John Ioannidis Aims His Bazooka at Nutrition Science (2018)

A much, much deeper understanding of how humans work.

Controversially, I think we need to do less research. The publish-or-perish culture has created a perverse incentive to crank out junk papers. Most working scientists will privately admit that most research isn't actually advancing our understanding of nature, it's just a desperate effort to dredge up something sufficiently novel to publish. Conversely, there's a substantial amount of research that is potentially useful to clinicians, but is languishing unread in some third-tier journal. Most research is never published at all because it supports the null hypothesis, but we can't do good meta-analyses based on a cherry-picked set of studies. We're glutted with data, but we have a remarkable paucity of actionable information getting to the people who need it.

The problem isn't that scientists are doing a bad job, but that the funding mechanisms of science incentivise the wrong kind of work. We should be focusing a far larger proportion of our funding - perhaps even a majority - on replication, meta-analysis and dissemination. Primary research is only one small part of the information architecture of science, but it dominates our spending.

In the case of nutrition, we're spending huge amounts of money on figuring out whether coffee increases or decreases your life expectancy by a fraction of a percent, but almost nothing on behavioural research to figure out how to stop people from gorging themselves to death on food they know to be terrible for them. There's a morbidly obese elephant in the room, but we're preoccupied by the micronutrient-rich mice scuttling around the periphery.

But more quality science and less garbage science would help. Doing 10 studies with a small likelyhood of any of them having a true result is less helpful than 1 high quality study that almost certainly is true.

As a response, I work at a company that is automating biology, with a goal of making much more reliable clean data for machine learning, but even then, it's very expensive. A decent robot arm costs well over $50K (I could build an equivalent for $1-2K, but at a cost of $100K of my time) and all the other equipment is often in the $100K+ range. Just to automate what you could hire a human to do for $75-100K a year!

So yeah, some scientists are doing it wrong, but even the folks doing it right are still wasteful. I think it's endemic to the enterprise, but we could still do better.

Considering the fact that 90% of phase 1 drugs never get approved and that study seems quite biased. That study conveniently assumes that all those medicinal chemists, who've worked decades and never seen a drug approved, simply don't exist

Is there purity? No, not in the absolute sense. However, we're not some homogeneous mix either. Genetic, environmental and other such patterns do form a number different subgroups which while imperfect, remain useful.

From the page you linked to: "13% of bowel cancer cases in the UK are caused by eating processed meat.

Bowel cancer risk is 17-30% higher per 100-120g/day of red meat intake, meta-analyses have shown.

Bowel cancer risk is 9-50% higher per 25-50g/day of processed meat intake, meta-analyses have shown; however bowel cancer risk was not associated with processed meat intake in a pooled analysis of UK case-control studies. Colon cancer risk is 12% higher per 1mg/day of haem iron intake, a meta-analysis showed; though bowel cancer risk was not associated with dietary iron intake in a pooled analysis of UK cohort studies.

Serrated bowel polyp risk is 23% higher in people with the highest versus lowest levels of red meat intake, a meta-analysis showed."

https://www.cancerresearchuk.org/health-professional/cancer-...

It’s a bit ironic you are questioning the study with regards to causality and then mention obesity exploding to record levels with an unsubstantiated, implied connection to the promotion of eating less fat. Your coment would have been perfect without the obesity comment since your comment is decrying linking things without sufficient causal evidence.

> Conclusion. Modifiable risk factors, such as physical activity, diet, smoking cessation and mid-day naps, might depict the “secrets” of the long-livers; these findings suggest that the interaction of environmental, behavioral together with clinical characteristics may determine longevity. This concept must be further explored in order to understand how these factors relate and which are the most important in shaping prolonged life.

It could be that the quantity red meat is solely responsible for their longevity, or has nothing to do with it, or plays some role with the many other confounding variables.

[0] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3051199/

One of the points made by Ioannidis is that we’re far too easily swayed to believe what sounds plausible when no one has yet asked “Could this be for some other reason altogether that we aren’t testing for?” and then tests for those reasons. That flaw applies here: studying just one community automatically implies that the results are invalid, simply because there’s no evaluation of other communities that share various attributes that could be hidden factors for the benefit.

EDIT: Per one article, that study “would try to cut through the stories and establish the facts about Ikaria’s longevity”. It has succeeded in establishing that, factually, Ikaria’s longevity is a thing relative to other populations. It has not established which of the properties of Ikaria are responsible. (For example, perhaps the island is slightly radioactive, and old people thrive in a slightly higher radioactivity.) Extrapolating that their diet is responsible is an instance of the A/X error described in the above-linked article.

In my opinion, the difference is the low cost of broadcasting ideas. Before, if you didn't believe in a main stream idea, you could share your theories with people near you. Now, it's easy for people who don't believe in the main stream to form communities and reinforce each other's beliefs.