Unusually High Risks of COVID-19 Mortality with Age-Related Comorbidities: An Adjusted Meta-Analysis Method to Improve the Risk Assessment of Mortality Using the Comorbid Mortality Data

Burden, risk factors, and projections of ischemic heart disease in China (1990-2021): findings from the 2021 GBD study

Background

Ischemic heart disease (IHD) remains a major public health challenge in China. This study aimed to comprehensively analyze the burden of IHD, its risk factors, and future trends from 1990 to 2021 using the Global Burden of Disease database.

Methods

We assessed IHD trends in incidence, prevalence, mortality, and disability-adjusted life years (DALYs) stratified by age (greater than 15 years) and gender. Age-standardized rates, average annual percentage changes, and joinpoint regression analyses were used to evaluate temporal trends. Decomposition and frontier analyses were conducted to identify key contributors to the IHD burden, while future projections were generated for the next 15 years.

Results

In 2021, the number of IHD incident cases, prevalent cases, deaths, and DALYs in China were 3.17, 3.25, 3.57, and 2.62 times higher than those in 1990, respectively. Age-standardized mortality rates and age-standardized DALYs rates demonstrated an initial increase, followed by a gradual decline. Males showed higher IHD burden during middle age, while elderly females surpassed males in the later years. Aging, high systolic blood pressure, ambient particulate matter pollution, elevated low-density lipoprotein cholesterol, and smoking were the primary drivers of IHD burden. Future projections suggest a declining incidence and prevalence among males but increasing trends in females, with DALYs expected to rise significantly in the female population.

Conclusions

The burden of IHD in China has evolved significantly over the past three decades, driven by demographic and environmental factors. While prevalence and incidence have risen, mortality and DALYs have shown a recent decline, reflecting shifts in disease patterns. Age and gender disparities are evident, with middle-aged males and elderly females disproportionately affected. Key contributors, such as high blood pressure and pollution, highlight the need for targeted interventions. Gender-specific public health strategies, alongside improved environmental and health policies, are essential to mitigating the future burden of IHD in China.

Machine learning approaches to predict the need for intensive care unit admission among Iranian COVID-19 patients based on ICD-10: A cross-sectional study

Background & Aim

Timely identification of the patients requiring intensive care unit admission (ICU) could be life-saving. We aimed to compare different machine learning algorithms to predict the requirements for ICU admission in COVID-19 patients.

Methods

We screened all patients with COVID-19 at six academic hospitals in Tehran comprising our study population. A total of 44,112 COVID-19 patients (≥18 years old) were included, among which 7722 patients were hospitalized. We used a Random Forest algorithm to select significant variables. Then, prediction models were developed using the Support Vector Machine, Naıve Bayes, logistic regression, lightGBM, decision tree, and K-Nearest Neighbor algorithms. Sensitivity, specificity, accuracy, F1 score, and receiver operating characteristic-Area Under the Curve (AUC) were used to compare the prediction performance of different models.

Results

Based on random Forest, the following predictors were selected: age, cardiac disease, cough, hypertension, diabetes, influenza & pneumonia, malignancy, and nervous system disease. Age was found to have the strongest association with ICU admission among COVID-19 patients. All six models achieved an AUC greater than 0.60. Naıve Bayes achieved the best predictive performance (AUC = 0.71).

Conclusion

Naïve Bayes and lightGBM demonstrated promising results in predicting ICU admission needs in COVID-19 patients. Machine learning models could help quickly identify high-risk patients upon entry and reduce mortality and morbidity among COVID-19 patients.

No evidence that ACE2 or TMPRSS2 drive population disparity in COVID risks

Early in the SARS-CoV2 pandemic, in this journal, Hou et al. (BMC Med 18:216, 2020) interpreted public genotype data, run through functional prediction tools, as suggesting that members of particular human populations carry potentially COVID-risk-increasing variants in genes ACE2 and TMPRSS2 far more often than do members of other populations. Beyond resting on predictions rather than clinical outcomes, and focusing on variants too rare to typify population members even jointly, their claim mistook a well known artifact (that large samples reveal more of a population's variants than do small samples) as if showing real and congruent population differences for the two genes, rather than lopsided population sampling in their shared source data. We explain that artifact, and contrast it with empirical findings, now ample, that other loci shape personal COVID risks far more significantly than do ACE2 and TMPRSS2-and that variation in ACE2 and TMPRSS2 per se unlikely exacerbates any net population disparity in the effects of such more risk-informative loci.

Has mortality in the United States returned to pre-pandemic levels? An analysis of provisional 2023 data

BACKGROUND

The COVID-19 pandemic, which started in 2020, resulted in greater all-cause mortality in 2020 and in subsequent years. Whether all-cause mortality remains elevated in 2023 compared to pre-pandemic numbers is unknown.

METHODS AND RESULTS

The United States (US) Center for Disease Control Wide-Ranging, Online Data for Epidemiologic Research database was used to compare mortality rates between 2019 and provisional data for 2022 and 2023. Age-adjusted mortality rates (AAMRs) for all-cause as well as top causes of mortality were collected. Mortality based on subgroups by sex, age, and ethnicity was also collected. All-cause AAMRs between 2018 and 2023 per 100,000 individuals were 723.6, 715.2, 835.4, 879.7, (provisionally) 798.8, and (provisionally) 738.3, respectively, with AAMRs in 2023 remaining above 2019 pre-pandemic levels. Similar trends were noted in subgroups based on sex, ethnicity, and most age groups. Mortality attributed directly to COVID-19 peaked in 2021 as the 3rd leading cause of death and dropped to the 10th leading cause in 2023. Provisional mortality rate trends for 2023 suggest that rates for diseases of the heart increased during the pandemic but appear to have returned to or dipped below pre-pandemic levels.

CONCLUSION

Provisional 2023 all-cause mortality rates in the US have decreased from the 2021 peak associated with the COVID-19 pandemic but remain above the pre-pandemic baseline. Mortality from some conditions, including diseases of the heart, appears to have recovered from the impact of the COVID-19 pandemic.

