Natália Coelho Mendonça(Natália Mendonça)
Of note, your charts with simulated data don’t take into account that there was a midcentury slowdown in the increase in BMI percentiles, which, as I said in the post, probably contributes to the appearance of an abrupt change in the late 20th century.
If I ate like that, not only would I get obese and diabetic
What’s the best evidence we have of that, in your opinion?
I think that, when you cite that chart, it’s useful for readers if you point out that it’s the output of a statistical model created using NCHS data collected between 1959 and 2006.
Thank you for the feedback, I’ll try to rephrase that section. It does seem that a lot of the disagreement here is semantic.
Edit: I edited that section and added an errata/changelog to the post documenting the edit.
I not believe that your brain has a lipostat: https://www.frontiersin.org/articles/10.3389/fnut.2022.826334/full.
There’s an extra period in the URL, so the link doesn’t work. But this is intriguing and I’ll look into it — thank you!
Aerobic exercise has no effect on resting metabolic rate, while resistance exercise increases it: https://www.tandfonline.com/doi/abs/10.1080/02640414.2020.1754716. The claim in the article you link (which even the article treats with a degree of skepticism) may be explained by the runners running more efficiently as the race progressed: it’s certainly not plausible that the athletes’ resting metabolic rates dropped by 1,300 kcal/day, and no such claim is made in the article linked in support of the claim by the first article (https://www.science.org/content/article/study-marathon-runners-reveals-hard-limit-human-endurance).
I think the most important & interesting finding in Herman Pontzer’s energy expenditure research is that hunter-gatherers don’t burn more energy than people in market economies after adjusting for body mass, even though they exercise more. From the ground-breaking Pontzer et al. (2012):
These lines are the output of a statistical model, based on cohort- and age-associated changes in BMI observed in NCHS data collected between 1959 and 2006. I edited the post to make that clearer.
On not getting contaminated by the wrong obesity ideas
I myself have 4-year timelines
Is that a mean, median or mode? Also, what does your probability distribution look like? E.g. what are its 10th, 25th, 75th and/or 90th percentiles?
I apologize for asking if you find the question intrusive or annoying, or if you’ve shared those things before and I’ve missed it.
Oh my, I completely misunderstood your previous comment. I apologize.
ETA: I’d completely misunderstood Elizabeth’s comment. This comment I wrote does not make sense as a reply to it. I’m keeping my comment here with this disclaimer on the top because I wanted to make these points somewhere, but keep that in mind.
the fact that we’ve known about it for >10 years and it hasn’t spread widely suggests to me that it’s unlikely to be a silver bullet.
I don’t know exactly what you mean by “unlikely to be a silver bullet,” but I want to outline the reasons I think this diet is nowhere close to being a $20 bill lying on the sidewalk, as some people seem to think it is:
very restrictive diets are very socially costly to follow. If you regularly eat from college dining halls, cafeterias at work, restaurants, other people’s homes, etc. you’ll have a very hard time following an all-potato diet. Compare it to being vegan — outside of vegan-friendly places, it can be quite inconvenient to be one, and following an all-potato diet seems like it would be significantly worse than that.
very restrictive diets might cause weight loss that is too rapid to be healthy. Losing weight too quickly increases your chances of getting refeeding syndrome (if/when you go back to eating normally) and gallstone formation by quite a lot.
It is unclear that this diet doesn’t have the same exact problems as all other diets, that is, a high attrition rate[1] and weight regain upon cessation.
Investigating diets seems relatively uninteresting when (1) diets have a huge attrition+weight regain problem and (2) semaglutide and tirzepatide alone would massively reduce obesity rates if they were more popular, and there are drugs in preclinical trials that seem even more promising
- ^
I hope no one is taking the attrition rates calculated in their post at face value, given that all of their data is from people who literally signed up for a potato diet and hence there is a very obvious selection effect at play. Even if you do take them at face value, however, the attrition rate was like 40%-60% after 4 weeks, depending on how you slice it, compared to 18.8% after 3 months in this study that they mentioned[2], and ~30-50% per year in general in the diet studies they talked about.
- ^
They cited this study as having a “56.3%” attrition rate. I think they were probably referring to the fact that the attrition rate was 53.6% (not 56.3%) after 12 months. I don’t know why they chose to report that number, when the study also reported a 3-month attrition rate, which is much closer to the timescale of their own diet.
The next step would be a more serious experiment like the Potato Camp they mentioned.
