One of my scientific heroes is Bernard Katz. The closing words of his inaugural lecture, as professor of biophysics at UCL, hang on the wall of my office as a salutory reminder to refrain from talking about ‘how the brain works’. After speaking about his discoveries about synaptic transmission, he ended thus.
"My time is up and very glad I am, because I have been leading myself right up to a domain on which I should not dare to trespass, not even in an Inaugural Lecture. This domain contains the awkward problems of mind and matter about which so much has been talked and so little can be said, and having told you of my pedestrian disposition, I hope you will give me leave to stop at this point and not to hazard any further guesses."
The question of what to eat for good health is truly a topic about "which so much has been talked and so little can be said"
That was emphasized yet again by an editorial in the British Medical Journal written by my favourite epidemiologist. John Ioannidis. He has been at the forefront of debunking hype. Its title is “Implausible results in human nutrition research” (BMJ, 2013;347:f6698.
The gist is given by the memorable statement
"Almost every single nutrient imaginable has peer reviewed publications associating it with almost any outcome."
and the subtitle
“Definitive solutions won’t come from another million observational papers or small randomized trials“.
Being a bit obsessive about causality, this paper is music to my ears. The problem of causality was understood perfectly by Samuel Johnson, in 1756, and he was a lexicographer, not a scientist. Yet it’s widely ignored by epidemiologists.
The problem of causality is often mentioned in the introduction to papers that describe survey data, yet by the end of the paper, it’s usually forgotten, and public health advice is issued.
Ioannidis’ editorial vindicates my own views, as an amateur epidemiologist, on the results of the endless surveys of diet and health.
- Diet and health. What can you believe: or does bacon kill you (2009) in which I look at the World Cancer Research Fund’s evidence for causality (next to none in my opinion). Through this I got to know Gary Taubes, whose explanation of causality in the New York Times is the best popular account I’ve ever seen.
- How big is the risk from eating red meat now: an update (2012) This was based on the WCRF update – the risk was roughly halved though it didn’t say that in the press release.
- Another update. Red meat doesn’t kill you, but the spin is fascinating (2013). Update after the EPIC results in which the risk essentially vanished: good news which you could find only by digging into Table 3.
There is nothing new about the problem. It’s been written about many times. Young & Karr (Significance, 8, 116 – 120, 2011: get pdf) said "Any claim coming from an observational study is most likely to be wrong". Out of 52 claims made in 12 observational studies, not a single one was confirmed when tested by randomised controlled trials.
Another article cited by Ioannidis, "Myths, Presumptions, and Facts about Obesity" (Casazza et al , NEJM, 2013), debunks many myths, but the list of conflicts of interests declared by the authors is truly horrendous (and at least one of their conclusions has been challenged, albeit by people with funding from Kellogg’s). The frequent conflicts of interest in nutrition research make a bad situation even worse.
The quotation in bold type continues thus.
"On 25 October 2013, PubMed listed 291 papers with the keywords “coffee OR caffeine” and 741 with “soy,” many of which referred to associations. In this literature of epidemic proportions, how many results are correct? Many findings are entirely implausible. Relative risks that suggest we can halve the burden of cancer with just a couple of servings a day of a single nutrient still circulate widely in peer reviewed journals.
However, on the basis of dozens of randomized trials, single nutrients are unlikely to have relative risks less than 0.90 for major clinical outcomes when extreme tertiles of population intake are compared—most are greater than 0.95. For overall mortality, relative risks are typically greater than 0.995, if not entirely null. The respective absolute risk differences would be trivial. Observational studies and even randomized trials of single nutrients seem hopeless, with rare exceptions. Even minimal confounding or other biases create noise that exceeds any genuine effect. Big datasets just confer spurious precision status to noise."
"According to the latest burden of disease study, 26% of deaths and 14% of disability adjusted life years in the United States are attributed to dietary risk factors, even without counting the impact of obesity. No other risk factor comes anywhere close to diet in these calculations (not even tobacco and physical inactivity). I suspect this is yet another implausible result. It builds on risk estimates from the same data of largely implausible nutritional studies discussed above. Moreover, socioeconomic factors are not considered at all, although they may be at the root of health problems. Poor diet may partly be a correlate or one of several paths through which social factors operate on health."