Platelet Indices as Novel Surrogate Markers for the Prognosis of COVID-19 Infection: An Observational Study

Background  The new severe acute respiratory syndromecoronavirus 2 (SARS-CoV-2) causes severe acute respiratory illness accountable for causing the coronavirus disease 2019 (COVID-19) illness. Thrombotic issues, acute respiratory distress syndrome (ARDS), and cytokine storm are significant contributors to morbidity and mortality in patients with COVID-19. Elevated D-dimer levels and prothrombin times are further indicators of abnormal coagulation parameters in COVID-19 patients. This study aimed to study the platelet indices as prognostic markers in COVID-19 infection. Methods In this prospective observational study, 150 real-time reverse transcription-polymerase chain reaction (RT-PCR)-positive COVID-19 patients were enrolled between October 2020 and September 2021. All the subjects were screened and explained the study procedure in their native language. Following enrolment, a detailed history and physical examination were performed. Subsequently, laboratory investigations were performed, and patients were subjected to high-resolution computed tomography (HRCT) examination to classify patients into mild, moderate, and severe according to the severity of the illness. The platelet indices taken into account were plateletcrit (PCT) in percentage, platelet count (PLT) in lakh per microlitre, mean platelet volume (MPV) in femtolitres, and platelet distribution width (PDW) in femtolitres. Results The mean PLT was significantly greater among survivors than non-survivors (2.03 ± 0.72 versus 1.76 ± 0.47; p-value = 0.018). The mean MPV (10.42 ± 0.53 versus 9.22 ± 0.64; p-value <0.0001) and PDW (17.99 ± 1.53 versus 16.54 ± 0.91 fl; p-value <0.0001) were significantly greater among non-survivors than survivors. However, the mean PCT was significantly greater among survivors than non-survivors (0.22 ± 0.03% versus 0.18 ± 0.33%; p-value <0.0001). At a cut-off of 0.213, the sensitivity and specificity of PCT in predicting death were found to be 79.2% and 74.5%, respectively. At a cut-off of 16.75, the sensitivity and specificity of PDW in predicting death were found to be 68.8% and 59.8%, respectively. The findings demonstrated a relationship between elevated MPV and PDW and mortality and severe COVID-19 infection. Increased PCT was connected to higher survival, with a specificity and sensitivity of 87.5% and 75.5%, respectively, and MPV >9.75 may predict death. PDW >16.75 exhibited a specificity and sensitivity of 68.8% and 59.8%, respectively, in predicting death. With comparable sensitivity and specificity of 79.2% and 74.5%, PCT >0.213 may predict death. Conclusion In severely sick COVID-19 patients, platelet indices should be routinely calculated and can be utilized as simple, low-cost prognostic indicators.

A Simple Risk Formula for the Prediction of COVID-19 Hospital Mortality

SARS-CoV-2 respiratory infection is associated with significant morbidity and mortality in hospitalized patients. We aimed to assess the risk factors for hospital mortality in non-vaccinated patients during the 2021 spring wave in the Czech Republic. A total of 991 patients hospitalized between January 2021 and March 2021 with a PCR-confirmed SARS-CoV-2 acute respiratory infection in two university hospitals and five rural hospitals were included in this analysis. After excluding patients with unknown outcomes, 790 patients entered the final analyses. Out of 790 patients included in the analysis, 282/790 (35.7%) patients died in the hospital; 162/790 (20.5) were male and 120/790 (15.2%) were female. There were 141/790 (18%) patients with mild, 461/790 (58.3%) with moderate, and 187/790 (23.7%) with severe courses of the disease based mainly on the oxygenation status. The best-performing multivariate regression model contains only two predictors-age and the patient's state; both predictors were rendered significant (p < 0.0001). Both age and disease state are very significant predictors of hospital mortality. An increase in age by 10 years raises the risk of hospital mortality by a factor of 2.5, and a unit increase in the oxygenation status raises the risk of hospital mortality by a factor of 20.

Reduced SARS-CoV-2 mRNA vaccine immunogenicity and protection in mice with diet-induced obesity and insulin resistance

BACKGROUND

Obesity and type 2 diabetes mellitus (T2DM) are associated with an increased risk of severe outcomes from infectious diseases, including coronavirus disease 2019. These conditions are also associated with distinct responses to immunization, including an impaired response to widely used severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) mRNA vaccines.

OBJECTIVE

We sought to establish a connection between reduced immunization efficacy via modeling the effects of metabolic diseases on vaccine immunogenicity that is essential for the development of more effective vaccines for this distinct vulnerable population.

METHODS

A murine model of diet-induced obesity and insulin resistance was used to model the effects of comorbid T2DM and obesity on vaccine immunogenicity and protection.

RESULTS

Mice fed a high-fat diet (HFD) developed obesity, hyperinsulinemia, and glucose intolerance. Relative to mice fed a normal diet, HFD mice vaccinated with a SARS-CoV-2 mRNA vaccine exhibited significantly lower anti-spike IgG titers, predominantly in the IgG2c subclass, associated with a lower type 1 response, along with a 3.83-fold decrease in neutralizing titers. Furthermore, enhanced vaccine-induced spike-specific CD8+ T-cell activation and protection from lung infection against SARS-CoV-2 challenge were seen only in mice fed a normal diet but not in HFD mice.

CONCLUSIONS

The study demonstrated impaired immunity following SARS-CoV-2 mRNA immunization in a murine model of comorbid T2DM and obesity, supporting the need for further research into the basis for impaired anti-SARS-CoV-2 immunity in T2DM and investigation of novel approaches to enhance vaccine immunogenicity among those with metabolic diseases.

COVID-19 in Patients with Chronic Lymphocytic Leukemia: What Have We Learned?

BACKGROUND

Chronic lymphocytic leukemia (CLL) is a prevalent hematological malignancy (HM) characterized by inherent immunodeficiency, which is further pronounced by disease-directed therapy. The COVID-19 pandemic has had devastating outcomes, and although its impact has diminished over time, it continues to be a cause of significant morbidity and mortality, particularly among immunodeficient patients.

