A Comprehensive Rebuttal to Seed Oil Sophistry

Updated: Jan 11

The popular consciousness has accepted many dietary villains over the course of the last half century, ranging from fat, to protein, to salt, to carbohydrates. More often than not, dietary constituents that have fallen under such scrutiny have been exonerated in time, as more and more scientific data is brought to light. I suspect that there is a growing number of people who are now wrongfully demonizing vegetable oils as well. Both skepticism and generally negative attitudes toward these oils appear to have skyrocketed in recent years, and it can be seen seemingly everywhere.

From what I can tell, almost all of the claims regarding the negative health effects of vegetable oils are essentially rooted in either mechanistic or ecological research. Mechanistic research includes studies such as cell culture studies, animal studies, in-vitro studies, or even some short term human experiments. Ecological research is merely investigating largely unadjusted associations between population-level exposures and outcomes, such as the association between vegetable oil availability in the food supply and type 2 diabetes incidence, for example.

Despite the fact that it is absolutely true that these types of research can be incredibly valuable, it is also almost always extremely inappropriate to extrapolate from from these types of research to population-level health effects. In other words, in the absence of corroborating human outcome data, it is almost always dubious to make claims about what outcomes will happen, based on speculation about intuitive mechanisms. Especially if there is no high internal validity population-level outcome data that actually agrees with the mechanistic speculation to begin with.

Ultimately, mechanistic studies carry virtually no information about actual human disease risk itself. Keep this in mind as we parse through the lower- and higher-quality evidence as we go along. This will be important as we explore the various claims made about vegetable oils and their interactions with human health. Let’s start with something familiar and dive into cardiovascular disease.

  1. Abbreviations

  2. Cardiovascular Disease

  3. Lipoprotein Oxidation

  4. Heart Disease

  5. Cancer

  6. Total Cancer

  7. Skin Cancer

  8. Adiposity

  9. 2-Arachidonoylglycerol

  10. Energy Intake

  11. Thermogenesis

  12. Type 2 Diabetes Mellitus

  13. Fatty Liver Diseases

  14. Fat Overload Syndrome

  15. Non-alcoholic Fatty Liver Disease

  16. Autoimmune Diseases

  17. Rheumatoid Arthritis

  18. Systemic Lupus Erythematosus

  19. Degenerative Diseases

  20. Age-related Macular Degeneration

  21. Cognitive Decline

  22. Mechanistic Autofellatio

  23. Inflammation

  24. Lipid Peroxidation

  25. Discussion

  26. Bibliography


2-AG, 2-arachidonoylglycerol; 8-oxodG, 8-oxo-7,8-dihydro-20-deoxyguanosine; ACR, acrylamide; AMD, age-related macular degeneration; AMI, acute myocardial infarction; ANA, anti-nuclear antibody; ApoB, apolipoprotein B; BCC, basal cell carcinoma; BCSO, blackcurrant seed oil; CAO, canola oil; CAT, catalase; CB1, cannabinoid receptor 1; CD, conjugated dienes; CRP, C-reactive protein; EE, energy expenditure; EPO, evening primrose; FADS, fatty acid desaturase; FO, fish oil; GLA, gamma-linolenic acid; GPx, glutathione peroxidase; GR, glutathione reductase; HDL-C, high density lipoprotein cholesterol; HUB, healthy-user bias; IVLE, intravenous lipid emulsion; LA, linoleic acid; LAVAT, La Veterans Administration Hospital Study; LBM, lean body mass; LDL, low density lipoprotein; MCE, Minnesota Coronary Experiment; MDA, malondialdehyde; MS, multiple sclerosis; NSAID, nonsteroidal anti-inflammatory drugs; O2H2, hydroperoxides; O6:O3, omega-6/omega-3 ratio; PUFA, polyunsaturated fat; RA, rheumatoid arthritis; RCT, randomized controlled trials; RMR, resting metabolic rate; SCC; squamous cell carcinoma; SDHS, Sydney Diet Heart Study; SLE, systemic lupus erythematosus; SO, soybean oil; SOD, superoxide dismutase; SU, sunflower oil; T2DM, type 2 diabetes mellitus; TC, total cholesterol; TEF, thermic effect of feeding; TFA, trans-fat; TNF, tumor necrosis factor; TPN, total parenteral nutrition; VAT, visceral adipose tissue.



The primary mechanism by which vegetable oils are suggested to increase the risk of cardiovascular disease (CVD) is through the oxidation of polyunsaturated fats (PUFA), particularly linoleic acid (LA), in low density lipoprotein (LDL) phospholipid membranes. In the literature, this hypothesis seems to be largely spearheaded by DiNicolantonio and O'Keefe (2018) [1]. The authors' case against vegetable oils begins with the claim that vegetable oils contain fatty acids, particularly LA, which increase the susceptibility of LDL to oxidize, and that CVD risk is mediated through the oxidation of LDL.

It is also suggested that saturated fatty acids (SFA) are resistant to this sort of oxidative destruction, which subsequently implies that the substitution of SFA for LA in the diet might lower CVD risk. This hypothesis would seem to have many entailments that run contrary to the common understanding of how these different fats influence CVD risk. So, let's see if these claims and implication actually hold up to scrutiny.

As it turns out, we've thoroughly investigated the effects of altering the fatty acid composition of the diet on LDL oxidation rates in humans. For example, Mata et al. (1996) explored this question with one of the most rigorous studies of its kind. No statistically significant differences between the effects of high-SFA diets and high-PUFA diets on the lag time to LDL oxidation were observed [2].

However, diets high in monounsaturated fat (MUFA) diets might actually increase the lag time to LDL oxidation more than either SFA-rich or PUFA-rich diets. It would appear as though this effect could be explained due to the fact that MUFA might be better than SFA at replacing LA in LDL particles. So, it seems as though the implication that SFA could offer unique protection against LDL oxidation is less straightforward than originally suggested.

When comparing LDL that were enriched with PUFA to LDL that were enriched with MUFA, Kratz et al. (2002) observed a stepwise decrease in LDL lag time to oxidation with increasing PUFA, specifically linoleic acid (LA) [3]. These results cohere with the results of Mata el al., suggesting that MUFA increases the lag time to LDL oxidation more than SFA or PUFA.

In short, it would at least seem to be true that vegetable oil consumption, when investigated in isolation, increases the susceptibility of LDL to oxidize. But, don't get too excited. As we will discuss, these are paltry changes compared to what can be accomplished with antioxidants.

Despite Kratz et al. claiming that vitamin E doesn't mask the effects of dietary fatty acids on LDL susceptibility to oxidation, their reference for this claim does not clearly support this conclusion. The work of Reaven et al. (1994) was their supporting reference for this claim and it actually paints a slightly different picture [4]. The Lag time to LDL oxidation with vitamin E supplementation was more than double that which was observed in the previous study (or nearly any other random population sample from any other study that I've seen for that matter).

Reaven et al. discovered that the lag time to LDL oxidation was enormously high in all groups after a run-in diet that included 1200mg/day of vitamin E for three months before randomization. Compared to the previous study by Kratz et al. (2002), the LDL lag time to oxidation was 150% higher (~60 minutes to ~150 minutes). This is consistent with other research showing that the vitamin E content of LDL particles linearly increases the lag-time to LDL oxidation [5].

