Search

Objectives: To identify patient characteristics, health concerns, and barriers to care based on overweight or obesity., Methods: A 50-question anonymous survey was administered to patients. Data were compared by body mass index (BMI)., Results: Among 3976 distributed surveys, 899 were returned and 682 were analyzed. Most respondents were women (60%), White (94%), and married/partnered (65%) and had some college education (80%). Younger and unmarried persons had higher BMI ( P  < .001). Concern for quality of life ( P  < .001) and importance of lifestyle changes ( P  = .006) increased with BMI, but confidence in making changes decreased as BMI increased ( P  < .001). Perceived good health decreased with increasing BMI ( P  < .001), whereas depression and other comorbid conditions increased. Self-esteem decreased and stigma increased with higher BMI (both P  < .001). Weight discussions with clinicians ( P  < .001) and belief that clinicians should be involved in weight management ( P  = .002) increased with BMI, yet self-perception of being judged by clinicians also increased ( P  < .001). As BMI increased, delays in seeking health care increased ( P  < .001)., Conclusions: This survey study highlights perceived health concerns and barriers to care among persons with overweight and obesity. With higher BMI, self-esteem decreased, and stigma, self-perception of poor health, perception of being judged by clinicians, and delay in seeking medical care increased., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: J.O.E. serves on a scientific advisory board for Applied Aerosol Technologies and serves as a consultant to Exact Sciences and K Health, with reimbursement paid to Mayo Clinic. M.S.M. has research grants from Fresenius Kabi, Nestle, Realfood Blends, and VectivBio, and he is a consultant for NorthSea. S.L.B. serves on a scientific advisory board for CorMedix. All other authors have nothing to declare.All authors declare no support from any organization for the submitted work; no financial relationships with any organizations that might have an interest in the submitted work in the previous 3 years; and no other relationships or activities that could appear to have influenced the submitted work.

Background: Despite malnutrition being associated with increased mortality and morbidity, there continues to be great difficulty in defining criteria and implementing widespread screening. Tools used to diagnose decreased fat‐free mass (FFM [sarcopenia]) should be easy to use, relatively inexpensive, and safe. Bioelectrical impedance analysis (BIA) has the potential to meet these criteria, but reliability across body mass index (BMI) classes is a concern. Methods: A total of 176 healthy ambulatory participants (aged 18–65 years) were recruited equally (n = 44) in 4 BMI categories: (1) 18.5–24.9, (2) 25.0–29.9, (3) 30–34.9, and (4) ≥35.0. Participants were fasting overnight and had S‐MFBIA (InBody 770) measurements the next morning, with DXA being performed subsequently within 30 minutes. Results: The measurement (mean ± SD) for FFM with DXA was 52.8 ± 11.0, and BIA was 53.6 ± 11.0. Delta (S‐MFBIA vs DXA) was 0.8 ± 2.2 (5% limits of agreement −3.5 to +5.2), and concordance correlation coefficient (CCC) was 0.98 (95% CI, 0.97–0.98). The measurements (mean ± SD) for PBF with DXA was 37.5 ± 10.6% and S‐MFBIA was 36.6 ± 11.3%. Delta (S‐MFBIA vs DXA) was −0.9 ± 2.6 (5% limits of agreement 6.0 to +4.2), and CCC was 0.97 (95% CI, 0.96–0.98). The CCC according to the 4 BMI groups for FFM and PBF was between 0.96–0.98 and 0.90–0.94, respectively. Conclusions: FFM and PBF measured by S‐MFBIA had good agreement with DXA across all BMI categories measured in the current study of ambulatory participants. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of the present study was to investigate body mass index, multi-morbidity, and COVID-19 Risk Score as predictors of severe COVID-19 outcomes. Patients: Patients from this study are from a well-characterized patient cohort collected at Mayo Clinic between January 1, 2020 and May 23, 2020; with confirmed COVID-19 diagnosis defined as a positive result on reverse-transcriptase-polymerase-chain-reaction (RT-PCR) assays from nasopharyngeal swab specimens. Measures: Demographic and clinical data were extracted from the electronic medical record. The data included: date of birth, gender, ethnicity, race, marital status, medications (active COVID-19 agents), weight and height (from which the Body Mass Index (BMI) was calculated, history of smoking, and comorbid conditions to calculate the Charlson Comorbidity Index (CCI) and the U.S Department of Health and Human Services (DHHS) multi-morbidity score. An additional COVID-19 Risk Score was also included. Outcomes included hospital admission, ICU admission, and death. Results: Cox proportional hazards models were used to determine the impact on mortality or hospital admission. Age, sex, and race (white/Latino, white/non-Latino, other, did not disclose) were adjusted for in the model. Patients with higher COVID-19 Risk Scores had a significantly higher likelihood of being at least admitted to the hospital (HR = 1.80; 95% CI = 1.30, 2.50; P <.001), or experiencing death or inpatient admission (includes ICU admissions) (HR = 1.20; 95% CI = 1.02, 1.42; P =.028). Age was the only statistically significant demographic predictor, but obesity was not a significant predictor of any of the outcomes. Conclusion: Age and COVID-19 Risk Scores were significant predictors of severe COVID-19 outcomes. Further work should examine the properties of the COVID-19 Risk Factors Scale. [ABSTRACT FROM AUTHOR]