SUMMARY

In this review, we describe mechanisms of immune dysfunction in CLL in relation to COVID-19, provide an overview of the clinical outcomes of the disease in this patient population, and identify risk factors associated with severe morbidity and mortality. Additionally, we acknowledge the influence of the rapidly evolving landscape of new disease variants. The review further delineates the humoral and cellular responses to vaccination and their clinical efficacy in preventing COVID-19 in CLL patients. Moreover, we explore potential approaches to enhance these immune responses. Pre- and post-exposure prophylaxis strategies are discussed, along with description of common agents in the treatment of the disease in both outpatient and inpatient setting. Throughout the review, we emphasize the interplay between novel therapies for CLL and COVID-19 outcomes, prevention, and treatment and describe the impact of COVID-19 on the utilization of these novel agents. This information has the potential to guide clinical decision making in the management CLL patients.

KEY MESSAGES

CLL patients are at risk for severe COVID-19 infection. Vaccinations and COVID-19 directed therapy have improved outcomes in patients with CLL, yet clinical challenges persist.

Linking Sepsis with chronic arterial hypertension, diabetes mellitus, and socioeconomic factors in the United States: A scoping review

RATIONALE

Sepsis is a syndrome of life-threatening organ dysfunction caused by a dysregulated host immune response to infection. Social risk factors including location and poverty are associated with sepsis-related disparities. Understanding the social and biological phenotypes linked with the incidence of sepsis is warranted to identify the most at-risk populations. We aim to examine how factors in disadvantage influence health disparities related to sepsis.

METHODS

A scoping review was performed for English-language articles published in the United States from 1990 to 2022 on PubMed, Web of Science, and Scopus. Of the 2064 articles found, 139 met eligibility criteria and were included for review.

RESULTS

There is consistency across the literature of disproportionately higher rates of sepsis incidence, mortality, readmissions, and associated complications, in neighborhoods with socioeconomic disadvantage and significant poverty. Chronic arterial hypertension and diabetes mellitus also occur more frequently in the same geographic distribution as sepsis, suggesting a potential shared pathophysiology.

CONCLUSIONS

The distribution of chronic arterial hypertension, diabetes mellitus, social risk factors associated with socioeconomic disadvantage, and sepsis incidence, are clustered in specific geographical areas and linked by endothelial dysfunction. Such population factors can be utilized to create equitable interventions aimed at mitigating sepsis incidence and sepsis-related disparities.

Clinical characteristics and comorbidities associated with SARS-CoV-2 breakthrough infection in the University of California Healthcare Systems

BACKGROUND

To evaluate the degree to which clinical comorbidities or combinations of comorbidities are associated with SARS-CoV-2 breakthrough infection.

MATERIALS AND METHODS

A breakthrough infection was defined as a positive test at least 14 days after a full vaccination regimen. Logistic regression was used to calculate aORs, which were adjusted for age, sex, and race information.

RESULTS

A total of 110,380 patients from the UC CORDS database were included. After adjustment, stage 5 CKD due to hypertension (aOR: 7.33; 95% CI: 4.86-10.69; p<.001; power=1) displayed higher odds of infection than any other comorbidity. Lung transplantation history (aOR: 4.79; 95% CI: 3.25-6.82; p<.001; power= 1), coronary atherosclerosis (aOR: 2.12; 95% CI: 1.77-2.52; p<.001; power=1), and vitamin D deficiency (aOR: 1.87; 95% CI: 1.69-2.06; p<.001; power=1) were significantly correlated to breakthrough infection. Patients with obesity in addition to essential hypertension (aOR: 1.74; 95% CI: 1.51-2.01; p<.001; power=1) and anemia (aOR: 1.80; 95% CI: 1.47-2.19; p<.001; power=1) were at additional risk of breakthrough infection compared to those with essential hypertension and anemia alone.

CONCLUSIONS

Further measures should be taken to prevent breakthrough infection for individuals with these conditions, such as acquiring additional doses of the SARS-CoV-2 vaccine to boost immunity.

Development and validation of a scoring system to predict mortality in patients hospitalized with COVID-19: A retrospective cohort study in two large hospitals in Ecuador

OBJECTIVE

To develop and validate a scoring system to predict mortality among hospitalized patients with COVID-19.

METHODS

Retrospective cohort study. We analyzed 5,062 analyzed hospitalized patients with COVID-19 treated at two hospitals; one each in Quito and Guayaquil, from February to July 2020. We assessed predictors of mortality using survival analyses and Cox models. We randomly divided the database into two sets: (i) the derivation cohort (n = 2497) to identify predictors of mortality, and (ii) the validation cohort (n = 2565) to test the discriminative ability of a scoring system. After multivariate analyses, we used the final model's β-coefficients to build the score. Statistical analyses involved the development of a Cox proportional hazards regression model, assessment of goodness of fit, discrimination, and calibration.

RESULTS

There was a higher mortality risk for these factors: male sex [(hazard ratio (HR) = 1.32, 95% confidence interval (95% CI): 1.03-1.69], per each increase in a quartile of ages (HR = 1.44, 95% CI: 1.24-1.67) considering the younger group (17-44 years old) as the reference, presence of hypoxemia (HR = 1.40, 95% CI: 1.01-1.95), hypoglycemia and hospital hyperglycemia (HR = 1.99, 95% CI: 1.01-3.91, and HR = 1.27, 95% CI: 0.99-1.62, respectively) when compared with normoglycemia, an AST-ALT ratio >1 (HR = 1.55, 95% CI: 1.25-1.92), C-reactive protein level (CRP) of >10 mg/dL (HR = 1.49, 95% CI: 1.07-2.08), arterial pH <7.35 (HR = 1.39, 95% CI: 1.08-1.80) when compared with normal pH (7.35-7.45), and a white blood cell count >10 × 103 per μL (HR = 1.76, 95% CI: 1.35-2.29). We found a strong discriminative ability in the proposed score in the validation cohort [AUC of 0.876 (95% CI: 0.822-0.930)], moreover, a cutoff score ≥39 points demonstrates superior performance with a sensitivity of 93.10%, a specificity of 70.28%, and a correct classification rate of 72.66%. The LR+ (3.1328) and LR- (0.0981) values further support its efficacy in identifying high-risk patients.

