Natália

• Natália 7 Oct 2025 3:37 UTC

  65 points

  13

  on: Natália’s Shortform

  A few months ago, Scott Alexander summarized some of my arguments against SMTM’s lithium hypothesis of the obesity epidemic. SMTM recently replied to Scott’s summary, and I’d like to address their points here.

  Preamble

  Before working through SMTM’s specific points, there’s a fundamental issue to address. For lithium to help explain the obesity epidemic, we’d need evidence that human activity meaningfully increased lithium exposure over time.

  However, lithium exposure in the US appears to come primarily from natural rather than anthropogenic sources. I wasn’t aware of this when writing my original post, and I think it deserves careful consideration. As I’ll explain,

  • Several US regions in the 1960s had drinking water lithium levels that exceeded — by dozens of times — levels found in several high-obesity regions of the US today. Those high levels are explained by features of the natural environment, such as lithology and climate.

  • A 2022 study of US drinking water found that “Lithium in the source waters was mainly from natural sources” and “No correlations were found between Lithium and potential indicators of anthropogenic sources such as Co, Cu, and Ni that commonly present in Li-based industry leachate.”

  • Some areas of Argentina have naturally occurring lithium levels roughly 100 times higher than what is found in American drinking water, yet these populations have low obesity rates.

  I think this context is worth keeping in mind as we work through the specific arguments. Let me now turn to SMTM’s reply to each of Scott’s points.

  Scott’s first point

  Scott’s summary:

  1. Do you agree with the obesity increase being gradual over the course of the 20th century, rather than “an abrupt shift” as you describe in ACH?

  The SMTM authors appear to agree there’s little non-semantic disagreement between us on this question, so this can be skipped.

  Scott’s second point

  2. Do you agree that even medical lithium patients don’t have enough weight gain to cause the obesity epidemic? If so, why do you think that getting a tiny fraction of that much lithium would?

  Let’s go through SMTM’s reply.

  This is a great question. Let’s say that on average, people have gained 12 kilos since 1970, but that patients only gain an average of 6 kilos when they start taking medical lithium. This would be some evidence that lithium exposure isn’t responsible for the entire change in obesity since 1970. But it would be quite consistent with the idea that lithium caused some of the change in obesity since 1970, potentially as much as 50%.

  Let me clarify what this 6 kg figure means and why it’s important. The “6 kg” figure refers to an estimate in my original post, in which I estimated that clinical doses of lithium cause “zero to 6 kg of weight gain in the long term.” To clarify: 6 kg represents the upper end of that range, and applies to clinical doses specifically – not trace doses. Many studies on the effect of lithium on weight find a much smaller effect, and those that find an effect closer to 6 kg are observational studies that include patients on other obesogenic medications, such as anti-psychotics. Controlled studies find a much smaller effect for clinical doses of lithium, often consistent with 0 effect. This was covered in my original post.

  Moreover, as I said in my post, we want to explain a 22.5 kg change in average weight since the 1890s, not just the 12 kg change since 1970.

  The SMTM authors follow that up with:

  That said, we do think there’s some reason to suspect that lithium might be responsible for more than 50%. If everyone is already exposed to lithium in their diet, then the amount of weight gained by medical lithium patients when they add a higher dose will underestimate the total effect.

  To avoid underestimation, you can directly look up the obesity rates of lithium patients back when obesity rates were low. Quoting my original post, in which I do that:

  This can also be seen in studies that report the obesity rate or average BMI of patients taking lithium back when obesity rates were low. Chen & Silverstone (1990) (a review article that has been cited by SMTM) reviewed some studies reporting either of those figures, and in none of them was the obesity rate greater than 25% – even though most of those patients were probably on antipsychotics and/​or TCAs as well [which are know to cause weight gain]. So it’s difficult to imagine how lithium exposure could explain why the obesity rate is greater than 30% in several countries.

  But more fundamentally, even if the weight gain caused by clinical doses of lithium were arbitrarily high, “the idea that lithium caused some of the change in obesity since 1970” would only make sense if people nowadays are exposed to a lot more lithium than people in the 1960s, due to e.g. anthropogenic contamination. Are they?

  Has lithium exposure even increased?

  A Chemical Hunger states that drinking water lithium levels have increased in Interlude H: Well Well Well (a):

  [A]re we being exposed to more lithium now than we were back in the day? Our best source for this is that 1964 paper, Public water supplies of the 100 largest cities in the United States by Durfor & Becker, which as you may remember is available on Google Books. They report a median level lithium concentration of only 2.0 ng/​mL in the water supplies they analyzed. Based on this [and the 6-8.1 ng/​mL modern groundwater median], the median level in US drinking water seems to have increased 3-4x since 1964.

  But we don’t have enough data to estimate the average overall change in lithium in US drinking water with a few ng/​mL of precision. Lithium concentration in water varies a lot by region; the variation across space and type of water source is far greater than 3x-4x or 6 ng/​mL. Drinking water lithium levels exceeded 100 ng/​mL in some cities in the 1960s and 1970s, as covered in Interlude G: Li+, with values above 10 ng/​mL being ordinary in historical sources. The 6-8.1 ng/​mL median from the USGS groundwater study is well within typical values found in historical sources. Moreover, the 1964 study was focused on cities. In comparison, the recent USGS dataset is much more expansive, containing multiple rural measurements from the Mountain West backcountry, which has high levels of lithium, unlike the 1964 study.

