Obesity and diabetes as high‐risk factors for severe coronavirus disease 2019 (<scp>Covid</scp>‐19)

Zhou, Yue; Chi, Jen-Tsan; Lv, Wenshan; Wang, Yangang

doi:10.1002/dmrr.3377

Cited by 424 publications

(401 citation statements)

References 181 publications

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“…Another large-scale British primary care study of more than 17 million subjects revealed similar risk factors as above 17 . There is also a relatively large literature on the study of risk factors associated with severe or fatal disease [8][9][10][11][18][19][20][21] . Some commonly reported risk factors included age, sex, obesity, diabetes, hypertension, cardiometabolic and respiratory disorders.…”

Section: Prediction Of Severity/mortalitymentioning

confidence: 99%

Uncovering clinical risk factors and prediction of severe COVID-19: A machine learning approach based on UK Biobank data

Wong

Xiang

2020

Preprint

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Background: COVID-19 is a major public health concern. Given the extent of the pandemic, it is urgent to identify risk factors associated with severe disease. Accurate prediction of those at risk of developing severe infections is also important clinically. Methods: Based on the UK Biobank (UKBB data), we built machine learning(ML) models to predict the risk of developing severe or fatal infections, and to evaluate the major risk factors involved. We first restricted the analysis to infected subjects, then performed analysis at a population level, considering those with no known infections as controls. Hospitalization was used as a proxy for severity. Totally 93 clinical variables (collected prior to the COVID-19 outbreak) covering demographic variables, comorbidities, blood measurements (e.g. hematological/liver and renal function/metabolic parameters etc.), anthropometric measures and other risk factors (e.g. smoking/drinking habits) were included as predictors. XGboost (gradient boosted trees) was used for prediction and predictive performance was assessed by cross-validation. Variable importance was quantified by Shapley values and accuracy gain. Shapley dependency and interaction plots were used to evaluate the pattern of relationship between risk factors and outcomes. Results: A total of 1191 severe and 358 fatal cases were identified. For the analysis among infected individuals (N=1747), our prediction model achieved AUCs of 0.668 and 0.712 for severe and fatal infections respectively. Since only pre-diagnostic clinical data were available, the main objective of this analysis was to identify baseline risk factors. The top five contributing factors for severity were age, waist-hip ratio(WHR), HbA1c, number of drugs taken(cnt_tx) and gamma-glutamyl transferase levels. For prediction of mortality, the top features were age, systolic blood pressure, waist circumference (WC), urea and WHR. In subsequent analyses involving the whole UKBB population (N for controls=489987), the corresponding AUCs for severity and fatality were 0.669 and 0.749. The same top five risk factors were identified for both outcomes, namely age, cnt_tx, WC, WHR and cystatin C. We also uncovered other features of potential relevance, including testosterone, IGF-1 levels, red cell distribution width (RDW) and lymphocyte percentage. Conclusions: We identified a number of baseline clinical risk factors for severe/fatal infection by an ML approach. For example, age, central obesity, impaired renal function, multi-comorbidities and cardiometabolic abnormalities may predispose to poorer outcomes. The presented prediction models may be useful at a population level to help identify those susceptible to developing severe/fatal infections, hence facilitating targeted prevention strategies. Further replications in independent cohorts are required to verify our findings.

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Section: Prediction Of Severity/mortalitymentioning

confidence: 99%

Uncovering clinical risk factors and prediction of severe COVID-19: A machine learning approach based on UK Biobank data

Wong

Xiang

2020

Preprint

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“…Blacks and Latinos in the United States are more likely to live in areas with high poverty rates, less access to quality hospitals and health care, more air pollution, and other environmental risks (Fiscella, Franks, & Gold, 2000; Gradín, 2012; Woo et al, 2019). A higher prevalence of chronic conditions like diabetes and obesity among these populations may put them at greater risk of severe complications from COVID‐19 (Crossrow & Falkner, 2004; Hales, Caroll, Fryar, & Ogden, 2017; Spanakis & Golden, 2013; Zhou, Chi, Lv, & Wang, 2020). The coronavirus pandemic is an additional major burden to communities already struggling to stem the ongoing opioid crisis (Alexander et al, 2020; Becker & Fiellin, 2020; Volkow, 2020) as well as other health disparities.…”

Section: Higher Underlying Infection Risk and Worse Infection‐relatedmentioning

confidence: 99%

Addressing Major Health Disparities Related to Coronavirus for People With Behavioral Health Conditions Requires Strength‐Based Capacity Building and Intentional Community Partnership

