Fighting the SARS-CoV-2 pandemic requires a global approach to understanding the heterogeneity of vaccine responses.

Host genetic and environmental factors including age, biological sex, diet, geographical location, microbiome composition and metabolites converge to influence innate and adaptive immune responses to vaccines. Failure to understand and account for these factors when investigating severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccine efficacy may impair the development of the next generation of vaccines. Most studies aimed at identifying mechanisms of vaccine-mediated immune protection have focused on adaptive immune responses. It is well established, however, that mobilization of the innate immune response is essential to the development of effective cellular and humoral immunity. A comprehensive understanding of the innate immune response and environmental factors that contribute to the development of broad and durable cellular and humoral immune responses to SARS-CoV-2 and other vaccines requires a holistic and unbiased approach. Along with optimization of the immunogen and vectors, the development of adjuvants based on our evolving understanding of how the innate immune system shapes vaccine responses will be essential. Defining the innate immune mechanisms underlying the establishment of long-lived plasma cells and memory T cells could lead to a universal vaccine for coronaviruses, a key biomedical priority.

Precision Medicine and Heterogeneity of Treatment Effect in Therapies for ARDS.

ARDS is a clinically heterogeneous syndrome, rather than a distinct disease. This heterogeneity at least partially explains the difficulty in studying treatments for these patients and contributes to the numerous trials of therapies for the syndrome that have not shown benefit. Recent studies have identified different subphenotypes within the heterogeneous patient population. These different subphenotypes likely have variable clinical responses to specific therapies, a concept known as heterogeneity of treatment effect. Recognizing different subphenotypes and heterogeneity of treatment effect has important implications for the clinical management of patients with ARDS. This review presents studies that have identified different subphenotypes and discusses how they can modify the effects of therapies evaluated in trials that are commonly considered to have shown no overall benefit in patients with ARDS.

Intersubject Variation in ACE2 Protein Expression in Human Airway Epithelia and Its Relationship to Severe Acute Respiratory Syndrome Coronavirus 2.

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2 ) initiates entry into airway epithelia by binding its receptor, angiotensin-converting enzyme 2 (ACE2). METHODS: To explore whether interindividual variation in ACE2 abundance contributes to variability in coronavirus disease 2019 (COVID-19) outcomes, we measured ACE2 protein abundance in primary airway epithelial cultures derived from 58 human donor lungs. RESULTS: We found no evidence for sex- or age-dependent differences in ACE2 protein expression. Furthermore, we found that variations in ACE2 abundance had minimal effects on viral replication and induction of the interferon response in airway epithelia infected with SARS-CoV-2. CONCLUSIONS: Our results highlight the relative importance of additional host factors, beyond viral receptor expression, in determining COVID-19 lung disease outcomes.

Personalized Virus Load Curves for Acute Viral Infections.

We introduce an explicit function that describes virus-load curves on a patient-specific level. This function is based on simple and intuitive model parameters. It allows virus load analysis of acute viral infections without solving a full virus load dynamic model. We validate our model on data from mice influenza A, human rhinovirus data, human influenza A data, and monkey and human SARS-CoV-2 data. We find wide distributions for the model parameters, reflecting large variability in the disease outcomes between individuals. Further, we compare the virus load function to an established target model of virus dynamics, and we provide a new way to estimate the exponential growth rates of the corresponding infection phases. The virus load function, the target model, and the exponential approximations show excellent fits for the data considered. Our virus-load function offers a new way to analyze patient-specific virus load data, and it can be used as input for higher level models for the physiological effects of a virus infection, for models of tissue damage, and to estimate patient risks.

Impact of COVID-19 Pandemic on Weight and BMI among UK Adults: A Longitudinal Analysis of Data from the HEBECO Study.

COVID-19-related restrictions impacted weight and weight-related factors during the initial months of the pandemic. However, longitudinal analyses are scarce. An online, longitudinal study was conducted among self-selected UK adults (n = 1818), involving three surveys (May-June, August-September, November-December 2020), covering anthropometric, sociodemographic, COVID-19-related and behavioural measures. Data were analysed using generalised estimating equations. Self-reported average weight/body mass index (BMI) significantly increased between the May-June period and the August-September period (74.95 to 75.33 kg/26.22 kg/m2 to 26.36kg/m2, p < 0.001, respectively), and then significantly decreased to November-December (to 75.06 kg/26.27 kg/m2, p < 0.01), comparable to May-June levels (p = 0.274/0.204). However, there was great interindividual variation, 37.0%/26.7% increased (average 3.64 kg (95% confidence interval: 3.32, 3.97)/1.64 kg/m2 (1.49, 1.79)), and 34.5%/26.3% decreased (average 3.59 kg (3.34, 3.85)/1.53 kg/m2 (1.42, 1.63)) weight/BMI between May-June and November-December. Weight/BMI increase was significantly negatively associated with initial BMI, and positively associated with monthly high fat, salt and sugar (HFSS) snacks intake and alcohol consumption, and for BMI only, older age. Associations were time-varying; lower initial BMI, higher HFSS snacks intake and high-risk alcohol consumption were associated with maintaining weight/BMI increases between August-September and November-December. The average weight/BMI of UK adults fluctuated between May-June and November-December 2020. However, the substantial interindividual variation in weight/BMI trajectories indicates long-term health impacts from the pandemic, associated with food and alcohol consumption.