  The substantial spatial variation also means that even if we knew that drinking water lithium levels had universally increased by 3x-4x, that would still not explain the obesity epidemic, since geographical variation was far higher than 3x-4x in the 1960s. We know that there were communities in the 1960s with dozens of times more lithium in their water than some communities in the 2020s. As an example using the data we have, someone in El Paso, TX (~81 ng/​mL) or Lubbock, TX (~63 ng/​mL) was getting a lot more lithium in their water than someone living in southeastern United States today (~2 ng/​mL). Despite that, the latter is much more likely to suffer from obesity. Why?

  This is especially true internationally: people in some areas of Chile and Argentina have >100x more naturally occurring lithium in their water than people in the US, and yet they have low obesity rates (7%-17%). A 3x-4x increase, even if it were clear that it had happened, is small compared to that.

  Going back to SMTM’s post, it also says that the tail end of exposure has increased even more. “Back in 1964, the maximum level they recorded was 170 ng/​mL. In the modern data, the highest level is 1700 ng/​mL, 10x higher.” But again, that is not an apples-to-apples comparison, due to the Mountain West backcountry measurements in the USGS dataset. In particular, the maximum value, 1,700 ng/​mL, comes from this seemingly uninhabited point in Utah, which likely looks something like this.

  

  We can go ahead and plot the coordinates of all values higher than 170 ng/​mL across used wells in the USGS dataset.

  

  We can see that:

  • they are very few, only 12 out of the 3,140 samples,

  • nearly all of them are closer to 170 ng/​mL than 1700 ng/​mL, and

  • nearly all are in very sparsely populated areas.

  Lithium exposure is primarily natural, not anthropogenic

  Overall, variation in lithium levels in drinking water appears to largely come from features of the natural environment, with human activity playing a minor role, if any. This 2022 study of drinking water in the US, both surface water (median 3.9 ng/​mL) and groundwater (median 14 ng/​mL), says this explicitly, stating that “Lithium in the source waters was mainly from natural sources” and “No correlations were found between Lithium and potential indicators of anthropogenic sources such as Co, Cu, and Ni that commonly present in Li-based industry leachate.”

  If we want to explain the obesity epidemic with a contaminant, a contaminant for which variations in exposure are largely anthropogenic would be a better fit.

  ---

  Of course, water is not the only source of lithium. Could it be that lithium exposure substantially increased through food, in a way that dwarfs spatial variation? There’s been some discussion about how much lithium there is in food (more on this later), but the SMTM authors don’t appear to have argued that it has increased over time, which would be necessary for the hypothesis to work, or propose a mechanism by which that could have happened. Instead, the 2024 post Lithium Hypothesis of Obesity: Recap, written years after the focus of their research shifted to lithium in food, still relies on water lithium levels to argue that lithium exposure has increased – the same numbers, 2 ng/​mL for the 1960s and 6-8 ng/​mL for the 2020s, that I addressed a few paragraphs ago.

  So, there really is essentially no indication that lithium exposure has meaningfully increased due to anthropogenic activity, given the data we have, and especially not universally in a way that dwarfs geographical variation.

  I think this by itself is more important than the rest of what I have to say. But we’re still only halfway through Scott’s second point. For completeness, let’s keep going through the next arguments.

  As a reminder, Scott’s second point was:

  2. Do you agree that even medical lithium patients don’t have enough weight gain to cause the obesity epidemic? If so, why do you think that getting a tiny fraction of that much lithium would?

  The SMTM authors bring up the Pima to address the second part of the question.

  The Pima

  [T]here’s at least one population (the Pima of the Gila River Valley [in Arizona]) where long-term exposure to lithium in food and water was associated with striking rates of obesity and diabetes, suggesting that under some conditions, lithium levels found in food and water may be enough to cause serious weight gain.

  The Pima of Arizona were documented to have very high obesity rates. The best data I found was from this study, for which the obesity rate of 25-45 year olds exceeded 60% in the 1980s among men, and 70% among women.

  SMTM “did some back-of-the-envelope math and estimated that the Pima might have been getting around 15 mg of lithium per day from wolfberry jelly.” To put this in perspective: if the 15 mg/​day estimate is accurate, the Pima’s lithium consumption would be approximately 10x lower than clinical doses but ~400x higher than typical modern exposure. As I’ll explain later, multiple lines of evidence indicate that people (outside of very specific regions like the Andes) are exposed to roughly ~1,000x-10,000x less lithium than the typical patient taking lithium for bipolar. In comparison, 15 mg/​day is only ~10x lower than a clinical dose, meaning that the Pima were likely getting hundreds of times more lithium than you. Scott’s question was likely about typical exposure, not the Pima’s (estimated) high exposure.

  The Pima’s obesity rate of 60-70% was more than twice that of lithium patients in the same decade (<25%), despite the Pima receiving lower doses. Since many lithium patients were also taking other obesogenic medications, this pattern suggests that lithium alone cannot fully explain the Pima’s obesity rates. This suggests other factors are involved; maybe a weird and quite strong gene-environment interaction, with lithium or something else in their environment, limiting the usefulness of this data for explaining obesity rates in genetically dissimilar populations.

  Moreover, we have examples of populations taking sky-high doses of naturally occurring lithium, close to SMTM’s estimate for the Pima, and remaining lean.

  • In this study from Argentina, the median urine lithium concentration was 3.9 mg/​L, which is absolutely insane: >100x higher than what we find in other areas. People pee out about ~1.5L of urine per day, meaning median exposure was roughly 5.85 mg/​day, with the maximum in this sample being 14 mg/​L = 21 mg/​day. Meanwhile, the obesity rate of study participants, despite their staggeringly high lithium consumption, was 17%. These people likely get exposed to more environmental lithium per year than the typical person in the southeastern US does in a lifetime. And yet, their obesity rate is comparable to that of a lean European country.

