Natália Coelho Mendonça(Natália Mendonça)
I am not aware of a low BMI population in which consumption of processed western food is just as common as it is in high-obesity regions. This seems to be an important counterexample.
Update: I found a few papers from villages in Argentina with very high lithium exposure, one of which (whose subjects had urinary lithium concentrations ranging from 0.1-14 mg/L) found a positive association between lithium excretion and BMI (r=0.11), and did find that at such levels lithium increased plasma TSH levels. But only 17% of participants were obese,[1] even though the average urinary concentration was > 4 mg/L.
In the other one (which I think I’d seen before, actually) serum lithium concentration (which strongly correlated (0.84) with urinary excretion) was not found to be associated with BMI. The range of urinary concentration was 0.105–4.600 mg/L. Interestingly, higher exposure seemed to have been associated with smaller body size (both height and weight) for the adults in the study as well as the newborn children.
their average age was 37, median 34, and at that age people are pretty close to the highest BMI they’ll ever have, according to NHANES data.
If semaglutide works universally, or nearly so—and early studies are very promising—then that might be a strong hint as to what is causing the obesity epidemic.
Relatedly, this seems to be the distribution of weight changes on 15 mg of tirzepatide + lifestyle interventions, compared to lifestyle interventions alone (over 72 weeks, I think):
Ege Erdil was referring to a flurry of downvotes that this post got within ~30 minutes of being posted. I don’t remember the exact number, but at the time he made that edit the karma count was quite low due to them.
none of the studies you list can help very well to understand what lithium’s effects are at a normal dietary level.
This study found no association between serum lithium concentration in the general population in Germany and BMI. Also, as I mentioned in the post, the correlation between log(water lithium levels) and log(obesity %) across Texas counties is negative.
In that paragraph, I was addressing your claim that we don’t have evidence that the weight gain caused by lithium disappears at dietary doses.
After learning that 5% of the population has subclinical hypothyroidism, I’m actually more inclined to believe that normal doses of dietary lithium may be causing the obesity epidemic than I was before reading this post. However, my credence is still well below 50%, so I also still agree with your post’s title.
I’m a bit confused about this update. A lot of chronic diseases are very common. And as I showed, hypothyroidism has not been increasing over time. It might be decreasing. And, again, the geographical pattern of hypothyroidism does not match the geographical pattern of obesity.
The fact that lithium shows a dose-dependent response at therapeutic doses allows for dietary doses in the neighborhood of 20 ug/day to cause weight gain. What we’d need is evidence that the weight gain caused by lithium disappears at dietary doses.
We don’t have that here. The closest is the Rinker study, which found 13⁄28 MS patients reported weight gain and 10⁄28 reported weight loss. More patients gained than lost weight, so if anything it supports the conclusion that low-dose lithium can lead to weight gain.
Let us not conflate different meanings of “low-dose lithium” here. In the Rinker study, the dose that patients took was still more than 1000x greater than what people in e.g. New Zealand get from their food. That is not a small difference.
Relevantly, this study found no association between serum lithium concentration in the general population in Germany and BMI. Also, as I mentioned in the post, the correlation between log(water lithium levels) and log(obesity %) across Texas counties is negative.
It’s very clear to me that it’s fine (and often great!) to investigate implausible theories. It just seems to me that the SMTM authors are doing a very bad job at actually pursuing the truth, as demonstrated by the facts that, e.g.
they wrote a “literature review” that only includes studies that are outliers, refused to address the fact that they are outliers, and are actively trying to prevent their readers from knowing that (by refusing to approve my comment on their post despite having had the time to approve several comments that were made afterward)
they have misrepresented their sources several times (as I show in this section and this comment) and have refused to correct their posts after being told about it
they seem oddly uninterested in all of the other common side effects that lithium causes at therapeutic doses, even though it’s very easy to know that those side effects are significant by merely reading the Wikipedia page on lithium salts
they seem oddly uninterested in whether therapeutic doses of lithium cause enough weight gain to explain the obesity epidemic
I also think it would be more helpful if they told readers their credences on their hypotheses, and what led them to reach those credences. But this is a minor point compared to the 4 above.
FWIW, the first time I contacted them about those studies was 15 days before the publication of this post.
400 extra calories over a year (assuming 3500 calories = 1 pound) is an extra 41.7 pounds per year.
In reality, you’ll stop gaining weight at some point if you increase your caloric intake once and never change it again, because your energy expenditure will rise.
But even taking that into account, it does seem to me that 456 extra kcal per day is way too much.
Here’s an illustrative calculation. Herman Pontzer has the following equations relating body weight to total daily energy expenditure in his book (figure 3.4):
The average woman in the US weighed 65.5 kg in the 1970s, and 78 kg in the 2010s, so this predicts a TDEE increase of only 134 kcal for women. For men, the figure is 160 kcal. Those numbers are about a third of 456 kcal. So yes, a 456 kcal in average daily energy intake would be a jaw-dropping increase.
