Simulation Analysis and Comparison of Point of Care Testing and Central Laboratory Testing.

Background. In response to demand for fast and efficient clinical testing, the use of point-of-care testing (POCT) has become increasingly common in the United States. However, studies of POCT implementation have found that adopting POCT may not always be advantageous relative to centralized laboratory testing. Methods. We construct a simulation model of patient flow in an outpatient care setting to evaluate tradeoffs involved in POCT implementation across multiple dimensions, comparing measures of patient outcomes in varying clinical scenarios, testing regimes, and patient conditions. Results. We find that POCT can significantly reduce clinical time for patients, as compared to traditional testing regimes, in settings where clinic and central testing areas are far apart. However, as distance from clinic to central testing area decreased, POCT advantage over central laboratory testing also decreased, in terms of time in the clinical system and estimated subsequent productivity loss. For example, testing for pneumonia resulted in an estimated average of 27.80 (central lab) versus 15.50 (POCT) total lost productive hours in a rural scenario, and an average of 14.92 (central lab) versus 15.50 (POCT) hours in a hospital-based scenario. Conclusions. Our results show that POCT can effectively reduce the average time a patient spends in the system for varying condition profiles and clinical scenarios. However, the number of total lost productive hours, a more holistic measure, is greatly affected by testing quality, where POCT often is at a disadvantage. Thus, it is important to consider factors such as clinical setting, target condition, testing costs, and test quality when selecting appropriate testing regime.

Evolutionary triangulation: informing genetic association studies with evolutionary evidence.

Genetic studies of human diseases have identified many variants associated with pathogenesis and severity. However, most studies have used only statistical association to assess putative relationships to disease, and ignored other factors for evaluation. For example, evolution is a factor that has shaped disease risk, changing allele frequencies as human populations migrated into and inhabited new environments. Since many common variants differ among populations in frequency, as does disease prevalence, we hypothesized that patterns of disease and population structure, taken together, will inform association studies. Thus, the population distributions of allelic risk variants should reflect the distributions of their associated diseases. Evolutionary Triangulation (ET) exploits this evolutionary differentiation by comparing population structure among three populations with variable patterns of disease prevalence. By selecting populations based on patterns where two have similar rates of disease that differ substantially from a third, we performed a proof of principle analysis for this method. We examined three disease phenotypes, lactase persistence, melanoma, and Type 2 diabetes mellitus. We show that for lactase persistence, a phenotype with a simple genetic architecture, ET identifies the key gene, lactase. For melanoma, ET identifies several genes associated with this disease and/or phenotypes related to it, such as skin color genes. ET was less obviously successful for Type 2 diabetes mellitus, perhaps because of the small effect sizes in known risk loci and recent environmental changes that have altered disease risk. Alternatively, ET may have revealed new genes involved in conferring disease risk for diabetes that did not meet nominal GWAS significance thresholds. We also compared ET to another method used to filter for phenotype associated genes, population branch statistic (PBS), and show that ET performs better in identifying genes known to associate with diseases appropriately distributed among populations. Our results indicate that ET can filter association results to improve our ability to discover disease loci.

Genetics of Plasminogen Activator Inhibitor-1 (PAI-1) in a Ghanaian Population.

Plasminogen activator inhibitor 1 (PAI-1), a major modulator of the fibrinolytic system, is an important factor in cardiovascular disease (CVD) susceptibility and severity. PAI-1 is highly heritable, but the few genes associated with it explain only a small portion of its variation. Studies of PAI-1 typically employ linear regression to estimate the effects of genetic variants on PAI-1 levels, but PAI-1 is not normally distributed, even after transformation. Therefore, alternative statistical methods may provide greater power to identify important genetic variants. Additionally, most genetic studies of PAI-1 have been performed on populations of European descent, limiting the generalizability of their results. We analyzed >30,000 variants for association with PAI-1 in a Ghanaian population, using median regression, a non-parametric alternative to linear regression. Three variants associated with median PAI-1, the most significant of which was in the gene arylsulfatase B (ARSB) (p = 1.09 x 10(-7)). We also analyzed the upper quartile of PAI-1, the most clinically relevant part of the distribution, and found 19 SNPs significantly associated in this quartile. Of note an association was found in period circadian clock 3 (PER3). Our results reveal novel associations with median and elevated PAI-1 in an understudied population. The lack of overlap between the two analyses indicates that the genetic effects on PAI-1 are not uniform across its distribution. They also provide evidence of the generalizability of the circadian pathway's effect on PAI-1, as a recent meta-analysis performed in Caucasian populations identified another circadian clock gene (ARNTL).

Human and Helicobacter pylori coevolution shapes the risk of gastric disease.

Helicobacter pylori is the principal cause of gastric cancer, the second leading cause of cancer mortality worldwide. However, H. pylori prevalence generally does not predict cancer incidence. To determine whether coevolution between host and pathogen influences disease risk, we examined the association between the severity of gastric lesions and patterns of genomic variation in matched human and H. pylori samples. Patients were recruited from two geographically distinct Colombian populations with significantly different incidences of gastric cancer, but virtually identical prevalence of H. pylori infection. All H. pylori isolates contained the genetic signatures of multiple ancestries, with an ancestral African cluster predominating in a low-risk, coastal population and a European cluster in a high-risk, mountain population. The human ancestry of the biopsied individuals also varied with geography, with mostly African ancestry in the coastal region (58%), and mostly Amerindian ancestry in the mountain region (67%). The interaction between the host and pathogen ancestries completely accounted for the difference in the severity of gastric lesions in the two regions of Colombia. In particular, African H. pylori ancestry was relatively benign in humans of African ancestry but was deleterious in individuals with substantial Amerindian ancestry. Thus, coevolution likely modulated disease risk, and the disruption of coevolved human and H. pylori genomes can explain the high incidence of gastric disease in the mountain population.