GoodLyfe Anabolics/GH



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), 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.
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.

It’s not as simple as multiply by the same number ….
 
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.

It’s not as simple as multiply by the same number ….
You think that is all I did with that.

Understood.
 
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.

It’s not as simple as multiply by the same number ….

And keep thinking math works with gear you fucking idiot.

All you did with all that is prove my point

Have you ever read this book?

 
Lol …. “even you” huh, someone of your intelligence should be able to read one page before, nobody cares about your dorky ass vocabulary, search bar or read dum dum.
Well @Millard I believe I have cracked the code to become an esteemed member here in no time. I will have to up my game to see if I can muster up some of these high quality posts LMAO. That >3 like to 1 post ratio is so critical.

All great learnings.

And btw Phenom, I understand your pov that my initial comment may have been somewhat off-putting. I revised my statement above.

But just remember that math does not stop at y = mx+b where b=0 in your example above.

Good day Sir.
 
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Well @Millard I believe I have cracked the code to become an esteemed member here in no time. I will have to up my game to see if I can muster up some of these high quality posts LMAO. That >3 like to 1 post ratio is so critical.

All great learnings.

And btw Phenom, I understand your pov that my initial comment may have been somewhat off-putting. I revised my statement above.

But just remember that math does not stop at y = mx+b where b=0 in your example above.

Good day Sir.
Dude please. This isn’t T-Nation. Nobody here gives a fuck what kind of member or “guru” you were. You don’t need to keep shitposting to get your posts up hoping you become Elite or whatever.

Now I can see why you were banned. Just spamming every thread. Let it go man, there’s more to life than steroid forums. You don’t need to be a hero here.
 
Dude please. This isn’t T-Nation. Nobody here gives a fuck what kind of member or “guru” you were. You don’t need to keep shitposting to get your posts up hoping you become Elite or whatever.

Now I can see why you were banned. Just spamming every thread. Let it go man, there’s more to life than steroid forums. You don’t need to be a hero here.
Yeah. Thanks for the guidance and reaching out.

Amen on the more to life comment.

Hero? Hardly. But I get your point to try and keep it more y=mx or simpler for some members.

Shitposting? Nah. That appears more up @phenominal34 's alley. Helping someone with their math and logic is spam? You say so.

I aspire to be an unguru. Guru is so common these days.
 
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Yeah. Thanks for the guidance and reaching out.

Amen on the more to life comment.

Hero? Hardly. But I get your point to try and keep it more y=mx or simpler for some members.

Shitposting? Nah. That appears more up @phenominal34 's alley. Helping someone with their math and logic is spam? You say so.

I aspire to be an unguru. Guru is so common these days.

CB6E583E-2A9F-4FD6-B77C-64179E8489C7.gif
 


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), 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.
So older guys get higher e2 for the same level of testosterone. Did they control for body fat?
 
So older guys get higher e2 for the same level of testosterone. Did they control for body fat?
Remember they measured E2 and not fE2 so IIRC the older vs younger groups get confounded with their SHBG (with older guys having the higher SHBG). Too bad researchers did not use and track fT/fE2 in all this work but of course we are still trying to come up with harmonized measurement techniques. Hence folks are left confused having to sort through TT/E2 measurements when the free hormones would tell the story much more clearly.

And damn it I keep forgetting all that stuff is behind member wall. Sorry about that.

See table 1. Big differences.
 
Sheesh all I did was post my blood test results didn't mean to piss in everyone's Cheerios
No worries Brother. We are just calibrating in here haha. Rectangular hyperbolas ain't for everyone it seems. Take care.

Way to go using caution as you dip your toes in the anabolic waters.
 
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No worries Brother. We are just calibrating in here haha. Rectangular hyperbolas ain't for everyone it seems. Take care.

Way to go using caution as you dip your toes in the anabolic waters.
Airing on the side of caution can only benefit my health in the long term. Which sounds weird saying while I'm a cycle lol
 
Which sounds weird saying while I'm a cycle lol
Weird? Not at all. 250 to 300 mg/week as a first "cycle" is smart for someone already on TRT or considering the cruise and blast approach. 1 say 16 week cycle every year is sorta dumb in its typical format as it is 1 step forward and usually 1 step back unless you cycle, take 8 weeks off, then cycle again. Then it is more like 1 step forward and 0.25 to 0.5 steps back.


You see all these "start with 500 mg/week recommendations" with a front load sometimes which may be *fine* if you are young and healthy. But if you have an unknown heart issue or arrythmia it is not good to learn about it at the hospital cause you went into AFIB 2 weeks into your first blast at 500 mg/week with 1000 mg/week frontload.

Keep up the great work.
 
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Hi all,

I’m still wondering if anyone has run GL baby hulk center stage for a cycle? Experiences of any kind is appreciated. Here is my concept:

12 week run
GL Baby hulk TPP/NPP. 200/200 week
GL mast E 300-400 mg week
Aromasin 12.5 E3D
Telmisartan 40mg day

Mast e for its Sarm and noted mood lift since I respond well to it. Also, to hopefully counteract any NPP mental effects. Telmisartan for BP and it’s supposedly heart protection against nandrolone.
 
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