Progovac¹,

Cortés²,

Chambers³

et al. 2020

World Med & Health Policy

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Far from being an equalizer, as some have claimed, the COVID‐19 pandemic has exposed just how vulnerable many of our social, health, and political systems are in the face of major public health shocks. Rapid responses by health systems to meet increased demand for hospital beds while continuing to provide health services, largely via a shift to telehealth services, are critical adaptations. However, these actions are not sufficient to mitigate the impact of coronavirus for people from marginalized communities, particularly those with behavioral health conditions, who are experiencing disproportional health, economic, and social impacts from the evolving pandemic. Helping these communities weather this storm requires partnering with existing community‐based organizations and local governments to rapidly and flexibly meet the needs of vulnerable populations.

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“…and 9% as severely obese (BMI ≥ 40 kg/m 2 )-it is an important factor to consider in the context of COVID-19. 82,83 The CDC has identified that any degree of obesity is associated with poor prognosis in COVID-19 patients; severe obesity indicates even worse prognoses. 82,84 The virus is known to enters cells via ACE2 receptors.…”

Section: Policies For Recording Cases/deathsmentioning

confidence: 99%

“…[82][83][84] Thus, obese people may have a higher viral load. [82][83][84] Additionally, adipocytes may significantly contribute to production of angiotensin II, which, by inducing pulmonary vasoconstriction, causes more severe lung injury (measured as PaO2 to fraction of inspired oxygen) in COVID-19 patients. 82 Common markers of obesity are chronically increased levels of circulating proinflammatory cytokines.…”

Section: Policies For Recording Cases/deathsmentioning

confidence: 99%

“…82 Common markers of obesity are chronically increased levels of circulating proinflammatory cytokines. [82][83][84] Hypertrophic adipocytes become more prone to activating endoplasmic reticulum and mitochondrial stress responses, leading to chronic inflammation that impairs macrophage activation and migration, decreases formation of antibodies and memory T cells, and suppresses activation of effector cells-all of which reduce immune function. 83 Obesity also increases airway resistance, reduces lung volumes, and causes respiratory muscle inefficiency.…”

Section: Policies For Recording Cases/deathsmentioning

confidence: 99%

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A Comparison of COVID-19 Epidemiology, Pathophysiology, and Impact on Vulnerable Populations in Two States within the USA

Elengickal

Weber

Morris

et al. 2020

MRAJ

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Since late 2019, SARS-CoV-2 has differentially impacted geographies and population demographics as it spread. As of June 30, 2020, two hotspots within the United States of America—the states of Georgia and Michigan—exhibited similar numbers of cases while Michigan had over twice the case fatality rate (CFR) of Georgia. Given the similar populations, land areas, and pandemic timelines of these states, such a large difference is unexpected. The primary goal of this paper is to examine why Michigan experienced much higher COVID-19 mortality than Georgia, which may point to at-risk comorbidities and vulnerable populations. We examined publicly available data on demographics, rates of comorbidities, environmental factors, and other population differences at the state and local levels (the cities of Detroit, Michigan; Atlanta, Georgia; and Albany, Georgia) that have known or identified associations with health outcomes. We also outlined the timeline of the pandemic in each state to determine if the actions of state governments may have contributed to the observed difference in CFR. While the difference in state CFR may imply that Michigan handled the pandemic poorly, the data show that inherent characteristics of Detroit may have led to the higher statewide CFR. Notable differences between the states include elderly populations, agricultural statistics, and drinking habits. Notable differences between the cities included population density, health system quality, per capita income, race, education, media access, and air pollution. Hypertension (among blacks), diabetes (at the city level), chronic kidney disease, asthma, heart disease, and cancer differed in prevalence by location and were associated with increased severity and/or mortality of COVID-19. There were more deaths due to COVID-19 in African American communities and nursing homes in Michigan. A combination of these factors likely explains the differential impact between these two states.

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Obesity and diabetes as high‐risk factors for severe coronavirus disease 2019 (Covid‐19)

Cited by 424 publications

References 181 publications

Uncovering clinical risk factors and prediction of severe COVID-19: A machine learning approach based on UK Biobank data

Uncovering clinical risk factors and prediction of severe COVID-19: A machine learning approach based on UK Biobank data

Addressing Major Health Disparities Related to Coronavirus for People With Behavioral Health Conditions Requires Strength‐Based Capacity Building and Intentional Community Partnership

A Comparison of COVID-19 Epidemiology, Pathophysiology, and Impact on Vulnerable Populations in Two States within the USA

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