Performance of gene-expression profiling test score variability to predict future clinical events in heart transplant recipients.

BACKGROUND A single non-invasive gene expression profiling (GEP) test (AlloMap®) is often used to discriminate if a heart transplant recipient is at a low risk of acute cellular rejection at time of testing. In a randomized trial, use of the test (a GEP score from 0-40) has been shown to be non-inferior to a routine endomyocardial biopsy for surveillance after heart transplantation in selected low-risk patients with respect to clinical outcomes. Recently, it was suggested that the within-patient variability of consecutive GEP scores may be used to independently predict future clinical events; however, future studies were recommended. Here we performed an analysis of an independent patient population to determine the prognostic utility of within-patient variability of GEP scores in predicting future clinical events. METHODS We defined the GEP score variability as the standard deviation of four GEP scores collected ≥315 days post-transplantation. Of the 737 patients from the Cardiac Allograft Rejection Gene Expression Observational (CARGO) II trial, 36 were assigned to the composite event group (death, re-transplantation or graft failure ≥315 days post-transplantation and within 3 years of the final GEP test) and 55 were assigned to the control group (non-event patients). In this case-controlled study, the performance of GEP score variability to predict future events was evaluated by the area under the receiver operator characteristics curve (AUC ROC). The negative predictive values (NPV) and positive predictive values (PPV) including 95 % confidence intervals (CI) of GEP score variability were calculated. RESULTS The estimated prevalence of events was 17 %. Events occurred at a median of 391 (inter-quartile range 376) days after the final GEP test. The GEP variability AUC ROC for the prediction of a composite event was 0.72 (95 % CI 0.6-0.8). The NPV for GEP score variability of 0.6 was 97 % (95 % CI 91.4-100.0); the PPV for GEP score variability of 1.5 was 35.4 % (95 % CI 13.5-75.8). CONCLUSION In heart transplant recipients, a GEP score variability may be used to predict the probability that a composite event will occur within 3 years after the last GEP score. TRIAL REGISTRATION Clinicaltrials.gov identifier NCT00761787.

Variability of lung function predicts loss of asthma control following withdrawal of inhaled corticosteroid treatment

BACKGROUND One aspect of a multidimensional approach to understanding asthma as a complex dynamic disease is to study how lung function varies with time. Variability measures of lung function have been shown to predict response to beta(2)-agonist treatment. An investigation was conducted to determine whether mean, coefficient of variation (CV) or autocorrelation, a measure of short-term memory, of peak expiratory flow (PEF) could predict loss of asthma control following withdrawal of regular inhaled corticosteroid (ICS) treatment, using data from a previous study. METHODS 87 adult patients with mild to moderate asthma who had been taking ICS at a constant dose for at least 6 months were monitored for 2-4 weeks. ICS was then withdrawn and monitoring continued until loss of control occurred as per predefined criteria. Twice-daily PEF was recorded during monitoring. Associations between loss of control and mean, CV and autocorrelation of morning PEF within 2 weeks pre- and post-ICS withdrawal were assessed using Cox regression analysis. Predictive utility was assessed using receiver operator characteristics. RESULTS 53 out of 87 patients had sufficient PEF data over the required analysis period. The mean (389 vs 370 l/min, p<0.0001) and CV (4.5% vs 5.6%, p=0.007) but not autocorrelation of PEF changed significantly from prewithdrawal to postwithdrawal in subjects who subsequently lost control, and were unaltered in those who did not. These changes were related to time to loss of control. CV was the most consistent predictor, with similar sensitivity and sensitivity to exhaled nitric oxide. CONCLUSION A simple, easy to obtain variability measure of daily lung function such as the CV may predict loss of asthma control within the first 2 weeks of ICS withdrawal.

Coastal upwelling is linked to temporal genetic variability in the acorn barnacle Balanus glandula

Dispersal and recruitment are central processes that shape the geographic and temporal distributions of populations of marine organisms. However, significant variability in factors such as reproductive output, larval transport, survival, and settlement success can alter the genetic identity of recruits from year to year. We designed a temporal and spatial sampling protocol to test for genetic heterogeneity among adults and recruits from multiple time points along a similar to 400 km stretch of the Oregon (USA) coastline. In total, 2824 adult and recruiting Balanus glandula were sampled between 2001 and 2008 from 9 sites spanning the Oregon coast. Consistent with previous studies, we observed high mitochondrial DNA diversity at the cytochrome oxidase I locus (884 unique haplotypes) and little to no spatial genetic population structure among the 9 sites (Phi(ST) = 0.00026, p = 0.170). However, subtle but significant temporal shifts in genetic composition were observed among year classes (Phi(ST) = 0.00071, p = 0.035), and spatial Phi(ST) varied from year to year. These temporal shifts in genetic structure were correlated with yearly differences in the strength of coastal upwelling (p = 0.002), with greater population structure observed in years with weaker upwelling. Higher levels of barnacle settlement were also observed in years with weaker upwelling (p < 0.001). These data suggest the hypothesis that low upwelling intensity maintains more local larvae close to shore, thereby shaping the genetic structure and settlement rate of recruitment year classes.

