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Table 1 Why clinical utility is not part of this framework

From: Proposed guidelines to evaluate scientific validity and evidence for genotype-based dietary advice

This framework stops at the assessment of scientific validity. The recommendations provide clear and sufficient detail so that any opinion on health (or clinical) utility can be derived by the user (including the individual, dietician/nutritionist/medical doctor, companies and claim regulation bodies).

Clinical utility is the measure of the likelihood that the recommended therapy or intervention will lead to a beneficial outcome. Clinical utility is the most controversial aspect: it is often difficult to define and must take into consideration many factors including positive or negative psychological or motivational effects on the end user [81]. Others contend that clinical utility can only be thoroughly established through randomised clinical trials (RCT), but these are challenging for the personal genetics environment, includes diet, lifestyle and behavioural changes and has small cumulative effects over decades (see [82, 83] for an example of the current debate). A further problem is the precise definition of a clinical benefit. A gene-diet interaction may not be associated directly with disease risk, such as cardiovascular disease, but with intermediate phenotypes, e.g. lipid levels, hypertension and homocysteine, which are independent risk factors for disease. Some commentators require that clinical utility is demonstrated as a reduction in disease incidence. The majority view accepts that lowering of intermediate risk factors is acceptable (as is the case for phytosterols and their cholesterol lowering properties [50, 84]).

RCTs

In personalised nutrition research, RCTs with disease incidence as the endpoint are not practically feasible as they will require long-lasting nutritional changes, making compliance difficult and very expensive, at least in terms of primary prevention in healthy people––apart from any ethical problems. RCTs that address disease incidence reduction in middle-aged or elderly high-risk subjects, secondary prevention in individuals with disease and/or on effects on intermediate biomarkers or risk factors can be useful, but care is required in drawing conclusions. RCTs in nutrition and genetics are often complex, difficult to design and challenging to conduct in a reasonable time frame. Some examples given below illustrate this and may be helpful when interpreting RCT data for personalised diet and lifestyle evidence advice.

Primary prevention in high-risk groups

Genetics × diet × T2DM (type 2 diabetes mellitus)––The T allele of the TCF7L2 rs7903146SNP has been associated repeatedly with an increased risk of T2DM (2-fold in homozygotes [85]). Compared with non-risk allele carriers, individuals who carry the risk allele and who are at high risk phenotypically (glucose intolerance, pre-diabetes diagnosis) require a longer lasting and a more intense dietary and lifestyle recommendation to divert the trajectory from disease over a period of 12 months and to maintain health gains over a 4-year period [86]. Although useful, these findings have been obtained in clinical trials of unhealthy people, who typically were older. Thus, to be precise, it does not demonstrate, and cannot be used to claim, that specific dietary modifications in younger, healthier people will prevent the development of glucose intolerance or T2D in those carrying the risk allele. However, this evidence of gene × diet interactions in pre-diabetics is consistent with the evidence from other types of studies in healthy subjects (epidemiological, cohort, effects on biomarkers) and can provide supporting evidence, but not conclusive evidence, that specific dietary guidelines would be appropriate for healthy carriers of this TCF7L2 risk allele. This example shows how difficult it is to validate a gene-diet interaction but suggests that adjusting the environment will improve the individuals’ health.

The same TCF7L2 genetic variant was assessed in the recently published study from the PREDIMED project [14], a large randomised trial in 7018 high-cardiovascular-risk individuals comparing two Mediterranean (Med) diets and a control diet. TCF7L2 TT homozygotes at SNP rs7903146 had higher blood glucose levels, total cholesterol, LDL cholesterol and triglycerides but only when adherence to the Med diet was low. Furthermore, incidence of stroke was almost three times higher in TT homozygotes as in the control group, but this increased risk was completely dissolved in the Med diet group (Hazard Ratio, HR = 0.96). Thus, compared to the control diet, both Med diets were effective at reducing both risk biomarkers and disease incidence itself in a genotype specific manner. While this is a strong endorsement of the Mediterranean diet, it is also relevant that the age range was 55 to 80 years. This RCT supports the epidemiological evidence for health benefits of the Med diets for older persons, and those at increased risk of CVD, and can only suggest such benefits for other age groups who carry the TCF7L2 TT genotype at rs7903146.

Secondary prevention in subjects with pathology

MTHFR × folate × homocysteine on CVD risk––results of several large homocysteine-lowering clinical trials have been published over the last decade, and none reported any benefit in prevention of secondary CVD by folate supplementation. These results have been used widely to declare that there is no evidence that elevated plasma homocysteine levels are relevant for CVD and that there is no benefit in homocysteine-lowering in primary prevention [87,88,89]. However, these were all short-term trials in older people already suffering from (mainly) CVD and taking several medications, where incidence of further cardiovascular events was measured. None of the trials were performed in healthy people. Thus, the conclusion from these studies states that over the trial periods there was no apparent benefit in lowering homocysteine in ill people, i.e. as in secondary prevention. However, still lowering homocysteine by using folate may reduce risks of CVD in healthy people with high risk [53, 90, 91]. For instance, the China Stroke Primary Prevention Trial [13, 92], reported on a total of 20,702 adults with hypertension without history of stroke or myocardial infarction who participated in the study. That study compared a single-pill combination containing 10 mg of enalapril and 0.8 mg of folic acid with a tablet containing 10 mg of enalapril only. Among adults with hypertension, the combined use of enalapril and folic acid, compared with enalapril alone, significantly reduced the risk of first stroke (HR = 0.79). Analysis of the MTHFR 677 genotype showed further that the TT genotype had the largest risk reduction in the highest folate quartile (HR = 0.24), suggesting that individuals with the TT genotype may have a greater folate requirement.

MTHFR × riboflavin × hypertension––several RCTs have demonstrated that riboflavin supplementation contributes to blood pressure-lowering specifically in hypertensive carriers of the 677T allele [52, 93,94,95]. The trials do not prove primary prevention (i.e. they do not demonstrate that increasing riboflavin in 677T normotensives prevents development of hypertension), and they do not prove the ultimate health benefit of riboflavin to reduce incidence of heart disease. However, reducing blood pressure is considered to be a health benefit in itself, and although the results cannot be used to establish a genotype specific role of riboflavin in primary prevention of hypertension, they can be used to support other types of studies.

Overall, outcomes of RCTs can be useful for nutrition/lifestyle advice, but they need to be interpreted with care. Furthermore, it must be accepted that conducting an RCT in young healthy people with the aim of investigating the effect of nutrition on actual reduction of disease incidence over the long term is not feasible either ethically, economically or scientifically (see also [96] for discussion). On the other hand, the use of RCTs to study the effects on biomarkers that quantify health (i.e. not simple risk markers of impending disease) is a promising new approach [97].