Sage Journals: Discover world-class research

Abstract

Objectives

We assessed the relationship between obesity and all-cause mortality in patients with acute respiratory distress syndrome (ARDS).

Methods

In this retrospective cohort study, patient data were extracted from the eICU Collaborative Research Database and the Medical Information Mart for Intensive Care Database III. Body mass index (BMI) was grouped according to World Health Organization classifications: underweight, normal weight, overweight, obese. Cox regression models estimated hazard ratios (HRs) and 95% confidence intervals (CIs) of all-cause mortality related to obesity.

Results

Participants included 185 women and 233 men, mean age 70.7 ± 44.1 years and mean BMI 28.7 ± 8.1 kg/m². Compared with normal weight patients, obese patients tended to be younger (60.1 ± 13.7 years) and included more women (51.3% vs. 49.0%). In the unadjusted model, HRs (95% CIs) of 30-day mortality for underweight, overweight, and obesity were 1.57 (0.76, 3.27), 0.64 (0.39, 1.08), and 4.83 (2.25, 10.35), respectively, compared with those for normal weight. After adjustment, HRs (95% CIs) of 30-day mortality for underweight, overweight, and obesity were 1.82 (0.85, 3.90), 0.59 (0.29, 1.20), and 3.85 (1.73, 8.57), respectively, compared with the reference group; 90-day and 1-year all-cause mortalities showed similar trends.

Conclusions

Obesity was associated with increased all-cause mortality in patients with ARDS.

Keywords

Body mass index all-cause mortality acute respiratory distress syndrome Medical Information Mart for Intensive Care Database III version retrospective cohort study obesity

Introduction

Acute respiratory distress syndrome (ARDS) is an acute inflammatory lung injury associated with increased pulmonary vascular permeability, increased lung weight, and loss of aerated lung tissue, with severe hypoxemia.¹,² ARDS is a global health care challenge with a reported mortality rate of more than 34%.³ Despite advances in understanding of the mechanisms in ARDS, improvement in mortality remains less than satisfactory.⁴ Hence, there is a pressing need to identify prognostic predictors of ARDS to assist physicians in making medical decisions and identifying patients with high-risk.⁵

Obesity is considered to be a global epidemic resulting from a growing preference for energy-dense foods by populations worldwide. Obesity is a contributor to increased morbidity in hypertension, diabetes, coronary heart disease, cancer, and other diseases.⁶,⁷ However, recent studies have revealed an “obesity paradox” in that obesity remains a risk factor for some diseases (e.g., heart failure, cancer, and stroke), but paradoxically presents a survival benefit in patients who have already developed these diseases.^8–10 Several studies^11–14 have investigated the relationship between obesity and outcome of ARDS. Ni et al.¹⁵ conducted a meta-analysis pooling five trials. They found that compared with normal weight, obesity was more likely to result in lower mortality. Zhi et al.¹⁶ also reported an obesity paradox in ARDS in a systematic review and meta-analysis. However, several important confounding factors, including alcohol use, severity of disease, and cardiovascular disease, were not considered in these studies. Thus, whether an obesity paradox exists with respect to ARDS remains controversial.¹⁷ In our study, we evaluated the association between obesity and the risk of all-cause mortality in patients with ARDS.

Methods

Study population

We conducted a retrospective cohort study using the eICU Collaborative Research Database and the Medical Information Mart for Intensive Care Database III (MIMIC-III) version 1.4.^18,19 The MIMIC-III database includes 58,976 patients admitted to the intensive care unit (ICU) at Beth Israel Deaconess Medical Center from 2001 to 2012.¹⁸ The setting of this database has been approved by the Institutional Review Boards of Beth Israel Deaconess Medical Center and the Massachusetts Institute of Technology. To gain access to this database, we completed online courses required by the National Institutes of Health and completed all tests related to protecting human research participants. All personal information in the database has been de-identified to safeguard patient privacy. The reporting in our study conforms to the STROBE statement.