The effect of dosing vitamin E on increasing the lag time to LDL oxidation observed by Reaven et al. is approximately 7.5x stronger than the effects observed by either Kratz et al. or Mata et al., which involved altering the fatty acid composition of the diet. This strongly suggests that antioxidants are a much stronger lever to pull if one wants to decrease the susceptibility of LDL to oxidize.

All three studies used the same copper sulfate solution methodology to measure the lag-time to oxidation, as they both reference the same source for the methods developed by Esterbauer et al. (1989) [6]. As such, it wouldn't seem that the increase in LDL lag-time to oxidation seen with vitamin E supplementation would be due to a difference in measurement methods. So we have a decent proof of concept that antioxidants like vitamin E can probably modulate the lag-time to LDL oxidation to an enormous degree.

Which brings me back to the trial by Kratz et al. (2002), which compared olive oil (OO) versus sunflower oil (SU). Despite the fact that all diet groups were receiving roughly the same amount of vitamin E, the OO group ended up with significantly higher vitamin E status compared to the SU group. This is consistent with the observations that MUFA increases the lag time to LDL oxidation to a greater degree than SFA or PUFA. Which leads to higher vitamin E representation in the end.

It is well understood that higher PUFA intakes increase vitamin E requirements in humans [7]. However, the OO group also started with much higher status to begin with, which likely contributed to the effect. Lastly, the run-in diet was high in SFA. Such a diet is almost assuredly to be lower in vitamin E, which could exaggerate the effects of high-PUFA diets.

As discussed earlier, SFA is not vulnerable to lipid peroxidation in any way similar to that of PUFA. However, SFA being less vulnerable to lipid peroxidation typically means that sources of SFA contain very low amounts of antioxidants [8]. For example, both coconut oil and butter contain negligible vitamin E and have minimal polyphenols. The potential importance of polyphenol antioxidants in protecting LDL from oxidation has been demonstrated in research investigating OO [9].

Figure 2 from Marrugat et al. (2004) shows that virgin OO increases the lag time to oxidation to a greater degree than that of refined OO, with the primary differences between these different OOs being their polyphenol content. Many polyphenols act as antioxidants once they're inside our bodies, so these results are not particularly surprising.

It is likely that the overall dietary pattern matters more than PUFA, or even LA, in altering the susceptibility of LDL to oxidation. This principle has been demonstrated multiple times [10-13].

For example, in a trial by Aronis et al. (2007), diabetic subjects were placed on either a fast food diet that was formulated to be "Mediterranean diet-like" or assigned to follow their usual diet. There was also a third arm of non-diabetics placed on the Mediterranean-style fast food diet. Lag time to LDL oxidation was measured according to the same methodology as described above, with a copper sulfate solution.

It should be noted that the foods were specifically formulated to increase the lag time to LDL oxidation. However, what makes these results more impressive is that the fast food groups (groups A and B) were consuming twice as much PUFA than they were at baseline. Despite this we see some of the longest lag times to LDL oxidation observed in the literature in a population that has not been primed with megadoses of vitamin E for three months.

Altogether this would seem to divulge that diet quality matters more than PUFA, or even LA, for LDL oxidation in the aggregate. We've seen multiple times that low-PUFA or low-LA diets can be outperformed by high-PUFA or high-LA diets of better overall quality. Little things add up, and the effect of diet is greater than that of MUFA alone, SFA alone, or even polyphenols alone. Perhaps not vitamin E alone, though.

But let's be clear. Measuring the lag time to LDL oxidation in this way is highly contrived, and probably not reflective of normal physiological conditions. When you plop the LDL particles in a copper sulfate solution, eventually all of the LDL will oxidize. There's no escaping it. Naturally, the high-PUFA LDL will oxidize marginally sooner. But ultimately, this does not tell us anything about what would happen to these LDL particles under physiological conditions. For that, it might be more useful to look at markers like oxLDL.

A marker like oxLDL can give us a better sense of just how many oxidized LDL are likely to form in the blood after a particular intervention or in a particular context. This is important because merely looking at the lag time to oxidation could give us an exaggerated sense of what is likely to happen in vivo.

For example, it may be the case that when you expose LDL particles to copper sulfate, they oxidize in under an hour. But under physiological conditions, that same oxidation could potentially take days. If the LDL are cleared from the blood before oxidation can occur, then the results of the copper sulfate test are probably not very informative.

One particularly long crossover-style RCT by Palomäki et al. (2010) compared the effects of butter to that of canola oil (CAO) on oxLDL [14]. Overall the butter diet resulted in higher LDL and higher oxLDL. I wasn't able to find many PUFA-SFA substitution trials that measured oxLDL beyond this one study, unfortunately.

Again, I speculate that this is likely the result of SFA being a poor source of antioxidants. Or perhaps it's because baseline diets could have been higher in PUFA, and reducing vegetable oil intakes might just be cutting off robust sources of antioxidants and increasing LDL oxidation. There are not many studies investigating this, so it's not clear at the moment.

But, just for the sake of argument let's assume that high-PUFA diets do increase LDL oxidation relative to high-SFA diets. Would that actually be a bad thing? Perhaps not. One study by Oörni et al. (1997) has identified that oxidized LDL are less likely to be retained within the arterial intima when compared to native LDL [15]. If the LDL are oxidizing in the serum, perhaps this just opens up more disposal pathways for LDL and lowers its chances of getting retained in the subendothelial space to begin with.

Lastly, while we have established that vegetable oil consumption does appear to have an independent impact on LDL oxidation (though the effect is dwarfed by the effect of antioxidants), it is also true that oxLDL isn't actually a robust risk factor for CHD. Wu et al. (2006) discovered that the association between oxLDL and CHD does not survive adjustment for traditional risk factors like apolipoprotein (ApoB) or TC/high density lipoprotein cholesterol (HDL-C) [16].

Essentially this means that after accounting for ApoB or TC/HDL, risk is more closely tracking ApoB or TC/HDL-C, and is not particularly likely to be tracking oxLDL at all. So even if it were the case that diets high in vegetable oils simply increased oxLDL, it wouldn't appear that it moves the needle for risk in the real world. It would also suggest that the hypothetical detriments of increasing LDL oxidation aren't significant enough to offset the LDL-lowering benefits of a high-PUFA diet. As we will discuss later in this section.

In the above study by Wu et al. (2006), the Mercodia 4E6 antibody assay was used to measure oxLDL. Some have argued that this assay is invalid due to supposedly making poor distinctions between native LDL and oxLDL [17]. However, if the 4E6 assay was truly making poor distinctions between oxLDL and native LDL, the two biomarkers would essentially be proxying for one another to the point of being either interchangeable or even being the same thing. In this scenario, the results of the model would suggest extreme multicollinearity as indicated by similarly (extremely) wide confidence intervals for both results.

