Background: Long COVID, also known as post-acute sequelae of COVID-19 (PASC), is a poorly understood condition with symptoms across a range of biological domains that often have debilitating consequences. Some have recently suggested that lingering SARS-CoV-2 virus particles in the gut may impede serotonin production and that low serotonin may drive many Long COVID symptoms across a range of biological systems. Therefore, selective serotonin reuptake inhibitors (SSRIs), which increase synaptic serotonin availability, may be used to prevent or treat Long COVID. SSRIs are commonly prescribed for depression, therefore restricting a study sample to only include patients with depression can reduce the concern of confounding by indication. Methods: In an observational sample of electronic health records from patients in the National COVID Cohort Collaborative (N3C) with a COVID-19 diagnosis between September 1, 2021, and December 1, 2022, and a comorbid depressive disorder, the leading indication for SSRI use, we evaluated the relationship between SSRI use during acute COVID-19 and subsequent 12-month risk of Long COVID (defined by ICD-10 code U09.9). We defined SSRI use as a prescription for SSRI medication beginning at least 30 days before acute COVID-19 and not ending before SARS-CoV-2 infection. To minimize bias, we estimated relationships using nonparametric targeted maximum likelihood estimation to aggressively adjust for high-dimensional covariates. Results: We analyzed a sample (n = 302,626) of patients with a diagnosis of a depressive condition before COVID-19 diagnosis, where 100,803 (33%) were using an SSRI. We found that SSRI users had a significantly lower risk of Long COVID compared to nonusers (adjusted causal relative risk 0.92, 95% CI (0.86, 0.99)) and we found a similar relationship comparing new SSRI users (first SSRI prescription 1 to 4 months before acute COVID-19 with no prior history of SSRI use) to nonusers (adjusted causal relative risk 0.89, 95% CI (0.80, 0.98)). Conclusions: These findings suggest that SSRI use during acute COVID-19 may be protective against Long COVID, supporting the hypothesis that serotonin may be a key mechanistic biomarker of Long COVID.

Multiple sclerosis (MS) is an inflammatory demyelinating disease with heterogeneous clinical presentations and variable long-term disability accumulation. There are currently no standard criteria to accurately predict disease outcomes. In this study we investigated the cross-sectional relationship between disease phenotype and immune-modulating cytokines and chemokines in cerebrospinal fluid (CSF). We analyzed CSF from 20 DMT-naïve MS patients using Olink Proteomics' Target 96 Inflammation panel and correlated the resulting analytes with respect to (1) disease subtype, (2) patient age and sex, (3) extent of clinical disability, and (4) MRI segmental brain volumes. We found that intrathecal IL-4 correlated with higher Expanded Disability Status Scale (EDSS) scores and longer 25-foot walk times, and CD8A correlated with decreased thalamic volumes and longer 9-hole peg test times. Male sex was associated with higher FGF-19 expression, and Tumefactive MS with elevated CCL4. Several inflammatory markers were correlated with older age at the time of LP. Finally, higher intrathecal IL-33 correlated with increased MS lesion burden and multi-compartment brain atrophy. This study confirms immune heterogeneity underlying CSF profiles in MS, but also identifies several inflammatory protein biomarkers that may be of use for predicting clinical outcomes in future algorithms.

This is the September 2024 issue of the UMass Center for Clinical and Translational Science Newsletter containing news and events of interest.

Objectives: To address the challenges of sharing clinical research data through the implementation of cloud-based virtual desktops, enhancing collaboration among researchers while maintaining data security. Materials and methods: This case study details the deployment of virtual desktops at UMass Chan Medical School (UMass Chan). The process involved forming a Research Informatics Steering Executive workgroup, identifying key requirements, implementing Amazon WorkSpaces, and establishing configurable data management for research support. Results: Key lessons include the significance of collaboration, balancing user-friendliness and functionality, flexibility in data management, maximizing virtual desktop efficiency within budget constraints, and continuous user feedback. The implementation of virtual desktops supports secure collaborative research, advancing medical knowledge and improving healthcare outcomes. Discussion: The structured approach to implementing virtual desktops addresses data security, regulatory compliance, and real-time collaboration challenges. Continuous feedback and iterative improvements have enhanced the system's effectiveness. Conclusion: The successful implementation of virtual desktops at UMass Chan demonstrates the potential for such systems to support secure, collaborative research, offering insights for similar initiatives in other academic health centers.

