Publications

BACKGROUND

Secondhand smoke (SHS) exposure is associated with an increased risk of atherosclerotic heart disease and cardiac events. We sought to assess the effect of SHS on health-related quality of life (HRQOL) in patients with heart failure.

METHODS

Current nonsmokers with heart failure (N = 205) were enrolled in a cohort study. Exposure to SHS was assessed with a validated exposure questionnaire and a high-sensitivity assay for urinary cotinine level. Multidimensional HRQOL was evaluated with the RAND 36-Item Short Form Health Survey, which assesses 8 domains on a scale of 0 (worst) to 100 (best): physical functioning, bodily pain, role limitations due to physical health problems (role physical), role limitations due to emotional/personal problems (role emotional), emotional well-being, social functioning, energy/fatigue, and general health perceptions. A subset of patients (n = 75) agreed to assessment of functional status with a 6-minute walk test.

RESULTS

Self-reported exposure to SHS was associated with generally lower HRQOL scores in univariate analysis, with statistically and clinically significant reductions in 3 subscale scores: role physical (22.2 points), emotional well-being (11.0 points), and role emotional (16.2 points). Even after adjustment for clinical factors, such as age, sex, New York Heart Association class of heart failure, comorbidities, and medications, exposure to SHS remained an independent predictor of HRQOL scores in these domains. When increasing quartiles of urinary cotinine level were used as the exposure measure, qualitatively similar results were obtained.

CONCLUSIONS

Even low levels of SHS are associated with lower scores in several aspects of HRQOL. Physicians should advise patients with heart failure and their families to avoid SHS exposure.

OBJECTIVE

To provide a molecular mechanism that explains the association of the antiretroviral guanosine analogue, abacavir, with an increased risk of myocardial infarction.

DESIGN

Drug effects were studied with biochemical and cellular assays.

METHODS

Human platelets were incubated with nucleoside analogue drugs ex vivo. Platelet activation stimulated by ADP was studied by measuring surface P-selectin with flow cytometry. Inhibition of purified soluble guanylyl cyclase was quantified using an ELISA to measure cGMP production.

RESULTS

Pre-incubation of platelets in abacavir significantly increased activation in response to ADP in a time and dose-dependent manner. The active anabolite of abacavir, carbovir triphosphate, competitively inhibited soluble guanylyl cyclase activity with a K(i) of 55 μmol/l.

CONCLUSION

Abacavir competitively inhibits guanylyl cyclase, leading to platelet hyperreactivity. This may explain the observed increased risk of myocardial infarction in HIV patients taking abacavir.

Because the pathologic processes that underlie Alzheimer's disease (AD) appear to start 10 to 20 years before symptoms develop, there is currently intense interest in developing techniques to accurately predict which individuals are most likely to become symptomatic. Several AD risk prediction strategies - including identification of biomarkers and neuroimaging techniques and development of risk indices that combine traditional and non-traditional risk factors - are being explored. Most AD risk prediction strategies developed to date have had moderate prognostic accuracy but are limited by two key issues. First, they do not explicitly model mortality along with AD risk and, therefore, do not differentiate individuals who are likely to develop symptomatic AD prior to death from those who are likely to die of other causes. This is critically important so that any preventive treatments can be targeted to maximize the potential benefit and minimize the potential harm. Second, AD risk prediction strategies developed to date have not explored the full range of predictive variables (biomarkers, imaging, and traditional and non-traditional risk factors) over the full preclinical period (10 to 20 years). Sophisticated modeling techniques such as hidden Markov models may enable the development of a more comprehensive AD risk prediction algorithm by combining data from multiple cohorts. As the field moves forward, it will be critically important to develop techniques that simultaneously model the risk of mortality as well as the risk of AD over the full preclinical spectrum and to consider the potential harm as well as the benefit of identifying and treating high-risk older patients.