As a scientist there are a few things to drive my opinion it’s overblown:
- you can’t prove a negative (“this dye isn’t harmful”), all you can do is run a panel of tests and interpret the data
- food additives are tested in animal models at levels that are several orders of magnitude higher than what any human might consume. Animals are then autopsied to see if there are *any abnormalities in any organ system. This is done with several species.
- Cell models are also used to test things like carcinogeniticity, cell-specific toxicity, toxicity of chemicals formed when the additive breaks down, toxicity of trace impurities, etc. It’s quite extensive.
- Data is rarely 100% clear. You may get a signal in some animal model at 1000x expected exposure. What does it mean? Plenty of animals exhibit toxicity not seen in humans and slight abnormalities may or may not translate to humans. But the FDA tends to err on the side of caution, especially with food additives as there is little benefit to offset any risk.
- It’s not unusual to run 10 studies, find 9 are negative, 1 shows a signal but it’s not statistically significant. What RFK tends to do is cherry pick the 1 study and say “there is data to prove it’s harmful!” That’s not how science works. You look at the totality and quality of the data and make the best conclusion you can. Is it 100% foolproof? Of course not, but it’s pretty solid evidence that likely no harm will result.
- The one risk is the “unknown unknowns”. If you don’t know what to look for, you’ll never find it. But that’s true with everything we ingest - drugs, natural foods (peanuts and aflatoxin!), synthetic chemicals, water purification chemicals, etc, etc. We can only do the best with the knowledge we have.
- If you see 10 studies and 5 are positive (barely) and 5 are negative, either the effect is really small (I.e. you should worry more about other things) or it’s just noise.
- you can’t prove a negative (“this dye isn’t harmful”), all you can do is run a panel of tests and interpret the data
- food additives are tested in animal models at levels that are several orders of magnitude higher than what any human might consume. Animals are then autopsied to see if there are *any abnormalities in any organ system. This is done with several species.
- Cell models are also used to test things like carcinogeniticity, cell-specific toxicity, toxicity of chemicals formed when the additive breaks down, toxicity of trace impurities, etc. It’s quite extensive.
- Data is rarely 100% clear. You may get a signal in some animal model at 1000x expected exposure. What does it mean? Plenty of animals exhibit toxicity not seen in humans and slight abnormalities may or may not translate to humans. But the FDA tends to err on the side of caution, especially with food additives as there is little benefit to offset any risk.
- It’s not unusual to run 10 studies, find 9 are negative, 1 shows a signal but it’s not statistically significant. What RFK tends to do is cherry pick the 1 study and say “there is data to prove it’s harmful!” That’s not how science works. You look at the totality and quality of the data and make the best conclusion you can. Is it 100% foolproof? Of course not, but it’s pretty solid evidence that likely no harm will result.
- The one risk is the “unknown unknowns”. If you don’t know what to look for, you’ll never find it. But that’s true with everything we ingest - drugs, natural foods (peanuts and aflatoxin!), synthetic chemicals, water purification chemicals, etc, etc. We can only do the best with the knowledge we have.
- If you see 10 studies and 5 are positive (barely) and 5 are negative, either the effect is really small (I.e. you should worry more about other things) or it’s just noise.