CONCLUSION

Male sex, increasing age, hypoxemia, hypoglycemia or hospital hyperglycemia, AST-ALT ratio >1, elevated CRP, altered arterial pH, and leucocytosis were factors significantly associated with higher mortality in hospitalized patients with COVID-19. A statistically significant Cox regression model with strong discriminatory power and good calibration was developed to predict mortality in hospitalized patients with COVID-19, highlighting its potential clinical utility.

Clinical characteristics of COVID-19 patients infected by the Omicron variants in Macao, China: A cross-sectional study

Background and Aims

The evolving mutants of SARS-CoV-2 have made the COVID-19 pandemic sustained for over 3 years. In 2022, BA.4 and BA.5 were the Omicron variants dominating the spread globally. Although COVID-19 was no longer a Public Health Emergency of International Concern (PHEIC) as announced by WHO, the SARS-CoV-2 variants remain a challenge to global healthcare under the circumstances of withdrawal and loosening of personal protective behavior in the post-quarantine era. This study aims to acknowledge the clinical characteristics caused by Omicron BA.4/BA.5 in COVID-19 naive people and analyze possible factors affecting disease severities.

Methods

In this retrospective study, we report and analyze the clinical features of 1820 COVID-19 patients infected with the BA.4/BA.5 Omicron variants of SARS-CoV-2 during a local outbreak that occurred in Macao SAR, China, from June to July 2022.

Results

A total of 83.5% of patients were symptomatic eventually. The most common symptoms were fever, cough, and sore throat. Hypertension, dyslipidemia, and diabetes mellitus were the leading comorbidities. There were significantly more elderly patients (p < 0.001), more patients with comorbidity (p < 0.001) and more patients without vaccination or not completing the series (p < 0.001) in the "Severe to Critical" group. All deceased patients were elderly with at least three comorbidities and were partial to totally dependent in their daily lives.

Conclusion

Our data are consistent with a milder disease caused by BA.4/5 Omicron variants in the general population, while patients with old age and comorbidities have developed severe to critical diseases. Complete vaccination series and booster doses are effective strategies to reinforce protection against severe diseases and avoid mortality.

Expert Consensus: Main Risk Factors for Poor Prognosis in COVID-19 and the Implications for Targeted Measures against SARS-CoV-2

The clinical evolution of patients infected with the Severe Acute Respiratory Coronavirus type 2 (SARS-CoV-2) depends on the complex interplay between viral and host factors. The evolution to less aggressive but better-transmitted viral variants, and the presence of immune memory responses in a growing number of vaccinated and/or virus-exposed individuals, has caused the pandemic to slowly wane in virulence. However, there are still patients with risk factors or comorbidities that put them at risk of poor outcomes in the event of having the coronavirus infectious disease 2019 (COVID-19). Among the different treatment options for patients with COVID-19, virus-targeted measures include antiviral drugs or monoclonal antibodies that may be provided in the early days of infection. The present expert consensus is based on a review of all the literature published between 1 July 2021 and 15 February 2022 that was carried out to establish the characteristics of patients, in terms of presence of risk factors or comorbidities, that may make them candidates for receiving any of the virus-targeted measures available in order to prevent a fatal outcome, such as severe disease or death. A total of 119 studies were included from the review of the literature and 159 were from the additional independent review carried out by the panelists a posteriori. Conditions found related to strong recommendation of the use of virus-targeted measures in the first days of COVID-19 were age above 80 years, or above 65 years with another risk factor; antineoplastic chemotherapy or active malignancy; HIV infection with CD4+ cell counts < 200/mm³; and treatment with anti-CD20 immunosuppressive drugs. There is also a strong recommendation against using the studied interventions in HIV-infected patients with a CD4+ nadir <200/mm³ or treatment with other immunosuppressants. Indications of therapies against SARS-CoV-2, regardless of vaccination status or history of infection, may still exist for some populations, even after COVID-19 has been declared to no longer be a global health emergency by the WHO.

Oxygen Requirement in Overweight/Obese Kidney Transplant Recipients with COVID-19: An Observational Cohort Study

INTRODUCTION

Obesity is one of the components of the cardiometabolic syndrome that contributes to COVID-19 progression and mortality. Immunosuppressed individuals are at greater risk of the COVID-19 burden. Therefore, we sought to investigate the impact of the combination of overweight/obesity and kidney transplant on oxygen (O2) requirements in the COVID-19 setting.

METHODS

Retrospective analysis of 284 kidney transplant recipients (KTRs) from March/2020 to August/2020 in a single center. We investigated the risk factors associated with O2 requirements in overweight/obese KTRs.

RESULTS

Overall, 65.1% had a BMI (body mass index) ≥ 25 kg/m2, 52.4% were male, the mean age was 53.3 ± 11 years old, 78.4% had hypertension, and 41.1% had diabetes mellitus. BMI was an independent risk factor for O2 requirements (OR = 1.07, p = 0.02) alongside age, lymphopenia, and hyponatremia. When overweight/obese KTRs were older, smokers, they presented higher levels of lactate dehydrogenase (LDH), and lower levels of estimated glomerular filtration rate (eGFR), lymphocytes, and sodium at admission, and they needed O2 more often.

CONCLUSION

Being overweight/obese is associated with greater O2 requirements in KTRs, in particular in older people and smokers, with worse kidney allograft functions, more inflammation, and lower sodium levels. Therefore, the early identification of factors that predict a worse outcome in overweight/obese KTRs affected by COVID-19 contributes to risk stratification and therapeutic decisions.

