Targeting AKT1-E17K and the PI3K/AKT Pathway with an Allosteric AKT Inhibitor,ARQ 092

As a critical component in the PI3K/AKT/mTOR pathway, AKT has become an attractive target for therapeutic intervention. ARQ 092 and a next generation AKT inhibitor, ARQ 751 are selective, allosteric, pan-AKT and AKT1-E17K mutant inhibitors that potently inhibit phosphorylation of AKT. Biochemical and cellular analysis showed that ARQ 092 and ARQ 751 inhibited AKT activation not only by dephosphorylating the membrane-associated active form, but also by preventing the inactive form from localizing into plasma membrane. In endometrial PDX models harboring mutant AKT1-E17K and other tumor models with an activated AKT pathway, both compounds exhibited strong anti-tumor activity. Combination studies conducted in in vivo breast tumor models demonstrated that ARQ 092 enhanced tumor inhibition of a common chemotherapeutic agent (paclitaxel). In a large panel of diverse cancer cell lines, ARQ 092 and ARQ 751 inhibited proliferation across multiple tumor types but were most potent in leukemia, breast, endometrial, and colorectal cancer cell lines. Moreover, inhibition by ARQ 092 and ARQ 751 was more prevalent in cancer cell lines containing PIK3CA/PIK3R1 mutations compared to those with wt-PIK3CA/PIK3R1 or PTEN mutations. For both ARQ 092 and ARQ 751, PIK3CA/PIK3R1 and AKT1-E17K mutations can potentially be used as predictive biomarkers for patient selection in clinical studies.     

Clinical Features of Reported Ethylene Glycol Exposures in the United States

Background

Ethylene glycol is highly toxic and represents an important cause of poisonings worldwide. Toxicity can result in central nervous system dysfunction, cardiovascular compromise, elevated anion gap metabolic acidosis and acute kidney injury. Many states have passed laws requiring addition of the bittering agent, denatonium benzoate, to ethylene glycol solutions to reduce severity of exposures. The objectives of this study were to identify differences between unintentional and intentional exposures and to evaluate the utility of denatonium benzoate as a deterrent.

Methods and Findings

Using the National Poison Data System, we performed a retrospective analysis of reported cases of ethylene glycol exposures from January 2006 to December 2013. Outcome classification was summed for intentionality and used as a basis for comparison of effect groups. There were 45,097 cases of ethylene glycol exposures resulting in 154 deaths. Individuals more likely to experience major effects or death were older, male, and presented with more severe symptoms requiring higher levels of care. Latitude and season did not correlate with increased exposures; however, there were more exposures in rural areas. Denatonium benzoate use appeared to have no effect on exposure severity or number.

Conclusion

Deaths due to ethylene glycol exposure were uncommon; however, there were major clinical effects and more exposures in rural areas. Addition of denatonium benzoate was not associated with a reduction in exposures. Alternative means to deter ingestion are needed. These findings suggest the need to consider replacing ethylene glycol with alternative and less toxic agents.     

The Epidemiology of Hypertension in Uganda: Findings from the National Non-Communicable Diseases Risk Factor Survey

Background

Hypertension is an important contributor to global burden of disease and mortality, and is a growing public health problem in sub-Saharan Africa. However, most sub-Saharan African countries lack detailed countrywide data on hypertension and other non-communicable diseases (NCD) risk factors that would provide benchmark information for design of appropriate interventions. We analyzed blood pressure data from Uganda’s nationwide NCD risk factor survey conducted in 2014, to describe the prevalence and distribution of hypertension in the Ugandan population, and to identify the associated factors.

Methods

The NCD risk factor survey drew a countrywide sample stratified by the four regions of the country, and with separate estimates for rural and urban areas. The World Health Organization’s STEPs tool was used to collect data on demographic and behavioral characteristics, and physical and biochemical measurements. Prevalence rate ratios (PRR) using modified Poison regression modelling was used to identify factors associated with hypertension.

Results

Of the 3906 participants, 1033 were classified as hypertensive, giving an overall prevalence of 26.4%. Prevalence was highest in the central region at 28.5%, followed by the eastern region at 26.4%, western region at 26.3%, and northern region at 23.3%. Prevalence in urban areas was 28.9%, and 25.8% in rural areas. The differences between regions, and between rural-urban areas were not statistically significant. Only 7.7% of participants with hypertension were aware of their high blood pressure. The prevalence of pre-hypertension was also high at 36.9%. The only modifiable factor found to be associated with hypertension was higher body mass index (BMI). Compared to participants with BMI less than 25 kg/m², prevalence was significantly higher among participants with BMI between 25 to 29.9 kg/m² with an adjusted PRR = 1.46 [95% CI = 1.25–1.71], and even higher among obese participants (BMI ≥ 30 kg/m²) with an adjusted PRR = 1.60 [95% CI = 1.29–1.99]. The un-modifiable factor found to be associated with hypertension was older age with an adjusted PRR of 1.02 [95% CI = 1.02–1.03] per yearly increase in age.

