Body mass index, ventilator parameters, and mortality in acute respiratory distress syndrome: re-examining the obesity paradox

Introduction

Obesity has become a significant public health concern worldwide, affecting an estimated 13% of adults globally and up to 40% in high-income countries (1). While obesity is closely linked to numerous chronic conditions such as cardiovascular disease, diabetes mellitus, and metabolic syndrome, it has demonstrated paradoxical associations with mortality in specific acute conditions (2,3). For instance, in critically ill patients with sepsis and septic shock, higher body mass index (BMI) has been reported to correlate with lower or similar mortality rates compared to those with lower BMI (4,5). This phenomenon is often referred to as the “obesity paradox”.

Acute respiratory distress syndrome (ARDS) is a severe form of hypoxemic respiratory failure characterized by an intense inflammatory process in the lungs, necessitating mechanical ventilation (MV) for life support (6). Several studies have noted an “obesity paradox” in ARDS as well, observing lower or comparable mortality rates in obese patients relative to normal-weight or underweight individuals (7,8). However, the underlying mechanisms remain incompletely understood.

One potentially important aspect involves the unique respiratory physiology in obesity. Obese patients often exhibit decreased chest wall compliance, reduced functional residual capacity, and elevated intra-abdominal pressures, all of which can influence alveolar recruitment (9). These factors may impact key ventilator settings, including positive end-expiratory pressure (PEEP) and driving pressure. In non-obese ARDS patients, driving pressure has been identified as a significant predictor of mortality (10). Yet, whether this relationship holds for obese ARDS patients is controversial because elevated plateau or peak pressures in obesity can partly reflect decreased chest wall compliance rather than intrinsic lung overdistension (11). Thus, mechanically measured driving pressure may overestimate actual transpulmonary pressure in obese individuals.

This study aimed to (I) examine whether obesity is associated with lower or higher hospital mortality in ARDS, (II) elucidate how ventilatory parameters (especially PEEP and driving pressure) differ by BMI, and (III) explore whether these parameters play a differential prognostic role in obese compared to non-obese ARDS patients. We present this article in accordance with the STROBE reporting checklist (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1600/rc).

Methods

Study population

This retrospective cohort study was conducted at a tertiary referral hospital in Seoul, Korea. Adult patients (≥18 years) who fulfilled the Berlin definition of ARDS (6) and received MV for ≥48 hours between March 1, 2018, and February 28, 2021, were screened. Exclusion criteria were: (I) patients who received MV more than 48 hours after intensive care unit (ICU) admission; (II) those requiring extracorporeal membrane oxygenation; and (III) those lacking data to calculate BMI. Because patients receiving MV late during the ICU course may represent distinct etiologies or time courses of lung injury, we excluded these individuals to maintain a more homogeneous ARDS population. All clinical data were obtained from the Clinical Data Warehouse of Samsung Medical Center (DARWIN-C). This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The Institutional Review Board of Samsung Medical Center approved this study (No. 2022-12-016) and waived the requirement for informed consent due to the retrospective, observational nature of the study. Patient information was anonymized and de-identified before analysis.

BMI categories and data collection

We categorized patients into four groups according to the National Institutes of Health (NIH) guidelines: underweight (<18.5 kg/m²), normal weight (18.5 to <25 kg/m²), overweight (25 to <30 kg/m²), and obese (≥30 kg/m²) (12,13). Demographic and clinical data were extracted, including age, sex, comorbidities, initial sequential organ failure assessment (SOFA) score at ICU admission, and outcome variables such as ICU and hospital mortality. For MV parameters, values were recorded hourly from initiation up to 12 hours, and we used median values for analyses to account for early variability in ventilator settings. During the first 12 hours after the initiation of MV, ARDS patients received a deeper level of sedation [target Richmond Agitation-Sedation Scale (RASS) of −2 to −3] to minimize spontaneous inspiratory efforts. Neuromuscular blocking agents were used at the discretion of the attending physician in cases of severe hypoxemia or significant ventilator dyssynchrony. After this initial period, sedation was gradually lightened in accordance with our ICU protocol, which involves daily sedation interruption and spontaneous breathing trials to facilitate liberation from the ventilator. These weaning strategies, however, were not applied during the first 12 hours, when ventilator parameters were collected and mechanical power (MP) was calculated. MP was estimated with Becher’s simplified formula for pressure-controlled ventilation (PCV): MP_PCV = 0.098 ∙ RR ∙ Vt ∙ (ΔPinsp + PEEP) (14). We selected this formula instead of the van der Meijden equation because it requires fewer parameters, which are readily available on standard ICU ventilator monitors, thereby allowing rapid bedside calculation. Moreover, as our institution commonly applies PCV, the Becher formula has demonstrated good applicability in PCV-based protocols in previous studies. In this formula, 0.098 is a conversion factor for Joules per minute, RR is the respiratory rate (breaths/min), Vt is the tidal volume (L), and ΔPinsp is the change in airway pressure during inspiration. Additionally, dynamic lung-thorax compliance (LTCdyn) is the ratio of tidal volumes to driving pressure (mL/cmH₂O), providing an index of lung and thorax elasticity (15).