Of 58,976 distinct patients in the database, patients were included if (i) they were screened for ARDS, defined according to the American European Consensus Committee as follows: (1) hypoxemic respiratory failure requiring intubation and ratio of arterial oxygen partial pressure to fractional inspired oxygen ≤200 mmHg; (2) bilateral infiltrates on chest radiographs; (3) an absence of left atrial hypertension;²⁰,²¹ (ii) height and weight data were acquired on the first day of admission; (iii) the hospital stay was >2 days; and (iv) they were aged above 18 years. Exclusion criteria were >5% missing data and individual data values exceeding the mean ± 3 times the standard deviation.

Our institutional ethics review committee waived the need for approval of the study protocol and informed consent because this study involved a retrospective analysis using a public database.

Data extraction and management

We collected patient’s demographic data (age, sex, and ethnicity), clinical characteristics, comorbidities, and scale scores. Vital signs within 24 hours after ICU admission included heart rate, percutaneous oxygen saturation (SPO₂), systolic blood pressure (SBP), and diastolic blood pressure (DBP). Comorbidities included hypertension, diabetes mellitus (DM), coronary heart disease (CHD), chronic heart failure (CHF), atrial fibrillation (AF), and acute kidney injury (AKI). The Simplified Acute Physiology Score II (SAPS II), Sequential Organ Failure Assessment (SOFA),²² Glasgow Coma Scale (GCS), and Acute Physiology and Chronic Health Evaluation (APACHE) III²³ scores were included.²⁴

Body mass index (BMI) was calculated using height and weight recorded in the first 24 hours after patient admission. BMI was subdivided into four groups according to World Health Organization BMI classifications: underweight (BMI <18.5 kg/m²), normal weight (BMI 18.5–25 kg/m²), overweight (BMI 25–30 kg/m²), and obese (BMI ≥30 kg/m²).²⁵

Outcomes

Our primary outcome was 30-day mortality, and secondary outcomes were 90-day mortality and 1-year mortality. The date of enrollment was the start date of follow-up, and all participants were followed for at least 1 year. The date of death was obtained from Social Security Death Index records.

Statistical analyses

The statistical analysis was divided into three steps. First, participants were divided into groups according to baseline BMI. Continuous data are presented as mean with standard deviations and were compared using analysis of variance or the Kruskal–Wallis H test. Categorical data are presented as frequency and percentage and were compared using the χ² test and Fisher’s exact test. Second, Cox proportional hazards regression models were used to estimate hazard ratios (HRs) with 95% confidence intervals (CIs) for the association between BMI and mortality. The group with normal weight (BMI: 18.5–25 kg/m²) was considered the reference group. In Model 1, no covariates were adjusted; model 2 was adjusted for age, sex, and ethnicity; model 3 was adjusted for confounders including age, sex, ethnicity, SBP, heart rate, SPO_2, CHD, AF, DM, AKI, CHF, and APACHE III and SOFA scores. Confounders were selected based on change in the effect estimate over 10%.²⁶,²⁷ Third, smooth curve fitting (penalized spline method) was used to identify nonlinear correlations of BMI with 30-day mortality. A two-piecewise linear regression model was applied to examine the threshold effect of BMI on 30-day mortality. The inflection point of BMI, at which the correlation of BMI with 30-day mortality began to reverse, was determined using a recursive method.

We used IBM SPSS version 22.0 (IBM Corp., Armonk, NY, USA) for the statistical analysis. A P value <0.05 (two-sided) was considered statistically significant.

Results

Participant characteristics

For this study, from a total of 58,976 of individuals, 418 participants were ultimately included in the analysis (Figure 1). The baseline characteristics of 418 patients with ARDS are listed in Table 1. The participants included 185 women and 233 men, with mean age 70.7 ± 44.1 years and mean BMI 28.7 ± 8.1 kg/m². At baseline, obese patients tended to be younger (60.1 ± 13.7, P = 0.003) and included more women (51.3% vs. 49.0%, P = 0.001) compared with normal weight patients (Table 1). The proportion of patients with DM increased with increased BMI.

Figure 1.

Flow chart illustrating inclusion and exclusion criteria of study cohort.

Table 1.

Characteristics of study participants according to BMI.