This is puzzling to me. Randomizing people to different kinds of somewhat restrictive diets[1] seems like a way cheaper and more obvious experiment to test some of SMTM’s hypotheses, such that the potassium in potatoes clears out lithium or whatever.
It seems to me that they would have incurred little additional cost if they had randomized people in this study they already did, so I am somewhat confused about the choice not to have done that.
- ^
I say “somewhat restrictive” because I’m reluctant to advocate very restrictive diets, given the very low caloric intake reported by some people in SMTM’s blog post, and the increased risk of gallstones and refeeding syndrome that people incur by eating that little.
- ^
This metabolic ward study by Kevin Hall et al. found what the hyperpalatability hypothesis would expect.
I apologize for commenting so much on this post. But here is more evidence that, contra SMTM, being underweight is a lot less common now, not more common:
Underweight rates have decreased almost monotonically in the US over the past several decades.
The same trend can be seen in the rest of the world (the purple category is the percentage of the population that is underweight):
I don’t know why they say that being underweight is more common now, given that that is literally the opposite of the truth, and given that it is quite easy to figure that out by Googling.
It is true that the variance in BMI has increased, but that is entirely due to higher BMIs being more common. Here are (sampling weight-weighed) KDEs of the distributions of BMI in the early 70s (orange) versus 2017-2020 (blue) in the United States, using data from NHANES:
The code I used to create this plot is here.
Update: I have now looked into the raw TSH data from NHANES III (1988-1994) and compared it with data from the 2011-2012 NHANES. It seems that, although median TSH levels have increased a bit, the distribution of serum TSH levels in the general population aged 18-80 (including people with thyroid disorders) has gotten more concentrated around the middle; both very high levels (characteristic of clinical or subclinical hypothyroidism) and very low levels (characteristic of clinical or subclinical hyperthyroidism) are less common in the 2011-2012 NHANES compared to NHANES III. You can see the relevant table here. There might be bugs in my code affecting the conclusion of the analysis.
This paper, which pretty much used the same NHANES surveys, looked at a somewhat different thing (thyroid levels in a reference population without thyroid disorders or other exclusion criteria) but it seems to report the same finding w.r.t. high TSH levels: a lower proportion of the population in the latest survey meets the TSH criteria for clinical or subclinical hypothyroidism.
My last comment addresses this. They cover a broader range of methodologies. Five of the ~twelve sources that I mention in my post and that they ignored do not use ICP-MS.
On top of picking 5 of the ~20 estimates I mentioned to claim that low estimates of dietary lithium intake are “strictly outnumbered” by studies that arrive at much higher estimates, they also support that claim by misrepresenting some of their own sources. For example,
They say that “Magalhães et al. (1990) found up to 6.6 mg/kg in watercress at the local market,” but the study reports that as the lithium content per unit of dry mass, not fresh mass, of watercress (which the SMTM authors do not mention). This makes a big difference because Google says that watercresses are 95% water by weight.
They do the same thing with Hullin, Kapel, and Drinkall (1969). They do not mention that this study dried lettuce before measuring its lithium concentration, and reported lithium content per unit of dry mass. Google says that lettuce is 95% water by weight too, so this matters a lot.
They also mention a lot of other dry mass estimates, such as those from Borovik-Romanova (1965) and Ammari et al. (2011). This time they do disclose that those estimates are for dry mass, but they nevertheless present those estimates as contradicting Total Diet Studies, as if they were measuring the same kind of thing, when they are not.
Notably, they say “Duke (1970) found more than 1 mg/kg in some foods in the Chocó rain forest, in particular 3 mg/kg in breadfruit and 1.5 mg/kg in cacao,” and fail to mention that most of the foods in Duke (1970) have less than 0.5 mg/kg of lithium.
Also, only one of the examples SMTM used to claim that the Total Diet Studies are “outnumbered” actually attempted to quantify dietary lithium intake,[1] whereas almost all of the studies I’ve mentioned do that. This is important because a lot of the sources they cite that we don’t have access to (there are several of those) could be measuring lithium concentration in plant dry matter, as a lot of their sources that are available do, in which case seemingly high concentrations do not imply high dietary consumption.