  • In this other Argentinean study from the same area, the average lithium urine excretion of participants was 1.6 mg/​L, a bit less than 100x what we find in other areas, and the obesity rate was 7%, lower than the adult obesity rate of any European country.

  Scott’s third point

  3. Natalia lists several reasons to expect that trace lithium doses should have only trace effects – Gwern’s reanalysis showing few-to-no psych effects, some studies suggesting low doses have fewer side effects, and lack of any of the non-weight-gain side effects of lithium in trace users. What are your thoughts on this?

  Scott’s summary is a bit under-defined: it’s easier to discuss explicit ranges of lithium exposure, instead of a term like “trace lithium” which is vague and can encompass several orders of magnitude. And, of course, “effects” itself is vague.

  SMTM’s reply:

  We think there are several reasons to expect effects from trace and subclinical doses, especially with extremely long-term exposure.

  We’re only aware of one RCT of trace-level doses (Schrauzer & de Vroey, 1994), but this study found that taking 0.4 mg per day of lithium orally led to participants feeling happier, more friendly, more kind, less grouchy, etc., “without exception”, compared to placebo.

  When we surveyed redditors who took subclinical doses of lithium as a nootropic (ballpark 1-10 mg/​day), people commonly reported some non-weight-gain effects, like increased calm, brain fog, frequent urination, and decreased libido.

  Regarding SMTM’s survey of Redditors supplementing lithium, it’s important to note that there don’t appear to be reports of weight gain associated with a lithium dose under 20 mg/​day. That seems more directly relevant to their hypothesis than the evidence of non-weight-gain side effects at lower doses.

  Scott’s fourth point

  4. Do you agree that wild animals are not really becoming obese?

  The background is that I said this in my post:

  [SMTM’s source for the claim that wild animals are becoming more obese] only examined animal populations living in close proximity to humans; the “wildest” animal population they include is Norway rats living in human towns (and thus likely eating human food), which is probably not what people have in mind (a) when they think of “wild animals.” None of the animals in the study were in the wilderness or had wild diets.

  The SMTM authors appear to have conceded this point:

  Our use of the term followed our source, and while it’s natural that people misunderstood the term to mean something more broad, let’s clarify that we didn’t intend to imply we were making claims about mountain goats, sloths, or white-tailed deer.

  I appreciate this clarification. However, the claim is still included in several posts of A Chemical Hunger. This means that future readers may read “wild animals are becoming more obese” and misunderstand what the authors believe, since “wild” typically means “living in a state of nature.”

  Scott’s fifth point

  5. Do you agree that water has higher lithium levels at high altitudes (the opposite of what would be needed for lithium to explain the altitude-obesity correlation)?

  SMTM’s response:

  No. This claim is based on an analysis that contains several mistakes.

  Natália conducted an analysis of this dataset from the USGS and elevation data from Open Elevation API, and found a positive correlation of 0.46 between altitude and log(lithium concentration) in U.S. domestic-supply wells. We replicated this analysis and can confirm that’s the correlation coefficient you get. But this analysis is mistaken, for two main reasons.

  First of all, the statistical problem. Correlation tests estimate the population correlation by looking at the correlation in a random sample drawn from that population. But this sample isn’t random, and it’s not representative either. The data mostly come from Nebraska, certain parts of Texas, and the East Coast. Some states are not represented at all. Really, look at the map below; it’s so much Nebraska. Even if there is a correlation within this dataset, there’s no reason to expect it’s a meaningful estimate of the correlation in the U.S. as a whole.

  While no dataset is perfect, the USGS groundwater data was the best dataset I had at the time. As I explained in my post, I used data from “all wells whose coordinates were available.” The dataset provides evidence that drinking water lithium levels are higher at higher altitudes. SMTM’s argument is that the dataset may not be representative. This decreases our confidence, but not the direction of the evidence.

  Moreover, we know why lithium concentrations in water are higher at higher altitudes in the US, making it less likely that the actual real-world correlation goes the other way. As this paper explains, it is due to the geology and climate of the Mountain West.

  But even if this were a random sample, this analysis would still be mistaken, because it’s a sample from the wrong population. Natália’s analysis only covers domestic-supply wells. It excludes public-supply wells, and it entirely omits surface water sources.

  This is a problem, because many people get their drinking water from public-supply wells, or from surface water. And it’s a problem because if there were a correlation between lithium levels and altitude, we’d expect to see it in surface water, not well water. Water drawn from wells has often been down there for thousands of years, while surface water is directly exposed to runoff, landfills, brine spills, power plants, and factory explosion byproducts. So we’d expect surface water to drive any correlation of obesity with altitude.

  I covered this back when Scott wrote his summary. It’s true that not everybody gets their water from wells – roughly half of the US population does, the rest getting it from surface water. I (and SMTM at the time) focused on groundwater specifically because we have more data about it, and because it is known to have more lithium than surface water. Though they now suggest the opposite, the SMTM authors were originally concerned about increases in groundwater exposure explaining obesity. From A Chemical Hunger:

  Well, back in the day, nobody got their water from deep, drilled wells. Nowadays, millions of people drink well water every single day. […] If water from drilled wells tends to have more lithium in it than water from shallow wells or surface water does, that would explain why people are exposed to more lithium now than they used to be, and could explain why the exposure is so universal.

  Again, groundwater has more lithium than surface water, not less. This can be seen at all percentiles, with the difference being severalfold, as shown in this 2022 US study and this worldwide meta-analysis.

  

  So current data do not appear to indicate that “runoff, landfills, brine spills, power plants, and factory explosion byproducts” result in surface water being unusually contaminated with lithium. As we have discussed before, the 2022 US study also explicitly says that lithium in drinking water (both groundwater and surface water) in the US comes primarily from natural sources, with little evidence of anthropogenic activity playing a role.

