We observe even better blood markers (and other biomarkers) on people doing CR.
No we don’t, we see immunosupression, elevation of some stress markers, and sex hormones crashing. The stronger support for IF is that you can lead a normal life and meet all the usual criteria we use for assessing health. With CR we improve some markers for health at the cost of others and hope we have chosen the correct ones to downgrade in our estimation of effectiveness for longevity.
If I based lifestyle changes on studies with similar levels of evidence to methionine restriction, I would be pulling a Ray Kurzweil and taking dozens of supplements. Rat/mouse models should NOT inform lifestyle changes. Their purpose is to explore hypothesis space cheaply.
Rat/mouse models should NOT inform lifestyle changes. Their purpose is to explore hypothesis space cheaply.
Your epistemology seems flawed. We have multiple lines of evidence and a proper Bayesian agent will take these all into account. This is especially the case when certain types of evidence will only be available after a period of several decades or even several generations. In such cases, we need to rely on the limited evidence we have at present.
In any case, your original point was that the evidence for methionine restriction was worse that the evidence for CR, which you claimed was “pretty bad”. Yet your grounds for concluding that the evidence for CR was “pretty bad” were partly based on practical considerations which do not apply to methionine restriction, so that argument doesn’t really work.
Let’s drop CR for now then and focus on Methionine. The hypothesis here is that I should be dropping foods with known health benefits like salmon[1] and whey[2] from my diet based on a rodent model. I can’t find any studies on human methionine restriction, do you have any pointers?
Also, is there any reason I couldn’t achieve the same effect with a methionine inhibitor like SAMe? Another plausible idea is that methionine is not a problem itself but is merely a symptom of a different issue, such as glycine deficiency (we don’t eat connective tissue much anymore).
The dairy meta-analysis you provide was supported by the “Dairy Innovation Australia Limited”, which makes me skeptical of their findings, given the multiple ways in which data can be aggregated to yield specific conclusions, and the evidence documenting the degree to which financial incentives can affect scientific findings. I haven’t looked at the other study, but I’m happy to accept that the evidence for the health benefits of fish is strong.
I haven’t researched the literature on methionine restriction extensively, but here’s a paper I found after a quick Google Scholar search.
More generally, I think having answers to questions of the following sort would be very helpful in these discussions: “To what degree do hypotheses confirmed by non-human animal models are later vindicated by experimental studies?” and “To what degree do hypotheses confirmed by correlational human studies are later vindicated by experimental studies?”
“To what degree do hypotheses confirmed by correlational human studies are later vindicated by experimental studies?”
That depends on what you mean by a ‘correlational study.’ People who analyze observational data with causality in mind spend a lot of time thinking about potential confounding and what to do about it. For example, here’s an analysis of a very large longitudinal dataset with the aim of determining a causal effect:
which does very sensible things. If you look at sensible analyses of observational data, then ‘the vindication rate’ will be related to how often the needed assumptions actually hold. If you look at non-sensible analyses (e.g. that aren’t adjusting for confounder bias, and so on), then it’s just garbage, no reason to expect better than chance then.
No we don’t, we see immunosupression, elevation of some stress markers, and sex hormones crashing. The stronger support for IF is that you can lead a normal life and meet all the usual criteria we use for assessing health. With CR we improve some markers for health at the cost of others and hope we have chosen the correct ones to downgrade in our estimation of effectiveness for longevity.
If I based lifestyle changes on studies with similar levels of evidence to methionine restriction, I would be pulling a Ray Kurzweil and taking dozens of supplements. Rat/mouse models should NOT inform lifestyle changes. Their purpose is to explore hypothesis space cheaply.
Your epistemology seems flawed. We have multiple lines of evidence and a proper Bayesian agent will take these all into account. This is especially the case when certain types of evidence will only be available after a period of several decades or even several generations. In such cases, we need to rely on the limited evidence we have at present.
In any case, your original point was that the evidence for methionine restriction was worse that the evidence for CR, which you claimed was “pretty bad”. Yet your grounds for concluding that the evidence for CR was “pretty bad” were partly based on practical considerations which do not apply to methionine restriction, so that argument doesn’t really work.
Let’s drop CR for now then and focus on Methionine. The hypothesis here is that I should be dropping foods with known health benefits like salmon[1] and whey[2] from my diet based on a rodent model. I can’t find any studies on human methionine restriction, do you have any pointers? Also, is there any reason I couldn’t achieve the same effect with a methionine inhibitor like SAMe? Another plausible idea is that methionine is not a problem itself but is merely a symptom of a different issue, such as glycine deficiency (we don’t eat connective tissue much anymore).
[1] http://circ.ahajournals.org/content/109/22/2705.short
[2] http://www.ncbi.nlm.nih.gov/pubmed/21338538
The dairy meta-analysis you provide was supported by the “Dairy Innovation Australia Limited”, which makes me skeptical of their findings, given the multiple ways in which data can be aggregated to yield specific conclusions, and the evidence documenting the degree to which financial incentives can affect scientific findings. I haven’t looked at the other study, but I’m happy to accept that the evidence for the health benefits of fish is strong.
I haven’t researched the literature on methionine restriction extensively, but here’s a paper I found after a quick Google Scholar search.
More generally, I think having answers to questions of the following sort would be very helpful in these discussions: “To what degree do hypotheses confirmed by non-human animal models are later vindicated by experimental studies?” and “To what degree do hypotheses confirmed by correlational human studies are later vindicated by experimental studies?”
That depends on what you mean by a ‘correlational study.’ People who analyze observational data with causality in mind spend a lot of time thinking about potential confounding and what to do about it. For example, here’s an analysis of a very large longitudinal dataset with the aim of determining a causal effect:
http://www.hsph.harvard.edu/wp-content/uploads/sites/1138/2012/09/ije_2009.pdf
which does very sensible things. If you look at sensible analyses of observational data, then ‘the vindication rate’ will be related to how often the needed assumptions actually hold. If you look at non-sensible analyses (e.g. that aren’t adjusting for confounder bias, and so on), then it’s just garbage, no reason to expect better than chance then.
Not sure of actual base-rate but I know it is very poor, which is why most researchers don’t take animal models very seriously.
Not sure, I think also very poor, but having a measure of this would be very valuable.