Complement Activation-Independent Attenuation of SARS-CoV-2 Infection by C1q and C4b-Binding Protein

The complement system is a key component of the innate immune response to viruses and proinflammatory events. Exaggerated complement activation has been attributed to the induction of a cytokine storm in severe SARS-CoV-2 infection. However, there is also an argument for the protective role of complement proteins, given their local synthesis or activation at the site of viral infection. This study investigated the complement activation-independent role of C1q and C4b-binding protein (C4BP) against SARS-CoV-2 infection. The interactions of C1q, its recombinant globular heads, and C4BP with the SARS-CoV-2 spike and receptor binding domain (RBD) were examined using direct ELISA. In addition, RT-qPCR was used to evaluate the modulatory effect of these complement proteins on the SARS-CoV-2-mediated immune response. Cell binding and luciferase-based viral entry assays were utilised to assess the effects of C1q, its recombinant globular heads, and C4BP on SARS-CoV-2 cell entry. C1q and C4BP bound directly to SARS-CoV-2 pseudotype particles via the RBD domain of the spike protein. C1q via its globular heads and C4BP were found to reduce binding as well as viral transduction of SARS-CoV-2 spike protein expressing lentiviral pseudotypes into transfected A549 cells expressing human ACE2 and TMPRSS2. Furthermore, the treatment of the SARS-CoV-2 spike, envelope, nucleoprotein, and membrane protein expressing alphaviral pseudotypes with C1q, its recombinant globular heads, or C4BP triggered a reduction in mRNA levels of proinflammatory cytokines and chemokines such as IL-1β, IL-8, IL-6, TNF-α, IFN-α, and RANTES (as well as NF-κB) in A549 cells expressing human ACE2 and TMPRSS2. In addition, C1q and C4BP treatment also reduced SARS-CoV-2 pseudotype infection-mediated NF-κB activation in A549 cells expressing human ACE2 and TMPRSS2. C1q and C4BP are synthesised primarily by hepatocytes; however, they are also produced by macrophages, and alveolar type II cells, respectively, locally at the pulmonary site. These findings support the notion that the locally produced C1q and C4BP can be protective against SARS-CoV-2 infection in a complement activation-independent manner, offering immune resistance by inhibiting virus binding to target host cells and attenuating the infection-associated inflammatory response.

Artificial Intelligence-Assisted Meta-Analysis of the Frequency of ACE I/D Polymorphisms in Centenarians and Other Long-Lived Individuals

Current research on the angiotensin-converting-enzyme (ACE) gene has yielded controversial results on whether different ACE polymorphisms are linked with human longevity. ACE polymorphisms are a risk factor for Alzheimer's disease and age-onset diseases that may contribute to the mortality of older people. Our goal is to consolidate existing studies, using artificial intelligence-assisted software to come to a more precise understanding of the role of the ACE gene in human longevity. The I (insertion) and D (deletion) polymorphisms in the intron are correlated with the levels of circulating ACE; homozygous D (DD) is high, and homozygous I (II) is low. Here, we performed a detailed meta-analysis of the I and D polymorphisms using centenarians (100+ years old), long-lived subjects (85+ years old), and control groups. ACE genotype distribution was analyzed across a total of 2054 centenarians and 12,074 controls, as well as 1367 long-lived subjects between the ages of 85-99, using the inverse variance and random effects methods. The ACE DD genotype was found to be favored in centenarians (OR: 1.41 (95% CI: 1.19-1.67), p < 0.0001) with a heterogeneity of 32%, and the II genotype slightly favored the control groups (OR: 0.81 (95% CI: 0.66-0.98), p = 0.03) with a heterogeneity of 28%, corroborating results from previous meta-analyses. Novel to our meta-analysis, the ID genotype was found to be favored in control groups (OR: 0.86 (95% CI: 0.76-0.97), p = 0.01) with a heterogeneity of 0%. The long-lived group showed a similar positive association between the DD genotype and longevity (OR: 1.34 (95% CI: 1.21-1.48), p < 0.0001) and a negative association between the II genotype and longevity (OR: 0.79 (95% CI: 0.70-0.88), p < 0.0001). The long-lived ID genotype did not show significant findings (OR: 0.93 (95% CI: 0.84-1.02), p = 0.79). In conclusion, the results suggest a significant positive association of the DD genotype with human longevity. However, despite the previous study, the results do not confirm a positive association of the ID genotype with human longevity. We suggest a few important paradoxical implications: (1) inhibition of ACE can increase longevity in model systems from nematodes to mammals, seemingly opposite to the finding in humans; (2) exceptional longevity associated with homozygous DD is also associated with age-related diseases with higher mortality risks in homozygous DD. We discuss ACE, longevity, and age-related diseases.

Ct, IL-18 polymorphism, and laboratory biomarkers for predicting chemosensory dysfunctions and mortality in COVID-19

Aim

Patients with COVID-19 often experience chemosensory dysfunction. This research intends to uncover the association of RT-PCR Ct value with chemosensory dysfunctions and SpO2. This study also aims to investigate Ct, SpO2, CRP, D-dimer, and -607 IL-18 T/G polymorphism in order to find out predictors of chemosensory dysfunctions and mortality.

Materials & methods

This study included 120 COVID-19 patients, of which 54 were mild, 40 were severe and 26 were critical. CRP, D-dimer, RT-PCR, and IL-18 polymorphism were evaluated. Results & conclusion: Low Ct was associated with SpO2 dropping and chemosensory dysfunctions. IL-18 T/G polymorphism did not show an association with COVID-19 mortality; conversely, age, BMI, D-dimer and Ct values did.

Burden of illness associated with overweight and obesity in patients hospitalized with COVID-19 in the United States: analysis of the premier healthcare database from April 1, 2020 to October 31, 2020

BACKGROUND

SARS-CoV-2 (COVID-19) continues to be a major public health issue. Obesity is a major risk factor for disease severity and mortality associated with COVID-19.

OBJECTIVE

This study sought to estimate the healthcare resource use and cost outcomes in patients hospitalized with COVID-19 in the United States (US) according to body mass index (BMI) class.

METHODS

Retrospective cross-sectional study analyzing data from the Premier Healthcare COVID-19 database for hospital length-of-stay (LOS), intensive care unit (ICU) admission, ICU LOS, invasive mechanical ventilator use, invasive mechanical ventilator use duration, in-hospital mortality, and total hospital costs from hospital charge data.

