Studies linking diet and health need to improve dramatically

Studies linking diet and health need to improve dramatically

The researchers created a star-based metric that rates the quality of evidence for a link between a given behavior — like eating red meat — and a particular health outcome.Credit: Education Images/Universal Images Group/Getty

Does eating red meat shorten lifespan? Certain researchers certainly think so. Work such as the Global Burden of Disease, Injury and Risk Factor Study1 led the World Health Organization and the United States Department of Agriculture to advise people to limit their consumption of unprocessed red meat, in order to protect against diseases such as type 2 diabetes and various cancers.

Other researchers are less sure. Red meat consumption goals, set by public health officials and expert groups, vary widely, with some advising people to eat no more than 14 grams a day and others stating no limit. recommended. This sends a confusing message, which in itself is not good for public health.

It’s not just about red meat: the evidence base surrounding many broader nutritional and health advice is also disputed. Now, a new approach could help health policymakers better assess the quality of studies assessing potential health risks. A team from the Institute for Health Metrics and Evaluation (IHME) at the University of Washington in Seattle has created a star-based metric that rates the quality of evidence for a link between a given behavior – like eating meat flushed or smoking – and a particular health outcome2. A score of five stars means that the link is clearly established; a star means either there is no association between the two factors or the evidence is too weak to draw a firm conclusion.

What the researchers call “burden of proof” analysis doesn’t by itself resolve thorny issues such as the risks of red meat or the benefits of vegetables. But as a judgment on the quality of the research available, it can help signal to research funders where better evidence is needed for stronger conclusions.

How is the number of stars constructed? What are its parameters — and can the methodology itself be considered rigorous research? The IHME team did several things to try to quantify the effects of various biases in the studies assessed. An epidemiological study, for example, may be biased in different ways compared to a study testing the outcomes of health interventions. The researchers also eliminated what can be a common source of bias in research, namely the assumption that health risks increase exponentially with the parameter studied, for example blood pressure or red meat consumption. not transformed. And they tried to account for the bias that can arise when sample sizes are small.

Applying this framework to studies assessing a total of 180 questions produced mostly unsurprising results. Studies assessing an association between smoking and various cancers, for example, get a five-star rating3. Likewise, high systolic blood pressure – the force exerted by the heart to pump blood – has a five-star association with the narrowing of blood vessels called ischemic heart disease.4.

Studies evaluating diet and its effects on health score significantly lower. The IHME analysis, for example, finds only weak evidence for an association between consumption of unprocessed red meat and outcomes such as colorectal cancer, type 2 diabetes and ischemic heart disease.5. He finds no relationship in studies that explore whether eating unprocessed red meat leads to two types of strokes. There is stronger, but not overwhelming, evidence that eating vegetables reduces the risk of stroke and ischemic heart disease6.

In some cases, lower star ratings could be due to the size of the effect: for example, any health risk from eating red meat is likely to be small compared to the huge impact that smoking puts a strain on the body. Above all, the lower-rated results demonstrate that studies in these areas need to improve if they are to produce convincing results.

It is difficult to distinguish the effect of a single dietary component from those of the complex variety of exposures over a person’s lifetime. This would require larger studies, with a diverse pool of participants and strict control of their daily diet. Such studies will involve collaboration between research groups with different expertise and access to participants in different environmental settings – something that funders should encourage. It is a preferred company. A small risk for an individual does not mean a small impact on public health: a low-risk behavior can have a large impact at the population level if it is very common.

The literature in the field of responsible research and innovation highlights how metrics in science must always be questioned for their robustness and rigor. There should be broad consultation and, where possible, unintended consequences of using metrics should be anticipated, as shown by initiatives such as the San Francisco Declaration on Research Assessment and the Manifesto. from Leiden. This work must come sooner rather than later.

We have evidence that underpowered clinical studies, lacking the necessary controls to make sense of the data, do not help. If funders don’t focus their efforts on producing quality data, the public will remain confused, weary, distrustful, and deprived of the information they need to make informed health and lifestyle choices.

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