Variable	Underweight (BMI <18.5 kg/m²)	Normal weight (BMI 18.5–25 kg/m²)	Overweight (BMI 25–30 kg/m²)	Obese (BMI ≥30 kg/m²)	P-value
Baseline characteristics
N	22	143	103	150
Age (years), n (%)	72.4 ± 53.8	77.5 ± 53.4	76.3 ± 53.1	60.1 ± 13.7	0.003
Sex, n (%)					0.001
Female	4 (18.2)	70 (49.0)	34 (33.0)	77 (51.3)
Male	18 (81.8)	73 (51.0)	69 (67.0)	73 (48.7)
Ethnicity, n (%)					0.256
White	14 (63.6)	97 (67.8)	82 (79.6)	97 (64.7)
Black	2 (9.1)	13 (9.1)	6 (5.8)	18 (12.0)
Other	6 (27.3)	33 (23.1)	15 (14.6)	35 (23.3)
Vital signs within 24 hours after ICU admission
Heart rate, beats/minute	77.2 ± 14.2	77.8 ± 14.8	81.0 ± 14.3	82.1 ± 15.3	<0.001
SBP, mmHg	125.3 ± 16.3	125.6 ± 17.5	124.0 ± 19.0	125.0 ± 18.7	0.037
DBP, mmHg	62.6 ± 10.6	62.0 ± 10.9	60.8 ± 11.6	61.6 ± 11.1	0.005
SPO₂, %	92.5 ± 2.1	92.5 ± 2.2	91.4 ± 2.8	92.4 ± 2.7	0.238
Severity of organ dysfunction
SOFA	5.7 ± 3.2	5.5 ± 3.3	5.5 ± 3.4	5.5 ± 3.3	0.678
GCS	13.4 ± 2.5	13.9 ± 2.5	13.9 ± 2.4	13.1 ± 3.2	0.033
APACHE III	51.5 ± 13.6	52.0 ± 17.3	49.5 ± 21.2	55.7 ± 23.2	0.106
Comorbidities, n (%)
CHD					0.163
No	19 (86.4)	105 (73.4)	77 (74.8)	124 (82.7)
Yes	3 (13.6)	38 (26.6)	26 (25.2)	26 (17.3)
AF					0.059
No	17 (77.3)	96 (67.1)	73 (70.9)	121 (80.7)
Yes	5 (22.7)	47 (32.9)	30 (29.1)	29 (19.3)
CHF					0.384
No	17 (77.3)	108 (75.5)	77 (74.8)	124 (82.7)
Yes	5 (22.7)	35 (24.5)	26 (25.2)	26 (17.3)
Hypertension					0.303
No	21 (95.5)	125 (87.4)	92 (89.3)	125 (83.3)
Yes	1 (4.5)	18 (12.6)	11 (10.7)	25 (16.7)
DM					0.006
No	16 (72.7)	128 (89.5)	84 (81.6)	111 (74.0)
Yes	6 (27.3)	15 (10.5)	19 (18.4)	39 (26.0)
AKI					0.176
No	7 (31.8)	25 (17.5)	17 (16.5)	20 (13.3)
Yes	15 (68.2)	118 (82.5)	86 (83.5)	130 (86.7)
Mortality, n (%)
30-day	9 (40.9)	35 (24.5)	16 (15.5)	25 (16.7)	0.019
90-day	13 (59.1)	50 (35.0)	24 (23.3)	43 (28.7)	0.006
1-year	15 (68.2)	71 (49.7)	38 (36.9)	60 (40.0)	0.017

Data are presented as mean ± SD unless otherwise noted.

P value: difference among the four groups.

BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; SPO₂, percutaneous oxygen saturation; CHD, coronary heart disease; CHF, chronic heart failure; AF, atrial fibrillation; DM, diabetes mellitus; SOFA, Sequential Organ Failure Assessment; GCS, Glasgow Coma Scale; ICU, intensive care unit; APACHE, Acute Physiology and Chronic Health Evaluation; AKI, acute kidney injury.