RESULTS

After adjustment for patient age, gender, and race, patients with COVID-19 and overweight or obesity had longer durations for mean hospital LOS (normal BMI = 7.4 days, class 3 obesity = 9.4 days, p < .0001) and ICU LOS (normal BMI = 6.1 days, class 3 obesity = 9.5 days, p < .0001) than patients with normal weight. Patients with normal BMI had fewer days on invasive mechanical ventilation compared to patients with overweight and obesity classes 1-3 (6.7 days vs. 7.8, 10.1, 11.5, and 12.4, respectively, p < .0001). The predicted probability of in-hospital mortality was nearly twice that of patients with class 3 obesity compared to patients with normal BMI (15.0 vs 8.1%, p < .0001). Mean (standard deviation) total hospital costs for a patient with class 3 obesity is estimated at $26,545 ($24,433-$28,839), 1.5 times greater than the mean for a patient with a normal BMI at $17,588 ($16,298-$18,981).

CONCLUSIONS

Increasing levels of BMI class, from overweight to obesity class 3, are significantly associated with higher levels of healthcare resource utilization and costs in adult patients hospitalized with COVID-19 in the US. Effective treatment of overweight and obesity are needed to reduce the burden of illness associated with COVID-19.

Reduced SARS-CoV-2 mRNA vaccine immunogenicity and protection in mice with diet-induced obesity and insulin resistance

Background

Obesity and Type 2 Diabetes Mellitus (T2DM) are associated with an increased risk of severe outcomes from infectious diseases, including COVID-19. These conditions are also associated with distinct responses to immunization, including an impaired response to widely used SARS-CoV-2 mRNA vaccines.

Objective

To establish a connection between reduced immunization efficacy via modeling the effects of metabolic diseases on vaccine immunogenicity that is essential for the development of more effective vaccines for this distinct vulnerable population.

Methods

We utilized a murine model of diet-induced obesity and insulin resistance to model the effects of comorbid T2DM and obesity on vaccine immunogenicity and protection.

Results

Mice fed a high-fat diet (HFD) developed obesity, hyperinsulinemia, and glucose intolerance. Relative to mice fed a normal diet (ND), HFD mice vaccinated with a SARS-CoV-2 mRNA vaccine exhibited significantly lower anti-spike IgG titers, predominantly in the IgG2c subclass, associated with a lower type 1 response, along with a 3.83-fold decrease in neutralizing titers. Furthermore, enhanced vaccine-induced spike-specific CD8 + T cell activation and protection from lung infection against SARS-CoV-2 challenge were seen only in ND mice but not in HFD mice.

Conclusion

We demonstrate impaired immunity following SARS-CoV-2 mRNA immunization in a murine model of comorbid T2DM and obesity, supporting the need for further research into the basis for impaired anti-SARS-CoV-2 immunity in T2DM and investigation of novel approaches to enhance vaccine immunogenicity among those with metabolic diseases.

Capsule summary

Obesity and type 2 diabetes impair SARS-CoV-2 mRNA vaccine efficacy in a murine model.

Biological and disease hallmarks of Alzheimer’s disease defined by Alzheimer’s disease genes

An increasing number of genes associated with Alzheimer’s disease (AD genes) have been reported. However, there is a lack of an overview of the genetic relationship between AD and age-related comorbidities, such as hypertension, myocardial infarction, and diabetes, among others. Previously, we used Reactome analysis in conjunction with the AD genes to identify both the biological pathways and the neurological diseases. Here we provide systematic updates on the genetic and disease hallmarks defined by AD genes. The analysis identified 50 pathways (defined as biological hallmarks). Of them, we have successfully compiled them into a total of 11 biological hallmarks, including 6 existing hallmarks and 5 newly updated hallmarks. The AD genes further identified 20 diverse diseases (defined as disease hallmarks), summarized into three major categories: (1) existing hallmarks, including neurological diseases; (2) newly identified hallmarks, including common age-related diseases such as diabetes, hypertension, other cardiovascular diseases, and cancers; (3) and other health conditions; note that cancers reportedly have an inverse relation with AD. We previously suggested that a single gene is associated with multiple neurological diseases, and we are further extending the finding that AD genes are associated with common age-related comorbidities and others. This study indicates that the heterogeneity of Alzheimer’s disease predicts complex clinical presentations in people living with AD. Taken together, the genes define AD as a part of age-related comorbidities with shared biological mechanisms and may raise awareness of a healthy lifestyle as potential prevention and treatment of the comorbidities.

Mortality Risk Factors for Coronavirus Infection in Hospitalized Adults in Brazil: A Retrospective Cohort Study

BACKGROUND

The COVID-19 pandemic has presented high morbidity and mortality, with associated high socioeconomic costs. Brazil ranks third in the number of COVID-19 cases, behind only India and the United States.

OBJECTIVE

To analyze risk factors for mortality in adults hospitalized with COVID-19 in Brazil.

METHODS

Observational retrospective cohort study including data from all Brazilian states and regions. The study included information from 468,226 in-hospital patients from all regions of Brazil from 1 January 2021 to 31 July 2021. Data from the influenza epidemiological surveillance system were used. The participants were adults hospitalized with COVID-19. A Cox regression model was used to analyze factors associated with mortality in adults with COVID-19.

RESULTS

The in-hospital mortality lethality was 37.5%. The risk factors associated with COVID-19 mortality were older age, with a linear increase with increments in age, male sex, black or mixed race, low education level, comorbidities, use of ventilatory support, and living in the southeast, north, or northeast regions of the country.

CONCLUSIONS

Our results illustrate the severity of the COVID-19 pandemic in Brazil and reinforce that policies and practices to deal with this disease should focus on groups and regions with higher risk, whereas public policies should promote nonpharmacological measures and vaccination in the Brazilian population.