If oxLDL and native LDL were truly proxying for one another in the model in this fashion, we'd expect the confidence intervals for each relative risk to be inflated and more likely non-significant. But, there is no evidence of extreme multicollinearity in the results. Therefore, it is unlikely that the 4E6 antibody assay is actually making poor distinctions between oxLDL and native LDL. This is important to consider, because the argument for extreme multicollinearity is the primary criticism used against the 4E6 antibody assay's usefulness. But the argument doesn't actually pan out.

It is instead suggested by skeptics of the 4E6 antibody assay that measures of oxidized phospholipids, such as the E06 antibody assay, are more robust measures of oxLDL than the 4E6 antibody assay. However, the E06 antibody assay is actually more vulnerable to the exact type of confounding that has been suggested for the 4E6 antibody assay. This is explained on Mercodia's website [18].

“The proprietary mouse monoclonal antibody 4E6 is developed by professors Holvoet and Collen at the University of Leuven in Belgium. It is directed against a conformational epitope in the ApoB100 moiety of LDL that is generated as a consequence of substitution of at least 60 lysine residues of Apo B100 with aldehydes (Holvoet 2006). This number of substituted lysines corresponds to the minimal number required for scavenger-mediated uptake of oxidized LDL.”

Essentially, the 4E6 antibody assay makes a clear distinction between native LDL and oxLDL by only binding to ApoB that has been modified sufficiently, as to not be recognizable by LDL receptors. As described on the Mercodia website, the 4E6 antibody only binds to ApoB particles that have >60 of their lysine residues modified by aldehydes, which is the threshold for initiating binding affinity with scavenger receptors, and foreclosing binding affinity with LDL receptors. This makes the 4E6 antibody assay an excellent assay for clearly distinguishing between native LDL and oxLDL.

Mercodia goes on to discuss the reasons for why the E06 antibody assay could be problematic for assessing oxLDL.

“Substituting aldehydes can be produced by peroxidation of lipids of LDL, resulting in the generation of oxidized LDL. However, lipid peroxidation is not required. Indeed, aldehydes that are released by endothelial cells under oxidative stress or by activated platelets may also induce the oxidative modification of Apo B100 in the absence of lipid peroxidation of LDL.”

Because lipid peroxidation of the LDL particle's phospholipid membrane is not required for an LDL particle to oxidize, a measurement of oxPL could easily mistake a minimally oxidized LDL particle as an oxLDL. For this reason, it is likely that the 4E6 antibody assay is likely to better reflect the actual number of oxLDL [19].

This is relevant because the immune cells that mediate the formation of atherosclerotic plaques only tend to take up maximally oxidized LDL particles, not minimally oxidized LDL particles [20][21]. Maximally oxidized LDL have to be disposed of through scavenger receptor-mediated pathways, rather than LDL receptor-mediated pathways.

If minimally oxidized LDL likely contribute as little to foam cell formation as native LDL, why favour measures of minimally oxidized LDL such as the E06 antibody assay over measures of maximally oxidized LDL such as the 4E6 antibody assay?

Unfortunately, so far no studies have attempted to explore the relationship between oxLDL, as measured by the E06 antibody assay, and CHD outcomes after adjustment for total ApoB. The closest we have is a single study by Tsimikas et al. (2006) that found no correlation between oxPL/ApoB and ApoB [22].

However, if ApoB and oxPL tend to vary in tandem, the oxPL/ApoB ratio might not be expected to change very much from subject to subject. If that is the case, then we would not expect oxPL/ApoB to correlate very well at all. It would be nice to see univariable and multivariable models presented that test for independent effects of these biomarkers.

Nevertheless, if merely having more LA in your body meant that you would increase lipoprotein oxidation and thus get more CHD, we would not expect results like those of the meta-analysis conducted by Marklund et al. (2019) [23]. When investigated closely, the higher your tissue representation, the lower your risk of both CHD and ischemic stroke.

It’s important to note that there was not a single statistically significant increase in risk observed in any of the included cohorts when comparing the lowest tissue LA to the highest tissue LA. One of the more interesting findings was from a regression analysis of the Scottish Heart Health Extended Cohort by Wood et al. (1984), which found a strong inverse correlation between adipose LA and CHD incidence [24].

This certainly isn't what we would expect if the chain of causality is LA -> OxLDL -> CHD. However, as we'll discuss in the next section, this is what we'd expect if the chain of causality is SFA -> LDL -> CHD. So, let's go ahead and dive into that data next.


Now that we've established that there is scant evidence validating oxLDL as a robust, independent marker of CVD risk, let's move on to actual human outcomes. It is a common belief among certain diet camps that PUFA actually increases CVD risk, and that SFA may even lower the risk of CVD. But is there evidence for this claim?

Let's start with the epidemiological evidence, being sure to keep the original question in mind— does substituting PUFA for SFA lower CVD rates? There have been multiple meta-analyses that have aimed to answer this question, however the application of meta-analysis methods in this context can produce some significant interpretative challenges. Let me explain why.

The relationship between SFA and CVD is nonlinear, and significantly influenced by the degree of replacement with PUFA. As such, the linear summation of prospective cohort studies can hide the nonlinear effect. For example, let's have a look at one of the most heavily-cited meta-analyses by Siri-Tarino et al. (2010), which investigated the relationship between SFA and CVD in prospective cohort studies [25].

As we can see, the aggregated results are null (P = 0.22). However, the I² (a measure of heterogeneity) is 41%, and no attempt was made to investigate the source of that heterogeneity. If we take the time to calculate the intake ranges of each cohort study (when possible), we can test for nonlinearity.

After stratifying the included studies by intake range, we see a statistically significant 30% increase in risk in subgroup one (~25±15g/day) and null results for subgroups two and three (35±15g/day and 45±15g/day, respectively). This is precisely what we'd expect to see if the risk threshold presupposed by the Dietary Guidelines was correct. McGee et al. (1984) needed to be removed for this analysis because SFA intake ranges could not be determined from the provided data.

But perhaps this is an isolated finding. There are many other meta-analyses on this subject [26-29]. Maybe the other meta-analyses on this subject would find something different? Let's find out.

Applying the same methodology to all of the available meta-analyses yields the same result. This time subgroup two is representing the intake threshold presupposed by the Dietary Guidelines (~25±15g/day). We see similar increases in risk in the same subgroup across all published meta-analyses investigating the relationship between SFA and CVD (RR 1.14 [1.07-1.22], P<0.0001).

You may be asking why subgroups one, three, and four are null, whereas subgroup two is not. The answer is quite simple. It's because those ranges are not crossing the threshold of effect. The range of intake represented in subgroup one exists below the threshold of effect, whereas subgroups three and four exist above the threshold of effect. As such, comparing lower intakes to higher intakes within those ranges does not show an additional increase in risk. The relationship between SFA and CVD is sigmoidal.

Comparing intakes that exist on either the floor or ceiling of the risk curve will typically produce null results. Only when the middle intake range is investigated is the increase in risk observable.

But how does PUFA play into this? To answer this question, let's make our own meta-analyses. Let's take all of the cohort studies that fall within that middle range and plot them out for both SFA and PUFA.

Inclusion Criteria:

  • Prospective cohort studies investigating the relationship between SFA and/or PUFA and CVD.

