phenominal34
Member
All you did with all that is prove my point … just cause your at 1000 total test and 30 e2 doesn’t mean if your at 5000 total test that e2 automatically will be 150.
Switch from Arimidex to Aromasin
Rectangular hyperbola. More background on the MM kinetics math: https://febs.onlinelibrary.wiley.com/doi/full/10.1111/febs.16124forums.t-nation.com
View attachment 268162
From paper's methods section:
We employed two models to explore the relationship between testosterone and its metabolites. First, we used an empirical power law to assess the relationship of posttreatment circulating testosterone levels to E2 levels. We used exploratory plots stratified by age group, with scatterplot smoothing using generalized additive models (GAM) (24); these models apply semiparametric locally weighted smoothing to diagnose nonlinearities in associations. These results were used to motivate the fitting of parametric models of E2 on total testosterone and covariates using generalized linear models (GLM), assuming a γ distribution for outcomes and a log link function. The overall fit of the parametric models was measured by the Akaike Information Criterion (25)—a penalized likelihood-based statistic of model deviation from the data—for GLM and coefficient of determination (R2) for linear models. The statistical significance of individual regression coefficients was assessed using Wald tests. To assess the independent contributions of individual covariates to model fit while controlling for the influence of other variables, a forward stepwise fitting procedure was used. Results were considered statistically significant if null hypotheses of no association could be rejected at the 0.05 level. Analyses were performed using R (26) version 2.9.2 (R Foundation, Vienna, Austria).
We also analyzed the data using a mechanistic model in which the four relationships (i.e. total E2 vs. total testosterone, total DHT vs. total testosterone, free E2 vs. free testosterone, and free DHT vs. free testosterone) were modeled using rectangular hyperbolae, i.e. Y = A X/ (B + X). Four equations (with corresponding A and B values) were derived for young men and old men, respectively. Based on pharmacokinetic principles and Michaelis-Menten kinetics (see Appendix A, published on The Endocrine Society’s Journals Online web site at http://jcem.endojournals.org/ (http://jcem.endojournals.org)), the parameter A corresponds to the ratio of Vmax (the maximal rate of conversion of total or free testosterone to the metabolite) divided by MCR (the metabolic clearance rate of the total or free metabolite); the B parameter corresponds to the Km value for the enzymatic conversion of total or free testosterone to the metabolite. The model parameter estimation (including the se of the estimate, se, and R2 value) was performed using the 2D Michaelis-Menten equation curve fitting software available online [dead link so I removed]. Parameter estimates are derived using a downhill simplex method, with initial values derived from a genetic algorithm (personal correspondence from zunzun.com). The data points were equally weighted in the present analyses. Statistical comparison of the A and B parameters between young and old men were based on unpaired t tests. Because the results of the empiric power model and mechanistic model were similar, only the results of the mechanistic model are described in detail.
It’s not as simple as multiply by the same number ….