Association between BMI and outcome of ARDS

Table 2 displays the effect sizes in the association between BMI and outcome of ARDS. For 30-day mortality, 85 deaths occurred during the follow-up period. In Model 1 (unadjusted model), the HRs (95% CIs) of 30-day mortality for underweight, overweight, and obesity were 1.57 (0.76, 3.27), 0.64 (0.39, 1.08), and 4.83 (2.25, 10.35), respectively, (P < 0.0001) in comparison with normal weight. In Model 2, after adjusting for age, sex, and ethnicity, a similar trend was observed for 30-day mortality and the risk was more evident with higher BMI. In Model 3, after adjusting for confounders including age, sex, ethnicity, SBP, heart rate, SPO_2, CHD, AF, DM, AKI, CHF, and APACHE III and SOFA scores, the HRs (95% CIs) of 30-day mortality for underweight, overweight, and obesity were 1.82 (0.85, 3.90), 0.59 (0.29, 1.20), and 3.85 (1.73, 8.57) respectively, compared with the reference group. For 90-day mortality and 1-year mortality, a similar trend was also observed, and the risk was more evident with higher BMI.

Table 2.

HRs (95% CIs) for mortality across groups of BMI.

BMI	Model 1		Model 2		Model 3
BMI	HR (95% CI)	P value	HR (95% CI) lepara	P value	HR (95% CI) lepara	P value
30-day all-cause mortality
BMI classification
Underweight	1.57 (0.76, 3.27)	0.2252	1.50 (0.71, 3.19)	0.2875	1.82 (0.85, 3.90)	0.1221
Normal weight	ref	ref	ref
Overweight	0.64 (0.39, 1.08)	0.0927	0.58 (0.32, 1.06)	0.0743	0.59 (0.29, 1.20)	0.1455
Obese	4.83 (2.25, 10.35)	<0.0001	4.29 (1.93, 9.53)	0.0004	3.85 (1.73, 8.57)	0.0019
90-day all-cause mortality
BMI classification
Underweight	1.72 (0.94, 3.17)	0.0808	1.52 (0.80, 2.86)	0.1983	1.94 (0.93, 2.89)	0.2095
Normal weight	ref	ref	ref
Overweight	0.62 (0.38, 1.01)	0.0538	0.60 (0.37, 1.01)	0.0502	1.18 (0.78, 1.80)	0.4347
Obese	3.62 (1.76, 7.45)	0.0005	3.90 (1.84, 8.31)	0.0004	3.01 (1.42, 6.39)	0.0041
1-year all-cause mortality
BMI classification
Underweight	1.60 (0.92, 2.80)	0.0983	1.63 (0.92, 2.92)	0.0957	1.96 (1.09, 3.52)	0.0239
Normal weight	ref	ref	ref
Overweight	0.67 (0.45, 1.00)	0.0480	0.71 (0.47, 1.06)	0.0942	0.79 (0.56, 1.13)	0.2044
Obese	3.01 (1.49, 6.09)	0.0021	3.27 (1.58, 6.78)	0.0014	2.84 (1.38, 5.82)	0.0044

Model 1: no covariates were adjusted.

Model 2 adjusted for age, sex, and ethnicity.

Model 3 adjusted for age, sex, ethnicity, SBP, heart rate, SPO_2, CHD, AF, DM, AKI, CHF, APACHE III, and SOFA.

BMI, body mass index; HR: hazard ratio; CI: confidence interval; ref: reference; SBP, systolic blood pressure; SPO₂, percutaneous oxygen saturation; CHD, coronary heart disease; CHF, chronic heart failure; AF, atrial fibrillation; DM, diabetes mellitus; SOFA, Sequential Organ Failure Assessment; APACHE, Acute Physiology and Chronic Health Evaluation; AKI, acute kidney injury.

To demonstrate nonlinearity in BMI and 30-day mortality, we performed smooth curve fitting (penalized spline method). After adjusting for age, sex, ethnicity, SBP, heart rate, SPO_2, CHD, AF, DM, AKI, CHF, and APACHE III and SOFA scores, nonlinear relationships were observed (Figure 2).

Figure 2.

Fitting curve between BMI and log RR for 30-day mortality.

Discussion

In this observational database study, we found that obesity was associated with increased risk of short- and long-term all-cause mortality in patients with ARDS, compared with their normal weight counterparts. However, there was no significant difference in odds of mortality among patients with ARDS who were underweight, overweight, and normal weight. Moreover, the relationship between BMI and ARDS mortality was nonlinear.

