Literature DB >> 32628537

Obesity and COVID-19 in New York City: A Retrospective Cohort Study.

Parag Goyal1, Joanna Bryan Ringel1, Mangala Rajan1, Justin J Choi1, Laura C Pinheiro1, Han A Li2, Graham T Wehmeyer2, Mark N Alshak2, Assem Jabri1, Edward J Schenck1, Ruijun Chen3, Michael J Satlin1, Thomas R Campion1, Musarrat Nahid1, Maria Plataki1, Katherine L Hoffman1, Evgeniya Reshetnyak1, Nathaniel Hupert1, Evelyn M Horn1, Fernando J Martinez1, Roy M Gulick1, Monika M Safford1.   

Abstract

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Year:  2020        PMID: 32628537      PMCID: PMC7384267          DOI: 10.7326/M20-2730

Source DB:  PubMed          Journal:  Ann Intern Med        ISSN: 0003-4819            Impact factor:   25.391


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Background: Some reports suggest that obesity could be a risk factor for complications in coronavirus disease 2019 (COVID-19) (1). Several mechanisms could explain this. First, adipocytes, which activate the inflammatory cascade, can increase risk for thromboembolism and susceptibility to the cytokine storm described in COVID-19 (2). Second, obesity negatively affects lung mechanics, which could predispose obese persons to more severe respiratory distress and failure (3). Finally, obesity can alter mitochondrial bioenergetics in lung epithelial cells and increase risk for acute lung injury (4). However, some have suggested an obesity paradox in some critical illnesses, including acute respiratory distress syndrome, where patients with obesity may have improved outcomes; whether this phenomenon occurs in patients with COVID-19 is unclear (5). Objective: To study the association between obesity and outcomes among a diverse cohort of 1687 persons hospitalized with confirmed COVID-19 at 2 New York City hospitals. Methods and Findings: This retrospective observational cohort study included consecutive adults with confirmed COVID-19 who were hospitalized between 3 March and 15 May 2020 at an 862-bed quaternary referral center or a 180-bed community hospital in New York City. We excluded 46 patients who did not have height or weight data available to calculate body mass index (BMI). Patient data were manually abstracted (1) from the electronic health record through 6 June 2020. We determined BMI on the basis of the most recent height and weight listed in the electronic health record. Height and weight were collected during hospitalization for 95.5% of the cohort; the remaining BMIs were collected during ambulatory encounters within 3 months of hospitalization. We defined BMI categories as underweight (<18.5 kg/m2), normal (18.5 to 24.9 kg/m2), overweight (25.0 to 29.9 kg/m2), mild to moderate obesity (30.0 to 39.9 kg/m2), and morbid obesity (≥40.0 kg/m2). To examine the association between BMI and in-hospital mortality, we used a Cox proportional hazards model adjusted for age, sex, race, smoking, diabetes, hypertension, chronic obstructive pulmonary disease, asthma, end-stage renal disease, coronary artery disease, heart failure, and cancer. These characteristics were chosen on the basis of risk factors for severe COVID-19 identified by the Centers for Disease Control and Prevention. We also examined for effect modification by age, sex, and race. To examine the association between BMI and respiratory failure, defined as a need for invasive mechanical ventilation, we used a Fine and Gray model to account for the competing risk for death and adjusted for the same 12 variables used in the model for mortality. We excluded the underweight group from this analysis because of low numbers. Finally, we repeated the adjusted Cox proportional hazards model analysis for mortality among persons with respiratory failure, again excluding the underweight group. To account for missing data (12% for race), we did multiple imputation. We examined 1687 patients, whose median BMI was 27 kg/m2 (interquartile range, 23.5 to 31.3 kg/m2); 31.1% were obese. Participants in higher BMI categories were younger (Table). At the time of this report, only 69 persons remained hospitalized, including 3 who remained on invasive mechanical ventilation. Median follow-up was 7 days (interquartile range, 4 to 17 days).
Table. Characteristics of 1687 Hospitalized Patients With COVID-19, According to BMI*
We found a J-shaped pattern for in-hospital mortality. The fully adjusted hazard of dying was highest for underweight persons, was lowest for overweight persons, and progressively increased with higher degrees of obesity (Figure). This observation was similar across age (P for interaction = 0.32), sex (P = 0.59), and race (P = 0.57). For respiratory failure, the fully adjusted hazard ratio (HR) was lowest among persons with normal weight and progressively increased with higher BMI class (Figure). Finally, among those with respiratory failure, we found a similar J-shaped pattern for in-hospital mortality; HRs were similar to those in the full cohort, albeit with wider CIs (normal as the reference: HR, 1; overweight: HR, 0.76 [95% CI, 0.52 to 1.12]; mild to moderate obesity: HR, 0.82 [CI, 0.53 to 1.27]; morbid obesity: HR, 1.29 [CI, 0.58 to 2.86]).
Figure.