Prerequisite for COVID-19 Prediction: A Review on Factors Affecting the Infection Rate

Since the year 2020, coronavirus disease 2019 (COVID-19) has emerged as the dominant topic of discussion in the public and research domains. Intensive research has been carried out on several aspects of COVID-19, including vaccines, its transmission mechanism, detection of COVID-19 infection, and its infection rate and factors. The awareness of the public related to the COVID-19 infection factors enables the public to adhere to the standard operating procedures, while a full elucidation on the correlation of different factors to the infection rate facilitates effective measures to minimize the risk of COVID-19 infection by policy makers and enforcers. Hence, this paper aims to provide a comprehensive and analytical review of different factors affecting the COVID-19 infection rate. Furthermore, this review analyses factors which directly and indirectly affect the COVID-19 infection risk, such as physical distance, ventilation, face masks, meteorological factor, socioeconomic factor, vaccination, host factor, SARS-CoV-2 variants, and the availability of COVID-19 testing. Critical analysis was performed for the different factors by providing quantitative and qualitative studies. Lastly, the challenges of correlating each infection risk factor to the predicted risk of COVID-19 infection are discussed, and recommendations for further research works and interventions are outlined.

CALMS: Modelling the long-term health and economic impact of Covid-19 using agent-based simulation

We present our agent-based CoronAvirus Lifelong Modelling and Simulation (CALMS) model that aspires to predict the lifelong impacts of Covid-19 on the health and economy of a population. CALMS considers individual characteristics as well as comorbidities in calculating the risk of infection and severe disease. We conduct two sets of experiments aiming at demonstrating the validity and capabilities of CALMS. We run simulations retrospectively and validate the model outputs against hospitalisations, ICU admissions and fatalities in a UK population for the period between March and September 2020. We then run simulations for the lifetime of the cohort applying a variety of targeted intervention strategies and compare their effectiveness against the baseline scenario where no intervention is applied. Four scenarios are simulated with targeted vaccination programmes and periodic lockdowns. Vaccinations are targeted first at individuals based on their age and second at vulnerable individuals based on their health status. Periodic lockdowns, triggered by hospitalisations, are tested with and without vaccination programme in place. Our results demonstrate that periodic lockdowns achieve reductions in hospitalisations, ICU admissions and fatalities of 6-8% compared to the baseline scenario, with an associated intervention cost of £173 million per 1,000 people and targeted vaccination programmes achieve reductions in hospitalisations, ICU admissions and fatalities of 89-90%, compared to the baseline scenario, with an associated intervention cost of £51,924 per 1,000 people. We conclude that periodic lockdowns alone are ineffective at reducing health-related outputs over the long-term and that vaccination programmes which target only the clinically vulnerable are sufficient in providing healthcare protection for the population as a whole.

COVID-19 mortality surveillance in Lebanon

Since the beginning of the COVID-19 pandemic, the Epidemiological surveillance program of the Lebanese Ministry of Public Health has launched a rapid surveillance system for collecting COVID-19-related mortality data. In this study, we document the Lebanese experience of COVID-19 mortality surveillance and provide an analysis of the epidemiological characteristics of confirmed deaths. The implementation of the rapid COVID-19 mortality surveillance system, data sources, and data collection were described. A retrospective descriptive analysis of the epidemiological characteristics of confirmed cases occurring in Lebanon between February 20, 2020, and September 15, 2021, was performed. Epidemiological curves of Covid-19 confirmed cases and deaths as well as the geographic distribution map of mortality rates were generated. Between February 21, 2020, and September 15, 2021, a total of 8163 COVID-19-related deaths were reported with a predominance of males (60.4%). More than 60% were aged 70 years or above. Of all deaths, 84% occurred at hospitals and 16% at home. The overall cumulative mortality rate was 119.6 per 100,000. The overall case fatality ratio (CRF) was 1.3%. Of the total deaths, 82.2% had at least one underlying medical condition. The top reported COVID-19 comorbidities associated with COVID-19-related deaths are cardiovascular diseases including hypertension (59.1%), diabetes (37.2%), kidney diseases including dialysis (11%), cancer (6.7%), and lung diseases (6.3%). The CFR was 30.9% for kidney diseases, 20.2% for cancer, 20.2% for lung diseases, 18.1% for liver diseases, 14% for diabetes, and 12.2% for cardiovascular diseases. Considering the limited human and financial resources in Lebanon due to the economic and political crisis, the rapid mortality surveillance system can be considered successful. Improving this system is important and would contribute to better detection of deaths from emerging and re-emerging diseases during health crises.

A Machine Learning Model for Predicting Hospitalization in Patients with Respiratory Symptoms during the COVID-19 Pandemic

A machine learning approach is a useful tool for risk-stratifying patients with respiratory symptoms during the COVID-19 pandemic, as it is still evolving. We aimed to verify the predictive capacity of a gradient boosting decision trees (XGboost) algorithm to select the most important predictors including clinical and demographic parameters in patients who sought medical support due to respiratory signs and symptoms (RAPID RISK COVID-19). A total of 7336 patients were enrolled in the study, including 6596 patients that did not require hospitalization and 740 that required hospitalization. We identified that patients with respiratory signs and symptoms, in particular, lower oxyhemoglobin saturation by pulse oximetry (SpO2) and higher respiratory rate, fever, higher heart rate, and lower levels of blood pressure, associated with age, male sex, and the underlying conditions of diabetes mellitus and hypertension, required hospitalization more often. The predictive model yielded a ROC curve with an area under the curve (AUC) of 0.9181 (95% CI, 0.9001 to 0.9361). In conclusion, our model had a high discriminatory value which enabled the identification of a clinical and demographic profile predictive, preventive, and personalized of COVID-19 severity symptoms.

Glycemic Control and Clinical Outcomes in U.S. Patients With COVID-19: Data From the National COVID Cohort Collaborative (N3C) Database

OBJECTIVE

The purpose of the study is to evaluate the relationship between HbA1c and severity of coronavirus disease 2019 (COVID-19) outcomes in patients with type 2 diabetes (T2D) with acute COVID-19 infection.

