Examinando por Autor "Glei, Dana"
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Ítem Predicting survival from telomere length versus conventional predictors: a multinational population-based cohort study(PLOS One; Volumen 11, Número 4, 2016) Glei, Dana; Risques, Rosa Ana; Rehkopf, David H.; Dow, William H.; Rosero Bixby, Luis; Weinstein, Maxine; Goldman, NoreenTelomere length has generated substantial interest as a potential predictor of aging- related diseases and mortality. Some studies have reprted significant associations, but few have tested its ability to discriminate between decedents and survivors compared with a broad range of well-established predictors that include both biomarkers and commonly collected self-reprted data. Our aim here was to quantify the prognostic value of leuko- cyte telomere length relative to age, sex, and 19 other variables for predicting five-year mortality among older persons in three countries. We used data from nationally represen- tative surveys in Costa Rica (N = 923, aged 61+), Taiwan (N = 976, aged 54+), and the U. S. (N = 2672, aged 60+). Our study used a prospective cohort design with all-cause mor- tality during five years post-exam as the outcome. We fit Cox hazards models separately by country, and assessed the discriminatory ability of each predictor. Age was, by far, the single best predictor of all-cause mortality, whereas leukocyte telomere length was only somewhat better than random chance in terms of discriminating between decedents and survivors. After adjustment for age and sex, telomere length ranked between 15th and 17th (out of 20), and its incremental contribution was small; nine self-reprted variables (e.g., mobility, global self-assessed health status, limitations with activities of daily living, smoking status), a cognitive assessment, and three biological markers (C-reactive protein, serum creatinine, and glycosylated hemoglobin) were more powerful predictors of mortality in all three countries. Results were similar for cause-specific models (i.e., mortality from cardiovascular disease, cancer, and all other causes combined). Leukocyte telomere length had a statistically discernible, but weak, association with mortality, but it did not predict survival as well as age or many other self-reprted variables. Although telomere length may eventually help scientists understand aging, more powerful and more easily obtained tools are available for predicting survival.Ítem Self-reported versus performance-based measures of physical function: prognostic value for survival(Demographic Research; Volumen 30, Número 7, 2014) Glei, Dana; Rosero Bixby, Luis; Chiou, Shu-Ti; Weinstein, Maxine; Goldman, NoreenBackground: Although previous studies have indicated that performance assessments strongly predict future survival, few have evaluated the incremental value in the presence of controls for self-reprted activity and mobility limitations. Objective: We assess and compare the added value of four tests -- walking speed, chair stands, grip strength, and peak expiratory flow (PEF) -- for predicting all-cause mortality. Methods: Using population-based samples of older adults in Costa Rica (n = 2290, aged 60+) and Taiwan (n = 1219, aged 53+), we estimate proportional hazards models of mortality for an approximate five-year period. Receiver Operator Characteristic (ROC) curves are used to assess the prognostic value of each performance assessment. Results: Self-reprted measures of physical limitations contribute substantial gains in mortality prediction, whereas performance-based assessments yield modest incremental gains. PEF provides the greatest added value, followed by grip strength. Our results suggest that including more than two performance assessments may provide little improvement in mortality prediction. Conclusions: PEF and grip strength are often simpler to administer in home interview settings, impose less of a burden on some respondents, and, in the presence of self-reprted limitations, appear to be better predictors of mortality than do walking speed or chair stands. Comments: Being unable to perform the test is often a strong predictor of mortality, but these indicators are not well-defined. Exclusion rates vary by the specific task and are likely to depend on the underlying demographic, health, social and cultural characteristics of the sample.