Outcome

The primary outcome was hospital mortality, defined as death during the index hospital stay. Secondary outcomes were ICU mortality, ventilator-free days at day 28, success in weaning from MV, and ICU/hospital lengths of stay.

Statistical analysis

Continuous variables were expressed as median [interquartile range (IQR)] and compared using the Mann-Whitney U test or Kruskal-Wallis test, as appropriate. Categorical variables were analyzed using Chi-squared or Fisher’s exact test. Variables yielding P<0.05 in univariate analysis, along with certain clinically significant a priori variables, were entered into a multiple logistic regression model to determine independent predictors of hospital mortality. Because patients with hematologic malignancies and solid tumors are known to have intrinsically higher risks of hospital mortality, we conducted a sensitivity analysis to assess whether our primary findings were driven by the inclusion of these patients. For this analysis, we repeated the multivariable logistic regression after excluding patients with hematologic malignancies and solid tumors, using the same covariates as in the primary analysis. We report odds ratios (ORs) with 95% confidence intervals (CIs). A two-tailed P<0.05 was considered statistically significant. Data were analyzed using R Statistical Software (Version 4.4.2; R Foundation for Statistical Computing, Vienna, Austria).

Results

Baseline clinical characteristics

A total of 595 patients were ultimately included. Of these, 84 (14.1%) were underweight, 371 (62.4%) had normal BMI, 109 (18.3%) were overweight, and 31 (5.2%) were obese (Figure 1). Table 1 presents the baseline characteristics stratified by BMI group. Underweight patients were the oldest (median age of 70 years), whereas obese patients were the youngest (median of 59 years). Although the proportion of females was highest in the obese group (51.6%), this difference did not reach significance (P=0.17). Chronic liver disease was more frequent in higher BMI groups (highest in the obese group, P<0.01). Chronic kidney disease was also most prevalent in the obese group. Notably, normal BMI individuals had a higher prevalence of hematologic malignancy and solid tumors compared to the other groups, potentially contributing to differences in overall severity and mortality. In addition, initial SOFA score was highest in the obese group [median (IQR), 13.0 (8.5–14.0), P=0.047].

Figure 1 Study flowchart. BMI, body mass index; ECMO, extracorporeal membrane oxygenation; ICU, intensive care unit; MV, mechanical ventilation.

Ventilatory variables and clinical outcomes

Ventilatory parameters and outcomes by BMI group are summarized in Table 2. The median PEEP increased with BMI category, from 5.0 cmH₂O in underweight patients to 7.0 cmH₂O in normal BMI patients and 8.0 cmH₂O in both overweight and obese patients (P=0.02). Obese patients had the highest median driving pressure [18.0 (13.5−20.0) cmH₂O] compared to underweight or normal BMI patients [15.0 (12.0−18.0) cmH₂O] (P=0.03). The incidence of tracheostomy was highest in the underweight group (39.3%) and lowest in the obese group (19.4%, P=0.02).

Regarding clinical outcomes, ICU mortality did not significantly differ among the BMI groups (P=0.41). The number of ventilator-free days at day 28 was comparable among groups (P=0.52), as was the weaning success rate (P=0.41). Hospital mortality also showed no significant difference across groups (P=0.56). However, the length of hospital stay was shortest in the obese group [13.0 (10.0−25.0) days] (P=0.03).

Distribution of hospital mortality by BMI and ventilator subgroups

Figure 2 illustrates hospital mortality stratified by BMI category and ventilator parameters (PEEP or driving pressure). In PEEP subgroups (5–7 vs. ≥8 cmH₂O), hospital mortality tended to be higher in the normal BMI group within the lower PEEP range, but this was not significant. A similar pattern emerged for driving pressure, where normal BMI patients in the low-driving pressure subgroup (<15 cmH₂O) had the highest mortality. Figure 3 illustrates a restricted cubic spline curve of the relationship between BMI and hospital mortality, indicating an inverted U-shaped pattern: mortality was higher within the normal BMI range, with somewhat lower mortality at both lower and higher BMI extremes.

