Sunday, September 17, 2017

The PURE Study

The Prospective Urban Rural Epidemiology study (PURE study) has lately been making the rounds in the media following 3 publications that I will discuss below

The PURE study is a very large prospective cohort study with 135335 participants from 18 countries, that were followed up for a median of 7.4 years.  The PURE study is quite unique as the vast majority of prospective cohort studies for long term diet and disease relationships use North American and European cohorts (with the occasional Japanese cohort), whereas the PURE study uses 18 countries, includes countries in other parts of the world, and those middle and lower income levels.  They categorised the countries into the following regions:

“Regions included China, south Asia (Bangladesh, India, and Pakistan), North America, Europe (Canada, Poland, and Sweden), South America (Argentina, Brazil, Chile, and Colombia), Middle East (Iran, occupied Palestinian territory, Turkey, and United Arab Emirates), southeast Asia (Malaysia), and Africa (South Africa and Zimbabwe)”


In this study, fruit, vegetable and legume consumption was combined for the main analyses because legumes are sometimes categorised as vegetables.  In addition potatoes and other tubers weren’t counted as vegetables, and fruit and vegetable juices weren’t counted as fruit and vegetables

Combined fruit, vegetable and legume consumption was associated with more education and physical activity, a higher intake of calories (2869 kcal for ≥ 8 serves vs 1442 kcal for < 1 serve), red meat, and white meat (both absolute intake and relative to energy intake), less smoking and slightly lower starch intake

The study found that fruit, vegetable and legume consumption was associated with lower mortality up to 3-4 serves per day (375-500g), which was due to both CVD and non-CVD mortality, and there was no additional benefit with higher consumption.  Fruit, vegetable and legume consumption on their own were associated with lower mortality, with no additional benefit beyond 2 serves per day for fruit and vegetables each, and 1 serving per month to less than 1 serving per week for legumes.  Also, raw vegetable intake was more strongly inversely associated with mortality than cooked vegetable intake



The results of this study are pretty much what would expect if you’ve read the Mensink meta-analysis.  There are some surprises such as MUFA and PUFA intake being associated with higher LDL-C, PUFA intake being more strongly associated with higher LDL-C than MUFA, and carbohydrate intake being only weakly associated with triglycerides (~0.08 mmol/l from 20% points of carbohydrate)

The study emphasises the ApoB:ApoA1 ratio (the ratio of LDL particles to HDL particles).  This measure is better than LDL-C and HDL-C [9] [10] [11], because the cholesterol in those particles is in some ways irrelevant, it’s about LDL and HDL function and particle number at least partly implies net function

In addition, protein didn’t have much relationship with any measure of blood lipids, and dietary cholesterol had no significant relationship with on ApoB despite being associated with higher LDL-C



This paper attracted the most media attention by far.  For the purposes of this blog post I’m going to focus on total mortality as it tells you more than just looking at one cause of death and it avoids the bias of cherry picking subgroups to fit a particular narrative

  
Carbohydrate intake (by % of energy intake) was associated with an increased total mortality (Q5 vs Q1: RR = 1.28), which was due to an increase risk of death from non-CVD causes (Q5 vs Q1: RR = 1.36).  Whereas fat and protein intake were both inversely associated with lower total mortality (Q5 vs Q1: RR = 0.77 and 0.88), which in both cases was due to an reduced risk of non-CVD mortality (Q5 vs Q1: RR = 0.70 and 0.85)*

When protein intake was separated by animal and plant protein, they found that “Animal protein intake was associated with lower risk of total mortality and no significant association was observed between plant protein and risk of total mortality”

* “Among noncardiovascular disease mortality, in all regions except Africa, the most common cause of mortality was cancer followed by respiratory diseases. In Africa, infectious disease was the first and respiratory disease was the second most common cause of non-cardiovascular disease mortality.”

  
Fat intake was then broken down to SFA, MUFA and PUFA.  Intake of all three classes of fats was inversely associated with total mortality (Q5 vs Q1: RR = 0.86, 0.81, 0.80).  which in all cases was due to an reduced risk of non-CVD mortality (Q5 vs Q1: RR = 0.86, 0.79, 0.75).