Conclusions

The prevalence of hypertension in Uganda is high, with no significant differences in distribution by geographical location. Only 7.7% of persons with hypertension were aware of their hypertension, indicating a high burden of undiagnosed and un-controlled high blood pressure. Thus a big percentage of persons with hypertension are at high risk of hypertension-related cardiovascular NCDs.     

Quality Control Measures over 30 Years in a Multicenter Clinical Study: Results from the Diabetes Control and Complications Trial / Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) Study

Implementation of multicenter and/or longitudinal studies requires an effective quality assurance program to identify trends, data inconsistencies and process variability of results over time. The Diabetes Control and Complications Trial (DCCT) and the follow-up Epidemiology of Diabetes Interventions and Complications (EDIC) study represent over 30 years of data collection among a cohort of participants across 27 clinical centers. The quality assurance plan is overseen by the Data Coordinating Center and is implemented across the clinical centers and central reading units. Each central unit incorporates specific DCCT/EDIC quality monitoring activities into their routine quality assurance plan. The results are reviewed by a data quality assurance committee whose function is to identify variances in quality that may impact study results from the central units as well as within and across clinical centers, and to recommend implementation of corrective procedures when necessary. Over the 30-year period, changes to the methods, equipment, or clinical procedures have been required to keep procedures current and ensure continued collection of scientifically valid and clinically relevant results. Pilot testing to compare historic processes with contemporary alternatives is performed and comparability is validated prior to incorporation of new procedures into the study. Details of the quality assurance plan across and within the clinical and central reading units are described, and quality outcomes for core measures analyzed by the central reading units (e.g. biochemical samples, fundus photographs, ECGs) are presented.     

Serum Metabolite Biomarkers Discriminate Healthy Smokers from COPD Smokers

COPD (chronic obstructive pulmonary disease) is defined by a fixed expiratory airflow obstruction associated with disordered airways and alveolar destruction. COPD is caused by cigarette smoking and is the third greatest cause of mortality in the US. Forced expiratory volume in 1 second (FEV1) is the only validated clinical marker of COPD, but it correlates poorly with clinical features and is not sensitive enough to predict the early onset of disease. Using LC/MS global untargeted metabolite profiling of serum samples from a well-defined cohort of healthy smokers (n = 37), COPD smokers (n = 41) and non-smokers (n = 37), we sought to discover serum metabolic markers with known and/or unknown molecular identities that are associated with early-onset COPD. A total of 1,181 distinct molecular ions were detected in 95% of sera from all study subjects and 23 were found to be differentially-expressed in COPD-smokers vs. healthy-smokers. These 23 putative biomarkers were differentially-correlated with lung function parameters and used to generate a COPD prediction model possessing 87.8% sensitivity and 86.5% specificity. In an independent validation set, this model correctly predicted COPD in 8/10 individuals. These serum biomarkers included myoinositol, glycerophopshoinositol, fumarate, cysteinesulfonic acid, a modified version of fibrinogen peptide B (mFBP), and three doubly-charged peptides with undefined sequence that significantly and positively correlate with mFBP levels. Together, elevated levels of serum mFBP and additional disease-associated biomarkers point to a role for chronic inflammation, thrombosis, and oxidative stress in remodeling of the COPD airways. Serum metabolite biomarkers offer a promising and accessible window for recognition of early-stage COPD.     

Reproducibility of Volumetric Computed Tomography of Stable Small Pulmonary Nodules with Implications on Estimated Growth Rate and Optimal Scan Interval

Purpose

To use clinically measured reproducibility of volumetric CT (vCT) of lung nodules to estimate error in nodule growth rate in order to determine optimal scan interval for patient follow-up.

Methods

We performed quantitative vCT on 89 stable non-calcified nodules and 49 calcified nodules measuring 3–13 mm diameter in 71 patients who underwent 3–9 repeat vCT studies for clinical evaluation of pulmonary nodules. Calculated volume standard deviation as a function of mean nodule volume was used to compute error in estimated growth rate. This error was then used to determine the optimal patient follow-up scan interval while fixing the false positive rate at 5%.

Results

Linear regression of nodule volume standard deviation versus the mean nodule volume for stable non-calcified nodules yielded a slope of 0.057±0.002 (r² = 0.79, p<0.001). For calcified stable nodules, the regression slope was 0.052±0.005 (r² = 0.65, p = 0.03). Using this with the error propagation formula, the optimal patient follow-up scan interval was calculated to be 81 days, independent of initial nodule volume.

Conclusions

Reproducibility of vCT is excellent, and the standard error is proportional to the mean calculated nodule volume for the range of nodules examined. This relationship constrains statistical certainty of vCT calculated doubling times and results in an optimal scan interval that is independent of the initial nodule volume.     

Improving the Rank Precision of Population Health Measures for Small Areas with Longitudinal and Joint Outcome Models

Objectives

The University of Wisconsin Population Health Institute has published the County Health Rankings since 2010. These rankings use population-based data to highlight health outcomes and the multiple determinants of these outcomes and to encourage in-depth health assessment for all United States counties. A significant methodological limitation, however, is the uncertainty of rank estimates, particularly for small counties. To address this challenge, we explore the use of longitudinal and pooled outcome data in hierarchical Bayesian models to generate county ranks with greater precision.

