Identification of Host Cytosolic Sensors and Bacterial Factors Regulating the Type I Interferon Response to Legionella pneumophila

Legionella pneumophila is a gram-negative bacterial pathogen that replicates in host macrophages and causes a severe pneumonia called Legionnaires' Disease. The innate immune response to L. pneumophila remains poorly understood. Here we focused on identifying host and bacterial factors involved in the production of type I interferons (IFN) in response to L. pneumophila. It was previously suggested that the delivery of L. pneumophila DNA to the host cell cytosol is the primary signal that induces the type I IFN response. However, our data are not easily reconciled with this model. We provide genetic evidence that two RNA-sensing proteins, RIG-I and MDA5, participate in the IFN response to L. pneumophila. Importantly, these sensors do not seem to be required for the IFN response to L. pneumophila DNA, whereas we found that RIG-I was required for the response to L. pneumophila RNA. Thus, we hypothesize that bacterial RNA, or perhaps an induced host RNA, is the primary stimulus inducing the IFN response to L. pneumophila. Our study also identified a secreted effector protein, SdhA, as a key suppressor of the IFN response to L. pneumophila. Although viral suppressors of cytosolic RNA-sensing pathways have been previously identified, analogous bacterial factors have not been described. Thus, our results provide new insights into the molecular mechanisms by which an intracellular bacterial pathogen activates and also represses innate immune responses

Indicators of "Healthy Aging" in older women (65-69 years of age). A data-mining approach based on prediction of long-term survival

<p>Abstract</p> <p>Background</p> <p>Prediction of long-term survival in healthy adults requires recognition of features that serve as early indicators of successful aging. The aims of this study were to identify predictors of long-term survival in older women and to develop a multivariable model based upon longitudinal data from the Study of Osteoporotic Fractures (SOF).</p> <p>Methods</p> <p>We considered only the youngest subjects (<it>n </it>= 4,097) enrolled in the SOF cohort (65 to 69 years of age) and excluded older SOF subjects more likely to exhibit a "frail" phenotype. A total of 377 phenotypic measures were screened to determine which were of most value for prediction of long-term (19-year) survival. Prognostic capacity of individual predictors, and combinations of predictors, was evaluated using a cross-validation criterion with prediction accuracy assessed according to time-specific AUC statistics.</p> <p>Results</p> <p>Visual contrast sensitivity score was among the top 5 individual predictors relative to all 377 variables evaluated (mean AUC = 0.570). A 13-variable model with strong predictive performance was generated using a forward search strategy (mean AUC = 0.673). Variables within this model included a measure of physical function, smoking and diabetes status, self-reported health, contrast sensitivity, and functional status indices reflecting cumulative number of daily living impairments (HR ≥ 0.879 or RH ≤ 1.131; P < 0.001). We evaluated this model and show that it predicts long-term survival among subjects assigned differing causes of death (e.g., cancer, cardiovascular disease; P < 0.01). For an average follow-up time of 20 years, output from the model was associated with multiple outcomes among survivors, such as tests of cognitive function, geriatric depression, number of daily living impairments and grip strength (P < 0.03).</p> <p>Conclusions</p> <p>The multivariate model we developed characterizes a "healthy aging" phenotype based upon an integration of measures that together reflect multiple dimensions of an aging adult (65-69 years of age). Age-sensitive components of this model may be of value as biomarkers in human studies that evaluate anti-aging interventions. Our methodology could be applied to data from other longitudinal cohorts to generalize these findings, identify additional predictors of long-term survival, and to further develop the "healthy aging" concept.</p

Difficulty and help with activities of daily living among older adults living alone with cognitive impairment

IntroductionThere is limited research on difficulties with activities of daily living (I/ADLs) among older adults living alone with cognitive impairment, including differences by race/ethnicity.MethodsFor U.S. Health and Retirement Study (2000-2014) participants aged 55+ living alone with cognitive impairment (4,666 individuals; 9,091 observations), we evaluated I/ADL difficulty and help.ResultsAmong 4.3 million adults aged 55+ living alone with cognitive impairment, an estimated 46% reported an I/ADL difficulty; 72% reported not receiving help with an I/ADL. Women reported more difficulty than men. Compared to white women, black women were 22% more likely to report a difficulty without help, and Latina women were 36% more likely to report a difficulty with help. Among men, racial/ethnic differences in outcomes were not significant. Patterns of difficulty without help by race/ethnicity were similar among Medicaid beneficiaries.DiscussionFindings call for targeted efforts to support older adults living alone with cognitive impairment

ESTIMATING HEALTH EXPECTANCY IN PRESENCE OF MISSING DATA: AN APPLICATION USING HID SURVEY

In this article we estimate health transition probabilities using longitudinal data collected in France for the survey on handicaps, disabilities and dependencies from 1998 to 2001. Life expectancies with and without disabilities are estimated using a Markov-based multi-state life table approach with two non-absorbing states: able to perform all activities of daily living (ADLs) and unable or in need of help to perform one or more ADLs, and the absorbing state of death. The loss of follow-up between the two waves induces biases in the probabilities estimates: mortality estimates were biased upwards; also the incidence of recovery and the onset of disability seemed to be biased. Since individuals were not missing completely at random, we correct this bias by estimating health status for drop-outs using a non parametric model. After imputation, we found that at the age of 70 disability-free life expectancy decreases by 0.5 years, whereas the total life expectancy increases by 1 year. The slope of the stable prevalence increases, but it remains lower than the slope of the cross sectional prevalence. The gender differences on life expectancy did not change significantly after imputation. Globally, there is no evidence of a general reduction in ADL disability, as defined in our study. The added value of the study is the reduction of the bias induced by sample attrition