* SFA was the only class of fatty acids to be inversely associated with stroke mortality.  This isn’t the first hint that such a relationship may exist, as the Siri-Tarino meta-analysis also found an inverse relationship between SFA intake and stroke (events not deaths) but one that didn’t reach significance (RR = 0.81, CI = 0.62-1.05, P = 0.11).  This may be a bit of surprise, particularly to diet heart true believers, but then the lipid and diet heart hypotheses were always based on coronary heart disease, not stroke or other manifestations of CVD.

  
They then separated the data by Asian and non-Asian regions, which found the relationships between total mortality with carbohydrate, total fat, SFA and MUFA intake are stronger in non-Asian countries, and that PUFA intake is associated with lower mortality in Asian countries, but not non-Asian ones

  
So what to make of these results?

If we take these results on face value it challenges a few dietary dogmas in conventional dietary advice (CDA):

  • CDA: low fat (20-35%), high carbohydrate (45-65%) diets decrease the risk of chronic disease [1].  PURE: reducing fat intake below 35% and increasing carbohydrate intake above 45% of total energy intake is associated with higher total mortality*
  • CDA: SFA increases the risk of CHD and/or PUFA decreases the risk of CHD [2].  PURE: SFA and PUFA were not associated with CHD and the RR for SFA and PUFA were near identical (1.17 and 1.12)
  • CDA: plant protein is healthy, animal protein is unhealthy [3].  PURE: animal protein is healthy, plant protein is neutral
  • CDA: recommends 5 servings of fruit and vegetables per day.  PURE: no additional benefit beyond 3-4 serves of combined fruits, vegetables, and legumes per day 

In the discussion the authors cite previous observational studies that did not find a relationship between total fat and carbohydrate intake with mortality, but that previous observational studies in North American and European cohort have a narrower and lower range of carbohydrate intakes compared to the PURE study (~35-56% vs 46-77%).  Regarding fat and carbohydrate intakes, one could make an argument based on these results that the relationship between health/mortality and intake of fat and carbohydrate generally exists as an inverse U-shaped curve, just like protein and micronutrients.  Where, within the range of intakes in the PURE study, higher fat and lower carbohydrate intake sits higher on the inverse U-shaped curve.  But if we were to just look at the PURE study, the exact shape of the inverse U-shaped curve is unknown because the PURE study can’t make any good conclusions about fat intakes > 35% and carbohydrate intakes < 45% of total energy intake

One of the most common argument from mainstream sources is that the study simply shows that refined carbohydrates are bad.  A strong line of evidence to back this up is that combined fruit, vegetable and legume intake was associated with lower mortality in another of the recent publications from the PURE study [4], not to mention the many other observational studies that find fruit & vegetables = good, refined carbohydrates = bad.  But this argument cuts both ways if you’re looking to have a discussion on the health effects of fats vs carbohydrates.  Just as fruit, vegetables, whole grains and legumes don’t have the same health effect as refined carbohydrates; full fat dairy, eggs, avocado, nuts and seeds don’t have the same health effects as refined oils.  Even though these days many conventional and alternative experts will proudly declare (particularly when the latest study doesn’t go their way), that they are beyond nutrients and instead focus on foods or eating patterns (however they define that), most will still object or have some problem with a non-extreme (≤ 10% or ≥ 70%) intake of either fat or carbohydrate.  They still care

Another explanation for the results is that there was insufficient adjustment for confounding variables.  In the contexts of standard western observational studies health consciousness is a major confounding variable.  Those who better follow conventional dietary advice do so mostly because they are more health conscious, and because they are more health conscious they are also more likely to exercise regularly and less likely to smoke, etc.  Therefore, when extracting the health effects of a given nutrient or food, observational studies are also extracting the health effects of eating patterns and lifestyle.  I have serious doubts that multivariate analyses always sufficiently adjust for such confounding variables.  For example, a very large observational study was published early this year on meat and mortality.  Basically it found red meat increased the risk total mortality and white meat decreased it.  Is red meat and white meat so different, or are these divergent outcomes a product of who eats red meat vs who eats white meat?  The answer probably lies more with the latter.  The strongest associations for red meat and white meat were respiratory mortality and liver disease, probably just simply due to differences smoking and drinking, and insufficient adjustments for these confounding variables [5].  This is a similar result with another observational study that found saturated fat was associated with respiratory mortality and it being one of the stronger associations, not CHD or CVD by the way, and also that SFA intake was associated with smoking [6].  The PURE study has a unique issue in that it’s also got the country’s income as a confounding variable.  For example, if country A eats a higher carb diet and has a lower life expectancy than country B, then an observational study that either combines data from both countries (like the PURE study) or compares two countries (an ecological study) could easily find that that carbohydrate intake is associated with an increased risk of death and in a more complicated data set may have difficulty in adjusting for these confounding variables.  So ideally the data would be separated by each country or at least income level but that may leave the study quite underpowered.  Even though the researchers adjusted for confounders there were some strange associations and ones that are quite unusual to see