  But what if well water usage is biased? Maybe people at higher altitudes are not using wells that often. That is what they argued in A Chemical Hunger, and they repeat this argument in their reply. From Interlude H: Well Well Well:

  Colorado is the leanest state but has pretty high levels of lithium in its well water. Alabama is quite obese but levels of lithium in the well water there are relatively low. What gives? [...] But this doesn’t matter, because almost none of the drinking water in Colorado comes from wells. Instead, most of the drinking water in Colorado comes from surface water, and most of that water comes directly from pure snowmelt.

  But notice how they only use this argument for Colorado, not the other high-lithium states. It turns out that Colorado is an outlier. The highest lithium levels in the nation’s wells are found in New Mexico, Utah, Arizona and Nevada, all of which get most of their drinking water from wells, according to the data we have available. In general, people in high-altitude states are more likely to get their drinking water from wells, not less.

  This means all evidence we have points to people at higher altitudes likely having more lithium in their drinking water, in the US:

  1. Groundwater has more lithium than surface water,

  2. People at high altitudes get more of their drinking water from wells, and

  3. Wells at high altitude have more lithium.

  ---

  A Chemical Hunger led many readers to believe that lithium explains why obesity is more common at lower altitudes in the US. In order for that to make sense, we’d presumably want positive evidence showing that lithium exposure is systematically higher at lower altitudes in the US – not just arguments about why it may be higher, despite available evidence we’ve seen pointing in the opposite direction. If we want to attempt to explain the altitude pattern with the contamination hypothesis (though as I’ve argued, that’s not necessary), it might be worth considering contaminants that show higher concentrations at lower altitudes.

  ---

  Taking a step back from the individual points, I think it’s worth considering what would make the lithium hypothesis compelling as an explanation for the obesity epidemic.

  SMTM’s responses demonstrate various ways the lithium hypothesis could potentially be consistent with the available evidence. However, I think the question Scott and other readers are interested in is: given the alternative explanations available, what makes lithium stand out as the “top suspect”? There are other potential contaminants to consider, as well as non-contaminant explanations. The SMTM authors have stated that “the evidence [is] compelling” and “the case in favor of lithium is quite strong,” while skeptics are “lithium deniers” engaging in “mental gymnastics.” I think what readers crave is an explanation of how that could be the case in light of the evidence I presented.

  SMTM’s sixth point

  We also want to call attention to a 6th point that Scott doesn’t mention. If we were to phrase it as one of his questions, it might go something like this:

  6. You did a literature review of lithium concentrations in food and found that some foods contain more than 1 mg/​kg of lithium, which implies that people might be getting subclinical doses from their daily diet. Natália disputes this and says that the best available data shows less than 0.5 mg/​kg lithium in every single food. Do you agree?

  In my original post, I pointed out that most studies of lithium concentration in food in the literature find low levels, compatible with a daily dietary intake of (very roughly) ~40 μg/​day, thousands of times lower than the typical clinical exposure. The SMTM authors conducted original/​independent research showing that the method used to analyze lithium in solid food affects the results, and in their opinion, higher values, which would put daily intake at closer to the 1 mg/​day order of magnitude – only about 100 times lower than clinical exposure – are more accurate than values that yield lower results. In particular, they think HNO3 digestion, the method used in a lot of the literature, underestimates the concentration of lithium, whereas dry ashing is more accurate.

  However, we can also look at food lithium levels in context with other measures. We can compare them with other indicators of exposure that don’t have the HNO3 digestion issue, such as lithium content in blood, urine and drinking water. And what we find is that 1) they are internally consistent, and 2) they are more consistent with a ~40 μg/​day level of exposure than ~1 mg/​day (outside of the areas of Chile and Argentina with very high naturally occurring lithium, and low obesity rates, that we’ve discussed above).

  Blood

  From my original post:

  The Canadian Health Measures Survey found that the median whole blood lithium concentration was 0.000068 mEq/​L in a nationally representative sample of 5,752 subjects, with data collected from 2009 to 2011. Whole blood lithium concentrations are 65% the value of plasma concentrations (a), which are in turn similar to those in serum, so that corresponds to a serum concentration of 0.0001 mEq/​L. This value is 4,000 times lower than the lower end of the therapeutic range [0.4 mEq/​L], and the 95th percentile (0.00029 mEq/​L) is 1,383 times lower.

  Those are comparisons against the lowest acceptable therapeutic serum concentration of lithium (0.4 mEq/​L). A more typical value is 0.8 mEq/​L, putting the general population median and 95th percentile at 8,000 times and 2,766 times lower, respectively.

  Urine

  Lithium is known to be largely rapidly eliminated via the kidneys. This means that urinary lithium excretion is a good marker of exposure. I searched for studies measuring lithium in urine and found:

  • A median of roughly ~15 μg/​L in Italian children, with a reference range of 4.8–71.7 μg/​L in morning samples.

  • A geometric mean of 9.6 μg/​L and a range of 0.8-40.5 μg/​L in the UK.

  • A geometric mean of 23 μg/​L and a range of 4-237 μg/​L in Germany.

  • A geometric mean of 23.5 μg/​L in Japanese workers with no workplace lithium exposure.

  • A median of 24 μg per 24 hours in a sample of people with end-stage kidney disease in the Netherlands.

  More studies available here. In comparison, patients on therapeutic doses excrete a median of ~50 mg/​L in their urine. But they also produce more urine, often ~3-4 L per day compared to the typical 1.5 L (though some don’t have this side-effect, and others can produce >10 L/​day). Overall, urine excretion in these studies appears to be about ~30 μg/​day, compared to 150-200 mg/​day for patients on lithium therapy – roughly a 5,800x difference.