RESEARCH DESIGN AND METHODS

We conducted a retrospective study using observational data from the National COVID Cohort Collaborative (N3C), a longitudinal, multicenter U.S. cohort of patients with COVID-19 infection. Patients were ≥18 years old with T2D and confirmed COVID-19 infection by laboratory testing or diagnosis code. The primary outcome was 30-day mortality following the date of COVID-19 diagnosis. Secondary outcomes included need for invasive ventilation or extracorporeal membrane oxygenation (ECMO), hospitalization within 7 days before or 30 days after COVID-19 diagnosis, and length of stay (LOS) for patients who were hospitalized.

RESULTS

The study included 39,616 patients (50.9% female, 55.4% White, 26.4% Black or African American, and 16.1% Hispanic or Latino, with mean ± SD age 62.1 ± 13.9 years and mean ± SD HbA1c 7.6% ± 2.0). There was an increasing risk of hospitalization with incrementally higher HbA1c levels, but risk of death plateaued at HbA1c >8%, and risk of invasive ventilation or ECMO plateaued >9%. There was no significant difference in LOS across HbA1c levels.

CONCLUSIONS

In a large, multicenter cohort of patients in the U.S. with T2D and COVID-19 infection, risk of hospitalization increased with incrementally higher HbA1c levels. Risk of death and invasive ventilation also increased but plateaued at different levels of glycemic control.

Associations among political voting preference, high-risk health status, and preventative behaviors for COVID-19

BACKGROUND

We investigate the relationships among political preferences, risk for COVID-19 complications, and complying with preventative behaviors, such as social distancing, quarantine, and vaccination, as they remain incompletely understood. Since those with underlying health conditions have the highest mortality risk, prevention strategies targeting them and their caretakers effectively can save lives. Understanding caretakers' adherence is also crucial as their behavior affects the probability of transmission and quality of care, but is understudied. Examining the degree to which adherence to prevention measures within these populations is affected by their health status vs. voting preference, a key predictor of preventative behavior in the U. S, is imperative to improve targeted public health messaging. Knowledge of these associations could inform targeted COVID-19 campaigns to improve adherence for those at risk for severe consequences.

METHODS

We conducted a nationally-representative online survey of U.S. adults between May-June 2020 assessing: 1) attempts to socially-distance; 2) willingness/ability to self-quarantine; and 3) intention of COVID-19 vaccination. We estimated the relationships between 1) political preferences 2) underlying health status, and 3) being a caretaker to someone with high-risk conditions and each dependent variable. Sensitivity analyses examined the associations between political preference and dependent variables among participants with high-risk conditions and/or obesity.

RESULTS

Among 908 participants, 75.2% engaged in social-distancing, 94.4% were willing/able to self-quarantine, and 60.1% intended to get vaccinated. Compared to participants intending to vote for Biden, participants who intended to vote for Trump were significantly less likely to have tried to socially-distance, self-quarantine, or intend to be vaccinated. We observed the same trends in analyses restricted to participants with underlying health conditions and their caretakers Underlying health status was independently associated with social distancing among individuals with obesity and another high-risk condition, but not other outcomes.

CONCLUSION

Engagement in preventative behavior is associated with political voting preference and not individual risk of severe COVID-19 or being a caretaker of a high-risk individual. Community based strategies and public health messaging should be tailored to individuals based on political preferences especially for those with obesity and other high-risk conditions. Efforts must be accompanied by broader public policy.

Intention of Parents to Immunize Children against SARS-CoV-2 in Italy

Several countries have targeted adolescents for immunization against SARS-CoV-2 to mitigate COVID-19 spread. In Italy, immunization for children ≥ 12 years has been available starting from June 2021. We conducted a cross-sectional study to investigate the knowledge, attitude and intention to vaccinate children < 18 years in Italian families. We used a multinomial logistic regression model to investigate factors associated with intention to vaccinate. We collected a total of 1696 responses. Among the 491 families of children ≥ 12 years, 41.2% would not vaccinate their children and 21.2% were uncertain, while among the 1205 families of children < 12 years, 36.1% would not vaccinate and 33.8% were uncertain. Determinants of intention to vaccinate both age groups were perceived safety and efficacy of vaccines and perceived risk of transmitting infection to adults. For children < 12 years, additional determinants were perceived risk of being infected and being hospitalized because of COVID-19. In view of the expanding strategy to vaccinate adolescents and the availability of immunization for children < 12 years, our results call for a communication strategy targeted at families of children focused on the safety and efficacy of COVID-19 vaccine in children and on the dynamics of infection spread across different age groups. As perceptions in families are volatile and may change rapidly over time, repeated surveys for measuring attitudes to vaccinate would be advisable.

Two Opposing Functions of Angiotensin-Converting Enzyme (ACE) That Links Hypertension, Dementia, and Aging

A 2018 report from the American Heart Association shows that over 103 million American adults have hypertension. The angiotensin-converting enzyme (ACE) (EC 3.4.15.1) is a dipeptidyl carboxylase that, when inhibited, can reduce blood pressure through the renin–angiotensin system. ACE inhibitors are used as a first-line medication to be prescribed to treat hypertension, chronic kidney disease, and heart failure, among others. It has been suggested that ACE inhibitors can alleviate the symptoms in mouse models. Despite the benefits of ACE inhibitors, previous studies also have suggested that genetic variants of the ACE gene are risk factors for Alzheimer’s disease (AD) and other neurological diseases, while other variants are associated with reduced risk of AD. In mice, ACE overexpression in the brain reduces symptoms of the AD model systems. Thus, we find two opposing effects of ACE on health. To clarify the effects, we dissect the functions of ACE as follows: (1) angiotensin-converting enzyme that hydrolyzes angiotensin I to make angiotensin II in the renin–angiotensin system; (2) amyloid-degrading enzyme that hydrolyzes beta-amyloid, reducing amyloid toxicity. The efficacy of the ACE inhibitors is well established in humans, while the knowledge specific to AD remains to be open for further research. We provide an overview of ACE and inhibitors that link a wide variety of age-related comorbidities from hypertension to AD to aging. ACE also serves as an example of the middle-life crisis theory that assumes deleterious events during midlife, leading to age-related later events.