  • Endpoints directly related to CVD, coronary heart disease, ischemic heart disease, or myocardial infarction (events, mortality, incidences, etc) are acceptable.

  • Risk estimates stratified from lowest to highest SFA and/or PUFA intakes.

  • Cohorts must fit into one of four intake categories:

  1. 0-10g/day (lower range) to 20-30g/day (upper range)

  2. 10-20g/day (lower range) to 30-40g/day (upper range)

  3. 20-30g/day (lower range) to 40-50g/day (upper range)

  4. >30g/day (lower range). Min. 20g/day difference from lower to upper range

Exclusion Criteria:

  • Studies pooling results across multiple cohorts from different countries.

  • Risk adjustments controlling for serum cholesterol and hyperlipidemic medications.

  • Cohorts consisting of people with pre-existing CVD and/or currently taking hyperlipidemic medications.

  • Studies investigating the same cohorts as other included studies. Tie-breakers are decided based on differences in study quality (e.g., chosen subgroups, endpoints, multivariate adjustment models, etc).

Drag-netting the literature yielded a total of 74 studies. 20 studies were excluded due to reporting irrelevant endpoints (e.g., stroke, cerebral hemorrhage, atrial fibrillation, etc). Of the 41 remaining studies, 32 studies were excluded due to including duplicate cohorts, adjusting for blood lipids, being multinational, failing to specify intake ranges, or having an intake range that did not fall into one of the prespecified subgroups.

Results for Saturated Fat:

Altogether there were 21 studies that met all of the inclusion criteria [30-50]. Cohorts were stratified across four subgroups, based on absolute grams-per-day intakes of SFA. Total pooled results across all subgroups showed a non-significant increase in risk (RR 1.07 [0.97-1.18], P=0.16). Subgroup two was the only subgroup to reach statistical significance (RR 1.24 [1.10-1.40], P=0.0005).

Again, we see the exact same thing. The increase in risk occurs in the same subgroup, in the same intake range— the same intake range presupposed by the Dietary Guidelines to increase risk.

Results for Polyunsaturated Fat:

Altogether there were 10 studies that met all of the inclusion criteria. In order to preserve the PUFA to SFA ratio, cohorts were stratified across four subgroups, based on absolute grams-per-day intakes of SFA. Total pooled results across all subgroups showed a statistically significant reduction in CVD risk (RR 0.91 [0.83-1.00], P=0.04). Subgroup two was the only subgroup to reach statistical significance (RR 0.87 [0.80-0.93], P=0.0002).

This second forest plot was not stratified by PUFA intake. As mentioned above, it was instead stratified by SFA intake. This way the ratio of SFA to PUFA would be better preserved. This is important for understanding the relationship between these two dietary fats.

Both SFA and PUFA have inverse sigmoidal relationships with CVD. The more PUFA you replace with SFA, the higher your risk. The more SFA you replace with PUFA, the lower your risk.

This is exactly the same relationship we see in the RCTs as well [51]. Recently, Hooper et al. (2020) published an enormous meta-analysis and meta-regression analysis for the Cochrane Collaboration, which investigated CVD-related outcomes of many PUFA-SFA substitution RCTs.

Their analysis shows that substituting SFA for PUFA, and crossing the 10% of energy threshold as SFA, increases CVD risk in the aggregate. Especially for CVD events, CVD mortality, CHD mortality, and non-fatal acute myocardial infarction (AMI).

Cochrane also performed multiple meta-regression analyses on the included data. Meta-regression is a tool used to investigate the relationship between two variables when they are controlled by a moderator variable. In this case, the two variables in question are SFA intakes (or PUFA-SFA substitution) and CVD. Multiple moderator variables were modeled.

The only statistically significant moderator variable was total cholesterol (TC) (P=0.04). This suggests that the chain of causality is SFA -> TC -> CVD. This therefore suggests that to the extent that a dietary modification reduces TC, reductions in CVD should follow. This is confirmed in their exploratory analysis later on in the paper.

Additional exploratory meta-analyses by Hooper et al. (2020) also further divulge that SFA reduction lowers total CVD events (analysis 1.35), the best replacement for SFA is PUFA from vegetable oils (analysis 1.44), and the effect is likely via lowering TC (analysis 1.51). This evidence dovetails perfectly with the epidemiological evidence discussed above.

Not included in many of Cochrane's analyses were two studies that are often offered up as damning counterevidence, and often cited in support the notion that vegetable oils increase CVD risk. These two trials are the Sydney Diet Heart Study (SDHS) by Woodhill et al. (1978) and the Minnesota Coronary Experiment (MCE) by Frantz et al. (1989) [52-53]. These trials were both designed such that SFA was to be replaced with PUFA in the intervention groups in the form of vegetable oil-based margarines, and PUFA was to be replaced with SFA in the control groups.

Both trials achieved significant differences in TC during the course of both of the trials. However, the SDHS did not achieve a significant difference in the magnitude of the reduction, and the final difference in TC was only around 12mg/dL. While statistically significant, it is questionable whether or not either of these changes in TC were clinically significant. On the other hand, the MCE saw significant reductions in TC.

Neither trial saw a statistically significant difference in either CVD mortality or total mortality.

However, in the aggregate, there is a significant increase in CVD mortality between the two trials. This is concerning, seeing as though these two trials are often touted as the best designed trials that have been conducted in the investigation of this research question, aside from the LA Veterans Administration Hospital Study (LAVAT) by Dayton et al. (1969) [54]. However, the MCE's design ended up allowing participants to enter in and exit out of the trial at their leisure, and this ended up resulting in a mean follow-up time of around 1.5 years. Which is abysmal.

It should be noted that the SDHS was a secondary prevention trial, which means that the subjects had already had a single CVD event at the time of enrollment. Given the questionable clinical relevance of the final differences in TC, it is even more ambiguous how meaningful the findings were. It has been observed that differences in SFA intake don't always change CVD outcomes in high-risk populations [55].

Nevertheless, we're left wondering precisely why we didn't see the predicted effect in either of these trials. Aside from the revolving door protocol that was used in the MCE, the designs were not particularly terrible as far as large-scale RCTs go. So, why didn't we see the effect that we see in nearly every other trial of similar design and quality, as mentioned above?

The answer likely lies in the types of fat-based products that were provided to the vegetable oil groups in these two trials. The vegetable oil groups were the groups that were meant to increase PUFA intake. PUFA was provided to subjects primarily in the form of corn oil (CO) based margarines. This is a problem. Margarines are typically made primarily from unsaturated fats, which are liquid at room temperature. In order to make the product solid and spreadable, the oils need to be hardened. There are essentially two standard ways to achieve this effect.

First, we can harden oils through hydrogenation or through emulsification. However, non-hydrogenated margarines were not commonplace on the market prior to the 1970s [56]. Both the SDHS and the MCE were conducted during the 1960s. At the time, hydrogenation was the preferred method to harden margarines. However, this has the secondary effect of generating trans fatty acids (TFA).

TFAs are associated with an increased risk of many diseases, including CVD. However, TFAs are the only known fatty acid subtypes that have been associated with increased CVD mortality when replacing SFA on a population-level [57].

