1,781 research outputs found

    Phenotype standardization for statin-induced myotoxicity

    Get PDF
    Statins are widely used lipid-lowering drugs that are effective in reducing cardiovascular disease risk. Although they are generally well tolerated, they can cause muscle toxicity, which can lead to severe rhabdomyolysis. Research in this area has been hampered to some extent by the lack of standardized nomenclature and phenotypic definitions. We have used numerical and descriptive classifications and developed an algorithm to define statin-related myotoxicity phenotypes, including myalgia, myopathy, rhabdomyolysis, and necrotizing autoimmune myopathy.</p

    Lack of association between first myocardial infarction and past use of erythromycin, tetracycline, or doxycycline.

    Get PDF
    To evaluate the association of prior treatment with antibiotics active against Chlamydia pneumoniae with the risk for incident myocardial infarction, we conducted a population-based case-control study. We found that use of erythromycin, tetracycline, or doxycycline during the previous 5 years was not associated with risk for first myocardial infarction. These results suggest little or no association between the use of these antibiotics and the risk for first myocardial infarction in the primary prevention setting

    Using built environment characteristics to predict walking for exercise

    Get PDF
    Background: Environments conducive to walking may help people avoid sedentary lifestyles and associated diseases. Recent studies developed walkability models combining several built environment characteristics to optimally predict walking. Developing and testing such models with the same data could lead to overestimating one's ability to predict walking in an independent sample of the population. More accurate estimates of model fit can be obtained by splitting a single study population into training and validation sets (holdout approach) or through developing and evaluating models in different populations. We used these two approaches to test whether built environment characteristics near the home predict walking for exercise. Study participants lived in western Washington State and were adult members of a health maintenance organization. The physical activity data used in this study were collected by telephone interview and were selected for their relevance to cardiovascular disease. In order to limit confounding by prior health conditions, the sample was restricted to participants in good self-reported health and without a documented history of cardiovascular disease. Results: For 1,608 participants meeting the inclusion criteria, the mean age was 64 years, 90 percent were white, 37 percent had a college degree, and 62 percent of participants reported that they walked for exercise. Single built environment characteristics, such as residential density or connectivity, did not significantly predict walking for exercise. Regression models using multiple built environment characteristics to predict walking were not successful at predicting walking for exercise in an independent population sample. In the validation set, none of the logistic models had a C-statistic confidence interval excluding the null value of 0.5, and none of the linear models explained more than one percent of the variance in time spent walking for exercise. We did not detect significant differences in walking for exercise among census areas or postal codes, which were used as proxies for neighborhoods. Conclusion: None of the built environment characteristics significantly predicted walking for exercise, nor did combinations of these characteristics predict walking for exercise when tested using a holdout approach. These results reflect a lack of neighborhood-level variation in walking for exercise for the population studied.University of Washington Royalty Research fund award; by contracts R01-HL043201, R01-HL068639, and T32-HL07902 from the National Heart, Lung, and Blood Institute; and by grant R01-AG09556 from the National Institute on Aging

    Predicting Future Years of Life, Health, and Functional Ability: A Healthy Life Calculator for Older Adults

    Get PDF
    Introduction Planning for the future would be easier if we knew how long we will live and, more importantly, how many years we will be healthy and able to enjoy it. There are few well-documented aids for predicting our future health. We attempted to meet this need for persons 65 years of age and older. Methods Data came from the Cardiovascular Health Study, a large longitudinal study of older adults that began in 1990. Years of life (YOL) were defined by measuring time to death. Years of healthy life (YHL) were defined by an annual question about self-rated health, and years of able life (YABL) by questions about activities of daily living. Years of healthy and able life (YHABL) were the number of years the person was both Healthy and Able. We created prediction equations for YOL, YHL, YABL, and YHABL based on the demographic and health characteristics that best predicted outcomes. Internal and external validity were assessed. The resulting CHS Healthy Life Calculator (CHSHLC) was created and underwent three waves of beta testing. Findings A regression equation based on 11 variables accounted for about 40% of the variability for each outcome. Internal validity was excellent, and external validity was satisfactory. As an example, a very healthy 70-year-old woman might expect an additional 20 YOL, 16.8 YHL, 16.5 YABL, and 14.2 YHABL. The CHSHLC also provides the percent in the sample who differed by more than 5 years from the estimate, to remind the user of variability. Discussion The CHSHLC is currently the only available calculator for YHL, YABL, and YHABL. It may have limitations if today’s users have better prospects for health than persons in 1990. But the external validity results were encouraging. The remaining variability is substantial, but this is one of the few calculators that describes the possible accuracy of the estimates. Conclusion The CHSHLC, currently at http://diehr.com/paula/healthspan, meets the need for a straightforward and well-documented estimate of future years of healthy and able life that older adults can use in planning for the future

