Relationship between ultrasound bone parameters, lung function, and body mass index in healthy student population

The effect of metabolic health and obesity on lung function: A cross sectional study of 114,143 participants from Kangbuk Samsung Health Study

Objective

Although the role of obesity-induced metabolic abnormalities in impaired lung function is well-established, the risk of impaired lung function among obese individuals without metabolic abnormalities, referred to metabolically-healthy obesity (MHO), is largely unexplored. Therefore, we evaluated the impact of MHO on lung function in a large health-screening cohort.

Methods

114,143 subjects (65,342 men, mean age and BMI: 39.6 years and 23.6) with health examinations in 2019 were divided into four groups as follows: metabolically healthy non-obese (MHNO), MHO, metabolically unhealthy non-obese (MUHNO), and metabolically unhealthy obese (MUHO). Metabolic health was defined as fewer than two metabolic syndrome components. Obesity was defined as BMI ≥25 kg/m². Adjusted odds ratios (aORs), using MHNO as a reference, were calculated to determine lung function impairment.

Results

Approximately one-third (30.6%) of the study subjects were obese. The prevalence of MHO was 15.1%. Subjects with MHO had the highest FEV1% and FVC% values but the lowest FEV1/FVC ratio (p<0.001). These results persisted after controlling for covariates. Compared with MHNO, the aORs (95% confidence interval) for FEV1% < 80% in MHO, MUHNO and MUHO were 0.871 (0.775–0.978), 1.274 (1.114–1.456), and 1.176 (1.102–1.366), respectively (P for trend = 0.014). Similarly, the aORs in MHO, MUHNO, and MUHO were 0.704 (0.615–0.805), 1.241 (1.075–1.432), and 1.226 (1.043–1.441), respectively, for FVC% < 80% (p for trend = 0.013). However, the aORs for FEV1/FVC<0.7 were not significantly different between groups (p for trend = 0.173).

Conclusions

The MHO group had better lung function than other groups. However, longitudinal follow-up studies are required to validate our findings.

Mortality Beyond the First Year After Spinal Cord Injury: Does Body Mass Index Matter?

OBJECTIVE

To examine the association between body mass index (BMI [calculated as weight in kilograms divided by height in meters squared]) and mortality after the first year post spinal cord injury (SCI) overall and across demographic and injury characteristics.

DESIGN

Cohort study.

SETTING

Sixteen Spinal Cord Injury Model Systems (SCIMS) centers.

PARTICIPANTS

SCIMS Database participants age 20 years or older and having a BMI assessment during the 2007-2011 wave of data collection.

INTERVENTIONS

Not applicable.

MAIN OUTCOME MEASURES

All-cause mortality rate. Life table method and log-rank test were used to estimate and compare mortality rates across BMI groups and other factors. Cox proportional hazard regression model was conducted to estimate hazard ratio (HR) and 95% confidence interval (CI).

RESULTS

A total of 2346 participants (N=2346) with SCI were classified into 1 of the 8 BMI groups: <18.5 (6.9%), 18.5-19.9 (7.3%), 20.0-22.49 (15.0%), 22.5-24.9 (18.8%), 25.0-27.49 (17.5%), 27.5-29.9 (13.2%), 30.0-34.9 (13.5%), and ≥35.0 (7.8%). Compared with people with BMI of 22.5-29.9, a higher mortality risk was observed among people with BMI<18.5 (HR, 1.76; 95% CI, 1.25-2.49), 18.5-19.9 (HR, 1.51; 95% CI, 1.06-2.15), and ≥35.0 (HR, 1.51; 95% CI, 1.11-2.07) after adjusting for confounding factors (sex, age at the time of BMI assessment, marital status, neurologic status). The U-shape BMI-mortality relationship varied by age, sex, neurologic status, and years since injury.

CONCLUSIONS

To improve life expectancy after SCI, health care professionals could focus on weight management among patients with relatively low and extremely high BMI, defined by demographic and injury-related characteristics. Future studies should explore factors that contribute to such a higher mortality after SCI, including pre-existing conditions, poor diet and/or nutrition, and cardiorespiratory fitness.