Basically, TFAs are fucking dangerous— more dangerous than SFA. The CO-based margarine used in the SDHS was "Miracle" brand margarine [58]. According to Dr. Robert Grenfell of the National Cardiovascular Health Director at the Australian Heart Foundation and the Deputy Chairman of the Sydney University Nutrition Research Foundation, Bill Shrapnel, Miracle brand margarine was up to 15% TFA at the time the study was conducted [59].

“When this study began, Miracle margarine contained approximately 15 per cent trans fatty acids, which have the worst effect on heart disease risk of any fat. The adverse effect of the intervention in this study was almost certainly due to the increase in trans fatty acids in the diet”

If Woodhill et al. (1978) truly fed the subjects in the vegetable oil group (group F) as much of that margarine as they seem to be claiming to in their publication, that could be around 5.7g/day of TFA.

The margarine used in the MCE was likely to be Fleischmann's Original, due to the availability and popularity at the time of the intervention. As well as the fact that the product was developed in direct response to pro-PUFA research that was being conducted at the time.

The only other two margarines that were potentially available in the region at the time the MCE was conducted were Imperial margarine and Mazola margarine. Some Imperial products remained high in TFA until very recently [60]. Mazola was pretty much the only non-hydrogenated margarine on the market at the time.

The answer to the question of which margarine was used in the MCE is anybody's guess at this point. But if I were generous, I'd say that based on the margarines that were available (and most likely to be used) at the time the MCE was conducted, there is roughly a 67% chance that the trial was potentially confounded by TFA in the vegetable oil diet. Though Ramsden et al. (2016) remain skeptical that confounding such as this was likely [61].

However, even if the MCE was not confounded by TFA, we would still have a good reason to exclude it from consideration. Like I mentioned earlier, the trial was designed in such a way that the participants had the liberty to enter and exit the trial at their leisure. In the aggregate, there was only a 12-18 month follow-up time. As such, the trial has significantly less power than all other trials that investigated this particular sort of dietary substitution.

Ramsden et al. have argued at length that the control group was likely confounded by TFA to a greater degree than the vegetable oil group. In their view, this could indicate that LA is actually worse for you than TFA. Essentially they claim that vegetable shortening was added to the control group in order to boost the SFA content of the diet, and because vegetable shortening is hydrogenated, it must have contained a large amount of TFA. This is dubious.

Fully hydrogenated oils contain very little TFA [62]. However, partially hydrogenated oils such as most soft margarines of the period contain high levels of TFA. Here is a selection of SOs taken from the USDA's SR28 Nutrient Database [63].

As you can see, a tablespoon of partially hydrogenated SO margarine contains 2200% more TFA than a tablespoon of fully hydrogenated SO shortening.

Ramsden et al. (2016) also acknowledge that the vegetable oil group's diet used soft margarines that were likely to contain some TFA. However, they also under-appreciate the fact that fully hydrogenated vegetable shortening contains pretty much the same amount of TFA as its unadulterated, non-hydrogenated counterpart. In fact, this is precisely why fully hydrogenated shortenings are still on the market despite the TFA ban in most developed countries. It's because those fats never had much TFA to begin with.

“Because the trans fatty acid contents of MCE study diets are not available, one could speculate that the lack of benefit in the intervention group was because of increased consumption of trans fat. Indeed, in addition to liquid corn oil the intervention diet also contained a serum cholesterol lowering soft corn oil polyunsaturated margarine, which likely contained some trans fat. The MCE principal investigator (Ivan Frantz) and co-principal investigator (Ancel Keys), however, were well aware of the cholesterol raising effects of trans fat prior to initiating the MCE.77 Moreover, Frantz and Keys previously devised the diets used in the institutional arm of the National Diet Heart Feasibility Study (NDHS), which achieved the greatest reductions in serum cholesterol of all NDHS study sites.2 Hence, it is highly likely that this experienced MCE team selected products containing as little trans fat as possible to maximize the achieved degree of cholesterol lowering. Perhaps more importantly, it is clear from the MCE grant proposal that common margarines and shortenings (major sources of trans fat) were important components of the baseline hospital diets and the control diet (but not the intervention diet). Thus, confounding by dietary trans fat is an exceedingly unlikely explanation for the lack of benefit of the intervention diet.”

Given that we can soundly infer that the SDHS study was confounded by TFA, and the MCE was likely to be confounded by TFA, the results of these trials start to make a lot more sense. The effect sizes, and the direction of effect, mirror what we see in epidemiological research, as well. When TFA replaces SFA, risk goes up. When PUFA replaces SFA, risk goes down. If it is still to be maintained by skeptics that vegetable oils increase the risk of CVD, rather than lowering it, they must satisfy the burden of proof with extraordinary evidence.

I would surely be remiss if I did not briefly acknowledge one trial that was left out of the analysis by Hooper et al. (2020), which was the Lyon Diet Heart Study [64]. The reason this trial is noteworthy is due to it being the only experimental investigation of the diet-heart hypothesis that actually made an effort to reduce LA alongside SFA. The trial also saw one of the largest reductions in CVD risk ever recorded in this body of literature, with a massive 73% reduction in CVD risk over four years. Skeptics of vegetable oils would like to attribute this effect to the lowering of LA in the diet, but is there any merit to this?

The trial did achieve statistically significant differences in LA intake between groups, with 5.3% of energy as LA in the control group and only 3.6% of energy as LA in the intervention group [65]. However, if we calculate the absolute intakes of LA for both groups using each group’s total daily calories, we discover that the absolute differences in LA intake between groups amounted to about 4.5g/day— equal to approximately 1.5 teaspoons of SO. In the most clinical, scientific vernacular, this would be referred to as sweet fuck all.

So, why did the trial see such a massive reduction in risk? Likely because, as far as multifactorial interventions go, this trial did its best to knock the ball out of the park. Just take a look at the diets.

Firstly, the groups achieved a difference of 10g/day in SFA and the between-group differences in SFA cross the 25g/day threshold that we discussed earlier. Secondly, the intervention group was consuming significantly fewer foods that are positively associated with CVD, such as whole meat, processed meat, and butter. Lastly, the intervention group was consuming significantly higher amounts of fruit and vegetables (considered together), as well as unsaturated fats, which are strongly inversely associated with CVD.

Bottom line is that there is a lot going on in these diets that could be strongly modifying CVD risk. To attribute the massive 73% reduction in CVD risk to 4.5g/day of LA is frankly dubious, and not replicated anywhere else in the literature, despite it being directly tested. Virtually all other trials that were well controlled found the opposite effect of increasing LA. Honestly, based on what is currently known about LA, I would sooner suspect that the lower LA in the intervention group was likely working against them, not for them.

Lastly, the relationship between LA and CVD has been investigated using Mendelian randomization (MR). You can think of MR as being a method of investigation that is halfway between an RCT and an epidemiological study. Essentially, you find people with gene variants that influence a particular exposure (such as higher tissue LA) and compare their outcomes to other people who do not have those gene variants.

The study methods of MR also assume that genes are randomly distributed in the population, and this should significantly mitigate the potential for certain types of confounding like the healthy-user bias (HUB). It is generally accepted that this methodology offers superior internal validity for these sorts of research questions than typical methods used in the field of epidemiology. It's essentially giving you the best idea of the magnitude of the effect, independent of confounding, that is achievable without lifelong RCTs.