  Total Diet Studies

  Total Diet Studies, which the SMTM authors claim could be incorrect due to the method used for analysis, produce numbers consistent with the above, roughly 10 to 100 μg per day, 2,000 to 20,000 times lower than a typical therapeutic dose of ~200 mg.

  Those three different lines of evidence of lithium exposure all fit together quite nicely. By contrast, dietary intakes above 1 mg/​day would not make sense given this context. The contrast with the urine excretion data is particularly salient, since we expect nearly all lithium consumed to be peed out each day.^[1] Values above the 1 mg/​day range, even if they happened only occasionally, would also run into the problem I explained here: there would likely be other pieces of evidence of massive lithium exposure in the population, especially in vulnerable groups such as the elderly and those with kidney disease, who have difficulty eliminating lithium.

  ---

  Which exchanges?

  In their post, the SMTM authors say they had exchanges with “[me] and [my husband] Matthew,” that they “responded to our comments for a while” but “found [us] difficult to deal with,” and that this was part of why they didn’t respond to my post earlier. As far as I can tell, I wasn’t involved in those exchanges. They appear to have been with Matthew (1, 2) (who was not involved in writing my post, beyond briefly reviewing it), not me, and occurred in January 2022, before I read A Chemical Hunger in depth. I recall SMTM replying to me only once before.

  They say some of those early exchanges were deleted; I looked for evidence of deleted conversations (e.g., tweets from SMTM replying to deleted tweets that could’ve been mine) and didn’t find any.

  If I’m overlooking something, I’m happy to review any specific examples that involved me. My aim is simply to keep the timeline clear so readers can evaluate my arguments on their own terms, independently of Matthew’s separate exchanges.

  1. ^

     From Schaller, 2013:

     An increased absorption of lithium and its salts can be detected by analysing lithium in the biological body fluids, i.e. whole blood, serum, plasma, urine or saliva. In therapy control in the field of psychiatry, lithium is determined in plasma or serum (El Balkhi et al. 2009; Grandjean and Aubry 2009). The usefulness of saliva as a matrix for the detection of lithium is subject to debate (Obach et al. 1988). Urinalysis is preferred for the quantification of occupational or environmental exposures. In the literature, there are numerous data available for the parameter lithium in urine as reference value of environmental exposure (Abou-Shakra et al. 1989; Dol et al. 1992; Heitland and Köster 2004, 2006; Iguchi et al. 1999). Furthermore, the fact that more than 90% of the lithium absorbed by the organism is excreted via the kidneys is an argument in favour of lithium in urine as parameter (Arancibia et al. 1986).

     In an Argentinian environmental study, it could also be demonstrated that lithium excretion with the urine is a good way of documenting the intake of lithium from the drinking water. To evaluate exposure to various toxic metals, the concentrations of 31 elements were analysed in the drinking water and in the urine of village inhabitants in a region with potential geogenic exposure. In 161 women from San Antonio de los Cobres in the province of Salta, a village with a very high lithium exposure (1000 µg/​l) in the drinking water, the median lithium excretion was 4550 µg/​l (range 852–14 300 µg/​l) (Concha et al. 2010).

  2. ^

• Natália 27 Sep 2025 19:28 UTC

  14 points

  14

  in reply to: Elizabeth’s comment on: Why you should eat meat—even if you hate fac­tory farming

  Your own screenshot shows that pescatarians do better than vegans (not statistically significant, but neither is the difference between vegans and omnivores). And if you break it down by sex (and continue to ignore statistical significance), veganism is the worst choice for women after unconstrained omnivorism

  I addressed both of those points in my comment above. From my comment:

  The pescatarian mortality rate appears slightly lower than the vegan one, but in practice the difference is too small and the confidence intervals are too wide to tease out which one is actually lower. When broken down by sex, nearly all of the effect is concentrated in men, while all diets are pretty similar to each other in women.

  To explain this more in-depth, you cannot conclude from this table that “veganism is the worst choice for women after unconstrained omnivorism.” The confidence intervals of the adjusted hazard ratios for the “vegetarian” diets for women are essentially identical. It’s not just about failing to meet an arbitrary threshold of statistical significance—the difference between those diets has a very high p-value, not a p-value that almost approaches significance but falls just short of it.

  Meanwhile, the adjusted hazard ratio of veganism is significant in men, compared to omnivorism. Quoting from my old comment:

  The 95% confidence intervals of the adjusted hazard ratios for overall mortality, for men, were [0.56, 0.92] and [0.57, 0.93] for vegan and pescatarian diets, respectively, and for women the CIs are [0.72, 1.07] and [0.78, 1.20], respectively. For women, the confidence intervals for all diets are [0.78, 1.2], [0.83, 1.07], [0.72, 1.07] and [0.7, 1.22].

  What these CIs indicate is that there was likely no difference between pescatarian and vegan diets for men, both of which are better than omnivorism, and likely no difference between any of the diets for women.

  The CIs for women specifically look so similar that you could pretend that all of those CIs came from different studies examining the exact same diet, and write a meta-analysis with them, and readers of the meta-analysis would think, “oh, cool, there’s no heterogeneity among the studies!”

  In fact, we can go ahead and run a meta-analysis of those aHRs (the ones for women), pretending they’re all for the same diet, and quantitatively check the heterogeneity we get. Doing so, with a random-effects meta-analysis, we find that the $I^2$ is exactly 0%, as is the ${\tau }^2$. The p-value for heterogeneity is 0.92. Whereas this study should update us a little bit on pescatarian diets being better than vegan diets for women, these differences are almost certainly due to chance. No one would suspect that these are actually different diets if you had a meta-analysis with those numbers.