Purpose: Paxlovid is effective in reducing COVID-19 hospitalization and mortality. This study characterized Paxlovid use and evaluated racial/ethnic disparities over time among community-dwelling adults at high risk of progression to severe COVID-19 disease. Methods: This retrospective cohort study used the National COVID Cohort Collaborative (N3C) data and included individuals aged 18 years or older diagnosed with COVID-19 between January 2022 and December 2023. The study cohort included nonhospitalized individuals who were at high risk of COVID-19 progression, and selected the first COVID-19 episode in each quarter, including reinfection episodes. Paxlovid use was defined as receiving Paxlovid within ±5 days of a COVID-19 diagnosis. We used descriptive statistics to characterize Paxlovid use overall and by calendar quarter and race/ethnicity. We used a generalized estimating equations (GEE) models to quantify the association of race/ethnicity with Paxlovid use controlling for age, gender, and clinical characteristics. Results: Among 1 264 215 individuals at high risk of disease progression (1 404 607 episodes), Paxlovid use increased from 1.2% in January-March 2022 to 35.1% in October-December 2023. Paxlovid use was more common among non-Hispanic White individuals (23.9%) than non-Hispanic Black (16.5%) and Latinx/e (16.7%) patients. After adjusting age, gender, and clinical characteristics, Paxlovid use was less likely among non-Hispanic Black (odds ratio [OR] 0.69, 95% confidence interval [CI] 0.68-0.70) and Latinx/e (OR 0.72, CI 0.71-0.73) patients than non-Hispanic White patients. Conclusions: Among a large, diverse cohort of community-dwelling individuals with COVID-19, nearly two out of three eligible individuals did not receive Paxlovid, and minoritized racial/ethnic groups were less likely to use Paxlovid than their non-Hispanic White individuals.

This study explored why some elderly females do not adhere to their bone density tests. It found that factors like age, race, marital status, insurance type, social vulnerability index, and vaccination status influence completion of these tests. Addressing these differences could improve the management of bone health in older adults. Purpose: This study investigated factors influencing the cancellation of dual-energy x-ray absorptiometry (DXA) scans among females aged 65 and above during the COVID-19 pandemic. Methods: Utilizing a dataset of 19,066 females from 2021 to 2023, the research employed chi-squared tests and logistic regression analyses to examine demographic, socio-economic, and health-related determinants of DXA scan adherence. Results: Key findings revealed that younger seniors, White patients, married individuals, those with commercial/private or Medicare insurance, and vaccinated persons were more likely to complete DXA scans. In contrast, Asian and African American females, along with those from higher Social Vulnerability Index areas, showed lower completion rates. Conclusion: These results highlight the need for tailored strategies to improve osteoporosis screening adherence, focusing on identified demographic groups to enhance overall healthcare outcomes in osteoporosis management.

