BMI, sex and outcome in patients admitted to western Sweden during the covid-19 pandemic
Our database made it possible to capture all cases from the population in the VGR region that were admitted for emergency care with a laboratory-confirmed diagnosis of COVID-19. In total, we were able to extract information on BMI in 75% of the patients included in the present analysis. In general, baseline findings (Table 1) in hospitalized patients were in good agreement with previous observations under similar conditions.6.12. Thus, the majority of patients were old (> 65 years), more often men, overweight and with a high incidence of cardiometabolic comorbidities.
Obese patients were much younger than those who were normal or underweight. In the whole cohort, more than half (56%) of overweight patients (BMI ≥ 30) were under 65 years of age, while most (57%) of underweight patients (BMI <18.5) were 65 years or older, which probably reflects general weakness in the elderly with severe covid-19. Other comorbidities and risk factors seemed balanced between the groups, in addition to dyslipidemia and diabetes which were more common in the obese group. Vital signs during the first 24 hours were mostly within normal limits, except for increased respiratory rate and impaired oxygen saturation, which were most common in the obese subcohort. Among vital signs, blood pressure and heart rate did not vary between the different BMI classes, which can be accurately interpreted as an indication of comparable treatment regimens across BMI classes for cardiovascular risk factors and similarities in overall cardiovascular health status. However, the phenotype of diabetes, differences in detailed treatment regimens or disease duration cannot be understood in terms of blood pressure and heart rate, and interpretations of similarities must be made with caution. Although characteristics of included patients were consistent with other observations of hospital populations, the distribution with respect to age, BMI, and underlying disease also reflects the composition of the background population. This will also be affected by factors such as society's health, health care, the spread of infection in society and the age distribution in the population. The exact composition of hospitalized patients will therefore vary between countries.
In the whole cohort, only a minority of patients were assigned IC, with higher mortality (18%) in IC compared with RC (14%). However, the majority of deaths (84.1%) occurred in RC, which probably reflects a population that could not tolerate HFOT or other IC measures. The skewed distribution of BMI in relation to age was likely to covariate with BMI and age-related outcomes. Overall, the results were in good agreement with the expected results of increased mortality risk associated with older age, male gender and cardiometabolic comorbidities, confirming a striking excess risk associated with age13,15,16,20. The explanations are partly increased fragility and comorbidities, although the very sharp increase in risk with older age is not fully reported. Similarly, the increased risk of death among men has previously been observed but not definitively explained13,15,16,20.
Note that BMI was not associated with death, an issue previously addressed with contradictory results4,8,9,10,11,21,22. The relative risk of IC assignment was inversely associated with age, with a progressively smaller proportion of patients being assigned IC with increasing age. This is in line with standard considerations as comorbidities and weakness increase with age and limit the potential benefits of IC and the individual’s ability to withstand the requirements of HFOT or IC.
Our data showed significantly increased odds of IC among patients with a BMI ≥ 30. There also appeared to be an interaction with sex for women. IC at COVID-19 was generally indicated due to respiratory failure, presented as strenuous breathing work (increased respiratory rate) or hospital hypoxia. Circulatory failure was rare in our study, and COVID-19 is primarily a respiratory disease. The explanation for the increased proportion of obese patients admitted to IC is probably multifactorial. However, obesity and obesity limit the ability to breathe with several mechanisms that can be emphasized: First, altered mechanical properties of the chest wall and lungs due to fat deposits in the mediastinum and abdominal cavities will alter the normal breathing pattern, and increase respiratory work.23. The compliance of the lungs and chest wall is affected, and dormant lung volumes are reduced by overweight and obesity23.24. Effects on airway tone and weakness will also lead to increased airway closure, increased airway reactivity and lack of uniform ventilation. Obesity is also associated with increased hormonal activity and inflammation, through circulating adipokines and cytokines, directly linked to airway inflammation, hypersensitivity and obstruction, and cell damage that further compromises respiratory function. The adipogenic hormone leptin, which increases in obesity, can affect respiratory function and airway obstruction. All of the above may contribute to increased symptoms, respiratory failure and more severe course of covid-19 in obese people23,24,25,26.