Even though MR data doesn't really need to be mentioned, because we have actual RCT data on this subject. However, it's worthwhile to add it in here just to hammer home how utterly consistent this evidence actually is. This time the data is being sourced from the UK Biobank, which is a massive prospective cohort study that also leverages a repository of biological samples from over 500,000 subjects [66].

Here we see that Park et al. (2021) observed the same relationship yet again. Genetically elevated serum LA was inversely associated with AMI, whereas genetically elevated serum AA was positively associated with AMI. The gene variants that were investigated were related to the function of fatty acid desaturase (FADS) enzymes.

It would seem that those who are less able to convert LA to AA have a decreased risk of AMD, whereas those who are more able to convert LA to AA have an increased risk. Considering the fact that AA conversion is highly regulated, as well as the fact that dietary LA is virtually unavoidable, it seems unlikely that modulating dietary LA would be a reliable way to modulate the conversion of LA into AA [67]. On balance, these findings support increasing dietary LA for CVD prevention.

Overall, the available evidence overwhelmingly favours the inclusion of vegetable oils in the diet, especially if consumed to the exclusion of SFA, for reducing CVD risk. The evidence that would need to be bought forward to topple this paradigm would need to be truly extraordinary.



The data most often cited in support of the notion that vegetable oils increase the risk of cancer comes from a post-hoc analysis of the aforementioned study, the LAVAT, by Pearce et al. (1971) [68]. This is likely because this is some of the only randomized controlled trial (RCT) data that actually shows this increase in risk [69]. As we will discuss, this is quite easy to cherry-pick.

The LAVAT was a double-blind RCT was first reported on by Dayton et al. (1969), and aimed to investigate the effects of substituting vegetable oils for animal fat on the risk of CVD. However, cancer was among their secondary endpoints. The researchers actually took enormous care to ensure that the substitution of vegetable oil for animal fat was the only substitution the subjects were making. Even going so far as providing ice cream made out of vegetable oils rather than dairy fat.

It's important to preface this discussion with an acknowledgment that the trial observed statistically significant increases in dietary LA and LA tissue representation in the vegetable oil group. LA was higher across all measured tissue compartments when comparing subjects with an adherence of at least 88% to control.

Essentially, the concern that increasing vegetable oils in the diet increases cancer risk originates from the following figure. Figure 1 from the original analysis by Pearce at al (1971) clearly shows that the cumulative deaths due to carcinoma rise faster in the vegetable oil group than in the control group after around two years into the study.

I must admit that the graph looks pretty scary, and narratives surrounding the findings of this study had me convinced for a while too. However, this graph is incredibly misleading when taken out of context.

Firstly, cancer deaths were not a pre-specified primary endpoint of the trial itself, which means it is questionable whether or not the study was even appropriately powered or equipped to investigate this endpoint in any rigorous way. Secondly, the results are not statistically significant, despite the above figure showing what appears to be a massive divergence in cancer outcomes.

“During the diet phase (see figure) there were 31 carcinoma deaths in the experimental group and 17 in the control group (χ² = 3.668, p = 0.06).”

Pearce et al. also performed an analysis wherein they adjusted for the differences in relative adherence between groups. After the adjustment, the findings were consistent with what would be expected from random distribution.

“Many of the cancer deaths in the experimental group were among those who did not adhere closely to the diet. This reduces the possibility that the feeding of polyunsaturated oils was responsible for the excess carcinoma mortality observed in the experimental group. However, there were significantly more low adherers in the entire experimental group than in the controls (table VI). In both groups, the numbers of cancer deaths among the various adherence strata are compatible with random distribution (table V). A high incidence among high adherers would be expected if some constituent of the experimental diet were contributing to cancer fatality.”

This basically means that the excess cancer deaths seen in the vegetable oil group were very likely due to chance, and wrongly attributed to poor adherence. For this reason the authors conclude that the high vegetable oil diet was not likely to be the cause of the increase in cancer mortality. In fact, they specifically mention that their findings were an outlier in the context of the wider literature of the time.

Pearce et al. also included a table that stratifies cancer outcomes by the degree of adherence per group.

As mentioned above, the vegetable oil group had significantly more low adherers. As we can see, about 33% of the excess cancer was occurring among low adherers in the vegetable oil group. Let's see what happens when we not only remove the low adherer subgroup, but also compare the between-group difference in carcinoma risk among the highest adherers.

Removing the low adherers nullifies the effect of the vegetable oil diet on cancer outcomes. There are also other plausible explanations for the effect as well. For example, the vegetable oil group contained many more moderate smokers than the animal fat group.

Moderate smokers were defined as those smoking 0.5-1 packs of cigarettes per day. As you can see, observed cancer among moderate smokers was 3x higher in the vegetable oil group, and this is where all the excess cancer risk is accumulating.

This means that low adherers in the vegetable oil group were likely to be smokers, and the smokers were likely to get more cancer. As we would expect [70].

To suggest that the results of the LAVAT demonstrate a higher cancer risk for those consuming vegetable oils is just to misunderstand or misrepresent the data that Pearce et al. reported. The study did not actually divulge an independent effect of vegetable oil intake on cancer mortality. In fact, if we only look at high adherers, there are no statistically significant differences in cancer.

As the authors reported, if the increase in cancer risk was actually a consequence of the vegetable oil diet, we would see more cancer risk among higher adherers. But we don't. We see no statistically significant differences.

Overall, there is insufficient evidence to declare higher vegetable oil intakes to be an independent risk factor for cancer. The available data suggests that, at worst, higher vegetable oil intakes likely have a neutral effect compared to high animal fat intakes. But, the LAVAT was not the only trial to report cancer as a secondary endpoint. Three other trials that substituted vegetable oils for SFA also reported cancer outcomes.

When considering all of the studies in the aggregate (subgroup 2.1.1), we see that the effect of substituting vegetable oils for SFA on cancer is null. But, the LAVAT is still boasting its (dubiously) large effect size. When we remove the moderate smokers, the increased risk is nullified (subgroup 2.1.2). However, further excluding all regular smokers pushes the aggregated results to a non-significant decrease in cancer risk with substituting vegetable oils for SFA (subgroup 2.1.3).

The direction of effect that we see is actually consistent with the wider epidemiology investigating the relationship between LA biomarkers and cancer mortality. Here we see a strong inverse association between tissue LA representation and cancer risk overall [71].

However, this is total cancer. There is still some controversy surrounding whether or not vegetable oils may increase the risk of other cancers. So, let's dive into that literature next.


There is a meta-analysis by Ruan et al. (2020) investigating the limited epidemiological research on the subject. The results suggest that higher intakes of PUFA could also increase the risk of skin cancer in prospective cohort studies [72]. However, if we take a look at the author's data, we have some troubling findings.

Increasing PUFA intake seems to increase the risk of squamous cell carcinoma (SCC). However, a single study by Park et al. (2018) is contributing 92.6% of the weight [73].