For example, despite a being inversely associated with total mortality intakes of total fat, SFA, MUFA and PUFA all had RRs above 1.00 for myocardial infarction (1.12, 1.17, 1.12 and 1.12), but in each case were nowhere near significance (P = 0.40 for all of them).  Conversely, the RR for carbohydrate intake and myocardial infarction was 0.90, despite carbohydrate intake being associated with higher total mortality.  This is an unexpected result as in another of the papers, all classes of fats were associated with a lower ApoB:ApoA1 ratio, total-C:HDL-C ratio and triglyceride:HDL-C ratio, whereas carbohydrate intake was associated with a higher ratios of all three.  An explanation that may partly explain these results is that richer countries eat more fats and less carbohydrate, live longer, and get more heart disease, and this wasn’t able to be sufficiently adjusted for.  Another example is from the fruit, vegetable, and legume study where an intake of 1 serving of legumes per month to less than 1 serving per week, in other words probably like 2 servings of legumes per month, was associated with 20% lower mortality vs having none at all.  I doubt that ~2 serves of legumes per month would have such an impact


I initially found the fat versus carbohydrate debate that this study reignited to be quite old.  Despite the popular version of the Mediterranean diet and the standard western diet having very similar fat and carbohydrate intakes, it’s clear that they have very divergent health effects.  This difference in health outcomes is far more likely due to differences in diet quality rather than a few percentage points difference in fat and carbohydrate intake.  It can be easy to live in a bubble where this knowledge is taken for granted.  But this bubble bursts on occasion and then it becomes apparent that many in the general population (including nutrition students!!!) place a large emphasis on reducing fat intake, believing fat increase the risk of weight gain and chronic disease, even though the such beliefs should be at least a decade or two out of date.

“For prevention of chronic diseases, nutrient-based recommendations are more difficult to translate to the public. Few individuals can accurately estimate their daily calorie consumption, much less their intake of total fat or specific fatty acids [78, 79]. Interestingly, while 67% of consumers are trying to limit their fat intake, few are aware of how much fat they should actually be eating [76]. Only 22% of consumers correctly identified the recommended range of calories from fat [9]. Sixty percent of consumers believed that fat intake should be less than 14% of daily calories [9].” [7]

These beliefs are not harmless either as people are enticed by health halo around low fat junk food and reject the possibility of low carb diets being especially therapeutic for some diseases such as type 2 diabetes and not that difficult to maintain

The PURE study is one prospective cohort study among many, but a very large and unique one.  To draw a robust conclusions about diet and disease relationships you should look at the other observational studies rather than just cherry pick the one that supports your narrative (assuming RCTs aren’t available).  At the very least, the PURE study will teach some people about the importance of confounding variables, although I suspect the many of the same people complaining about PURE will forget about confounding variables and the difficulties in adjusting for them when championing the next ‘red meat is bad for you’ study

* The authors recommended a 35% fat and 50-55% carbohydrate intake [8], but this doesn’t line up with their results (maybe being politically correct?) and also only leaves a protein intake of 10-15%.  Whereas the results of the PURE study alone would suggest protein:fat:carbohydrate should be 20:35:45, which is almost what western countries eat anyway (minus a few % protein + a few % alcohol)

Sunday, July 23, 2017

New Paper on Glucose Dose and Suppression of Endogenous Glucose Production

Our research group recently published a paper in Diabetes titled ‘The Effect of Ingested Glucose Dose on the Suppression of Endogenous Glucose Production in Humans’.  The paper has been uploaded as an electronic form and will be properly published in an issue in a few weeks.  The paper requires a subscription to read so I’ll summarise it below:

