CYP2D6 Allelic Variants *34, *17-2, *17-3, and *53 and a Thr309Ala Mutant Display Altered Kinetics and NADPH Coupling in Metabolism of Bufuralol and Dextromethorphan and Altered Susceptibility to Inactivation by SCH 66712.

Metabolic phenotype can be affected by multiple factors, including allelic variation and interactions with inhibitors. Human CYP2D6 is responsible for approximately 20% of cytochrome P450-mediated drug metabolism but consists of more than 100 known variants; several variants are commonly found in the population, whereas others are quite rare. Four CYP2D6 allelic variants-three with a series of mutations distal to the active site (*34, *17-2, *17-3) and one ultra-metabolizer with mutations near the active site (*53), along with reference *1 and an active site mutant of *1 (Thr309Ala)-were expressed, purified, and studied for interactions with the typical substrates dextromethorphan and bufuralol and the inactivator SCH 66712. We found that *34, *17-2, and *17-3 displayed reduced enzyme activity and NADPH coupling while producing the same metabolites as *1, suggesting a possible role for Arg296 in NADPH coupling. A higher-activity variant, *53, displayed similar NADPH coupling to *1 but was less susceptible to inactivation by SCH 66712. The Thr309Ala mutant showed similar activity to that of *1 but with greatly reduced NADPH coupling. Overall, these results suggest that kinetic and metabolic analysis of individual CYP2D6 variants is required to understand their possible contributions to variable drug response and the complexity of personalized medicine.

County-level job automation risk and health: Evidence from the United States.

RATIONALE: Previous studies have observed a positive association between automation risk and employment loss. Based on the job insecurity-health risk hypothesis, greater exposure to automation risk could also be negatively associated with health outcomes. OBJECTIVE: The main objective of this paper is to investigate the county-level association between prevalence of workers in jobs exposed to automation risk and general, physical, and mental health outcomes. METHODS: As a preliminary assessment of the job insecurity-health risk hypothesis (automation risk → job insecurity → poorer health), a structural equation model was used based on individual-level data in the two cross-sectional waves (2012 and 2014) of General Social Survey (GSS). Next, using county-level data from County Health Rankings 2017, American Community Survey (ACS) 2015, and Statistics of US Businesses 2014, Two Stage Least Squares (2SLS) regression models were fitted to predict county-level health outcomes. RESULTS: Using the 2012 and 2014 waves of the GSS, employees in occupational classes at higher risk of automation reported more job insecurity, that, in turn, was associated with poorer health. The 2SLS estimates show that a 10% increase in automation risk at county-level is associated with 2.38, 0.8, and 0.6 percentage point lower general, physical, and mental health, respectively. CONCLUSION: Evidence suggests that exposure to automation risk may be negatively associated with health outcomes, plausibly through perceptions of poorer job security. More research is needed on interventions aimed at mitigating negative influence of automation risk on health.

Falls in rural elders: an empirical study of risk factors.

BACKGROUND: Our objective was to analyze and determine several risk factors of falls in the rural elderly population. Our study examined several variables believed to be positively correlated with the likelihood of an elderly person sustaining a fall. METHODS: Analysis was performed on a prospective study of 308 community-dwelling rural elders during a span of 6 years. Factors such as prescription medication, eyesight, age, sex, recent hospitalizations, and personal health ratings were analyzed through a series of advanced empirical techniques. RESULTS: Of the five significant variables found in our analysis, four were different types of prescription drugs. The biometric results suggest the probability of falling increases by as much as 4% for each year of age. Prescription painkillers increase the probability of falling by 20% to 85%. Arthritis medication decreases the probability of falling by 20% to 60%. CONCLUSION: Although our results are necessary to the body of falls research, further study is required for precise determination of those medications found to be statistically significant in our analysis.