The results of epidemiological studies showing the impact of obesity on outcomes in patients with ARDS remain controversial¹⁶,²⁸,²⁹ In 1999, Fleischmann et al.³⁰ first reported that increased BMI reduced mortality in patients with end-stage renal disease. This connection was also observed in CHF, CHD, chronic obstructive pulmonary disease, and critical illness.^31–34 The phenomenon was called the “obesity paradox”. In a meta-analysis,¹⁶ it was suggested that ARDS morbidity was higher for patients with obesity than those with normal weight, but obesity was significantly associated with a reduction in ARDS mortality.¹⁵,¹⁶ However, these studies did not adjust for cardiovascular disease or AKI. Obese patients are more likely to have comorbidities, cardiovascular disease, respiratory illness, and AKI. Our study used detailed clinical, laboratory, and physiologic data to strengthen our findings. We adjusted for multiple factors such as cardiovascular risk and severity of disease, and we applied curve fitting to the relationship between BMI and ARDS prognosis; a U-shaped relationship was observed. According to two-stage analysis, the cutoff point of BMI was 31.5 kg/m². Although the lower mortality rate in the overweight BMI range was surprising, this finding had been demonstrated in the general population as well for all-cause mortality.³⁵

The incidence of ARDS is higher in obese individuals; however, the mortality rate of ARDS is lower in obese patients than in normal-weight patients. The underlying pathophysiologic mechanisms are still undefined; possible explanations are as follows. First, the inflammatory status of obese patients with ARDS may be attenuated or altered. ARDS is characterized by an overreaction to inflammation in the lungs.³⁶ Obese patients with respiratory failure are reported to have lower plasma levels of proinflammatory cytokines than normal-weight patients.¹³ Moreover, Weisberg et al.³⁷ showed that the increased adipocyte volume in healthy obese people can lead to the accumulation of M1 subsets of macrophages (proinflammatory properties). Because macrophages are known to switch between M1/M2 phenotypes during critical illness,³⁸ protection against severe disease in obese patients may depend on switching to predominantly M2 (anti-inflammatory properties) activation in the already large number of macrophages. Second, lipids and lipoproteins in adipose tissue can bind potentially toxic metabolites. The additional antioxidant stores in obese patients may help them during the initial catabolic phase, thus contributing to survival.³⁹ Third, previous studies have suggested that the likelihood of requiring mechanical ventilation in obese patients is higher than in patients with low or normal weights. The application of mechanical ventilation may reduce the likelihood of respiratory muscle weakness, which partly explains the relative decrease in mortality among obese patients with ARDS. Another theory is that obesity leads to a protective preconditioning response that may induce a low-grade inflammatory state. In this process, the lungs may be protected from a more aggressive secondary attack, such as via ventilator-induced lung injury or sepsis.⁴⁰ Further research is needed to clarify the mechanisms underlying the relatively low mortality rate in obese patients with ARDS.

Several study limitations should be acknowledged. First, this was a retrospective cohort study; we cannot prove a causal relationship between mortality and BMI. Second, as with any cohort study, we aimed to adjust for possible risk factors, such as cardiovascular diseases and comorbidities; however, residual confounders cannot be completely ruled out, including proinflammatory factors, marital status, and other known or unknown confounders. Third, BMI values used were only those within the first 24 hours of admission; however, the relationship between subsequent changes in BMI and prognosis was not evaluated. BMI on admission may not be a good index of obesity owing to many factors. For example, positive fluid balance during resuscitation may falsely increase body weight. Using only baseline data increased the risk of misclassification bias.

Conclusions

In this retrospective single-center cohort, obesity was found to be associated with increased risk of short- and long-term all-cause mortality in patients with ARDS. After adjusting for multiple factors, the increase in risk declined but remained statistically significant. However, there was no significant difference in the odds of death among patients with ARDS who were underweight, overweight, and normal weight. The relationship between BMI and all-cause mortality in patients with ARDS was nonlinear.

