Obesity as a risk factor for COVID-19: an overview

Alberca, Ricardo Wesley; Oliveira, Lucas Ferrari de; Branco, Anna Cláudia Calvielli Castelo; Pereira, Newton Lindolfo; Sato, Maria Notomi

doi:10.1080/10408398.2020.1775546

Cited by 125 publications

(140 citation statements)

References 203 publications

(210 reference statements)

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“…Moreover, lactate dehydrogenase, a well-known marker for persistent viral infections may be of relevance as a potential biomarker for COVID-19 (10). Several manuscripts have highlighted the influence of comorbities in COVID-19 such as old age, systemic arterial hypertension, pregnancy and obesity (11)(12)(13)(14). Previous reports verified an immune dysregulation in circulating immune cells in the blood of COVID-19 patients, with a significant increase in neutrophil-to-lymphocyte ratio in COVID-19, and a further increase in severe COVID-19 (6).…”

Section: Resultsmentioning

confidence: 99%

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Frequencies of CD33+CD11b+HLA-DR–CD14–CD66b+ and CD33+CD11b+HLA-DR–CD14+CD66b– Cells in Peripheral Blood as Severity Immune Biomarkers in COVID-19

et al. 2020

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Frequencies of CD33+CD11b+HLA-DR–CD14–CD66b+ and CD33+CD11b+HLA-DR–CD14+CD66b– Cells in Peripheral Blood as Severity Immune Biomarkers in COVID-19

et al. 2020

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“…Patient comorbidities including hypertension, diabetes, obesity, and heart disease are associated with higher risk of severe COVID-19 disease. 3 , 4 , 19 , 20 , 21 while patients with additional common diseases including asthma and chronic obstructive lung disease (COPD) are advised to take extra precautions in the USA. 22 Reported COVID-19 inpatient mortality rates vary by city/region, 23 between countries, 1 , 3 , 4 , 16 , 17 , 19 , 23 , 24 , 25 and by disease severity.…”

Section: Introductionmentioning

confidence: 99%

Increase in COVID-19 inpatient survival following detection of Thromboembolic and Cytokine storm risk from the point of admission to hospital by a near real time Traffic-light System (TraCe-Tic)

Vizcaychipi¹,

Shovlin²,

McCarthy³

et al. 2020

The Brazilian Journal of Infectious Diseases

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Introduction Our goal was to evaluate if traffic-light driven personalized care for COVID-19 was associated with improved survival in acute hospital settings. Methods Discharge outcomes were evaluated before and after prospective implementation of a real-time dashboard with feedback to ward-based clinicians. Thromboembolism categories were “medium-risk” (D-dimer >1000 ng/mL or CRP >200 mg/L); “high-risk” (D-dimer >3000 ng/mL or CRP >250 mg/L) or “suspected” (D-dimer >5000 ng/mL). Cytokine storm risk was categorized by ferritin. Results 939/1039 COVID-19 positive patients (median age 67 years, 563/939 (60%) male) completed hospital encounters to death or discharge by 21st May 2020. Thromboembolism flag criteria were reached by 568/939 (60.5%), including 238/275 (86.6%) of the patients who died, and 330/664 (49.7%) of the patients who survived to discharge, p < 0.0001. Cytokine storm flag criteria were reached by 212 (22.6%) of admissions, including 80/275 (29.1%) of the patients who died, and 132/664 (19.9%) of the patients who survived, p < 0.0001. The maximum thromboembolism flag discriminated completed encounter mortality (no flag: 37/371 [9.97%] died; medium-risk: 68/239 [28.5%]; high-risk: 105/205 [51.2%]; and suspected thromboembolism: 65/124 [52.4%], p < 0.0001). Flag criteria were reached by 535 consecutive COVID-19 positive patients whose hospital encounter completed before traffic-light introduction: 173/535 (32.3% [95% confidence intervals 28.0, 36.0]) died. For the 200 consecutive admissions after implementation of real-time traffic light flags, 46/200 (23.0% [95% confidence intervals 17.1, 28.9]) died, p = 0.013. Adjusted for age and sex, the probability of death was 0.33 (95% confidence intervals 0.30, 0.37) before traffic light implementation, 0.22 (0.17, 0.27) after implementation, p < 0.001. In subgroup analyses, older patients, males, and patients with hypertension ( p ≤ 0.01), and/or diabetes ( p = 0.05) derived the greatest benefit from admission under the traffic light system. Conclusion Personalized early interventions were associated with a 33% reduction in early mortality. We suggest benefit predominantly resulted from early triggers to review/enhance anticoagulation management, without exposing lower-risk patients to potential risks of full anticoagulation therapy.

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“…COVID-19 has mild, moderate, and severe signs and symptoms. Mildto-moderate illness with symptoms of fever, dry cough, body aches, and shortness of breath occurs up to two weeks after exposure to the virus (6,7).…”

Section: Contextmentioning

confidence: 99%

Obesity and COVID-19

Moradi

2020

J Arch Mil Med

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Context: The emerging disease COVID-19 has made a major problem for people around the world, and treatment systems are facing hardships. Obesity is a risk factor for health, and COVID-19 is a global disease. Obesity may be a risk factor for this disease. The relationship between obesity and the disease was examined in this study. Evidence Acquisition: Three databases, PubMed, Scopus, and Embase, were examined. The search strategy and keyword combinations were (“COVID-19” OR “Coronavirus” OR “Wuhan virus” AND “Obesity” OR “Obese”). The results of related articles were used. Results: Obesity, diabetes, hypertension, cardiovascular, and respiratory diseases are the risk factors for COVID-19, especially in severe cases, which can affect the length of hospital stay and mortality. Conclusions: We know very little about this disease. Thus, much research is needed to conduct. Since obesity is a risk factor for this disease, exercise is proposed to prevent it.

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Obesity as a risk factor for COVID-19: an overview

Cited by 125 publications

References 203 publications

Frequencies of CD33+CD11b+HLA-DR–CD14–CD66b+ and CD33+CD11b+HLA-DR–CD14+CD66b– Cells in Peripheral Blood as Severity Immune Biomarkers in COVID-19

Frequencies of CD33+CD11b+HLA-DR–CD14–CD66b+ and CD33+CD11b+HLA-DR–CD14+CD66b– Cells in Peripheral Blood as Severity Immune Biomarkers in COVID-19

Increase in COVID-19 inpatient survival following detection of Thromboembolic and Cytokine storm risk from the point of admission to hospital by a near real time Traffic-light System (TraCe-Tic)

Obesity and COVID-19

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