This could be due to the fact that the study's adjustment model lacked several important covariates that may have plausibly changed the association. For example, the multivariate adjustment model adjusted for hair colour rather than skin tone. This is problematic because hair colour does not sufficiently capture the differential effects of skin tone on skin cancer [74].

In at least one of the included prospective cohort studies by Ibiebele et al. (2009) that investigated the relationship between LA and skin cancer included adjustments for skin colour, and their results were null [75]. This further casts doubt on the veracity of the findings reported by Park et al. (2018).

In fact, when tissue representation of LA was investigated by Wallingford et al. (2013) in a separate cohort study, non-significant reduction in the risk of basal cell carcinoma (BCC) can be observed [76]. Additionally, among those with previous skin cancers, higher tissue representation of LA was associated with a statistically significant decrease in risk of re-occurrence (RR 0.54 [0.35-0.82]) A very odd finding if having more LA inside your body is supposed to predispose you to developing skin cancer.

There is also one study by Harris et al. (2005) that investigates the relationship between red blood cell cis-LA content and squamous cell carcinoma [77]. While it is a case-control study and lacks the temporal component that is necessary to establish causality, it is some of the only human outcome data we have on the subject, and the results are null.

If one still believes that higher intakes of LA increases the risk of skin cancer, I have a treat for them. Do you remember the LAVAT we mentioned in the cancer section? It turns out that skin cancer was actually one of their secondary endpoints.

In table II from the post-hoc analysis by Pearce et al. (1971), we see that in the vegetable oil group, there were ten cases of skin cancer, whereas in the animal fat group there were 21. This produces a statistically significant increase in skin cancer risk for the animal fat group, whether or not we exclude or include the post-diet period (RR: 2.11 [1.01-4.43] and 2.09 [1.07-4.11], respectively).

As we discussed earlier, there were no statistically significant differences in cancer risk among heavy smokers, of which there were only two cases in the animal fat group. However, there was a non-significant increase in cancer risk among moderate smokers, of which there were 19 cases in the vegetable oil group.

This suggests that the increase in skin cancer seen in the animal fat group is not due to heavy smoking, as there were only two cases of cancer among heavy smokers in the animal fat group, and we have 21 cases of skin cancer. The vegetable oil group had lower rates of skin cancer despite the fact that there were more moderate smokers, and more cancer risk among moderate smokers, in the vegetable oil group. This increase in risk seen in the animal fat group can't be explained by differences in smoking habits.

Additionally, the methods of MR have also been used by Seviiri et al. (2021) to investigate the relationship between elevated plasma LA and skin cancer [78]. Data was collected for two different keratinocyte cancers, which were BCC and SCC, and the results are consistent with those found in the LAVAT.

There was a statistically significant 6% reduction in BCC risk with genetically elevated plasma LA, whereas AA showed a statistically significant 4% increase in risk. Oddly, the effect size of eicosapentaenoic acid (EPA) was actually larger than that of AA, which would seem to be at odds with the RCT data on the subject [79].

For SCC, the only statistically significant relationship between genetically elevated plasma PUFA was a 4% increased risk with plasma AA. The results for all other PUFA were null.

In conclusion, it is true that there are some cohort studies suggesting that higher intakes of PUFA may increase the risk of skin cancer. However, the results from the LAVAT and MR are actually stronger evidence and concordant in the opposite direction. As such, it would not appear likely that increasing vegetable oil consumption is an independent risk factor for developing skin cancer. In fact, increasing vegetable oil intake could actually reduce the risk of skin cancer.



The notion that vegetable oils are responsible for the obesity epidemic is extremely pervasive across a wide variety of niche diet communities. The hypothesis that dietary LA increases hunger through the passive production of an endocannabinoid known as 2-arachidonoylglycerol (2-AG) has been articulated by Watkins et al. (2015) [80]. They suggest that this endocannabinoid interacts with cannabinoid receptor type 1 (CB1) and facilitates obesity by stimulating appetite. Essentially, vegetable oils supposedly give us "the munchies" similarly to marijuana, as the story goes. But, does this mechanism actually work?

Indeed, this mechanism does appear to work— in mice [81]. In this study by Alvheim et al. (2012), mice were fed two different diets with varying fatty acid compositions. Essentially, mice were randomized to two diets that contained either moderate fat (35% of energy) or high fat (60% of energy). Within each diet group there were three distinct diet conditions. One of the diet conditions was low in LA (1% of energy), and the two remaining diet conditions were "high" in LA (8% of energy), with one of which also being supplemented with long-chain omega-3s.

By the end of the study, the mice receiving 8% of their energy from LA had consistently higher body weight, with a slightly mitigating effect of supplemented omega-3s in the mice fed a high-fat diet (chart e). The increases in body weight in the mice that were fed high-LA diets was commensurate with increases in 2-AG. Ergo, consuming a high-LA diet will increase 2-AG and facilitate obesity— in mice. But what about humans?

A study by Blüher et al. (2006) involving 60 subjects explored the correlation between body fatness and a number of markers related to the endocannabinoid system. There ended up being significant correlations between circulating 2-AG and obesity [82].

However, there is an issue. Unlike mice, circulating levels of the 2-AG precursor, ARA, did not differ between lean and obese subjects. For this reason, the authors go on to express skepticism toward the hypothesis that 2-AG synthesis is driven passively by the supply of precursors in humans. Instead, they point out that there is more evidence that obesity itself acts to inhibit the degradation of 2-AG.

“Which mechanisms lead to increased endocannabinoid levels in abdominal obesity? One possibility is the increased supply of precursors for endocannabinoid biosynthesis and/or increased activity of enzymes involved in endocannabinoid synthesis. When studying circulating levels of the precursor arachidonic acid and of oleoylethanolamide, a molecule with endocannabinoid structure and synthesized by the same enzymes that do not activate [cannabinoid] receptors, we did not find any significant correlation with measures of adiposity.

Thus, decreased endocannabinoid degradation must be considered as a second possibility. Given the overwhelming mass of adipose tissue compared with other organs, a contribution of adipocytes to endocannabinoid inactivation, which may be disturbed in visceral obese subjects, is an attractive hypothesis.”

This would not be surprising, considering that the impaired clearance and/or degradation of metabolic substrates is a well-characterized phenomenon in human obesity. Not only that, but there is also a large body of research divulging that the production of 2-AG from its precursors is regulated, not passive [83].

While somewhat tangential to the point, it is interesting to note that we have tested the effects of selective CB1-antagonism in humans with a pharmaceutical called Rimonabant [84]. Overall, this drug does appear to reduce energy intake and result in weight loss that is equal to just over a quarter-pound per week.

The reason this is tangential is because CB-antagonists such as Rimonabant are not specifically targeting LA metabolism. All this research tells us is that the endocannabinoid system is involved with the regulation of body weight in humans, but it does not tell us what independent contributions are being made by 2-AG, or even dietary LA for that matter.


If we wish to explore the effects of dietary LA on appetite, there have been a number of short-term fatty acid substitution trials investigating this question. One trial by Naughton et al. (2018) showed an effect of LA on hunger hormones, and a non-significant reduction in prospective energy intake compared to control and high-oleate diets [85]. However, the authors spuriously claim that there was no effect of the high-LA diet in reducing prospective energy intake. This is an example of the interaction fallacy, because there was actually non-inferiority between the high-LA diet and the other diet conditions.