Footnotes

Declaration of conflicting interest

The authors declare that there is no conflict of interest.

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

ORCID iD

Ren-Lai Zhu

References

Dyck

Zylak

CJ.

Acute respiratory distress in adults.

Radiology 1973; 106: 497–501.

Garber

Hébert

Yelle

, et al. Adult respiratory distress syndrome: a systemic overview of incidence and risk factors. Crit Care Med 1996; 24: 687–695.

Bellani

Laffey

Pham

, et al. Epidemiology, Patterns of Care, and Mortality for Patients With Acute Respiratory Distress Syndrome in Intensive Care Units in 50 Countries. JAMA 2016; 315: 788–800.

Wang

Gong

Ying

, et al. Relation between Red Cell Distribution Width and Mortality in Critically Ill Patients with Acute Respiratory Distress Syndrome. Biomed Res Int 2019; 2019: 1942078.

Zhang

Prediction model for critically ill patients with acute respiratory distress syndrome. PLoS One 2015; 10: e0120641.

Dehal

Garrett

Tedders

, et al. Body mass index and death rate of colorectal cancer among a national cohort of U.S. adults. Nutr Cancer 2011; 63: 1218–1225.

Lohmann

Goodwin

Chlebowski

, et al. Association of Obesity-Related Metabolic Disruptions With Cancer Risk and Outcome. J Clin Oncol 2016; 34: 4249–4255.

Sharma

Lavie

Borer

, et al. Meta-analysis of the relation of body mass index to all-cause and cardiovascular mortality and hospitalization in patients with chronic heart failure. Am J Cardiol 2015; 115: 1428–1434.

Hogue

Jr Stearns

Colantuoni

, et al. The impact of obesity on outcomes after critical illness: a meta-analysis. Intensive Care Med 2009; 35: 1152–1170.

10.

Niedziela

Hudzik

Niedziela

, et al. The obesity paradox in acute coronary syndrome: a meta-analysis. Eur J Epidemiol 2014; 29: 801–812.

11.

O'Brien

Jr Phillips

Ali

, et al . Body mass index is independently associated with hospital mortality in mechanically ventilated adults with acute lung injury. Crit Care Med 2006; 34: 738–744.

12.

Gong

Bajwa

Thompson

, et al. Body mass index is associated with the development of acute respiratory distress syndrome. Thorax 2010; 65: 44–50.

13.

Stapleton

Dixon

Parsons

, et al. The association between BMI and plasma cytokine levels in patients with acute lung injury. Chest 2010; 138: 568–577.

14.

Soto

Frank

Christiani

, et al. Body mass index and acute kidney injury in the acute respiratory distress syndrome. Crit Care Med 2012; 40: 2601–2608.

15.

Luo

, et al. Can body mass index predict clinical outcomes for patients with acute lung injury/acute respiratory distress syndrome? A meta-analysis. Crit Care 2017; 21: 36.

16.

Zhi

Xin

Ying

, et al. “Obesity Paradox” in Acute Respiratory Distress Syndrome: Asystematic Review and Meta-Analysis. PLoS One 2016; 11: e0163677.

17.

Karampela

Chrysanthopoulou

Christodoulatos

, et al. Is There an Obesity Paradox in Critical Illness? Epidemiologic and Metabolic Considerations. Curr Obes Rep 2020; 9: 231–244.

18.

Johnson

Pollard

Shen

, et al. MIMIC-III, a freely accessible critical care database. Sci Data 2016; 3: 160035.

19.

Wang

Gong

Ying

, et al. Association of Initial Serum Total Calcium Concentration with Mortality in Critical Illness. Biomed Res Int 2018; 2018: 7648506.

20.

Artigas

Bernard

Carlet

, et al. The American-European Consensus Conference on ARDS, part 2: Ventilatory, pharmacologic, supportive therapy, study design strategies, and issues related to recovery and remodeling. Acute respiratory distress syndrome. Am J Respir Crit Care Med 1998; 157: 1332–1347.

21.

Bernard

Artigas

Brigham

, et al. The American-European Consensus Conference on ARDS. Definitions, mechanisms, relevant outcomes, and clinical trial coordination. Am J Respir Crit Care Med 1994; 149: 818–824.