Of course, this 456 kcal number is based on self-report data, so it’s not likely to be that accurate. Stephan Guyenet mentions a better estimate on The Hungry Brain based on food disappearance data from the USDA, which is only 218 kcal/day, much closer to my estimates of how much TDEE has changed.
I agree with Eliezer et al. that CICO by itself cannot explain the obesity epidemic, but “a 456 kcal increase is not that much” is a bizarre argument.
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It’s Probably Not Lithium
Note: controlling for SES, altitude and race essentially eliminates this correlation (it becomes 0.026642, p=0.26, n=1764 counties, essentially no different from what you’d get by random chance.)
Here are a few jointplots I created with seaborn (“percent_obese” is from Elizabeth’s original dataset, “OBESITY_CrudePrev” is from the PLACES 2021 Zip Code Tabulation Area-level obesity prevalence estimates):
Yesterday I learned that the CDC actually provides zip code-level (well, technically, Zip Code Tabulation Area-level) obesity rate estimates on their website. Using them, these are the correlations I found, for contaminants with >10,000 entries:
Unfortunately, you can see that there is a very low correlation between the obesity rates in this ZCTA-level dataset (“OBESITY_CrudePrev”) and the “percent_obese” column in your dataset (0.54). As far as I know, all of these small area obesity rate estimates are created with fancy statistical modeling and interpolation to deal with missing data and small sample sizes, so this probably reflects differences in statistical methodology.
I don’t know which dataset is better. And, to make matters worse, when looking into this I found county-level obesity rate estimates on the CDC website (from the Diabetes Surveillance System) that have very low correlations with both of these!
(“DSS” stands for Diabetes Surveillance System, “PLACES” is the CDC project that created ZCTA-level estimates and also has its own county-level estimates, and “CHR” refers to the County Health Rankings. Elizabeth’s dataset uses data from the 2021 edition of the CHR. The 2022 edition uses a dataset identical to the 2021 release of PLACES.)
Here are a few differences between the datasets that I’ve noticed:
The PLACES and 2021 CHR estimates substantially negatively correlate with median household income and % Asian, but the DSS estimates barely do at all. (N = almost all counties).
The PLACES estimates weakly positively correlate with log(groundwater lithium concentration).[1] By contrast, lithium and log(lithium) are both weakly negatively correlated with the DSS and 2021 CHR obesity estimates. (N = approximately 1⁄10 of all counties).
The DSS estimates have sharp state boundaries: Texas and Georgia, for example, look a lot less obese than surrounding states. Those sharp borders do not exist in the 2021 CHR or 2021 PLACES estimates:
PLACES is a new project, so most of the older county-level obesity estimates seem to be from the DSS. But the DSS has changed its method of making estimates recently, and this seems to have had substantial consequences: a substantial negative correlation with income had been found in their estimates in 2013, before the change, even though I could find no correlation with the DSS’s new estimates.
I hope someone tells me that I downloaded the wrong DSS datasets somehow, because all of them are really weird and I don’t know what to do with them. The lack of a meaningful correlation between % Asian and obesity rate in those datasets, despite the fact that Asian Americans are much less likely to be obese than everyone else in the US, is extremely suspicious, as are the sharp state borders. But I probably shouldn’t just ignore them. For now, I guess I might just take a reasonably-weighted average of all datasets and use that when investigating what correlates with obesity rates?
I’d also want to know where the 2021 CHR dataset comes from? This page (control-F “2021”) seems to indicate they use PLACES data, but this document indicates they use 2017 DSS data, and in practice, their dataset doesn’t correlate well with either of those. I am probably misunderstanding something. Or maybe that data is from before the DSS changed their methodology.
I obtained groundwater lithium concentration data from here. The dataset provides explicit coordinates for a subset of the wells, and that was the data I used to obtain county-level lithium concentration estimates. I haven’t managed to figure out the coordinates for the wells in the rest of the dataset.
Many of you have read Slime Mold Time Mold’s series on the hypothesis that environmental contaminants are driving weight gain. I haven’t done a deep dive on their work, but their lit review is certainly suggestive.
I think this is as good a place as any to point out that the SMTM authors have been repeatedly misleading about their evidence and unwilling to correct their mistakes, both on A Chemical Hunger and elsewhere. Here are a few examples that come to my mind at the moment:
They claim that Texas “tends to be more obese along its border with Lousiana [sic], which is also where the highest levels of lithium were reported,” but their own source says that lower levels of lithium, not higher, are found along Texas’s border with Louisiana. A commenter on their post has pointed out that error, as have I on a Twitter thread, but the authors have not edited their post or addressed this in any other way. (Incidentally, the correlation between drinking water lithium levels and obesity rates across Texas counties is negative).