  Since the total meta-analytic aHR is also very close to 1, it also looks like none of the diets are meaningfully associated with increased or decreased mortality for women, though there was a slight trend towards lower mortality compared to the nonvegetarian reference diet (p-value: 0.11).

• Natália 27 Sep 2025 18:19 UTC

  65 points

  8

  on: Why you should eat meat—even if you hate fac­tory farming

  For example, famously Adventists are vegetarians and live longer than the average population. However, vegetarian is importantly different from vegan. Also, Adventists don’t drink or smoke either, which might explain the difference.

  Nit: we actually do have a study that looks at the mortality rate of vegan Adventists in particular. They fare well, having a nearly significantly lower mortality rate than omnivores after adjustments for drinking, smoking and other things:

  

  As you can see, groups that restrict meat intake in some form all trend towards having a lower mortality rate than the “nonvegetarian” group. The pescatarian mortality rate appears slightly lower than the vegan one, but in practice the difference is too small and the confidence intervals are too wide to tease out which one is actually lower. When broken down by sex, nearly all of the effect is concentrated in men, while all diets are pretty similar to each other in women.

  Make of that what you will; I am not claiming there’s a causal effect, though note that the study does control for a lot of confounders, such as smoking, drinking, age, race, income, education, marital status, etc.

  Meat consumption is a predictor of longer life expectancy. This relationship remained significant when influences of caloric intake, urbanization, obesity, education and carbohydrate crops were statistically controlled.

  You will get different results when examining this question worldwide (as in this study) vs in a developed country. Worldwide, meat is expensive, especially red meat, and so its consumption correlates with wealth. There have been several observational studies examining the relationship between vegetarianism and mortality or chronic diseases, you can find a non-cherrypicked list of systematic reviews here. Most find favorable outcomes for vegetarians. Though again, make of that what you will, I am not claiming it’s causal. In developed countries, of course, vegetarianism likely correlates with conscientiousness, which affects health in other ways. But these studies arguably should be mentioned in any overview of the health effects of vegetarianism that aims to be representative and balanced.

  Beef, especially pasture-raised (different from grass-fed). Factory farming of cows is far less bad than other animals. They often have access to the outdoors for a large percentage of their lives. They are cuter so we treat them better. Also, since they’re massive, even if their lives are quite bad, if you ate exclusively cow for a year, you most likely wouldn’t finish a single cow. Compare that to a chicken, which might last you a day. The same logic applies to dairy.

  I think there should be a bit more research on this; it’s not obvious to everyone that eating beef causes less suffering than eating chicken, or if it’s actually the other way around. This depends a lot on your moral intuitions, so the answer won’t be the same for everyone. From what I’ve seen, EAs tend to ascribe more moral worth to smaller animals than most other people would, leading to the conclusion that eating larger animals causes less suffering.

  However, my impression is that ~everyone who’s looked into it typically does agree that consuming dairy, bivalves, and non-farmable^[1] species of fish like sardines causes very little suffering, with bivalves often being considered vegan.

  1. ^

     See this comment for the relevant difference between wild-caught and non-farmable species of fish

• Natália 8 Nov 2023 3:40 UTC

  4 points

  3

  in reply to: lc’s comment on: AMA: Earn­ing to Give

  He explains it in this post.

• Natália 31 Oct 2023 2:12 UTC

  7 points

  5

  in reply to: Zvi’s comment on: Book Re­view: Go­ing Infinite

  Minor nit: following strict rules without weighing the costs and benefits each time could be motivated by rule utilitarianism, not only by deontology. It could also be motivated by act utilitarianism, if you deem that weighing the costs and benefits every single time would not be worth it. (Though I don’t think EA veganism is often motivated by act utilitarianism).

• Natália 31 Oct 2023 1:40 UTC

  15 points

  5

  on: Pres­i­dent Bi­den Is­sues Ex­ec­u­tive Order on Safe, Se­cure, and Trust­wor­thy Ar­tifi­cial Intelligence

  This made me wonder about a few things:

  • How responsible is CSET for this? CSET is the most highly funded longtermist-ish org, as far as I can tell from checking openbook.fyi (I could be wrong), so I’ve been trying to understand them better, since I don’t hear much about them on LW or the EA Forum. I suspected they were having a lot of impact “behind the scenes” (from my perspective), and maybe this is a reflection of that?

  • Aaron Bergman said on Twitter that for him, “the ex ante probability of something at least this good by the US federal government relative to AI progress, from the perspective of 5 years ago was ~1%[.] Ie this seems 99th-percentile-in-2018 good to me”, and many people seemed to agree. Stefan Schubert then said that “if people think the policy response is “99th-percentile-in-2018″, then that suggests their models have been seriously wrong.” I was wondering, do people here agree with Aaron that this EO appeared unlikely back then, and, if so, what do you think the correct takeaway from the existence of this EO is?

• Natália 5 Oct 2023 18:02 UTC

  10 points

  4

  in reply to: jimrandomh’s comment on: EA Ve­gan Ad­vo­cacy is not truth­seek­ing, and it’s ev­ery­one’s problem

  Thanks for this information. When I did this, it was because I was misunderstanding someone’s position, and only realized it later. I’ll refrain from deleting comments excessively in the future and will use the “retract” feature when something like this happens again.

• Natália 4 Oct 2023 19:33 UTC

  3 points

  0

  in reply to: Stephen Bennett’s comment on: EA Ve­gan Ad­vo­cacy is not truth­seek­ing, and it’s ev­ery­one’s problem

  Hi, that was an oversight, I’ve edited it now.

