Clinical- and cost-effectiveness of LDL particle-guided statin therapy: A simulation study

AbstractWe used the Archimedes Model, a mathematical simulation model (Model) to estimate the clinical- and cost-effectiveness of using LDL particle concentration (LDL-P) as an adjunct or alternative to LDL cholesterol (LDL-C) to guide statin therapy. LDL-P by NMR has been shown to be a better measure of cardiovascular disease (CVD) risk than LDL-C, and may therefore be a better gauge of the need for and response to statin treatment. Using the Model, we conducted a virtual clinical trial comparing the use of LDL-C alone, LDL-P alone, and LDL-C and LDL-P together to guide treatment in the general adult population, and in high-risk, dyslipidemic subpopulations. In the general population, the 5-year major adverse cardiovascular event (MACE) relative risk reduction (RRR) of LDL-P alone compared to the control arm (LDL-C alone) was 5.0% (95% CI, 4.7–5.3; p p p p In the general population, the costs per quality-adjusted life year (QALY) associated with the use of LDL-P alone were $76,052 at 5 years and$8913 at 20 years and $142,825 at 5 years and$25,505 at 20 years with the use of both markers. In high-risk subpopulations, the use of LDL-P alone was cost-saving at 5 years; whereas the cost per QALY for the use of both markers was $14,250 at 5 years and$859 at 20 years for high-risk dyslipidemics, $19,192 at 5 years and$649 at 20 years for diabetics, and $9030 at 5 years and$7268 at 20 years for patients with prior CHD. In conclusion, the model estimates that using LDL-P to guide statin therapy may reduce the risk of CVD events to a greater extent than does the use of LDL-C alone and maybe cost-effective or cost-saving for high-risk patients

Cooperation, competition, and coalitions in enzyme-producing microbes: social evolution and nutrient depolymerization rates.

Extracellular enzymes represent a public good for microbial communities, as they break down complex molecules into simple molecules that microbes can take up. These communities are vulnerable to cheating by microbes that do not produce enzymes, but benefit from those produced by others. However, extracellular enzymes are ubiquitous and play an important role in the depolymerization of nutrients. We developed a multi-genotype, multi-nutrient model of a community of exoenzyme-producing microbes, in order to investigate the relationship between diversity, social interactions, and nutrient depolymerization. We focused on coalitions between complementary types of microbes and their implications for spatial pattern formation and nutrient depolymerization. The model included polymers containing carbon, nitrogen, or phosphorus, and eight genotypes of bacteria, which produced different subsets of the three enzymes responsible for hydrolyzing these polymers. We allowed social dynamics to emerge from a mechanistic model of enzyme production, action, and diffusion. We found that diversity was maximized at high rates of either diffusion or enzyme production (but not both). Conditions favoring cheating also favored the emergence of coalitions. We characterized the spatial patterns formed by different interactions, showing that same-type cooperation leads to aggregation, but between-type cooperation leads to an interwoven, filamentous pattern. Contrary to expectations based on niche complementarity, we found that nutrient depolymerization declined with increasing diversity due to a negative competitive effect of coalitions on generalist producers, leading to less overall enzyme production. This decline in depolymerization was stronger for non-limiting nutrients in the system. This study shows that social interactions among microbes foraging for complementary resources can influence microbial diversity, microbial spatial distributions, and rates of nutrient depolymerization