They claimed on Twitter that geospatial associations between drinking water contaminants and obesity rates in the US are probably not confounded by SES, because “SES isn’t really associated with obesity rates.” However, the correlation between obesity and ln(income) across n = 3110 U.S. counties was −0.486 in 2013, and my own analysis of 2019 data suggests the correlation was −0.65 in that year (using median household income data). [1] [2] [3] (Their response to the 2013 data [4] is pretty much just “this correlation didn’t exist 30 years ago,” but I don’t see how that supports their statement that “SES isn’t really associated with obesity rates,” since that’s a statement in the present tense rather than the past tense.)
They claim that hypoxia probably cannot explain the effect of altitude on obesity, saying that “exercise in a low-oxygen environment does seem to reduce weight more than exercise in normal atmospheric conditions, but not by much.” However, when you read the abstract they linked to, you see that what they are calling “not by much” is a 60% increase in weight loss.
In several posts, they claim that wild animals have been getting more obese, citing Klimentidis et al. (2010). However, that paper does not make that claim; it doesn’t even examine body weight data from wild animals at all. When confronted about this on Twitter, they provided evidence that some white-tailed deer populations under increased predation from humans have been getting heavier over the past several decades, but there’s archeological evidence that they are simply returning to their normal historic body size after being smaller than normal for a while due to a temporary decrease in predation by humans (which increased their population density and thus competition for food). For sources and more details, see this Twitter thread.
(Unrelated to obesity) there’s a post in which they claim that “Sicilian lemons really ARE more like polar bear meat than they are like West Indian limes, at least for the purposes of treating scurvy” (implying that Sicilian lemons have lower vitamin C content than West Indian limes and polar bear meat). I investigated this and found that West Indian limes have ~60% of the vitamin C concentration of lemons, and that polar bear meat has much less vitamin C than either (but that all three of those can still prevent scurvy if eaten regularly at not-extremely-large portions, and lemons and limes both have enough to treat it).[5] They have been contacted about this, and their response was that we don’t know whether historical Sicilian limes had enough vitamin C to treat scurvy or not. Clearly, that is different from asserting (as they do in the post) that we know they don’t have enough vitamin C. But they have not edited their post.
ETA: What I initially said on point 5 was wrong (specifically, I embarrassingly confused lemons with limes at some point), and I have now fixed it.
Individual-level data yield a much weaker correlation (in my own analysis of NHANES 2017-2020 data, the correlation is −0.14 for white women in their 30s and 40s, and −0.05 for white men of the same age). But NHANES only records income levels in multiples of the poverty line up to 5, and individual-level data is known to be noisier than county-level data, so that probably explains the discrepancy. Moreover, in that specific context (figuring out whether geospatial associations between drinking water contaminants and obesity rates are confounded by SES or not) county-level data are more relevant than the individual-level data.
For comparison, my own analysis suggests that the correlation between altitude and obesity rates across US counties (which the SMTM authors think is a big deal) is −0.35. The altitude value I used for each county in my analysis was the average altitude of the centroids of its census tracts, which gives you the closest thing to a population-weighted average altitude by county that you can get with cheap and fast computation. I haven’t published the details of this analysis yet, but you can ask me for the Google Colab notebook and I’ll share it with you.
They address the 2013 data in the paragraph starting with “The studies that do find a relationship between income and obesity tend to qualify it pretty heavily.”
Livers tend to be more vitamin C-rich than other tissues in the animal body, so I looked for data for them too, and found that, for several animals, their vitamin C content ranged from lower than that of West Indian limes to higher than that of lemons. So West Indian limes did not stand out in my data as being unusually lacking in vitamin C.
Oops, sorry.
The map of average elevation by county looks similar, but you are right that this matters very little because people are disproportionally likely to live in low elevations.
Also, the mouth of the Mississippi basin does seem to be at a lower elevation than the West Coast:
Do we have evidence of that? As far as I can tell, the SMTM authors merely argue that some specific brands of processed food were available starting from the late 19th century, not that “highly processed food became dominant before the great obesity-ing started,” which is a much stronger claim.
Also, as I argued in this post, the obesity epidemic arguably started way before the SMTM authors often seem to imply it did. Quoting myself:
Here are a few charts from the sources I linked to. From this VoxEU article:
A chart by Random Critical Analysis, using data from this book:
A chart made with fancy statistical modeling, from this paper (note that the x-axis is each cohort’s birth year):
This is also relevant (from Random Critical Analysis as well):
We can see that BMI increases superlinearly with body fat %, so if body fat % is linearly growing in a population, BMIs will accelerate.