). Confirmatory factor analysis was used in 15 of the included articles. It provided reassuring results in identifying valid dietary constructs characterizing the populations under consideration. Based on the available evidence, only suggestive conclusions can be derived on reproducibility across different statistical solutions. Nevertheless, most identified DPs showed good reproducibility, fair relative validity, and good construct validity.

Multi-study factor analysis.

We introduce a novel class of factor analysis methodologies for the joint analysis of multiple studies. The goal is to separately identify and estimate (1) common factors shared across multiple studies, and (2) study-specific factors. We develop an Expectation Conditional-Maximization algorithm for parameter estimates and we provide a procedure for choosing the numbers of common and specific factors. We present simulations for evaluating the performance of the method and we illustrate it by applying it to gene expression data in ovarian cancer. In both, we clarify the benefits of a joint analysis compared to the standard factor analysis. We have provided a tool to accelerate the pace at which we can combine unsupervised analysis across multiple studies, and understand the cross-study reproducibility of signal in multivariate data. An R package (MSFA), is implemented and is available on GitHub.

GPCR-specific autoantibody signatures are associated with physiological and pathological immune homeostasis.

Autoantibodies have been associated with autoimmune diseases. However, studies have identified autoantibodies in healthy donors (HD) who do not develop autoimmune disorders. Here we provide evidence of a network of immunoglobulin G (IgG) autoantibodies targeting G protein-coupled receptors (GPCR) in HD compared to patients with systemic sclerosis, Alzheimer's disease, and ovarian cancer. Sex, age and pathological conditions affect autoantibody correlation and hierarchical clustering signatures, yet many of the correlations are shared across all groups, indicating alterations to homeostasis. Furthermore, we identify relationships between autoantibodies targeting structurally and functionally related molecules, such as vascular, neuronal or chemokine receptors. Finally, autoantibodies targeting the endothelin receptor type A (EDNRA) exhibit chemotactic activity, as demonstrated by neutrophil migration toward HD-IgG in an EDNRA-dependent manner and in the direction of IgG from EDNRA-immunized mice. Our data characterizing the in vivo signatures of anti-GPCR autoantibodies thus suggest that they are a physiological part of the immune system.