    Life expectancy associated with different ages at diagnosis of type 2 diabetes in high-income countries:23 million person-years of observation

    Get PDF
    Background:The prevalence of type 2 diabetes is increasing rapidly, particularly among younger age groups. Estimates suggest that people with diabetes die, on average, 6 years earlier than people without diabetes. We aimed to provide reliable estimates of the associations between age at diagnosis of diabetes and all-cause mortality, cause-specific mortality, and reductions in life expectancy. Methods:For this observational study, we conducted a combined analysis of individual-participant data from 19 high-income countries using two large-scale data sources: the Emerging Risk Factors Collaboration (96 cohorts, median baseline years 1961–2007, median latest follow-up years 1980–2013) and the UK Biobank (median baseline year 2006, median latest follow-up year 2020). We calculated age-adjusted and sex-adjusted hazard ratios (HRs) for all-cause mortality according to age at diagnosis of diabetes using data from 1 515 718 participants, in whom deaths were recorded during 23·1 million person-years of follow-up. We estimated cumulative survival by applying age-specific HRs to age-specific death rates from 2015 for the USA and the EU. Findings: For participants with diabetes, we observed a linear dose–response association between earlier age at diagnosis and higher risk of all-cause mortality compared with participants without diabetes. HRs were 2·69 (95% CI 2·43–2·97) when diagnosed at 30–39 years, 2·26 (2·08–2·45) at 40–49 years, 1·84 (1·72–1·97) at 50–59 years, 1·57 (1·47–1·67) at 60–69 years, and 1·39 (1·29–1·51) at 70 years and older. HRs per decade of earlier diagnosis were similar for men and women. Using death rates from the USA, a 50-year-old individual with diabetes died on average 14 years earlier when diagnosed aged 30 years, 10 years earlier when diagnosed aged 40 years, or 6 years earlier when diagnosed aged 50 years than an individual without diabetes. Using EU death rates, the corresponding estimates were 13, 9, or 5 years earlier. Interpretation: Every decade of earlier diagnosis of diabetes was associated with about 3–4 years of lower life expectancy, highlighting the need to develop and implement interventions that prevent or delay the onset of diabetes and to intensify the treatment of risk factors among young adults diagnosed with diabetes.</p

    Genetic determinants of cortical structure (thickness, surface area and volumes) among disease free adults in the CHARGE Consortium

    Get PDF
    Cortical thickness, surface area and volumes (MRI cortical measures) vary with age and cognitive function, and in neurological and psychiatric diseases. We examined heritability, genetic correlations and genome-wide associations of cortical measures across the whole cortex, and in 34 anatomically predefined regions. Our discovery sample comprised 22,824 individuals from 20 cohorts within the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium and the United Kingdom Biobank. Significant associations were replicated in the Enhancing Neuroimaging Genetics through Meta-analysis (ENIGMA) consortium, and their biological implications explored using bioinformatic annotation and pathway analyses. We identified genetic heterogeneity between cortical measures and brain regions, and 160 genome-wide significant associations pointing to wnt/β-catenin, TGF-β and sonic hedgehog pathways. There was enrichment for genes involved in anthropometric traits, hindbrain development, vascular and neurodegenerative disease and psychiatric conditions. These data are a rich resource for studies of the biological mechanisms behind cortical development and aging
    corecore