Methods

In our models we used pooled outcome data for three measure groups: (1) Poor physical and poor mental health days; (2) percent of births with low birth weight and fair or poor health prevalence; and (3) age-specific mortality rates for nine age groups. We used the fixed and random effects components of these models to generate posterior samples of rates for each measure. We also used time-series data in longitudinal random effects models for age-specific mortality. Based on the posterior samples from these models, we estimate ranks and rank quartiles for each measure, as well as the probability of a county ranking in its assigned quartile. Rank quartile probabilities for univariate, joint outcome, and/or longitudinal models were compared to assess improvements in rank precision.

Results

The joint outcome model for poor physical and poor mental health days resulted in improved rank precision, as did the longitudinal model for age-specific mortality rates. Rank precision for low birth weight births and fair/poor health prevalence based on the univariate and joint outcome models were equivalent.

Conclusion

Incorporating longitudinal or pooled outcome data may improve rank certainty, depending on characteristics of the measures selected. For measures with different determinants, joint modeling neither improved nor degraded rank precision. This approach suggests a simple way to use existing information to improve the precision of small-area measures of population health.     

The Role of ARF6 in Biliary Atresia

Background & Aims

Altered extrahepatic bile ducts, gut, and cardiovascular anomalies constitute the variable phenotype of biliary atresia (BA).

Methods

To identify potential susceptibility loci, Caucasian children, normal (controls) and with BA (cases) at two US centers were compared at >550000 SNP loci. Systems biology analysis was carried out on the data. In order to validate a key gene identified in the analysis, biliary morphogenesis was evaluated in 2-5-day post-fertilization zebrafish embryos after morpholino-antisense oligonucleotide knockdown of the candidate gene ADP ribosylation factor-6 (ARF6, Mo-arf6).

Results

Among 39 and 24 cases at centers 1 and 2, respectively, and 1907 controls, which clustered together on principal component analysis, the SNPs rs3126184 and rs10140366 in a 3’ flanking enhancer region for ARF6 demonstrated higher minor allele frequencies (MAF) in each cohort, and 63 combined cases, compared with controls (0.286 vs. 0.131, P = 5.94x10^-7, OR 2.66; 0.286 vs. 0.13, P = 5.57x10^-7, OR 2.66). Significance was enhanced in 77 total cases, which included 14 additional BA genotyped at rs3126184 only (p = 1.58x10^-2, OR = 2.66). Pathway analysis of the 1000 top-ranked SNPs in CHP cases revealed enrichment of genes for EGF regulators (p<1 x10^-7), ERK/MAPK and CREB canonical pathways (p<1 x10^-34), and functional networks for cellular development and proliferation (p<1 x10^-45), further supporting the role of EGFR-ARF6 signaling in BA. In zebrafish embryos, Mo-arf6 injection resulted in a sparse intrahepatic biliary network, several biliary epithelial cell defects, and poor bile excretion to the gall bladder compared with uninjected embryos. Biliary defects were reproduced with the EGFR-blocker AG1478 alone or with Mo-arf6 at lower doses of each agent and rescued with arf6 mRNA.

Conclusions

The BA-associated SNPs identify a chromosome 14q21.3 susceptibility locus encompassing the ARF6 gene. arf6 knockdown in zebrafish implicates early biliary dysgenesis as a basis for BA, and also suggests a role for EGFR signaling in BA pathogenesis.     

Change in HbA1c Levels between the Age of 8 Years and the Age of 12 Years in Dutch Children without Diabetes: The PIAMA Birth Cohort Study

Objective

HbA1c is associated with cardiovascular risk in persons without diabetes and cardiovascular risk accumulates over the life course. Therefore, insight in factors determining HbA1c from childhood onwards is important. We investigated (lifestyle) determinants of HbA1c at age 12 years and the effects of growth on change in HbA1c and the tracking of HbA1c between the age of 8 and 12 years.

Study Design and Methods

Anthropometric measurements were taken and HbA1c levels were assessed in 955 children without diabetes aged around 12 years participating in the PIAMA birth cohort study. In 363 of these children HbA1c was also measured at age 8 years. Data on parents and children were collected prospectively by questionnaires.

Results

We found no significant association between known risk factors for diabetes and HbA1c at age 12 years. Mean(SD) change in HbA1c between ages 8 and 12 years was 0.6(0.7) mmol/mol per year (or 0.1(0.1) %/yr). Anthropometric measures at age 8 and their change between age 8 and 12 years were not associated with the change in HbA1c. 68.9% of the children remained in the same quintile or had an HbA1c one quintile higher or lower at age 8 years compared to age 12 years.

Conclusion

The lack of association between known risk factors for diabetes and HbA1c suggest that HbA1c in children without diabetes is relatively unaffected by factors associated with glycaemia. HbA1c at age 8 years is by far the most important predictor of HbA1c at age 12. Therefore, the ranking of HbA1c levels appear to be fairly stable over time.