The aim of the study was to get a better understanding of the regulation of endogenous glucose production (EGP) under physiological conditions, as most of the research on EGP has come from studies using the euglycemic-hyperinsulinemic clamp (a method that involves infusing glucose and insulin to maintain normal glucose levels and high insulin levels over time)

We used the dual tracer technique to accurately measure EGP before and after different glucose doses (25g, 50g, 75g) in healthy young males.  The dual tracer involves two stable (non-radioactive) labelled glucose tracers* – one that is included in the glucose drink, and the other is infused at a variable rate** to mimic the fall in endogenous glucose.  The dual tracer technique can also estimate the rate of glucose entering the bloodstream from the drink (Ra) and the rate of glucose exiting the bloodstream into tissues (Rd)***

The main findings are:

  • Consistent with previous research, different glucose doses produced a dose dependent insulin response but near identical glucose responses****
  • The 25g, 50g and 75g glucose doses all resulted in a near identical suppression of EGP*****
  • Whereas Ra and Rd were dose dependent, such that higher glucose doses results in a greater rate of glucose absorption and glucose disposal 

In the discussion we mention:

  • These results indicate that the suppression of EGP is likely the first line of defence (so to speak) to deal with oral glucose loads, which makes sense as inhibiting EGP is faster and requires less insulin than stimulating significant glucose disposal in muscle.  Minimising insulin secretion may also be the reason why we have a glucose response at all to low glucose doses (as healthy people clearly have the capacity to have almost no glucose response if their bodies were wired up to do that)
  • The fact that larger glucose doses result in greater insulin responses but near identical glucose responses means that something else besides the in blood glucose must be responsible for the difference in the insulin response, probably incretins 

So what are the practical implications of this paper?  This paper is mainly aiming to address textbook physiology type questions so, like many scientific papers, isn’t very actionable on an individual level.  Although it should go towards easing some of the anxiety of those who think that getting a glucose response from a banana or potato (~25g carbs) means that they are 'metabolically broken’, as this is normal and doesn’t necessarily mean that double or triple the carbohydrate intake will double or triple their glucose response.

* These are 1 and 2 atomic mass units heavier than regular glucose and this difference can be detected using gas chromatography-mass spectrometry (GC-MS)

** This avoids potential issues in calculating EGP such as paradoxical increases in EGP shortly after a glucose load and negative EGP during the euglycemic-hyperinsulinemic clamp.  There is a slightly amusing example of this in the following paper, where they find that one group of mice has negative EGP during the clamp, while the other group of mice has ‘impaired’ EGP because theirs is only ~0

*** Although this is more accurately done using the triple tracer technique, which involves infusing a second tracer at a variable rate to mimic the Ra

**** Previous studies have consistently found that different glucose loads (33g vs. 66g vs. 100g, 50g vs. 100g, etc*) produce a dose dependent insulin response but near identical glucose responses in healthy people.  In contrast, people with borderline or overt impaired glucose tolerance (2h glucose ≥ 7.8 mmol/l) have greater glucose responses from larger doses of glucose.  This begs the question, how is it that in healthy people, larger glucose doses don’t produce a greater glucose response?

***** EGP was suppressed by ~55% unlike the near complete suppression that is common from when using the euglycemic-hyperinsulinemic clamp.  This is because under physiological conditions insulin inhibits glycogenolysis but not gluconeogenesis, whereas the prolonged supraphysiological hyperinsulinemia from the clamp is necessary to persistently inhibit gluconeogenesis

Sunday, July 2, 2017

The AHA's Presidential Advisory on Dietary Fats and Cardiovascular Disease

The American Heart Association (AHA) recently released a presidential advisory on dietary fats and cardiovascular disease (CVD) [1].  As you would expect from the AHA, they claim that “…randomized controlled trials that lowered intake of dietary saturated fat and replaced it with polyunsaturated vegetable oil reduced CVD by ≈30%, similar to the reduction achieved by statin treatment” and conclude that “lowering intake of saturated fat and replacing it with unsaturated fats, especially polyunsaturated fats, will lower the incidence of CVD”.