Figure 2 Comparison of hospital mortality rates stratified by BMI groups and ventilatory parameters. (A) Hospital mortality was categorized by PEEP levels (5–7 vs. ≥8 cmH₂O) across BMI groups. (B) Hospital mortality was categorized by driving pressure levels across BMI groups. BMI, body mass index; PEEP, positive end-expiratory pressure; DP, driving pressure.

Figure 3 Restricted cubic spline curves depicting the association between hospital mortality and body mass index. CI, confidence interval.

Multivariable analyses

For the evaluation of clinical factors associated with hospital mortality, univariable analysis was performed (Table 3). Variables with P<0.05 and clinically relevant covariates were included in the multiple logistic regression analysis, comprising solid malignant tumors, hematologic malignancy, and lactate levels were associated with higher hospital mortality. Notably, neither obesity (BMI ≥30 kg/m²) nor driving pressure emerged as independent predictors of mortality after adjusting for confounding factors. A sensitivity analysis excluding patients with hematologic and solid malignancies showed no association between obesity groups and hospital mortality, consistent with the primary analysis (Table S1).

Discussion

In this single-center retrospective study of 595 ARDS patients, BMI had no significant independent association with hospital mortality despite the obese group having a higher severity of illness at baseline (SOFA) and higher measured ventilator pressures. Notably, normal BMI patients exhibited the highest mortality across various subgroup analyses, hinting at an inverted U-shaped mortality curve. However, these trends did not achieve statistical significance.

Our findings are consistent with previous reports suggesting that obesity in ARDS may not worsen outcomes and may be associated with comparable or lower mortality (7,8,16). Several factors have been proposed to explain the “obesity paradox” in patients with acute critical illness. First, obesity may confer more significant nutritional reserves, potentially mitigating the catabolic state in severe ARDS (17). Also, adipose tissue could modulate inflammatory responses differently, although this mechanism remains debated (18,19). Second, obese patients typically exhibit increased chest wall elastance, lower respiratory system compliance (20), reduced functional residual capacity, and an increased risk of airway closure leading to atelectasis formation (21). These physiological characteristics may cause obese patients to develop respiratory failure and require ICU admission at an earlier stage of their disease course, thereby presenting with a lower a-priori risk of mortality compared with non-obese patients. In addition, confounding factors, such as younger age or fewer oncologic comorbidities in obese patients, might play a role (22). In contrast, our cohort showed obese patients to have a higher prevalence of liver disease and chronic kidney disease, plus a higher median SOFA score, which potentially dilutes any “protective” effect of excess weight. In our study, normal BMI patients had higher proportions of hematologic malignancy and solid tumors. Previous studies suggest that prognosis may differ by BMI in patients with malignancies. Petrelli et al. reported that among patients with lung cancer, renal cell carcinoma, and melanoma, overweight or obese patients achieved better overall survival than normal-weight patients (23). Likewise, in adult AML, Medeiros et al. found that obese patients had higher rates of complete remission and reduced rates of resistant disease, although overall survival did not differ significantly (24). These findings suggest that the higher prevalence of malignancies in the normal BMI group may have contributed to the increased mortality observed in this group.

Driving pressure has emerged as an essential determinant of lung stress and mortality in ARDS (10,25,26). However, prior studies often pooled obese and non-obese patients, potentially overlooking the distinct respiratory mechanics of obesity (11,27). Obese patients have increased chest wall weight, diaphragmatic position, and intrathoracic pressures, which could inflate peak and plateau pressures without truly elevating transpulmonary pressure. As a result, the formula “driving pressure = plateau (or peak) – PEEP” may overestimate alveolar distending pressure in obesity. In this study, obese patients exhibited higher driving pressures but no corresponding increase in mortality. These findings are consistent with those reported by De Jong et al. (27), who similarly reported that driving pressure was less predictive of mortality in obese ARDS patients than in non-obese patients. Transpulmonary pressure measurements with esophageal manometry could be a more precise prospective investigation, allowing a more direct assessment of alveolar stress in obesity.

