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Biomedical subjects

Carter Coberley

Publications and source records attributed to Carter Coberley.

2 recordsLinked to original sources

Improvement of LDL-C laboratory values achieved by participation in a cardiac or diabetes disease management program.

Poor lipid control is a risk factor for cardiovascular diseases and diabetes complications. Frequently, however, patients with these diseases do not achieve blood lipid levels recommended by current standards of care. A retrospective study of 67,244 members eligible for disease management (DM) was initiated to evaluate the ability of interventions to promote improvement in low-density lipoprotein cholesterol (LDL-C) laboratory values for people with cardiovascular diseases or diabetes. The baseline trend in improving LDL-C values in the absence of DM was established. A two-year period prior to the start of the DM intervention was examined to measure the mean percent change in LDL-C values that was occurring in the population. The mean percent change observed for this pre-intervention group was then compared to the change in LDL-C values observed during the DM study period. A significant reduction in elevated LDL-C values (F-test; p < 0.0001) was observed for members who participated in the DM interventions, even when elevated LDL-C was defined as low as > or =70 mg/dL. Members with LDL-C values within threshold limits maintained these levels during the DM program. The significant reduction in elevated LDL-C values and maintenance of optimal values (< 100 mg/dL) was observed over the course of 3 years of participation in a DM program. A subset of the population also was examined to assess the impact of telephone intervention on reducing elevated LDL-C values. A significant relationship between receiving care calls and reduction in elevated LDL-C levels was observed; members who received calls achieved up to a 32.5% relative reduction in elevated LDL-C values compared to members who did not receive calls. In conclusion, these findings demonstrate the ability of DM interventions to assist a large, geographically diverse member population in reducing a clinical laboratory value.

Cardiovascular Diseases↗

Match/X, A gene expression pattern recognition algorithm used to identify genes which may be related to CDC2 function and cell cycle regulation.

Large-scale microarray gene expression studies can provide insight into complex genetic networks and biological pathways. A comprehensive gene expression database was constructed using Affymetrix GeneChip microarrays and RNA isolated from more than 6,400 distinct normal and diseased human tissues. These individual patient samples were grouped into over 700 sample sets based on common tissue and disease morphologies, and each set contained averaged expression data for over 45,000 gene probe sets representing more than 33,000 known human genes. Sample sets were compared to each other in more than 750 normal vs. disease pairwise comparisons. Relative up or downregulation patterns of genes across these pairwise comparisons provided unique expression fingerprints that could be compared and matched to a gene of interest using the Match/X trade mark algorithm. This algorithm uses the kappa statistic to compute correlations between genes and calculate a distance score between a gene of interest and all other genes in the database. Using cdc2 as a query gene, we identified several hundred genes that had similar expression patterns and highly correlated distance scores. Most of these genes were known components of the cell cycle involved in G2/M progression, spindle function or chromosome arrangement. Some of the identified genes had unknown biological functions but may be related to cdc2 mediated mechanism based on their closely correlated distance scores. This algorithm may provide novel insights into unknown gene function based on correlation to expression profiles of known genes and can identify elements of cellular pathways and gene interactions in a high throughput fashion.

Algorithms↗