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Mushroom Power
More and more people are talking about mushroom supplements and their health benefits. Men's Health published an article with the 'best 5' supplements. We looked at 2 of the most popular mushrooms in these supplements. Lion's Mane & Cordyceps.
Lion's Mane: According to men's health some data suggests that Lion's Mane mushrooms are brain food. In other words, the aptly-named fungi can stimulate nerve growth and improve cognitive function. What's more, they're increasingly known for helping with anxiety and depression as well as boosting sleep quality.
We looked at the current data on this.
Limited data in general - lots done in japan/asia (no surprise)
Most of this limited data is mice/animal studies + in vitro
Human data is extremely limited and with low subject sizes and higher than commercial doses. (not impossible doses however)
When people ask us about taking Lion's Mane, we don't really know, but also, what outcome are you actually looking for? What makes you want to take it? It's supposed to be helpful for your brain, right? On what metrics are you basing this? How are you measuring things?
So let's talk about supplements. If you're taking creatine, you know what the outcome will be. Maybe you want to get a couple more reps on your heavy lifts. Maybe you're trying to get faster at sprinting, basically getting stronger.
If you're taking something like protein, you're trying to supplement your protein intake, stay fuller longer, maintain muscle, build muscle, etc. When you take cyclic Dextrin, the carbohydrate supplement, you know you're doing it for energy reasons. Increased performance in carb-requiring activities.
Why Lions Main? What's the actual result you want? Unless you know what the outcome is, how can you know? If it says, "Oh, it's to improve your cognitive abilities". What does that mean? And how much do you need to do different things? It's all a little unclear.
Cordyceps: Common Claims: Vascular benefits and immune support, Boost energy utilization during exercise
Once again after looking at the available data on this
Limited data in general - lots done in Japan /asia again
Most of this limited data is mice/animal
The human data that shows increased exercise performance uses high doses and is on UNTRAINED participants (limited subject size) - Trained participants: Data shows NO effect
Once again, we're not saying it doesn't do anything, but from a performance standpoint, we don't think it does. If you know you're prone to anxiety, or if you already have anxiety, it might help.
Sports and performance anxiety go hand in hand, so who knows? In general, we need more research. However, it's difficult to say to someone, yeah, you should take it.
If someone wants to get stronger, I'll say, oh, try taking creatine. You're struggling to get enough protein to build muscle? Take some protein, supplement your protein. If you're running sprints, try some cyclic dextrin or if you're swimming, etc. There's things I can recommend, but with this, it's like I don't know what it is you're trying to improve.
Meta-Analysis, Gold Science?
I'm sure you've seen us get excited about meta-analyses of certain topics before. This is if they're done and reviewed properly, because statistical errors can mess up calculations and lead to mistakes.
Meta-analyses take multiple papers and put them all together for analysis to come to a stronger conclusion about a subject. As Tom has said in the past:
Instead of having 1 witness to a crime you've got a 100
We've got a brand new paper here With Great Power Comes Great Responsibility: Common Errors in Meta-Analyses and Meta-Regressions in Strength & Conditioning Research. Kadlec et al. (2022)
The researchers identified the 20 most frequently cited meta-analyses in the field of strength and conditioning, then checked them for statistical errors. A staggering 85% of these papers had at least one statistical error.
However, it shouldn't be too scary because it's common for large papers like meta-analyses to have errors. The key is identifying them and taking them into account when making your conclusions.
The checklist below will help you read a meta-analysis. You can hear a full breakdown at 34:00 on the audio podcast.
Is the data reported at the group-level or participant-level?
Are there enough studies or participants to draw strong conclusions?
Are these studies similar enough to even think about combining their data?
Are the measured outcomes similar enough to even think about combining them?
Have effect sizes been calculated correctly?
Have they properly accounted for any samples contributing multiple effect sizes?
Have they used an appropriate statistical model?
Are there any implausibly large effects, implausibly narrow confidence intervals, or outliers?
Does the funnel plot actually look like a symmetrical funnel?
Is there a great deal of unexplained heterogeneity?
Thanks to the team at MASS for providing this check list. Those guys do great work in the field of research so be sure to take a look.
Articles & Studies Mentioned
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