I recently published a meta-analysis on the randomised controlled trials (RCTs) that replaced saturated fat (SFA) with polyunsaturated fat (PUFA) to see if this intervention reduces the risk of coronary heart disease (CHD) (blog) [2].  I concluded that “available evidence from adequately controlled randomised controlled trials suggest replacing SFA with mostly n-6 PUFA is unlikely to reduce CHD events, CHD mortality or total mortality”.  So not surprisingly I disagree with the AHA’s presidential advisory and will explain why below.

Speaking of which, as my meta-analysis was published about a month ago some people asked if I thought the presidential advisory was a response to that.  I doubt it.  The authors would have to have been made aware of my meta-analysis (without there being significant media coverage), organise themselves, then write this paper and get it past review in less than month.  They also didn’t cite it my meta-analysis, but one could argue that could be because of other reasons.  I think it’s more likely that this is a response to the updated meta-analysis by Ramsden et al in April 2016 after recovering data from the Minnesota Coronary Survey [3].

In this post I’m going to focus on the presidential advisory, mostly the RCT evidence, and not the media coverage.  From what I’ve seen, the media coverage badly misrepresents the evidence.  They claim SFA is bad and draw special attention to coconut oil, claiming that coconut oil is worse than butter because it has more SFA.  This is a position you can only take if you ignore that SFA also increases HDL-C (as a result SFA doesn’t significantly affect the total-C:HDL-C ratio) and HDL-C is associated with a lower risk of CHD/CVD, ignore meta-analyses of observational studies, and ignore the almost total absence of RCTs for anything related to SFA and CHD/CVD other than replacing SFA with PUFA [1].  The conventional position is not that SFA is bad, but that replacing SFA with PUFA will reduce the risk of CHD/CVD.  Replacing SFA with MUFA is not a particularly defensible position unless you only look at blood lipids and ignore meta-analyses of observational studies.

The AHA’s Selection Criteria

The claim that RCTs where SFA was replaced with PUFA reduced the risk of CVD by ~30% doesn’t come from an earlier meta-analysis, but is from a meta-analysis the AHA did in the presidential advisory.  The AHA looked at previous systematic reviews and meta-analyses for RCTs (Mozaffarian et al 2010, Hooper et al 2015, and Chowdhury et al 2015) and applied the following inclusion criteria to select their core trials:

  1. Compared high SFA with high PUFA
  2. Didn’t include trans fats (TFA) as a major component
  3. Controlled the dietary intake of the intervention and control groups
  4. Had at least 2 years of sustained intake of the assigned diets
  5. Proved adherence by objective biomarkers such as serum cholesterol or blood or tissue levels of polyunsaturated fatty acids
  6. Collected and validated information on cardiovascular or coronary disease events 

These criteria are very reasonable.  (1) and (6) are actually essential.  In my meta-analysis I tried to account for (2) and (3) with my ‘adequately controlled’ and ‘inadequately controlled’ categories, and I reported changes in serum cholesterol for every trial (5).  The rationale for (4) makes some sense and the AHA say “trials of serum cholesterol–lowering agents show that a reduction in coronary heart disease (CHD) incidence occurs with a lag of 1 to 2 years”.  However, the primary reason given is that “the 2-year minimum duration is that changes in polyunsaturated fatty acids very slowly equilibrate with tissue fatty acid levels; it takes ≈2 years to achieve 60% to 70% of the full effect”.  This isn’t necessary for adherence and isn’t relevant for the proposed mechanism.  The proposed mechanism for PUFA reducing the risk of CHD is by lowering LDL-C, which short term feeding studies and the National Diet Heart Study found maximally occurs within days or a couple of weeks [4]

After applying their criteria, they included the following trials as part of their core trials:

  • Los Angeles Veterans Administration Trial (LAVAT)
  • Oslo Diet Heart Study (ODHS)
  • Finnish Mental Hospital Study (FMHS)
  • Medical Research Council Trial (MRCT) 

The Core Trials

When briefly summarising each trial, the AHA mentions key facts like the number of participants, the basics of the interventions and the number of events and deaths in each group.  However, the AHA doesn’t address the major issues in most of those trials which are discussed in my recent meta-analysis and I’ll also mention below:

Los Angeles Veterans Administration Trial

I see there being two key issues in LAVAT.  Firstly, that the researchers mostly omitted conventional margarines and hydrogenated shortenings (major sources of TFA) from the high PUFA diet [5].  Secondly, the α-tocopherol (vitamin E) intake in the high SFA group was 9.4-fold lower than the experimental group (22.6 mg vs. 2.4 mg) [6] and was deficient, being only 16.0% of the current RDA (15 mg) [7].  These issues were not reported in the monograph [8], which the AHA cites, but in the other papers that I cited here, which can be easily found by looking at the Cochrane meta-analyses (the AHA cites the one by Hooper et al 2015). 

