Improved prediction of all-cause mortality by a combination of serum total testosterone and insulin-like growth factor I in adult men

Objective: Lower levels of anabolic hormones in older age are well documented. Several studies suggested that low insulin-like growth factor I (IGF-I) or testosterone levels were related to increased mortality. The aim of the present study was to investigate the combined influence of low IGF-I and low testosterone on all-cause mortality in men. Methods and results: From two German prospective cohort studies, the DETECT study and SHIP, 3942 men were available for analyses. During 21,838 person-years of follow-up, 8.4% (n = 330) of men died. Cox model analyses with age as timescale and adjusted for potential confounders revealed that men with levels below the 10th percentile of at least one hormone [hazard ratio (HR) 1.38 (95% confidence-interval (CI) 1.06–1.78), p = 0.02] and two hormones [HR 2.88 (95% CI 1.32–6.29), p < 0.01] showed a higher risk of all-cause mortality compared to men with non-low hormones. The associations became non-significant by using the 20th percentile as cut-off showing that the specificity increased with lower cut-offs for decreased hormone levels. The inclusion of both IGF-I and total testosterone in a mortality prediction model with common risk factors resulted in a significant integrated discrimination improvement of 0.5% (95% CI 0.3–0.7%, p = 0.03). Conclusions: Our results prove that multiple anabolic deficiencies have a higher impact on mortality than a single anabolic deficiency and suggest that assessment of more than one anabolic hormone as a biomarker improve the prediction of all-cause mortality

Fuzzy Queueing Network Models of Computing Systems

Performance engineering of computing systems (software as well as hardware systems) which integrates performance modeling with the various phases of design and implementation has become an important and popular issue. However, especially in early phases of design and development, exact values for all model parameters are often unknown, leading to uncertainties in the model parametrization. For example, the analyst may have to construct a model based on information such as &quot;the mean service demand at device A will be about 30ms&quot;. Considering uncertainties in performance modeling and evaluation of computer and communication systems has been recognized to be of significant importance. A popular mathematical approach with a sophisticated theoretical background is the use of fuzzy numbers to model systems characterized by uncertain parameters. A fuzzy number is represented by a set of real numbers and an associated membership function. Based on techniques used in interval arithmetic, the ba..

Histogram-Based Performance Analysis for Computer Systems with Variabilities or Uncertainties in Workload

A conventional analytic model used for evaluating the performance of computer and communication systems accepts single values as model inputs and computes a single value for each performance measure of interest. However uncertainties regarding parameter values exist in different situations such as during early stages of system design. Although the clients in a system are statistically identical factors such as the time of the day and the current size of the data files can introduce variabilities in service demands for the server devices. Existence of uncertainties or variabilities in service demands makes the use of a single mean value for each model parameter inappropriate causing the conventional modelling approach to become ineffective. This paper proposes to use histograms for characterizing one or more model parameters that are associated with such uncertainty or variability and demonstrates its application with separable queueing network models. A histogram consists of a number o..

Mean Value Analysis for Computer Systems with Variabilities in Workload

When evaluating the performance of computer systems, often uncertainties or variabilities in service demands may be observed. Applying well known mean value analysis (MVA) for single- or multiclass queueing network models of such systems is inappropriate and ineffective, because these models fail to represent variations within a class. This paper proposes to use histograms for characterizing model parameters that are associated with uncertainty or variability and presents an adaptation of the single class MVA algorithm, which traditionally accepts single (mean) values for service demands, so that one or more input parameters can be specified as a histogram. The adapted algorithm generates a histogram output for the performance measures, thus providing a more detailed information (e.g. percentile values) than the mean values obtained from conventional MVA. The proposed technique is demonstrated on selected examples in different problem domains. It is shown, that the computational comple..