Intra-subject variability in attention-deficit hyperactivity disorder

BACKGROUND: This study is based on a comprehensive survey of the neuropsychological attention-deficit hyperactivity disorder (ADHD) literature and presents the first psychometric analyses of different parameters of intra-subject variability (ISV) in patients with ADHD compared to healthy controls, using the Continuous Performance Test, a Go-NoGo task, a Stop Signal Task, as well as N-back tasks. METHODS: Data of 57 patients with ADHD and 53 age- and gender-matched controls were available for statistical analysis. Different parameters were used to describe central tendency (arithmetic mean, median), dispersion (standard deviation, coefficient of variation, consecutive variance), and shape (skewness, excess) of reaction time distributions, as well as errors (commissions and omissions). RESULTS: Group comparisons revealed by far the strongest effect sizes for measures of dispersion, followed by measures of central tendency, and by commission errors. Statistical control of ISV reduced group differences in the other measures substantially. One (patients) or two (controls) principal components explained up to 67% of the inter-individual differences in intra-individual variability. CONCLUSIONS: Results suggest that, across a variety of neuropsychological tests, measures of ISV contribute best to group discrimination, with limited incremental validity of measures of central tendency and errors. Furthermore, increased ISV might be a unitary construct in ADHD.

Quantifying the periodicty of Inter Movement Intervals of Leg Movements

ims: Periodic leg movements in sleep (PLMS) are a frequent finding in polysomnography. Most patients with restless legs syndrome (RLS) display PLMS. However, since PLMS are also often recorded in healthy elderly subjects, the clinical significance of PLMS is still discussed controversially. Leg movements are seen concurrently with arousals in obstructive sleep apnoea (OSA) may also appear periodically. Quantitative assessment of the periodicity of LM/PLM as measured by inter movement intervals (IMI) is difficult. This is mainly due to influencing factors like sleep architecture and sleep stage, medication, inter and intra patient variability, the arbitrary amplitude and sequence criteria which tend to broaden the IMI distributions or make them even multi-modal. Methods: Here a statistical method is presented that enables eliminating such effects from the raw data before analysing the statistics of IMI. Rather than studying the absolute size of IMI (measured in seconds) we focus on the shape of their distribution (suitably normalized IMI). To this end we employ methods developed in Random Matrix Theory (RMT). Patients: The periodicity of leg movements (LM) of four patient groups (10 to 15 each) showing LM without PLMS (group 1), OSA without PLMS (group 2), PLMS and OSA (group 3) as well as PLMS without OSA (group 4) are compared. Results: The IMI of patients without PLMS (groups 1 and 2) and with PLMS (groups 3 and 4) are statistically different. In patients without PLMS the distribution of normalized IMI resembles closely the one of random events. In contrary IMI of PLMS patients show features of periodic systems (e.g. a pendulum) when studied in normalized manner. Conclusions: For quantifying PLMS periodicity proper normalization of the IMI is crucial. Without this procedure important features are hidden when grouping LM/PLM over whole nights or across patients. The clinical significance of PLMS might be eluded when properly separating random LM from LM that show features of periodic systems.

Present state of global wetland extent and wetland methane modelling: conclusions from a model inter-comparison project (WETCHIMP)

Global wetlands are believed to be climate sensitive, and are the largest natural emitters of methane (CH4). Increased wetland CH4 emissions could act as a positive feedback to future warming. The Wetland and Wetland CH4 Inter-comparison of Models Project (WETCHIMP) investigated our present ability to simulate large-scale wetland characteristics and corresponding CH4 emissions. To ensure inter-comparability, we used a common experimental protocol driving all models with the same climate and carbon dioxide (CO2) forcing datasets. The WETCHIMP experiments were conducted for model equilibrium states as well as transient simulations covering the last century. Sensitivity experiments investigated model response to changes in selected forcing inputs (precipitation, temperature, and atmospheric CO2 concentration). Ten models participated, covering the spectrum from simple to relatively complex, including models tailored either for regional or global simulations. The models also varied in methods to calculate wetland size and location, with some models simulating wetland area prognostically, while other models relied on remotely sensed inundation datasets, or an approach intermediate between the two. Four major conclusions emerged from the project. First, the suite of models demonstrate extensive disagreement in their simulations of wetland areal extent and CH4 emissions, in both space and time. Simple metrics of wetland area, such as the latitudinal gradient, show large variability, principally between models that use inundation dataset information and those that independently determine wetland area. Agreement between the models improves for zonally summed CH4 emissions, but large variation between the models remains. For annual global CH4 emissions, the models vary by ±40% of the all-model mean (190 Tg CH4 yr−1). Second, all models show a strong positive response to increased atmospheric CO2 concentrations (857 ppm) in both CH4 emissions and wetland area. In response to increasing global temperatures (+3.4 °C globally spatially uniform), on average, the models decreased wetland area and CH4 fluxes, primarily in the tropics, but the magnitude and sign of the response varied greatly. Models were least sensitive to increased global precipitation (+3.9 % globally spatially uniform) with a consistent small positive response in CH4 fluxes and wetland area. Results from the 20th century transient simulation show that interactions between climate forcings could have strong non-linear effects. Third, we presently do not have sufficient wetland methane observation datasets adequate to evaluate model fluxes at a spatial scale comparable to model grid cells (commonly 0.5°). This limitation severely restricts our ability to model global wetland CH4 emissions with confidence. Our simulated wetland extents are also difficult to evaluate due to extensive disagreements between wetland mapping and remotely sensed inundation datasets. Fourth, the large range in predicted CH4 emission rates leads to the conclusion that there is both substantial parameter and structural uncertainty in large-scale CH4 emission models, even after uncertainties in wetland areas are accounted for.