Another field that is notorious for producing false positives, wirh false attribution of causality, is the detection of biomarkers. A critical discussion can be found in the paper by Broadhurst & Kell (2006), "False discoveries in metabolomics and related experiments".
"Since the early days of transcriptome analysis (Golub et al., 1999), many workers have looked to detect different gene expression in cancerous versus normal tissues. Partly because of the expense of transcriptomics (and the inherent noise in such data (Schena, 2000; Tu et al., 2002; Cui and Churchill, 2003; Liang and Kelemen, 2006)), the numbers of samples and their replicates is often small while the number of candidate genes is typically in the thousands. Given the above, there is clearly a great danger that most of these will not in practice withstand scrutiny on deeper analysis (despite the ease with which one can create beautiful heat maps and any number of ‘just-so’ stories to explain the biological relevance of anything that is found in preliminary studies!). This turns out to be the case, and we review a recent analysis (Ein-Dor et al., 2006) of a variety of such studies."
The fields of metabolomics, proteomics and transcriptomics are plagued by statistical problems (as well as being saddled with ghastly pretentious names).
What’s to be done?
Barker Bausell, in his demolition of research on acupuncture, said:
[Page39] “But why should nonscientists care one iota about something as esoteric as causal inference? I believe that the answer to this question is because the making of causal inferences is part of our job description as Homo Sapiens.”
The problem, of course, is that humans are very good at attributing causality when it does not exist. That has led to confusion between correlation and cause on an industrial scale, not least in attempts to work out the effects of diet on health.
More than in any other field it is hard to do the RCTs that could, in principle, sort out the problem. It’s hard to allocate people at random to different diets, and even harder to make people stick to those diets for the many years that are needed.
We can probably say by now that no individual food carries a large risk, or affords very much protection. The fact that we are looking for quite small effects means that even when RCTs are possible huge samples will be needed to get clear answers. Most RCTs are too short, and too small (under-powered) and that leads to overestimation of the size of effects.
That’s a problem that plagues experimental pyschology too, and has led to a much-discussed crisis in reproducibility.
"Supplements" of one sort and another are ubiquitous in sports. Nobody knows whether they work, and the margin between winning and losing is so tiny that it’s very doubtful whether we ever will know. We can expect irresponsible claims to continue unabated.
The best thing that can be done in the short term is to stop doing large observational studies altogether. It’s now clear that inferences made from them are likely to be wrong. And, sad to say, we need to view with great skepticism anything that is funded by the food industry. And make a start on large RCTs whenever that is possible. Perhaps the hardest goal of all is to end the "publish or perish" culture which does so much to prevent the sort of long term experiments which would give the information we want.
Ioannidis’ article ends with the statement
"I am co-investigator in a randomized trial of a low carbohydrate versus low fat diet that is funded by the US National Institutes of Health and the non-profit Nutrition Science Initiative."
It seems he is putting his money where his mouth is.
Until we have the results, we shall continue to be bombarded with conflicting claims made by people who are doing their best with flawed methods, as well as by those trying to sell fad diets. Don’t believe them. The famous "5-a-day" advice that we are constantly bombarded with does no harm, but it has no sound basis.
As far as I can guess, the only sound advice about healthy eating for most people is
- don’t eat too much
- don’t eat all the same thing
You can’t make much money out of that advice.
No doubt that is why you don’t hear it very often.
Two relevant papers that show the unreliability of observational studies,
"Nearly 80,000 observational studies were published in the decade 1990–2000 (Naik 2012). In the following decade, the number of studies grew to more than 260,000". Madigan et al. (2014)
“. . . the majority of observational studies would declare statistical significance when no effect is present” Schuemie et al., (2012)
20 March 2014
On 20 March 2014, I gave a talk on this topic at the Cambridge Science Festival (more here). After the event my host, Yvonne Noblis, sent me some (doubtless cherry-picked) feedback she’d had about the talk.
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