Trends in Obesity Prevalence among US Older Adults in the Last Two Years of Life, 1998-2018.

CONTEXT: The prevalence of obesity has grown in the US over the decades. The temporal trends of body mass index categories in the last two years of life are poorly understood. OBJECTIVES: To describe the trends in body mass categories in the last two years of life over the past two decades controlling for other demographic changes. METHODS: We performed a cross-sectional study of prospectively collected survey data from the nationally representative Health and Retirement Study (HRS) among decedents who died between 1998 and 2018. We categorized BMI into five categories and calculated the proportion of decedents with each BMI category during each four epochs (1998-2003, 2004-2008, 2009-2013, 2014-2018). We examined trends in regression models with survey wave groupings modeled as an orthogonal polynomial and adjusted for factors commonly associated with BMI: sex, age, race, ethnicity, education, and tobacco use. RESULTS: The analytic cohort included 14,797 decedents. From 1998-2003 to 2014-2018 time periods, those categorized as having mild-to-moderate obesity in the last two years of life increased from 12.4% to 14.8% (linear trend P < 0.001), a 19% increase. Severe obesity increased from 1.9% to 4.3%, a 126% increase (linear trend P < 0.001). Underweight decreased from 9.9% to 5.9%, a 40% decrease (linear trend P < 0.001), adjusted for demographic factors. Adjusted quadratic temporal trends for BMI category were nonsignificant, except for in mild-to-moderate obesity. CONCLUSION: Severe obesity has increased greatly while underweight has decreased. As obesity increases in the final years of life, it is critical to assess how the existing and future palliative services and end of life care system address body size and weight.

Identifying small molecules for protecting chondrocyte function and matrix integrity after controlled compressive injury.

Objective: Articular cartilage injury is central for the development of post-traumatic osteoarthritis (PTOA). With few disease-modifying therapies successful at offsetting progressive osteoarthritis (OA), our goal is to use a high throughput screening platform of cartilage injury to identify novel chondroprotective compounds. Targeting articular cartilage damage immediately after injury remains a promising therapeutic strategy to overcome irreversible tissue damage. Method: We constructed a single impact-cartilage screening method using a multi-platen system that simultaneously impacts 48 samples and makes use of engineered cartilage tissue analogs (known as CTAs). Drug libraries were screened and assessed for their ability to alter two crucial biological responses to impact injuries, namely matrix degradation and cell stress. Results: Over 500 small molecules were screened for their ability to alter proteoglycan loss, matrix metalloproteinase activity, and cell stress or death. Fifty-five compounds passed through secondary screening and were from commercial libraries of natural and redox, stem cell related compounds, as well as protease, kinase and phosphatase inhibitors. Through secondary screening, 16 promising candidates exhibited activity on one or more critical function of chondrocytes. While many are mechanistically known compounds, their function in joint diseases is not known. Conclusion: This platform was validated for screening drug activity against a tissue engineered model of PTOA. Multiple compounds identified in this manner have potential application as early protective therapy for treating PTOA, and require further study. We propose this screening platform can identify novel molecules that act on early chondrocyte responses to injury and provide an invaluable tool for therapeutic development.

Cannabis use and medication nonadherence in bipolar disorder: A nationwide inpatient sample database analysis.

BACKGROUND: Medication nonadherence among bipolar disorder (BD) is often linked with comorbid substance use disorders. This study aims to investigate cannabis use disorder (CUD) association with medication noncompliance in hospitalized BD patients. METHODS: Using data on 266,303 BD hospitalizations between 2010 and 2014 from the US Nationwide Inpatient Sample database, we obtained medication noncompliance rates stratified by demographics and CUD. Logistic regression was used to evaluate factors associated with medication noncompliance. RESULTS: Overall mean age, the prevalence of CUD, and medication nonadherence were 41.58 (± 0.11) years, 15.0% and 16.1%, respectively. There were 56.6% females in the overall population. There was a significant difference in the characteristics of those in the medication nonadherence vs adherence groups, including age, sex, race, comorbid substance use, income, insurance type, hospital region, and hospital teaching status (p < 0.001). After adjusting for other variables using multivariate analysis, there remained a statistically significant association of medication nonadherence in BD hospitalization and CUD (OR 1.42, 95% CI 1.36-1.48). LIMITATION: Confounding multiple substance use could not be accounted for, and the retrospective nature of the database which includes only inpatients is prone to possible selection and reporting bias. CONCLUSION: CUD statistically predicts increased rates of medication nonadherence among patients with BD. Given the possible association of CUD with medication nonadherence among BD patients, collaborative work between general adult psychiatry and addiction services is imperative in improving the management outcome of patients with BD and comorbid CUD.

Genome Sequences of Six Cluster N Mycobacteriophages, Kevin1, Nenae, Parmesanjohn, ShrimpFriedEgg, Smurph, and SpongeBob, Isolated on Mycobacterium smegmatis mc2155.

The annotation of six cluster N Mycobacterium smegmatis phages (Kevin1, Nenae, Parmesanjohn, ShrimpFriedEgg, Smurph, and SpongeBob) reveals regions of genomic diversity, particularly within the central region of the genome. The genome of Kevin1 includes two orphams (genes with no similarity to other phage genes), with one predicted to encode an AAA-ATPase.

Genome Sequences of Chancellor, Mitti, and Wintermute, Three Subcluster K4 Phages Isolated Using Mycobacterium smegmatis mc2155.

Mycobacteriophages Chancellor, Mitti, and Wintermute infect Mycobacterium smegmatis mc2155 and are closely related to phages Cheetobro and Fionnbharth in subcluster K4. Genome sizes range from 57,697 bp to 58,046 bp. Phages are predicted to be temperate and to infect the pathogen Mycobacterium tuberculosis.