Of the trials that actually reported ad libitum energy intake in response to diets of varying fatty acid composition, no consistent effects are observed [86-91]. All together, the short-term evidence is largely a wash. In fact, this was remarked upon by Strik et al. (2010) in table 4 of one such publication, and they included the aggregated findings across a number of different trials investigating the relationship between fatty acid saturation, satiety, and energy intake [92]. Overall, when there was an effect of LA, it tended to decrease energy intake.

In one particularly well-done trial by Strik et al., participants in the three groups were given unlimited access to muffins made using either SFA, MUFA, or PUFA. They would consume these muffins ad libitum one variety at a time, with a washout period between different muffin types.

By the end, all three groups experienced all three muffin types. Each time researchers collected subjective data on satiety and the general satisfaction of the food experience. No differences in any parameters reached statistical significance.

There were no statistically significant differences in hunger, fullness, satisfaction, or prospective consumption (how much more subjects suspected they could eat at that moment). It appeared as though SFA trended toward a decrease in fullness, perhaps. There were also no observed differences in ad libitum energy intake.

“Mean total [energy intake] and energy contributed by CHO, fat and protein respectively at the ad lib lunch is presented for each treatment in Figure ​3. There was no significant difference in total [energy intake] between lipid treatments (treatment, P > 0.05). Mean (SEM) [energy intake] at lunch was 5275.9 (286.5) kJ, 5227.7 (403.9) kJ, and 5215.6 (329.5) kJ following the SFA-, PUFA-, and MUFA-rich breakfasts respectively.”

One interesting criticism is that the subjects' diets may have already been so high in LA that the hunger effects were blunted. This supposedly creates the illusion of equal satiety effects between fatty acids, because the hunger-generating effects are masked by a sort of LA tolerance, so to say. While this is possible, it doesn't seem like a particularly strong criticism. The researchers used a crossover-style design specifically to minimize this type of confounding.

I have also heard it proposed that in order to study this effect, researchers may need to run-in the subjects on an LA-depleting diet for months (or even years) leading up to the satiety tests. Luckily, that's not necessary, as this was also investigated in the previously referenced trial by Dayton et al. (1969), the LAVAT. In this ad libitum double-blind RCT, normal weight subjects were randomized to one of two groups. The first group was placed on a vegetable oil containing diet that yielded approximately 42.3g/day of LA, whereas the second group was placed on an animal fat containing diet that yielded only approximately 10.8g/day.

This difference in LA intake produced an enormous increase in adipose LA in the vegetable oil group. It's probably safe to say that these people reached a maximal saturation point, as evidenced by the hyperbolic curve in adipose LA representation over time.

The median LA representation in adipose tissue increased from around 8-11% of total fatty acids to over 30% of total fatty acids across the eight year study. The baseline levels are consistent with levels found in traditional populations like the Tsimane, Inuit, and the Navajo aboriginals [93-95]. While the data published by Martin et al. (2012) is representing the fatty acid composition of breast milk in the Tsimane, it is also true that the LA contents of both breast milk and adipose tissue correlate extremely tightly [96-98].

There were no statistically significant differences between the baseline tissue LA presentation in the LAVAT and measurements taken from either the Navajo or Tsimane (SD estimated for Navajo). However, tissue LA was statistically significantly higher among Inuit when compared to the baseline measurements observed in the LAVAT.

From this, we can likely infer that the subjects in the LAVAT were largely starting from ancestral levels of tissue LA, and increasing tissue LA to approximately threefold higher levels over the eight year trial period. So how did this threefold increase in tissue LA affect their body weight over time? Long story short, it didn't.

Some may speculate that perhaps the 10.8g of LA being consumed in the control group was simply too high, and that the hyperphagic effects of the LA could have been masked by both groups exceeding a particular threshold. This is difficult to reconcile with the fact that the LA intake of the animal fat group was perfectly within bounds when compared to all known estimates of preagricultural LA intakes, as mentioned above [99]. Also, this was an ad libitum trial in normal weight subjects and body weight remained within 2% of baseline.

The LA intakes in the vegetable oil group universally overshoot the upper bounds of all of those same estimates of preagricultural LA intakes. This means that the animal fat group was consuming levels of LA that were consistent with those consumed before the obesity epidemic occurred. However, the vegetable oil group was consuming a level of LA that far surpassed all known preagricultural estimates of LA consumption.

This wasn't the only RCT that substituted vegetable oils for animal fat to measure body weight over time. According to the secondary endpoint analysis done by Hooper et al. 2020 with the Cochrane Collaboration, Olso Diet-Heart saw a 2.5kg reduction in body weight during their study period, whereas the Medical Research Council saw no change in body weight as well [100-101].

Altogether, it does not appear as though vegetable oils uniquely increase body weight in humans. So, while vegetable oils may increase 2-AG and induce obesity in mice, this does not appear to pan out in humans. Large scale RCTs in humans do not support the hypothesis that vegetable oils lead to weight gain over time in humans.


The effect of varying PUFA and SFA in the diet on measures of energy expenditure (EE) have been tested numerous times and show unambiguously consistent results [102]. Overall, diets higher in PUFA and lower in SFA tend to increase EE in humans [103-104].

Here is an example from that body of literature. In this study by Lichtenbelt et al. (1997), we can clearly see a consistent effect across all six subjects with higher PUFA intakes increasing postprandial EE. The same trend was also observed for resting metabolic rate (RMR). Ultimately, high-SFA, low-PUFA diets tend to lower EE and RMR compared to high-PUFA, low-SFA diets. This finding is incredibly consistent, though PUFA and MUFA seem to trade blows in some studies, the overall trend of SFA being least thermogenic is clear.

The effects of high-PUFA feeding on fat oxidation have also been studied by Casas-Agustench et al. (2009) as well [105]. When using the respiratory quotient to compare the effects of diets that are high in PUFA, MUFA, and SFA on EE and fat oxidation, we see a similar trend emerge once again.

High-PUFA feeding resulted in higher postprandial EE as well as a higher thermic effect of feeding (TEF). Though the differences in the rate of fat oxidation did not reach significance between groups, there was an obvious trend that reflected the degree of fatty acid saturation. PUFA was the most thermogenic, SFA was the least thermogenic, and MUFA was somewhere in between.

The same results were observed by DeLany et al. (2000), only this time different dietary fats containing labeled carbon isotopes are used [106]. You can measure these isotopes in the breath in order to measure how much of the dietary fat a subject has consumed was burned in the time after a meal.

Using this methodology, we see that there is one type of SFA that is preferentially oxidized over all other FAs that were tested, and that is lauric acid. However, lauric acid might have ketogenic properties, so interpret with caution. Looking over the rest of the tested FAs, we see that PUFA once again has the highest oxidation rate, followed by MUFA, and SFA once again comes in last place.

Vegetable oils don't appear to make you fat. But even if they did, it does not appear that their effects on thermogenesis and EE are likely to be mediating factors. In fact, vegetable oils appear as though they could potentially have the opposite effect.