Moreover, a lot of their post is focused on speculating that ICP-MS (the technique used by most studies) systematically underestimates lithium concentration. However:
Van Cauwenbergh et al. (1999) use atomic absorption spectroscopy (AAS) instead of ICP-MS, and arrive at the second-lowest dietary lithium intake estimate I have ever found,
Iyengar et al. (1990) mention a lot of NA-MS measurements, all of which match the low estimates I’ve found,
Hamilton & Minski (1972) use spark source mass spectrometry (SSMS),
Evans et al. (1985) use flame atomic emission spectrophotometry, and
Clarke & Gibson (1988) use NA-MS.
All of those find very low concentrations of lithium in food.
Moreover, they themselves mention a paper that uses ICP-MS and finds high concentrations of lithium in food in Romania (Voica et al. (2020)).
These studies are a substantial fraction of all of the studies on lithium concentration in food that we have. So it seems to me that their whole focus on ICP-MS, and their claim that it “gives much lower numbers for lithium in food samples than every other analysis technique we’ve seen,” does not seem warranted.
Again, I don’t think that studies that find high concentrations of lithium in food are necessarily wrong. There is no market pressure for food to have 1 µg/kg rather than 1000 µg/kg of lithium, or the other way around, the way that there is market pressure for meals to have e.g. carbohydrate/fat ratios and energy densities within a specific optimal range. Consumers do not care about whether lithium concentration is 1 µg/kg or 1000 µg/kg. And we know that lithium concentration in e.g. water varies a lot according to lithology and climate, so we shouldn’t expect this to be uniform around the world. So I don’t see how it must be the case (as the SMTM authors claim) that all studies that find low concentrations are wrong.
- ^
The example was Schrauzer (2002), which bases its estimates on hair concentration rather than actual food measurements. Ken Gillman says that this paper “has a lot of non-peer-reviewed and secondary references of uncertain provenance and accuracy: it may be misleading in some important respects.” Also, interestingly, as I mentioned in my post, the highest estimate Schrauzer (2002) provides for dietary lithium intake is from China, not really a country with a huge obesity problem.
(Note that the Ken Gillman blog post has a typo: it says that the “typical total daily lithium intake from dietary sources has been quantified recently from the huge French “Total Diet Study” at 0.5 mg/day,” a value that is 10x too high.)
The SMTM authors just released a post (a) addressing some of the Total Diet Studies I found, where by “addressing” I mean that they picked a handful of them (5) and pretended that they are pretty much the only studies showing low lithium concentrations in food. (They don’t mention this blog post I wrote, nor do they mention me.)
Their post does not mention any of the following studies that were mentioned in my post, and that found low lithium concentrations in food:
Canada’s 2016-2018 TDS
Marcussen et al. (2013), from Vietnam
Turconi et al. (2009), from Italy
Noël et al. (2010), from France
Van Cauwenbergh et al. (1999), from Belgium, which also mentions estimates from:
Canada (Clarke & Gibson (1988))
Italy, Spain, Turkey and the US (Iyengar et al. (1991))
Japan (Shimbo et al. (1996)), and
the UK (Parr. etal. (1992))
The 3 estimates mentioned in this review from the WHO (ultimately from Iyengar et al. (1990)), from:
Turkey,
Finland, and
the US
The estimates mentioned in the EPA report, including those from:
Hamilton & Minski (1972), from the UK
Evans et al. (1985), also from the UK
Given that they say that the 5 TDSs they picked “disagree with basically every other measurement we’ve ever seen for lithium in food” (and repeat this point quite a few times in their post) it does not seem that they have read my post yet.
FT4 is not the same thing as T4. From Medical News Today:
In adults, normal levels of total T4 range from 5–12 micrograms per deciliter (mcg/dl) of blood. Normal levels of free T4 range from 0.8–1.8 nanograms per deciliter (ng/dl) of blood.
I haven’t converted these densities to molarities, so I haven’t compared these ranges with those provided by the ’88-’94 paper, but this distinction seems relevant.
What do you mean by “real money”? What effects on the world does it have that “fake money” doesn’t? M1 in the United States increased a lot during the COVID-19 pandemic, does that mean that the US dollar is no longer “real money”?
You seem to be claiming (though correct me if I’m wrong) that expansionary monetary policy can’t achieve its objectives. What makes you believe that?
I understand that excessive money-printing that leads to very high inflation can decrease confidence in a currency and make people purchase another currency if they’re able to do so. However, that seems meaningfully different from having a central bank try to print enough money to get to ~2% YoY inflation from a baseline of zero or negative inflation.
(Note: I don’t know much about monetary policy and could be confused about something.)