• Natália 4 Oct 2023 5:04 UTC

  2 points

  0

  in reply to: Stephen Bennett’s comment on: EA Ve­gan Ad­vo­cacy is not truth­seek­ing, and it’s ev­ery­one’s problem

  See this comment.

• Natália 4 Oct 2023 3:01 UTC

  5 points

  1

  on: EA Ve­gan Ad­vo­cacy is not truth­seek­ing, and it’s ev­ery­one’s problem

  Several people cited the AHS-2 as a pseudo-RCT that supported veganism (EDIT 2023-10-03: as superior to low meat omnivorism).

  […]

  My complaint is that the study was presented as strong evidence in one direction, when it’s both very weak and, if you treat it as strong, points in a different direction than reported

  [Note: this comment was edited heavily after people replied to it.]

  I think this is wrong in a few ways:

  1. None of the comments referred to “low meat omnivorism.” AHS-2 had a “semi-vegetarian” category composed of people who eat meat in low quantities, but none of the comments referred to it

  2. The study indeed found that vegans had lower mortality than omnivores (the hazard ratio was 0.85 (95% CI, 0.73–1.01)); your post makes it sound like it’s the opposite by saying that the association “points in a different direction than reported.” I think what you mean to say is that vegan diets were not the best option if we look only at the point estimates of the study, because pescatariansim was very slightly better. But the confidence intervals were wide and overlapped too much for us to say with confidence which diet was better.

  Here’s a hypothetical scenario. Suppose a hypertension medication trial finds that Presotex monotherapy reduced stroke incidence by 34%. The trial also finds that Systovar monotherapy decreased the incidence of stroke by 40%, though the confidence intervals were very similar to Presotex’s.

  Now suppose Bob learns this information and tells Chloe: “Alice said something misleading about Presotex. She said that a trial supported Prestotex monotherapy for stroke prevention, but the evidence pointed in a different direction than she reported.”

  I think Chloe would likely come out with the wrong impression about Presotex.

  3. My comment, which you refer to in this section, didn’t describe the AHS-2 as having RCT-like characteristics. I just thought it was a good observational study. A person I quoted in my comment (Froolow) was originally the person who mistakenly described it as a quasi-RCT (in another post I had not read at the time), but Froolow’s comment that I quoted didn’t describe it as such, and I thought it made sense without that assumption.

  4. Froolow’s comment and mine were both careful to notice that the study findings are weak and consistent with veganism having no effect on lifespan. I don’t see how they presented it as strong evidence.

  [Note: I deleted a previous comment making those points and am re-posting a reworded version.]

  What links here?

  • Stephen Bennett's comment on EA Ve­gan Ad­vo­cacy is not truth­seek­ing, and it’s ev­ery­one’s problem by Elizabeth (4 Oct 2023 15:50 UTC; 4 points)
  • Elizabeth's comment on EA Ve­gan Ad­vo­cacy is not truth­seek­ing, and it’s ev­ery­one’s problem by Elizabeth (EA Forum; 4 Oct 2023 23:29 UTC; 0 points)

• Natália 3 Oct 2023 21:57 UTC

  2 points

  0

  in reply to: Raemon’s comment on: EA Ve­gan Ad­vo­cacy is not truth­seek­ing, and it’s ev­ery­one’s problem

  I think the original post was a bit confusing in what it claimed the Faunalytics study was useful for.

  For example, the section

  The ideal study is a longitudinal RCT where diet is randomly assigned, cost (across all dimensions, not just money) is held constant, and participants are studied over multiple years to track cumulative effects. I assume that doesn’t exist, but the closer we can get the better.
  
  I’ve spent several hours looking for good studies on vegan nutrition, of which the only one that was even passable was the Faunalytics study.

  [...]

  A non-exhaustive list of common flaws:

  • Studies rarely control for supplements. [...]

  makes it sound like the author is interested on the effects of vegan diets on health, both with and without supplementation, and that they’re claiming that the Faunalytics study is the best study we have to answer that question. This is what I and Matthew would strongly disagree with.
  
  This post uses the Faunalytics study in a different (and IMO more reasonable) way, to show which proportion of veg*ans report negative health effects and quit in practice. This is a different question because it can loosely track how much veg*ans follow dietary guidelines. For example, vitamin B12 deficiency should affect close to 100% of vegans who don’t supplement and have been vegan for long enough, and, on the other side of the spectrum, it likely affects close to 0% of those who supplement, monitor their B12 levels and take B12 infusions when necessary.

  A “longitudinal RCT where diet is randomly assigned” and that controls for supplements would not be useful for answering the second question, and neither would the RCTs and systematic reviews I brought up. But they would be more useful than the Faunalytcis survey for answering the first question.

• Natália 3 Oct 2023 20:42 UTC

  2 points

  0

  in reply to: Raemon’s comment on: EA Ve­gan Ad­vo­cacy is not truth­seek­ing, and it’s ev­ery­one’s problem

  for me, the question is “what should vegan activist’s best guess be right now”

  Best guess of what, specifically?

• Natália 3 Oct 2023 5:07 UTC

  −3 points

  0

  in reply to: Stephen Bennett’s comment on: EA Ve­gan Ad­vo­cacy is not truth­seek­ing, and it’s ev­ery­one’s problem

  [deleted]

• Natália 3 Oct 2023 3:21 UTC

  1 point

  1

  in reply to: Vaniver’s comment on: EA Ve­gan Ad­vo­cacy is not truth­seek­ing, and it’s ev­ery­one’s problem

  [deleted]

• Natália 3 Oct 2023 2:05 UTC

  4 points

  3

  in reply to: Elizabeth’s comment on: EA Ve­gan Ad­vo­cacy is not truth­seek­ing, and it’s ev­ery­one’s problem

  The second point here was not intended and I fixed it within 2 minutes of orthonormal pointing it out, so it doesn’t seem charitable to bring that up. (Though I just re-edited that comment to make this clearer).