The relative distribution of a higher percentage of obesity in younger patients in covid-19 hospitals has been observed previously27. Previous studies have confirmed that weight gain and the acquisition of obesity earlier in life have a generally more serious prognosis than the development of obesity later in life.28.29 which is thought to be associated with accelerated development of cardiovascular and metabolic complications30. This is supported by the fact that high blood pressure, hyperlipidemia and diabetes are more common in the obese subcohort.4,21,23,25.
The over-risk of obesity in relation to the need for IC was observed only in women; in total, however, one third of all patients were overweight, which is a higher proportion than in the background population. Previous studies of BMI, obesity and lung function have found a negative association between BMI and lung function, more obvious in women than in men31. In addition, symptomatic shortness of breath and impaired lung function are more common in obese patients in women32. On the other hand, men are at a significantly higher risk of developing severe covid-19 and thus constitute a numerically dominant part of the patients on IC units.17. However, for influenza, although men are more susceptible to infection, women have more pronounced immune responses once infected, and animal experiments also support a greater inflammatory response, impaired repair of lung tissue, and hormonal triggers of pneumonia in the female sex.33. We do not know if this is also true for other viral lung infections, such as SARS-CoV-2, in women, but our findings on higher odds of IC treatment for women are consistent with such a mechanism. Finally, a pattern of body fat distribution of central fat concentration (android obesity) is related to more harmful changes in lung function compared to gynoid obesity (lower body distribution)27. However, the obesity pattern of patients in our study is not known, and the contribution of such a mechanism is still speculative. Nevertheless, the combination of obesity and female gender appears to carry specific respiratory vulnerability requiring IC in the case of COVID-19 infection.
Strengths and limitations
A strength of this study is the comprehensive coverage of inpatients with covid-19, which includes full availability for all patients in hospitals due to a laboratory-confirmed diagnosis of covid-19 from a complete region in western Sweden. The availability of comprehensive clinical data retrieved from EHR with near real-time availability also ensured a detailed data set. In addition, we believe that access to all patients treated with IC measures, even outside traditional IC units, in the temporary intermediate units during the pandemic, provides a more comprehensive representation of the hospitalized population with covid-19. Finally, as a result of the link between test data and hospital records, our data set included only patients with a laboratory-confirmed diagnosis of COVID-19. But our study also has some weaknesses. For example, BMI and risk factor diagnoses in the background population are not known, and we lack previous outpatient data from primary care provided by the private sector (approximately 40% of primary care). Measurement of individual contacts with public care for all citizens (under 2 years of hospital and specialized care) provides an estimate of 70% coverage of the population before the current hospital stay. We were able to retrieve BMI data from a total of 75% of the patients. It is likely that the mere presence of a BMI record is associated with a more vulnerable population with health problems related to either abnormally high or low BMI at baseline, which may represent a bias in the study. In addition, of great interest in the results of covid-19, we had no available measurements of weakness in the cohort, and no data on immigrant status or ethnicity. In addition, obstructive sleep apnea (OSA) is a major risk factor associated with BMI, cardiovascular disease and hypoventilation that have been linked to severe covid-19. However, it did not appear among the most common comorbidities obtained from EPR and was therefore not included in our study.
In addition, the limitations of BMI need to be addressed: Its shortcomings include inability to determine body composition, fat mass and allocation of body fat, which risks overestimating obesity in individuals with high muscle mass. Despite its shortcomings, BMI is still considered the best predictor of unhealthy weight and the most common anthropometric measure as a proxy for overweight and obesity, recommended by the WHO34. Its benefits include readiness and well-documented link to cardiovascular disease34.35. The results of hospital admissions and treatment will reflect the composition of the background population (median age, comorbidities, nutritional status and prevalence of obesity) and the organization of health care. In addition, in the case of a contagious disease, the spread of infection in the community and the speed of application of vaccination programs will affect the outcome. Therefore, our results have limited use for comparisons between different health care systems and general organization. Finally, specific medical treatment for covid-19 is not included in the present analyzes, as the focus is on conditions before, or early during hospitalization, to be related to decisions about level of care.