Obesity, Inflammation, and Clinical Outcomes in COVID-19: A Multicenter Prospective Cohort Study

CONTEXT

Obesity is a risk factor for coronavirus disease 2019 (COVID-19)-related outcomes; however, the mechanism remains unclear.

OBJECTIVE

The objective of this analysis was to determine whether inflammation mediates the association between obesity and COVID-19 outcomes.

DESIGN

The International Study of Inflammation in Covid-19 (ISIC): A Prospective Multi-Center Observational Study Examining the Role of Biomarkers of Inflammation in Predicting Covid-19 Related Outcomes in Hospitalized Patients.

SETTING

Ten hospitals in the United States and Europe.

PARTICIPANTS

Adults hospitalized specifically for COVID-19 between February 1, 2020, through October 19, 2022.

MAIN OUTCOME MEASURES

Inflammatory biomarkers, including soluble urokinase plasminogen activator receptor (suPAR), were measured at admission. Associations were examined between body-mass index (BMI, kg/m2) and a composite of death, need for mechanical ventilation, and renal replacement therapy, stratified by pre- and post-Omicron variants. The contribution of inflammation to the relationship between obesity and outcomes was assessed.

RESULTS

Among 4644 participants (mean age 59.3, 45.6% male, 21.8% BMI≥35), those with BMI>40 (n=485) had 55% higher odds of the composite outcome (95% CI[1.21 to 1.98]) compared to non-obese individuals (BMI<30, n=2358) in multivariable analysis. In multiple mediation analysis, only suPAR remained a significant mediator between BMI and composite outcome. Associations were amplified for participants younger than 65 years and with pre-Omicron variants.

CONCLUSION

Obesity is associated with worse outcomes in COVID-19, notably in younger participants and in the pre-Omicron era. Inflammation, as measured by suPAR, is a significant mediator of the association between obesity and COVID-19 outcomes.

Cardiovascular Risk Stratification of Patients Undergoing Hematopoietic Stem Cell Transplantation: The CARE-BMT Risk Score

BACKGROUND

Evidence guiding the pre-hematopoietic stem cell transplantation (HSCT) cardiovascular evaluation is limited. We sought to derive and validate a pre-HSCT score for the cardiovascular risk stratification of HSCT candidates.

METHODS AND RESULTS

We leveraged the CARE-BMT (Cardiovascular Registry in Bone Marrow Transplantation) study, a contemporary multicenter observational study of adult patients who underwent autologous or allogeneic HSCT between 2008 and 2019 (N=2435; mean age at transplant of 55 years; 4.9% Black). We identified the subset of variables most predictive of post-HSCT cardiovascular events, defined as a composite of cardiovascular death, myocardial infarction, heart failure, stroke, atrial fibrillation or flutter, and sustained ventricular tachycardia. We then developed a point-based risk score using the hazard ratios obtained from Cox proportional hazards modeling. The score was externally validated in a separate cohort of 919 HSCT recipients (mean age at transplant 54 years; 20.4% Black). The risk score included age, transplant type, race, coronary artery disease, heart failure, peripheral artery disease, creatinine, triglycerides, and prior anthracycline dose. Risk scores were grouped as low-, intermediate-, and high-risk, with the 5-year cumulative incidence of cardiovascular events being 4.0%, 10.3%, and 22.4%, respectively. The area under the receiver operating curves for predicting cardiovascular events at 100 days, 5 and 10 years post-HSCT were 0.65 (95% CI, 0.59-0.70), 0.73 (95% CI, 0.69-0.76), and 0.76 (95% CI, 0.69-0.81), respectively. The model performed equally well in autologous and allogeneic recipients, as well as in the validation cohort.

CONCLUSIONS

The CARE-BMT risk score is easy to calculate and could help guide referrals of high-risk HSCT recipients to cardiovascular specialists before transplant and guide long-term monitoring.

Portable Breath-Based Volatile Organic Compound Monitoring for the Detection of COVID-19 During the Circulation of the SARS-CoV-2 Delta Variant and the Transition to the SARS-CoV-2 Omicron Variant

Importance

Breath analysis has been explored as a noninvasive means to detect COVID-19. However, the impact of emerging variants of SARS-CoV-2, such as Omicron, on the exhaled breath profile and diagnostic accuracy of breath analysis is unknown.

Objective

To evaluate the diagnostic accuracies of breath analysis on detecting patients with COVID-19 when the SARS-CoV-2 Delta and Omicron variants were most prevalent.

Design, Setting, and Participants

This diagnostic study included a cohort of patients who had positive and negative test results for COVID-19 using reverse transcriptase polymerase chain reaction between April 2021 and May 2022, which covers the period when the Delta variant was overtaken by Omicron as the major variant. Patients were enrolled through intensive care units and the emergency department at the University of Michigan Health System. Patient breath was analyzed with portable gas chromatography.

Main Outcomes and Measures

Different sets of VOC biomarkers were identified that distinguished between COVID-19 (SARS-CoV-2 Delta and Omicron variants) and non-COVID-19 illness.