  The first point was already addressed here.
  
  I’m not sure what to say regarding the third point other than that I didn’t mean to imply that you “should have known and deliberately left out” that study. I just thought it was (literally) useful context. Just edited that comment.

  
  All of this also seems unrelated to this discussion. I’m not sure why me addressing your arguments is being construed as “holding [you] to an exacting standard.”

• Natália 3 Oct 2023 1:03 UTC

  6 points

  0

  on: Linkpost: They Stud­ied Dishon­esty. Was Their Work a Lie?

  Here are some Manifold questions about this situation (most from me):

• Natália 2 Oct 2023 18:20 UTC

  6 points

  11

  in reply to: Elizabeth’s comment on: EA Ve­gan Ad­vo­cacy is not truth­seek­ing, and it’s ev­ery­one’s problem

  That means they’re making two errors (overstating effect, and effect in wrong direction) rather than just one (overstating effect).

  Froolow’s comment claimed that “there’s somewhere between a small signal and no signal that veganism is better with respect to all-cause mortality than omnivorism.” How is that a misleading way of summarizing the adjusted hazard ratio 0.85 (95% CI, 0.73–1.01), in either magnitude or direction? Should he have said that veganism is associated with higher mortality instead?
  
  None of the comments you mentioned in that section claimed that veganism was associated with lower mortality in all subgroups (e.g. women). But even if they had, the hazard ratio for veganism among women was still in the “right” direction (below 1, though just slightly and not meaningfully). Other diets were (just slightly and not meaningfully) better among women, but none of the commenters claimed that veganism was better than all diets either.
  
  Unrelatedly, I noticed that in this comment (and in other comments you’ve made regarding my points about confidence intervals) you don’t seem to argue that the sentence “[o]utcomes for veganism are [...] worse than everything except for omnivorism in women” is not misleading.

• Natália 2 Oct 2023 17:50 UTC

  6 points

  2

  in reply to: jefftk’s comment on: EA Ve­gan Ad­vo­cacy is not truth­seek­ing, and it’s ev­ery­one’s problem

  To be clear, the study found that veganism and pescetarianism were meaningfully associated with lower mortality among men (aHR 0.72 , 95% CI [0.56, 0.92] and 0.73 , 95% CI [0.57, 0.93], respectively), and that no dietary patterns were meaningfully associated with mortality among women. I don’t think it’s misleading to conclude from this that veganism likely has neutral-to-positive effects on lifespan given this study’s data, which was ~my conclusion in the comment I wrote that Elizabeth linked on that section, which was described as “deeply misleading.”

• Natália 2 Oct 2023 15:50 UTC

  18 points

  7

  on: EA Ve­gan Ad­vo­cacy is not truth­seek­ing, and it’s ev­ery­one’s problem

  Outcomes for veganism are [...] worse than everything except for omnivorism in women.

  As I explained elsewhere a few days ago (after this post was published), this is a very misleading way to describe that study. The correct takeaway is that they could not find any meaningful difference between each diet’s association with mortality among women, not that “[o]utcomes for veganism are [...] worse than everything except for omnivorism in women.”

  It’s very important to consider the confidence intervals in addition to the point estimates when interpreting this study (or any study, really, when confidence intervals are available). They provide valuable context to the data.

  What links here?

  • EA Ve­gan Ad­vo­cacy is not truth­seek­ing, and it’s ev­ery­one’s problem by Elizabeth (28 Sep 2023 23:30 UTC; 334 points)
  • EA Ve­gan Ad­vo­cacy is not truth­seek­ing, and it’s ev­ery­one’s problem by Elizabeth (EA Forum; 29 Sep 2023 4:04 UTC; 128 points)
  • Elizabeth's comment on EA Ve­gan Ad­vo­cacy is not truth­seek­ing, and it’s ev­ery­one’s problem by Elizabeth (EA Forum; 4 Oct 2023 23:28 UTC; 1 point)

• Natália 30 Sep 2023 20:43 UTC

  1 point

  0

  in reply to: Elizabeth’s comment on: Ad­ven­tist Health Study-2 sup­ports pesc­etar­i­anism more than veganism

  I’d also like to point out that the sentences you describe as “slightly misleading” come immediately after I said “if you take the data at face value (which you shouldn’t)”. So as far as I can tell we’re in agreement here

  The first sentence is the title of the post, which has no hedging. The post also claims that“[i]f you’re going to conclude anything from these papers, it’s that fish are great,” which doesn’t seem to be the correct takeaway of this specific study given how wide and similar so many of the aHR 95% CIs are. You also claim “[o]utcomes for veganism are [...] worse than everything except for omnivorism in women” in another post without hedging.
  
  In any case, taking the data at face value doesn’t imply “ignoring the confidence intervals.” I don’t see why it would imply that.

  I thought you were using AHS2 to endorse much larger claims than you are.

  My original comment concluded that there was a “substantial probability” that vegan diets are healthier, and quoted someone (correctly) showing that vegans have lower mortality than omnivores in the AHS-2. I didn’t mean to claim it was the healthiest option for everyone; the comment is perfectly compatible with this null result for women. I also claimed that it wasn’t obvious that vegan diets can be expected to make you less healthy ex ante, modulo things like B12. I apologize if it wasn’t clear.

  What links here?

  • Natália's comment on EA Ve­gan Ad­vo­cacy is not truth­seek­ing, and it’s ev­ery­one’s problem by Elizabeth (3 Oct 2023 2:05 UTC; 4 points)