Introduction: The relationship between SARS-CoV-2 viral dynamics during acute infection and the development of long COVID is largely unknown. Methods: A total of 7361 asymptomatic community-dwelling people enrolled in the Test Us at Home parent study between October 2021 and February 2022. Participants self-collected anterior nasal swabs for SARS-CoV-2 RT-PCR testing every 24-48 hours for 10-14 days, regardless of symptom or infection status. Participants who had no history of COVID-19 at enrollment and who were subsequently found to have ≥1 positive SARS-CoV-2 RT-PCR test during the parent study were recontacted in August 2023 and asked whether they had experienced long COVID, defined as the development of new symptoms lasting 3 months or longer following SARS-CoV-2 infection. Participant's cycle threshold values were converted into viral loads, and slopes of viral clearance were modeled using post-nadir viral loads. Using a log binomial model with the modeled slopes as the exposure, we calculated the relative risk of subsequently developing long COVID with 1-2 symptoms, 3-4 symptoms, or 5+ symptoms, adjusting for age, number of symptoms, and SARS-CoV-2 variant. Adjusted relative risk (aRR) of individual long COVID symptoms based on viral clearance was also calculated. Results: 172 participants were eligible for analyses, and 59 (34.3%) reported experiencing long COVID. The risk of long COVID with 3-4 symptoms and 5+ symptoms increased by 2.44 times (aRR: 2.44; 95% CI: 0.88-6.82) and 4.97 times (aRR: 4.97; 95% CI: 1.90-13.0) per viral load slope-unit increase, respectively. Participants who developed long COVID had significantly longer times from peak viral load to viral clearance during acute disease than those who never developed long COVID (8.65 [95% CI: 8.28-9.01] vs. 10.0 [95% CI: 9.25-10.8]). The slope of viral clearance was significantly positively associated with long COVID symptoms of fatigue (aRR: 2.86; 95% CI: 1.22-6.69), brain fog (aRR: 4.94; 95% CI: 2.21-11.0), shortness of breath (aRR: 5.05; 95% CI: 1.24-20.6), and gastrointestinal symptoms (aRR: 5.46; 95% CI: 1.54-19.3). Discussion: We observed that longer time from peak viral load to viral RNA clearance during acute COVID-19 was associated with an increased risk of developing long COVID. Further, slower clearance rates were associated with greater number of symptoms of long COVID. These findings suggest that early viral-host dynamics are mechanistically important in the subsequent development of long COVID.

Obesity is a major cause of metabolic dysfunction-associated steatohepatitis (MASH) and is characterized by inflammation and insulin resistance. Interferon-γ (IFNγ) is a pro-inflammatory cytokine elevated in obesity and modulating macrophage functions. Here, we show that male mice with loss of IFNγ signaling in myeloid cells (Lyz-IFNγR2-/-) are protected from diet-induced insulin resistance despite fatty liver. Obesity-mediated liver inflammation is also attenuated with reduced interleukin (IL)-12, a cytokine primarily released by macrophages, and IL-12 treatment in vivo causes insulin resistance by impairing hepatic insulin signaling. Following MASH diets, Lyz-IFNγR2-/- mice are rescued from developing liver fibrosis, which is associated with reduced fibroblast growth factor (FGF) 21 levels. These results indicate critical roles for IFNγ signaling in macrophages and their release of IL-12 in modulating obesity-mediated insulin resistance and fatty liver progression to MASH. In this work, we identify the IFNγ-IL12 axis in regulating intercellular crosstalk in the liver and as potential therapeutic targets to treat MASH.

Introduction: The COVID-19 pandemic has significantly influenced surgical practices, with SARS-CoV-2 variants presenting unique pathologic profiles and potential impacts on perioperative outcomes. This study explores associations between Alpha, Delta, and Omicron variants of SARS-CoV-2 and surgical outcomes. Methods: We conducted a retrospective analysis using the National COVID Cohort Collaborative database, which included patients who underwent selected major inpatient surgeries within eight weeks post-SARS-CoV-2 infection from January 2020 to April 2023. The viral variant was determined by the predominant strain at the time of the patient's infection. Multivariable logistic regression models explored the association between viral variants, COVID-19 severity, and 30-d major morbidity or mortality. Results: The study included 10,617 surgical patients with preoperative COVID-19, infected by the Alpha (4456), Delta (1539), and Omicron (4622) variants. Patients infected with Omicron had the highest vaccination rates, most mild disease, and lowest 30-d morbidity and mortality rates. Multivariable logistic regression demonstrated that Omicron was linked to a reduced likelihood of adverse outcomes compared to Alpha, while Delta showed odds comparable to Alpha. Inclusion of COVID-19 severity in the model rendered the odds of major morbidity or mortality equal across all three variants. Conclusions: Our study examines the associations between the clinical and pathological characteristics of SARS-CoV-2 variants and surgical outcomes. As novel SARS-CoV-2 variants emerge, this research supports COVID-19-related surgical policy that assesses the severity of disease to estimate surgical outcomes.

This is the June 2024 issue of the UMass Center for Clinical and Translational Science Newsletter containing news and events of interest.