Browsing by Subject "risk factor"

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    Childhood bullying involvement predicts low-grade systemic inflammation into adulthood.
    (Proc Natl Acad Sci U S A, 2014-05-27) Copeland, WE; Wolke, D; Lereya, ST; Shanahan, L; Worthman, C; Costello, EJ
    Bullying is a common childhood experience that involves repeated mistreatment to improve or maintain one's status. Victims display long-term social, psychological, and health consequences, whereas bullies display minimal ill effects. The aim of this study is to test how this adverse social experience is biologically embedded to affect short- or long-term levels of C-reactive protein (CRP), a marker of low-grade systemic inflammation. The prospective population-based Great Smoky Mountains Study (n = 1,420), with up to nine waves of data per subject, was used, covering childhood/adolescence (ages 9-16) and young adulthood (ages 19 and 21). Structured interviews were used to assess bullying involvement and relevant covariates at all childhood/adolescent observations. Blood spots were collected at each observation and assayed for CRP levels. During childhood and adolescence, the number of waves at which the child was bullied predicted increasing levels of CRP. Although CRP levels rose for all participants from childhood into adulthood, being bullied predicted greater increases in CRP levels, whereas bullying others predicted lower increases in CRP compared with those uninvolved in bullying. This pattern was robust, controlling for body mass index, substance use, physical and mental health status, and exposures to other childhood psychosocial adversities. A child's role in bullying may serve as either a risk or a protective factor for adult low-grade inflammation, independent of other factors. Inflammation is a physiological response that mediates the effects of both social adversity and dominance on decreases in health.
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    COVID-19-Associated Mortality in US Veterans with and without SARS-CoV-2 Infection.
    (International journal of environmental research and public health, 2021-08-11) Suzuki, Ayako; Efird, Jimmy T; Redding, Thomas S; Thompson, Andrew D; Press, Ashlyn M; Williams, Christina D; Hostler, Christopher J; Hunt, Christine M

    Background

    We performed an observational Veterans Health Administration cohort analysis to assess how risk factors affect 30-day mortality in SARS-CoV-2-infected subjects relative to those uninfected. While the risk factors for coronavirus disease 2019 (COVID-19) have been extensively studied, these have been seldom compared with uninfected referents.

    Methods

    We analyzed 341,166 White/Black male veterans tested for SARS-CoV-2 from March 1 to September 10, 2020. The relative risk of 30-day mortality was computed for age, race, ethnicity, BMI, smoking status, and alcohol use disorder in infected and uninfected subjects separately. The difference in relative risk was then evaluated between infected and uninfected subjects. All the analyses were performed considering clinical confounders.

    Results

    In this cohort, 7% were SARS-CoV-2-positive. Age >60 and overweight/obesity were associated with a dose-related increased mortality risk among infected patients relative to those uninfected. In contrast, relative to never smoking, current smoking was associated with a decreased mortality among infected and an increased mortality in uninfected, yielding a reduced mortality risk among infected relative to uninfected. Alcohol use disorder was also associated with decreased mortality risk in infected relative to the uninfected.

    Conclusions

    Age, BMI, smoking, and alcohol use disorder affect 30-day mortality in SARS-CoV-2-infected subjects differently from uninfected referents. Advanced age and overweight/obesity were associated with increased mortality risk among infected men, while current smoking and alcohol use disorder were associated with lower mortality risk among infected men, when compared with those uninfected.
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    Predicting Outcomes Over Time in Patients With Heart Failure, Left Ventricular Systolic Dysfunction, or Both Following Acute Myocardial Infarction.
    (J Am Heart Assoc, 2016-06-27) Lopes, Renato D; Pieper, Karen S; Stevens, Susanna R; Solomon, Scott D; McMurray, John JV; Pfeffer, Marc A; Leimberger, Jeffrey D; Velazquez, Eric J
    BACKGROUND: Most studies of risk assessment or stratification in patients with myocardial infarction (MI) have been static and fail to account for the evolving nature of clinical events and care processes. We sought to identify predictors of mortality, cardiovascular death or nonfatal MI, and cardiovascular death or nonfatal heart failure (HF) over time in patients with HF, left ventricular systolic dysfunction, or both post-MI. METHODS AND RESULTS: Using data from the VALsartan In Acute myocardial iNfarcTion (VALIANT) trial, we developed models to estimate the association between patient characteristics and the likelihood of experiencing an event from the time of a follow-up visit until the next visit. The intervals are: hospital arrival to discharge or 14 days, whichever occurs first; hospital discharge to 30 days; 30 days to 6 months; and 6 months to 3 years. Models were also developed to predict the entire 3-year follow-up period using baseline information. Multivariable Cox proportional hazards modeling was used throughout with Wald chi-squares as the comparator of strength for each predictor. For the baseline model of overall mortality, the 3 strongest predictors were age (adjusted hazard ratio [HR], 1.35; 95% CI, 1.28-1.42; P<0.0001), baseline heart rate (adjusted HR, 1.17; 95% CI, 1.14-1.21; P<0.0001), and creatinine clearance (≤100 mL/min; adjusted HR, 0.86; 95% CI, 0.84-0.89; P<0.0001). According to the integrated discrimination improvement (IDI) and net reclassification improvement (NRI) indices, the updated model had significant improvement over the model with baseline covariates only in all follow-up periods and with all outcomes. CONCLUSIONS: Patient information assessed closest to the time of the outcome was more valuable in predicting death when compared with information obtained at the time of the index hospitalization. Using updated patient information improves prognosis over using only the information available at the time of the index event.