COVID-19: Understanding Inter-Individual Variability and Implications for Precision Medicine.

Coronavirus disease 2019 (COVID-19) is characterized by heterogeneity in susceptibility to the disease and severity of illness. Understanding inter-individual variation has important implications for not only allocation of resources but also targeting patients for escalation of care, inclusion in clinical trials, and individualized medical therapy including vaccination. In addition to geographic location and social vulnerability, there are clear biological differences such as age, sex, race, presence of comorbidities, underlying genetic variation, and differential immune response that contribute to variability in disease manifestation. These differences may have implications for precision medicine. Specific examples include the observation that androgens regulate the expression of the enzyme transmembrane protease, serine 2 which facilitates severe acute respiratory syndrome coronavirus 2 viral entry into the cell; therefore, androgen deprivation therapy is being explored as a treatment option in males infected with COVID-19. An immunophenotyping study of COVID-19 patients has shown that a subset develop T cytopenia which has prompted a clinical trial that is testing the efficacy of interleukin-7 in these patients. Predicting which COVID-19 patients will develop progressive disease that will require hospitalization has important implications for clinical trials that target outpatients. Enrollment of patients at low risk for progression of disease and hospitalization would likely not result in such therapy demonstrating efficacy. There are efforts to use artificial intelligence to integrate digital data from smartwatch applications or digital monitoring systems and biological data to enable identification of the high risk COVID-19 patient. The ultimate goal of precision medicine using such modern technology is to recognize individual differences to improve health for all.

Ethnic differences in thromboprophylaxis for COVID-19 patients: should they be considered?

Thromboembolic events contribute to morbidity and mortality in coronavirus disease 2019 (COVID-19). As a result, thromboprophylaxis using low-molecular-weight heparin (LMWH) is universally recommended for hospitalized patients based on multiple guidelines. However, ethnic differences with respect to thrombogenicity have been reported and the incidence of thromboembolic events is considered to be lower in the Asian population. Despite the importance of thromboprophylaxis, bleeding is also a side effect that should be considered. We examine the data relating to potential ethnic differences in thrombosis and bleeding in COVID-19. Although sufficient data is not yet available, current evidence does not oppose routine anticoagulant use and thromboprophylaxis using a standard dose of LMWH for admitted patients regardless of ethnicity based on our review.

Serological Assays Estimate Highly Variable SARS-CoV-2 Neutralizing Antibody Activity in Recovered COVID-19 Patients.

The development of neutralizing antibodies (NAbs) against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) following infection or vaccination is likely to be critical for the development of sufficient population immunity to drive cessation of the coronavirus disease of 2019 (COVID-19) pandemic. A large number of serologic tests, platforms, and methodologies are being employed to determine seroprevalence in populations to select convalescent plasma samples for therapeutic trials and to guide policies about reopening. However, the tests have substantial variations in sensitivity and specificity, and their ability to quantitatively predict levels of NAbs is unknown. We collected 370 unique donors enrolled in the New York Blood Center Convalescent Plasma Program between April and May of 2020. We measured levels of antibodies in convalescent plasma samples using commercially available SARS-CoV-2 detection tests and in-house enzyme-linked immunosorbent assays (ELISAs) and correlated serological measurements with NAb activity measured using pseudotyped virus particles, which offer the most informative assessment of antiviral activity of patient sera against viral infection. Our data show that a large proportion of convalescent plasma samples have modest antibody levels and that commercially available tests have various degrees of accuracy in predicting NAb activity. We found that the Ortho anti-SARS-CoV-2 total Ig and IgG high-throughput serological assays (HTSAs) and the Abbott SARS-CoV-2 IgG assay quantify levels of antibodies that strongly correlate with the results of NAb assays and are consistent with gold standard ELISA results. These findings provide immediate clinical relevance to serology results that can be equated to NAb activity and could serve as a valuable roadmap to guide the choice and interpretation of serological tests for SARS-CoV-2.

Digital phenotyping for mental health of college students: a clinical review.

Experiencing continued growth in demand for mental health services among students, colleges are seeking digital solutions to increase access to care as classes shift to remote virtual learning during the COVID-19 pandemic. Using smartphones to capture real-time symptoms and behaviours related to mental illnesses, digital phenotyping offers a practical tool to help colleges remotely monitor and assess mental health and provide more customised and responsive care. This narrative review of 25 digital phenotyping studies with college students explored how this method has been deployed, studied and has impacted mental health outcomes. We found the average duration of studies to be 42 days and the average enrolled to be 81 participants. The most common sensor-based streams collected included location, accelerometer and social information and these were used to inform behaviours such as sleep, exercise and social interactions. 52% of the studies included also collected smartphone survey in some form and these were used to assess mood, anxiety and stress among many other outcomes. The collective focus on data that construct features related to sleep, activity and social interactions indicate that this field is already appropriately attentive to the primary drivers of mental health problems among college students. While the heterogeneity of the methods of these studies presents no reliable target for mobile devices to offer automated help-the feasibility across studies suggests the potential to use these data today towards personalising care. As more unified digital phenotyping research evolves and scales to larger sample sizes, student mental health centres may consider integrating these data into their clinical practice for college students.