There’s the argument that smoking is a confounder in LAVAT.  The high PUFA and high SFA groups had similar number of low or non-smokers, but despite randomisation and a large number of participants, the experimental group had more moderate smokers and fewer heavy smokers [9].  My guess at a reasonably appropriate way to account for this is to take the incidence of endpoints in person years stratified by smoking status, weight them according to the total number of participants in each smoking stratification and sum that all together (see table below).  This results in the effect of higher moderate smokers and lower heavy smokers probably cancelling each other out to a large extent.  Interestingly, there was an interaction between smoking and the high SFA, vitamin E deficient control diet such that much of the increased risk of CHD/CVD in the high SFA group was in the moderate and heavy smokers (supporting an oxidative stress model of CHD)



Total incidence in person years
Average weighted incidence in person years

SFA
PUFA
RR
SFA
PUFA
RR
SD,MI
2.37
1.87
0.79
2.40
1.88
0.78
SD,MI,CI
3.18
2.16
0.68
3.21
2.18
0.68
Hard EP
3.51
2.38
0.68
3.51
2.38
0.68
CVD M
2.55
1.73
0.68
2.55
1.74
0.68

Oslo Diet Heart Study

The high PUFA group received a multifactorial dietary intervention that included advice to increase fish, shellfish and whole plant food consumption, advice to moderate sugar consumption and restrict shortening (a major source of TFA, and hydrogenated marine oils were a major source of fat in Norway at the time of trial), and the high PUFA group received sardines canned in cod liver oil [10].

This isn’t mentioned in the main publications of the trial [11] [12] ([11] is the one cited by the AHA), but is mentioned in the monograph [10], in an online version of the relevant chapter from the monograph [13], by Ramsden et al in the 2010 and 2016 versions of their meta-analysis [14] [15], and by Hoenselaar is his review [16] (and no doubt others have done the same in peer-reviewed journals).

ODHS should not have been included with the core trials for failing to meet the TFA (2) and controlled diet (3) criteria.  The AHA excluded DART and STARS because SFA was replaced with PUFA and carbohydrate, but included ODHS despite all the other dietary variables that are likely more meaningful than replacing SFA with carbohydrate, which the AHA doesn’t even think affects the risk of CHD/CVD.

Finnish Mental Hospital Study

The AHA includes FMHS as part of their core trials and refers to the core and non-core trials as ‘randomised controlled trials’.  However, FMHS isn’t a randomised trial.  Some have suggested that it’s a cluster randomised trial (in this case that the hospitals, rather than the patients, were randomly allocated to go on the high SFA or high PUFA diet first).  However, there is no mention of this in any of the publications from the trial.  Even if the researchers did flip a coin to randomly allocate the hospitals, a cluster randomisation with 2 clusters is probably quite inadequate.  Hooper et al 2012 [17] and 2015 [18] in their meta-analyses required at least 6 clusters and excluded FMHS for that reason.

A couple of issues in FMHS demonstrates that how 2 clusters can be inappropriate.  The control group in FMHS received more of a cardiotoxic antipsychotic drug called thioridazine in hospital N (0.82 vs. 1.79) and slightly less in hospital K (0.43 vs. 0.14), which averaged to an overall greater use in the control group (0.63 vs. 0.97) [14] [19].  Also, the participants in the control group remained in the hospitals longer than those in the experimental group, which led to an overestimation of the effect size also points to inadequate randomisation.  Fortunately the AHA correctly used the RR from incidence by age-adjusted person years to account for this.

In addition, due to the more detailed dietary information provided in FMHS [19], Ramsden et al [14] was able to estimate TFA intake in both of the groups and found TFA intake to be lower in the experimental group in both hospital K (0.0 vs. 2.0% of total energy intake) and hospital N (0.2 vs. 0.6% of total energy intake).