Obesity is a heterogeneous and multifaceted condition that cannot be fully captured by BMI alone. Critically ill patients with sarcopenia can coexist with obesity, which is called sarcopenic obesity (28). Other factors, such as malnutrition, and differences in body composition, may significantly influence outcomes, even among patients with normal BMI (29). Although methods such as computed tomography (CT)-derived assessment of muscle mass could provide a more precise evaluation (30), such data were not available in our retrospective cohort. Furthermore, conventional BMI cut-offs may not be appropriate for Asian populations, who generally have higher body fat and lower muscle mass at the same BMI compared with Western populations, thereby supporting the use of lower thresholds for defining obesity (13). Future studies incorporating more refined measures of body composition and nutritional status are warranted to better define the role of obesity phenotypes in ARDS outcomes. Another important factor that may influence outcomes in ARDS is the underlying etiology. Several studies have demonstrated that direct (pulmonary) and indirect (extrapulmonary) causes of ARDS differ in terms of gas exchange, systemic inflammation, and prognosis (31-33). Ruan et al. reported substantial etiology-associated heterogeneity, with differences in respiratory parameters, organ dysfunction, and biomarkers between direct and indirect ARDS (33). Luo et al. showed that direct and indirect ARDS differ not only in pathophysiology but also in predictors of mortality, with factors such as age and lung injury severity being more strongly associated with outcomes in direct ARDS (32). Because etiology was not systematically captured in our dataset, we could not stratify outcomes by direct versus indirect ARDS. This limitation should be considered when interpreting our results. Furthermore, prior studies during the coronavirus disease 2019 (COVID-19) pandemic have explored the relationship between obesity and ARDS outcomes. Daviet et al. found that obesity in patients with COVID-19 severe ARDS supported by ECMO was independently associated with improved 90-day survival, despite higher PEEP levels and respiratory system compliance (34). Telang et al. similarly reported that obesity was not an independent risk factor for increased hospital mortality among COVID-19 ARDS patients (35). Conversely, in patients with extreme obesity (BMI >40 kg/m²), Heubner et al. observed an increased risk of hospital mortality (36). Taken together, these findings suggest that while obesity may change ventilatory parameters (e.g., higher PEEP, altered compliance), such changes do not uniformly translate into worse outcomes, and that increased risk may emerge only in cases of extreme obesity or specific subgroups.

Our study has several limitations. First, it is a retrospective single-center study, which carries inherent risks of confounding and selection bias (e.g., excluding patients who started MV more than 48 hours after ICU admission). Second, the small sample size in the obese group (n=31) reduced statistical power, potentially obscuring significant differences. Moreover, while we applied the widely recognized NIH BMI categories, the definition of obesity in Asian populations remains debated; some guidelines suggest using lower BMI thresholds (≥25 kg/m²) to define obesity in Asians (37). Third, plateau pressure was not directly measured using an inspiratory hold maneuver. Nevertheless, in PCV with a sufficiently prolonged inspiratory time, the peak pressure likely approximated the plateau pressure. Fourth, peak inspiratory pressure was used rather than plateau pressure in driving pressure calculations; consequently, airway resistance and inspiratory flow may have inflated the measured driving pressures, especially in obese patients. Fifth, we did not collect detailed data on ARDS etiology, preventing us from distinguishing pulmonary from extrapulmonary causes. Our study period also overlapped with the COVID-19 pandemic, but COVID-19 status was not captured, which should be acknowledged as another limitation. Finally, as our population was predominantly Asian, the findings may not be fully generalizable to Western cohorts or other ethnicities with different fat distribution patterns.

Conclusions

In this single-center retrospective analysis of 595 ARDS patients, obesity was not statistically significantly associated with increased hospital mortality. Although obese patients had higher PEEP and driving pressure values than normal BMI patients, these ventilatory parameters did not lead to worse outcomes. Multivariable analysis confirmed that malignancies and elevated lactate were strong predictors of mortality, whereas obesity and driving pressure were not independent risk factors. These findings reinforce that BMI alone may be insufficient to stratify risk in ARDS, and further research incorporating body composition and obesity phenotypes is needed to refine risk assessment and ventilatory management in this population.

Acknowledgments

We would like to express our gratitude to the anonymous donor for contributing to the Medical Research Fund at Samsung Medical Center (SMO 1220811).

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1600/rc

Data Sharing Statement: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1600/dss

Peer Review File: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1600/prf

Funding: This research was supported by Chung-Ang University Research Grants in 2024 and the National Research Foundation of Korea (NRF) grant, funded by the Korean government (MIST) (RS-2023-00251935).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1600/coif). T.W.K. reports that this work was supported by a Chung-Ang University Research Grant (2024). R.E.K. reports that this work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (RS-2023-00251935). The other authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The Institutional Review Board of Samsung Medical Center approved this study (No. 2022-12-016) and waived the requirement for informed consent due to the retrospective, observational nature of the study.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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