22.

Vincent

Moreno

Takala

, et al. The SOFA (Sepsis-related Organ Failure Assessment) score to describe organ dysfunction/failure. On behalf of the Working Group on Sepsis-Related Problems of the European Society of Intensive Care Medicine. Intensive Care Med 1996; 22: 707–710.

23.

Knaus

Wagner

Draper

, et al. The APACHE III prognostic system. Risk prediction of hospital mortality for critically ill hospitalized adults. Chest 1991; 100: 1619–1636.

24.

Johnson

Stone

Celi

, et al. The MIMIC Code Repository: enabling reproducibility in critical care research. J Am Med Inform Assoc 2018; 25: 32–39.

25.

World Health Organization. A report about health. Retrieved from https://www.who.int/westernpacific/health-topics/obesity

26.

Sun

Que

Peng

, et al. The neutrophil-lymphocyte ratio: A promising predictor of mortality in coronary care unit patients - A cohort study. Int Immunopharmacol 2019; 74: 105692.

27.

Jaddoe

De Jonge

Hofman

, et al. First trimester fetal growth restriction and cardiovascular risk factors in school age children: population based cohort study. BMJ 2014; 348: g14.

28.

Maia

Cruz

De Oliveira

, et al. Effects of Obesity on Pulmonary Inflammation and Remodeling in Experimental Moderate Acute Lung Injury. Front Immunol 2019; 10: 1215.

29.

De Jong

Verzilli

Jaber

ARDS in Obese Patients: Specificities and Management.

Crit Care 2019; 23: 74.

30.

Fleischmann

Teal

Dudley

, et al. Influence of excess weight on mortality and hospital stay in 1346 hemodialysis patients. Kidney Int 1999; 55: 1560–1567.

31.

, et al. Increased body mass index linked to greater short- and long-term survival in sepsis patients: A retrospective analysis of a large clinical database. Int J Infect Dis 2019; 87: 109–116.

32.

Pepper

Demirkale

Sun

, et al. Does Obesity Protect Against Death in Sepsis? A Retrospective Cohort Study of 55,038 Adult Patients. Crit Care Med 2019; 47: 643–650.

33.

Yatabe

Yamashita

Yokoyama

Lower body mass index is associated with hospital mortality in critically ill Japanese patients. Asia Pac J Clin Nutr 2016; 25: 534–537.

34.

Newell

Bard

Goettler

, et al. Body mass index and outcomes in critically injured blunt trauma patients: weighing the impact. J Am Coll Surg 2007; 204: 1056–1061; discussion 1062-1054.

35.

Bhaskaran

Dos-Santos-Silva

Leon

, et al. Association of BMI with overall and cause-specific mortality: a population-based cohort study of 3·6 million adults in the UK. Lancet Diabetes Endocrinol 2018; 6: 944–953.

36.

Matthay

Ware

Zimmerman

GA.

The acute respiratory distress syndrome.

J Clin Invest 2012; 122: 2731–2740.

37.

Weisberg

McCann

Desai

, et al. Obesity is associated with macrophage accumulation in adipose tissue. J Clin Invest 2003; 112: 1796–1808.

38.

Porcheray

Viaud

Rimaniol

, et al. Macrophage activation switching: an asset for the resolution of inflammation. Clin Exp Immunol 2005; 142: 481–489.

39.

Langouche

Perre

Thiessen

, et al. Alterations in adipose tissue during critical illness: An adaptive and protective response? Am J Respir Crit Care Med 2010; 182: 507–516.

40.

Fernandez-Bustamante

Repine

JE.

Adipose-lung cell crosstalk in the obesity-ARDS paradox. J Pulm Respir Med 2013; 3: 144.

Impact of obesity on outcomes in patients with acute respiratory syndrome

Abstract

Objectives

Methods

Results

Conclusions

Keywords

Introduction

Methods

Study population

Data extraction and management

Outcomes

Statistical analyses

Results

Participant characteristics

Association between BMI and outcome of ARDS

Discussion

Conclusions

Footnotes

Declaration of conflicting interest

Funding

ORCID iD

References