Trajectories of physiological dysregulation predicts mortality and health outcomes in a consistent manner across three populations.

Mechanistic and evolutionary perspectives both agree that aging involves multiple integrated biochemical networks in the organism. In particular, the homeostatic physiological dysregulation (PD) hypothesis contends that aging is caused by the progressive breakdown of key regulatory processes. However, nothing is yet known about the specifics of how PD changes with age and affects health. Using a recently validated measure of PD involving the calculation of a multivariate distance (DM) from biomarker data, we show that PD trajectories predict mortality, frailty, and chronic diseases (cancer, cardiovascular diseases, and diabetes). Specifically, relative risks of outcomes associated with individual slopes in (i.e. rate of) dysregulation range 1.20-1.40 per unit slope. We confirm the results by replicating the analysis using two suites of biomarkers selected with markedly different criteria and, for mortality, in three longitudinal cohort-based studies. Overall, the consistence of effect sizes (direction and magnitude) across data sets, biomarker suites and outcomes suggests that the positive relationship between DM and health outcomes is a general phenomenon found across human populations. Therefore, the study of dysregulation trajectories should allow important insights into aging physiology and provide clinically meaningful predictors of outcomes.

Lagged association between powdery mildew leaf severity, airborne inoculum, weather, and crop losses in strawberry.

Knowledge about epidemiology and the impact of disease on yield is fundamental for establishing effective management strategies. The purpose of this study was to investigate the relationship between foliar strawberry mildew severity, Podosphaera aphanis airborne inoculum concentration, weather, and subsequent crop losses for day-neutral strawberry. The experiment was conducted at three, five, and four sites in 2006, 2007, and 2008, respectively, for a total of 12 epidemics. At each site, data were collected on 25 plants at 2-day intervals from the end of May to early October for a total of 60 to 62 samplings annually. First, seasonal crop losses were statistically described; then, a lagged regression model was developed to describe crop losses from the parameters that were significantly associated with losses. There was a strong positive linear relationship between seasonal crop losses and the area under the leaf disease progress curve (R(2) = 0.90) and daily mean airborne conidia concentration (R(2) = 0.86), and a negative linear relationship between crop losses and time to 5% loss (R(2) = 0.76) and time to 5% leaf area diseased (R(2) = 0.61). Among the 53 monitoring- and weather-based variables analyzed, percent leaf area diseased, log10-transformed airborne inoculum concentration, and weather variables related to temperature were significantly associated with crop losses. However, polynomial distributed lag regression models built with weather variables were not accurate in predicting losses, with the exception of a model based on a combined temperature and humidity variable, which provided accurate prediction of the data used to construct the model but not of independent data. Overall, the model based on log10-transformed airborne inoculum concentration did not provide accurate crop loss predictions. The model built using percent leaf area diseased with a time lag of 8 days (n = 4) and a polynomial degree of 2 provided a good description of the crop-loss data used to construct the model (r = 0.99 and 0.90) and of independent data (r = 0.92). For the 12 epidemics studied, 5% crop loss was reached when an average of 17% leaf area diseased was observed since the beginning of symptom development. These results indicate that information on foliar powdery mildew must be considered when making strawberry powdery mildew management decisions.