Medical Research Council Trial

I don’t see there being any major issues in MRCT and is the only one of the four core trials that I categorised as adequately controlled in my meta-analysis.  Some minor issues include: that the methods used to reduce SFA intake in the high PUFA group included forbidding “butter, other margarines, cooking-fat, other oils, fat meat, whole milk, cheese, egg yolk, and most biscuits and cakes”.  This was very would be expected to reduce TFA intake in the high PUFA group to some degree and these methods were very common in the diet heart trials.  And also that the participants were instructed to consume at least half of the soybean oil unheated and most of the participants achieve this by drinking the oil produced [20].  This doesn’t represent the way in which oils are usually used, which is for cooking, and cooking causes heat damage to oils.

The Non-Core Trials

The AHA considered the following to be non-core trials:

  • St Thomas Atherosclerosis Regression Study (STARS)
  • Diet And Reinfarction Trial (DART)
  • Houtsmuller et al (HDAT)
  • Rose Corn Oil Trial (RCOT)
  • Minnesota Coronary Survey (MCS)
  • Sydney Diet Heart Study (SDHS) 

Trial
Reason for exclusion
My comment
STARS
Replaced SFA with PUFA and carbohydrate
Carbohydrate was 10% lower (234.2 vs 267.1 g/d).  Doesn’t address what are likely to be far more meaningful dietary changes besides SFA, PUFA and carbohydrate (similar to ODHS, see my paper)
DART
Replaced SFA with PUFA and carbohydrate
Carbohydrate only increased from 44% to 46% of total energy intake
HDAT
Researchers were not blinded
This wasn’t one of their criteria, but it was a very badly reported study with lots of issues and unknowns
RCOT
Small number of participants (N =54) and short duration
Small number of participants wasn’t one of their criteria and wasn’t mentioned when discussing STARS.  Debatable whether it should have been included with the AHA criteria as it did have mostly 2 year follow up
MCS
Average duration 384 days, withdrawals, intermittent treatment
Could have used data from participants who remained in the study for ≥ 1 year as they had an average of 2.9 years on the diet
SDHS
Replaced SFA with PUFA and TFA
See below

Sometimes the exclusion of these trials was justified, sometimes it was not.  On balance the non-core trials are unfavourable for the diet heart hypothesis.  RCOT, MCS and SDHS are unfavourable, DART was pretty neutral, and while STARS and HDAT were favourable, they are both quite small.  So the exclusion of the non-core trials helped the AHA get an impressive RR when conducting a meta-analysis on their core trials, which is part of what makes me wonder whether the criteria were designed to get such a favourable result.

Also, in relation to MCS, the AHA also said “another concern is the use of lightly hydrogenated corn oil margarine in the polyunsaturated fat diet. This type of margarine contains trans linoleic acid, the type of trans fatty acid most strongly associated with CHD”.  However, this ignores several points made by Ramsden et al about TFA intake in MCS (see below):

“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. 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. 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.” [3]

It seems that the AHA uses TFA and multifactorial dietary interventions as justification to exclude trials when it’s convenient and ignores these issues when it’s not.

Should the Sydney Diet Heart Study be dismissed so easily?

Some have suggested that the high PUFA group in SDHS had a higher intake of TFA due to the use of Miracle Margarine, which has been suggested to have been rich in TFA at the time of the trial [21].  However, Ramsden et al [22] [23] has provided some arguments suggesting that TFA is likely to be a major factor in SDHS.  The AHA ignores this debate and uncertainty and confidently states that the study was comparing a high SFA diet with a high PUFA and TFA diet because the high PUFA group was given a margarine high in TFA.

“The Sydney Diet Heart Study showed that using a margarine rich in trans unsaturated fat to replace saturated fat increased CHD events, confirming similar adverse results in epidemiological studies.”

I’m going to play the AHA’s game and assume for this section that the high PUFA group in SDHS did in fact have a higher intake of TFA, but *spoiler alert*, they’re not going to like to outcome

To get a rough indication of the TFA intake in the high PUFA in SDHS let make a few assumptions and rough calculations.  (1) Assume that the Miracle Margarine used in SDHS was composed of 25-40% TFA, which based on study looking at the TFA content of safflower margarines of that time that was cited by Gutierrez* in her rapid response [21], and (2) assume that the high PUFA group replaced roughly half their original fat intake (which I think is a reasonable estimate given the change in SFA intake) with similar amount of miracle margarine and safflower oil.  Therefore Miracle Margarine provided about 9% of total energy intake and TFA intake from Miracle Margarine would be 2.25-3.60%.  Ramsden et al estimated that TFA intake in the high SFA group was 1.6% [14].  The high PUFA group were advised to restrict common margarines and shortenings (which are major sources of TFA), but let’s assume they were 50-75% compliant.  Therefore, a generous estimate of TFA intake would be 1.60% in the high SFA group and 2.65-4.40% in the high PUFA group.