HRs for in-hospital mortality and respiratory failure according to BMI.

The association between BMI and in-hospital mortality (blue triangle) is explained by a J-shaped curve, whereas that between BMI and respiratory failure (orange square) is linear. The solid blue lines indicate CIs for mortality, and the dashed orange lines indicate CIs for respiratory failure. Covariates in both models included age, sex, race, smoking, diabetes, hypertension, chronic obstructive pulmonary disease, asthma, end-stage renal disease, coronary artery disease, heart failure, and cancer. All analyses were done in STATA 14 (StataCorp) and SAS, version 9.4 (SAS Institute), with 2-sided statistical tests and significance levels of 0.05. HRs are provided with 95% CIs. BMI = body mass index; HR = hazard ratio.

HRs for in-hospital mortality and respiratory failure according to BMI.

The association between BMI and in-hospital mortality (blue triangle) is explained by a J-shaped curve, whereas that between BMI and respiratory failure (orange square) is linear. The solid blue lines indicate CIs for mortality, and the dashed orange lines indicate CIs for respiratory failure. Covariates in both models included age, sex, race, smoking, diabetes, hypertension, chronic obstructive pulmonary disease, asthma, end-stage renal disease, coronary artery disease, heart failure, and cancer. All analyses were done in STATA 14 (StataCorp) and SAS, version 9.4 (SAS Institute), with 2-sided statistical tests and significance levels of 0.05. HRs are provided with 95% CIs. BMI = body mass index; HR = hazard ratio. Conclusion: This study of 1687 adults hospitalized with COVID-19 in New York City showed that obesity was an independent risk factor for respiratory failure but not for in-hospital mortality. Our findings, at least in part, explain the extensive use of invasive mechanical ventilation reported in the United States (1), where the prevalence of obesity exceeds 40%. These findings thus support the need to consider the community-specific prevalence of obesity when planning a community's COVID-19 response and also suggest that risk conferred by obesity is similar across age, sex, and race. Our findings also provide insights about a possible obesity paradox in COVID-19. This study was limited to hospitalized adults in a single geographic location. The association between obesity and adverse outcomes could differ in other settings and thus merits additional investigation.
  5 in total

1.  Fatty acid synthase downregulation contributes to acute lung injury in murine diet-induced obesity.

Authors:  Maria Plataki; LiChao Fan; Elizabeth Sanchez; Ziling Huang; Lisa K Torres; Mitsuru Imamura; Yizhang Zhu; David E Cohen; Suzanne M Cloonan; Augustine Mk Choi
Journal:  JCI Insight       Date:  2019-07-09

Review 2.  The effect of obesity on lung function.

Authors:  Anne E Dixon; Ubong Peters
Journal:  Expert Rev Respir Med       Date:  2018-08-14       Impact factor: 3.772

3.  COVID-19: consider cytokine storm syndromes and immunosuppression.

Authors:  Puja Mehta; Daniel F McAuley; Michael Brown; Emilie Sanchez; Rachel S Tattersall; Jessica J Manson
Journal:  Lancet       Date:  2020-03-16       Impact factor: 79.321

4.  Does Coronavirus Disease 2019 Disprove the Obesity Paradox in Acute Respiratory Distress Syndrome?

Authors:  Ricardo J Jose; Ari Manuel
Journal:  Obesity (Silver Spring)       Date:  2020-06       Impact factor: 5.002

5.  Clinical Characteristics of Covid-19 in New York City.

Authors:  Parag Goyal; Justin J Choi; Laura C Pinheiro; Edward J Schenck; Ruijun Chen; Assem Jabri; Michael J Satlin; Thomas R Campion; Musarrat Nahid; Joanna B Ringel; Katherine L Hoffman; Mark N Alshak; Han A Li; Graham T Wehmeyer; Mangala Rajan; Evgeniya Reshetnyak; Nathaniel Hupert; Evelyn M Horn; Fernando J Martinez; Roy M Gulick; Monika M Safford
Journal:  N Engl J Med       Date:  2020-04-17       Impact factor: 176.079