Association Between Youth Smoking, Electronic Cigarette Use, and COVID-19

PURPOSE

This study aimed to assess whether youth cigarette and electronic cigarette (e-cigarette) use are associated with coronavirus disease 2019 (COVID-19) symptoms, testing, and diagnosis.

METHODS

An online national survey of adolescents and young adults (n = 4,351) aged 13-24 years was conducted in May 2020. Multivariable logistic regression assessed relationships among COVID-19-related symptoms, testing, and diagnosis and cigarettes only, e-cigarettes only and dual use, sociodemographic factors, obesity, and complying with shelter-in-place.

RESULTS

COVID-19 diagnosis was five times more likely among ever-users of e-cigarettes only (95% confidence interval [CI]: 1.82-13.96), seven times more likely among ever-dual-users (95% CI: 1.98-24.55), and 6.8 times more likely among past 30-day dual-users (95% CI: 2.40-19.55). Testing was nine times more likely among past 30-day dual-users (95% CI: 5.43-15.47) and 2.6 times more likely among past 30-day e-cigarette only users (95% CI: 1.33-4.87). Symptoms were 4.7 times more likely among past 30-day dual-users (95% CI: 3.07-7.16).

CONCLUSIONS

COVID-19 is associated with youth use of e-cigarettes only and dual use of e-cigarettes and cigarettes, suggesting the need for screening and education.

Association between underweight and pulmonary function in 282,135 healthy adults: A cross-sectional study in Korean population

In contrast to obesity, studies on the relationship between underweight and pulmonary function are still sparse. Thus, the objective of this study was to investigate the effect of being underweight on pulmonary function in a general population without apparent lung disease. A total of 282,135 retrospective cohort subjects between January 2012 and December 2014 in Korea were included. Using multivariate-adjusted analysis, the relationship between body mass index (BMI) and pulmonary function were assessed. Underweight individuals represented 5.5% of the total study population (n = 282,135), with most (87.9%) of them being females. Compare to normal weight and obese, underweight was associated with decreased pulmonary function. Forced expiratory volume in first second (FEV1), predicted FEV1 (%), forced vital capacity (FVC), predicted FVC (%), and peak expiratory flow (PEF) were lower in the underweight group than those in other groups after adjusting for age, sex, height, status of smoking, frequency of vigorous exercise, diabetes, and high-sensitivity C-reactive protein (hsCRP) (P < 0.001). Lower BMI tended to decrease pulmonary function parameters such as FEV1 (L), predicted FEV1 (%), FVC (L), predicted FVC (%), and PEF (L/sec) (P for trend <0.001). After adjusting for possible confounders, odds ratios (ORs; 95% confidence interval) for subjects with predicted FEV1% < 80% in underweight and normal weight groups compared to obese group (reference) were 2.10 (1.98–2.21), and 0.93 (0.90–0.97), respectively. ORs for subjects with predicted FVC% < 80% in underweight and normal weight groups compared to obese group (reference) were 4.90 (4.62–5.18) and 1.32 (1.27–1.38), respectively. This study demonstrated a proportional relationship between pulmonary function and the degree of BMI. We found that underweight status was independently associated with decreased pulmonary function in Korean population.

Inactivation of Pseudomonas aeruginosa biofilms formed under high shear stress on various hydrophilic and hydrophobic surfaces by a continuous flow of ozonated water

The inactivation of Pseudomonas aeruginosa biofilms grown on glass under high shear stress and exposed to a range of dissolved ozone concentrations (2, 5 and 7 ppm) at 10 and 20 min was investigated. The regression equation, log reduction (biofilm) = 0.64 + 0.59×(C - 2) + 0.33×(T - 10), described the dependence of biofilm inactivation on the dissolved ozone concentration (C, ppm) and contact time (T, min). The predicted D-values were 11.1, 5.7 and 2.2 min at 2, 5 and 7 ppm, respectively. Inactivation of biofilms grown on various surfaces was tested at a single dissolved ozone concentration of 5 ppm and a single exposure time of 20 min. Biofilms grown on plastic materials showed inactivation results similar to that of biofilms on glass, while biofilms grown on ceramics were statistically significantly more difficult to inactivate, suggesting the importance of utilizing non-porous materials in industrial and clinical settings.