High SFA group
High PUFA group
Baseline
Follow up
Baseline
Follow up
Total fat (%)
39.4
38.1
39.9
38.3
SFA (%)
15.9
13.5
16.4
9.8
MUFA (%)
14.9
13.8
14.9
11.5
PUFA (%)
6.6
8.9
6.6
15.1

The RR in SDHS was 1.57 for CHD mortality and 1.49 for total mortality.  The AHA cites two analyses of observational studies which found using a substation analysis that replacing 2% of energy from SFA with the same energy from TFA increased the risk of CVD mortality by 5% [24] and 16% [25], and total mortality by 16% [25].  Even assuming a TFA intake of 4.40% in the high PUFA group, using the 5% increased risk of CVD mortality with 2% SFA > TFA substitution results in an amended RR of 1.50 for CVD mortality.  So I’ll continue to be generous and use the 16% increased risk to calculate an altered RR in the table below.  The RR is still comfortably > 1.00 whether you assume a TFA intake in the high PUFA group of 2.65% or 4.40%.

TFA intake in high PUFA group
Altered RR for CVD mortality
Altered RR for total mortality
Assume 2.65%
1.44
1.37
Assume 4.40%
1.22
1.16

In summary, in the bottom row in the table above I’ve granted that Miracle Margarine is 40% TFA (upper-end), assumed it contributed about 9% of total energy intake, granted that the high PUFA group were only 50% compliant in restricting common margarines and shortenings, and finally used the most favourable of the AHA’s cited data for SFA > TFA substitution.  After doing all this to alter the RR to account for potential differences in TFA intake, SDHS is still an unfavourable study for the diet heart hypothesis!!!  This should be a serious wakeup call to the AHA and other diet heart advocates.

* In her rapid response, Gutierrez says “the PUFA-supplemented (intervention) group may have been provided with atherogenic trans fat, and the investigators cannot prove otherwise” [21].  However, this argument cuts both ways – ‘the high PUFA group may have had a lower or similar intake of TFA and critics cannot prove otherwise’ – and is similar to the stupid argument of ‘you can’t prove that God doesn’t exist’

Total Mortality

The AHA only conducted one meta-analysis that pooled the primary end-points from each of their core trials, which includes:

  • LAVAT: CVD mortality
  • ODHS: the number of participants with total CHD events (doesn’t count multiple events in the same person more than once, and includes ‘soft’ events like angina)
  • MRCT: the number of participants with total CHD events
  • FMHS: CHD mortality 

The figures here are all correct, even appropriately using the age-adjusted person years in FMHS, which is actually surprising given the previous meta-analyses

A major issue here is that the AHA doesn’t conduct a meta-analysis for total mortality.  This is important because whole point of trying to reduce your risk of CHD or CVD is to reduce your overall risk of morbidity and mortality.  One can argue that the AHA is justified to focus on CHD/CVD since that is the purpose of the association, but what good does it do to reduce your risk of CHD/CVD if doing so increases your risk of non-CHD/CVD morbidity and mortality, such that you would be no better off?

This is particularly relevant for the AHA’s selection of trials.  While the high PUFA group in LAVAT and FMHS had a lower risk of CHD and CVD mortality (RR = 0.80 and 0.59), they had a near identical risk of total mortality (RR = 0.98 and 1.01), because non-CHD/CVD mortality was higher [8] [26].  As those trials were both many times larger than ODHS and MRCT, and therefore have a much larger weighting, the pooled RR of the AHA’s core trials for total mortality is 0.98 (CI = 0.90-1.07, P = 0.65).


So will replacing SFA with PUFA reduce your risk of dying?  Even with the AHA’s selection of trials and even while ignoring the major issues in LAVAT, ODHS and FMHS, the answer is still no.