  5 in total
  36 in total

1.  Association of body mass index with morbidity in patients hospitalised with COVID-19.

Authors:  Maria Plataki; Di Pan; Parag Goyal; Katherine Hoffman; Jacky Man Kwan Choi; Hao Huang; Monika M Safford; Edward J Schenck
Journal:  BMJ Open Respir Res       Date:  2021-08

2.  Association of Obesity With COVID-19 Severity and Mortality: An Updated Systemic Review, Meta-Analysis, and Meta-Regression.

Authors:  Romil Singh; Sawai Singh Rathore; Hira Khan; Smruti Karale; Yogesh Chawla; Kinza Iqbal; Abhishek Bhurwal; Aysun Tekin; Nirpeksh Jain; Ishita Mehra; Sohini Anand; Sanjana Reddy; Nikhil Sharma; Guneet Singh Sidhu; Anastasios Panagopoulos; Vishwanath Pattan; Rahul Kashyap; Vikas Bansal
Journal:  Front Endocrinol (Lausanne)       Date:  2022-06-03       Impact factor: 6.055

Review 3.  Obesity Impact on SARS-CoV-2 Infection: Pros and Cons "Obesity Paradox"-A Systematic Review.

Authors:  Damiana-Maria Vulturar; Carmen-Bianca Crivii; Olga Hilda Orăsan; Emanuel Palade; Anca-Dana Buzoianu; Iulia Georgiana Zehan; Doina Adina Todea
Journal:  J Clin Med       Date:  2022-07-02       Impact factor: 4.964

Review 4.  Vascular Events, Vascular Disease and Vascular Risk Factors-Strongly Intertwined with COVID-19.

Authors:  Adrian Scutelnic; Mirjam R Heldner
Journal:  Curr Treat Options Neurol       Date:  2020-10-08       Impact factor: 3.598

5.  Erratum.

Authors: 
Journal:  Obes Rev       Date:  2021-08-03       Impact factor: 10.867

6.  Critical carE Database for Advanced Research (CEDAR): An automated method to support intensive care units with electronic health record data.

Authors:  Edward J Schenck; Katherine L Hoffman; Marika Cusick; Joseph Kabariti; Evan T Sholle; Thomas R Campion
Journal:  J Biomed Inform       Date:  2021-04-14       Impact factor: 8.000

7.  Body Mass Index and Risk for Intubation or Death in SARS-CoV-2 Infection : A Retrospective Cohort Study.

Authors:  Michaela R Anderson; Joshua Geleris; David R Anderson; Jason Zucker; Yael R Nobel; Daniel Freedberg; Jennifer Small-Saunders; Kartik N Rajagopalan; Richard Greendyk; Sae-Rom Chae; Karthik Natarajan; David Roh; Ethan Edwin; Dympna Gallagher; Anna Podolanczuk; R Graham Barr; Anthony W Ferrante; Matthew R Baldwin
Journal:  Ann Intern Med       Date:  2020-07-29       Impact factor: 25.391

8.  Obesity and mortality in critically ill COVID-19 patients with respiratory failure.

Authors:  Richard Dana; Aurélie Bannay; Pauline Bourst; Caroline Ziegler; Marie-Reine Losser; Sébastien Gibot; Bruno Levy; Gérard Audibert; Olivier Ziegler
Journal:  Int J Obes (Lond)       Date:  2021-06-10       Impact factor: 5.551

9.  Body mass index and Mini Nutritional Assessment-Short Form as predictors of in-geriatric hospital mortality in older adults with COVID-19.

Authors:  L Kananen; M Eriksdotter; A M Boström; M Kivipelto; M Annetorp; C Metzner; V Bäck Jerlardtz; M Engström; P Johnson; L G Lundberg; E Åkesson; C Sühl Öberg; S Hägg; D Religa; J Jylhävä; T Cederholm
Journal:  Clin Nutr       Date:  2021-07-29       Impact factor: 7.324

Review 10.  The Collision of Meta-Inflammation and SARS-CoV-2 Pandemic Infection.

Authors:  Gabrielle P Huizinga; Benjamin H Singer; Kanakadurga Singer
Journal:  Endocrinology       Date:  2020-11-01       Impact factor: 4.736

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