Results

Overall, 205 breath samples from 167 adult patients were analyzed. A total of 77 patients (mean [SD] age, 58.5 [16.1] years; 41 [53.2%] male patients; 13 [16.9%] Black and 59 [76.6%] White patients) had COVID-19, and 91 patients (mean [SD] age, 54.3 [17.1] years; 43 [47.3%] male patients; 11 [12.1%] Black and 76 [83.5%] White patients) had non-COVID-19 illness. Several patients were analyzed over multiple days. Among 94 positive samples, 41 samples were from patients in 2021 infected with the Delta or other variants, and 53 samples were from patients in 2022 infected with the Omicron variant, based on the State of Michigan and US Centers for Disease Control and Prevention surveillance data. Four VOC biomarkers were found to distinguish between COVID-19 (Delta and other 2021 variants) and non-COVID-19 illness with an accuracy of 94.7%. However, accuracy dropped substantially to 82.1% when these biomarkers were applied to the Omicron variant. Four new VOC biomarkers were found to distinguish the Omicron variant and non-COVID-19 illness (accuracy, 90.9%). Breath analysis distinguished Omicron from the earlier variants with an accuracy of 91.5% and COVID-19 (all SARS-CoV-2 variants) vs non-COVID-19 illness with 90.2% accuracy.

Conclusions and Relevance

The findings of this diagnostic study suggest that breath analysis has promise for COVID-19 detection. However, similar to rapid antigen testing, the emergence of new variants poses diagnostic challenges. The results of this study warrant additional evaluation on how to overcome these challenges to use breath analysis to improve the diagnosis and care of patients.

Leisure-time physical activity and all-cause mortality among adults with intellectual disability: the National Health Interview Survey

BACKGROUND

Adults with intellectual disabilities (IDs) have higher rates of morbidity and mortality compared with the general population, necessitating a need to identify modifiable targets for intervention to mitigate risk. While the benefits of leisure-time physical activity (PA) are well established in the general population; there is a dearth of evidence confirming its health benefits among adults with IDs. The purpose of this study was to examine the association between leisure-time PA and all-cause mortality among a population-based sample of adults with IDs.

METHODS

A total of 413 adults with IDs from 17 waves (1997-2014) of the National Health Interview Survey, a U.S. nationally representative survey, were studied. Minutes per week of leisure-time PA was ascertained by self-report and classified as poor, intermediate or ideal levels of PA.

RESULTS

Over a median follow-up of 7.2 years, 60 participants died. In a multivariable-adjusted model, higher levels of leisure-time PA were dose-dependently associated with a lower risk of all-cause mortality (P-trend = 0.008). The multivariable-adjusted hazard ratios (95% confidence interval) for all-cause mortality comparing participants with intermediate and ideal versus poor levels of PA were 0.43 (0.18, 1.04) and 0.30 (0.10, 0.87), respectively.

CONCLUSION

These findings show that leisure-time PA confers mortality benefit in adults with IDs and should be considered as a priority target for promoting health and longevity in this population.

Physical inactivity among parents of children with and without Down syndrome: the National Health Interview Survey

BACKGROUND

Emerging evidence suggests that parents of children with intellectual disabilities have poorer physical health than parents of typically developing children. However, it is unclear why. The purpose of this study was to examine differences in physical inactivity among a population-based sample of parents of children with and without Down syndrome.

METHODS

Data for this analysis come from 11 waves (2005-2016) of the National Health Interview Survey, a U.S. nationally representative survey. Minutes per week of leisure-time physical activity were ascertained by self-report with physical inactivity defined as reporting no leisure-time physical activity. Parents were classified as (1) parents of typically developing children, (2) parents of children with Down syndrome, (3) parents of children with a developmental disability that had a high functional impact (autism, cerebral palsy, vision impairment or hearing impairment), (4) parents of children with an intellectual or developmental disability, but who did not have Down syndrome or a high-impact developmental disabilities, and (5) parents of children with other special health care needs.

RESULTS

Parents of children with Down syndrome were more likely to be physically inactive compared with parents of typical children (odds ratio [OR]: 1.51 [95% confidence interval, CI: 1.08, 2.12]) and had the lowest likelihood among all subgroups of parents to children with developmental disabilities or special health care needs. Parents of children with Down syndrome also had a significantly greater likelihood of being physically inactive compared with parents of children with other special health care needs (OR: 1.56 [95% CI: 1.11, 2.19]), with developmental disabilities without high functional impact (OR: 1.58 [95% CI: 1.12, 2.24]) and with developmental disabilities with high functional impact (OR: 1.46 [95% CI: 1.03, 2.08]).

CONCLUSION

Parents of children with Down syndrome are more likely to be physically inactive compared with parents of typically developing children and parents of children with other developmental disabilities or special health care needs. These findings suggest that parents of children with Down syndrome are a population in urgent need for interventions/programmes that promote physical activity, particularly as child well-being is linked to caregiver health.

An interpretable health behavioral intervention policy for mobile device users

An increasing number of people use mobile devices to monitor their behavior, such as exercise, and record their health status, such as psychological stress. However, these devices rarely provide ongoing support to help users understand how their behavior contributes to changes in their health status. To address this challenge, we aim to develop an interpretable policy for physical activity recommendations that reduce a user's perceived psychological stress, over a given time horizon. We formulate this problem as a sequential decision-making problem and solve it using a new method that we refer to as threshold Q-learning (TQL). The advantage of the TQL method over traditional Q-learning is that it is "doubly robust" and interpretable. This interpretability is achieved by making model assumptions and incorporating threshold selection into the learning process. Our simulation results indicate that the TQL method performs better than the Q-learning method given model misspecification. Our analyses are performed on data collected from 79 healthy adults over a 7 week period, where the data comprise physical activity patterns collected from mobile devices and self-assessed stress levels of the users. This work serves as a first step toward a computational health coaching solution for mobile device users.