C H A P T E R
14 Epistemology of Nutrigenetic Knowledge Martin Kohlmeier Human Research Core and Nutrigenetics Laboratory, UNC Nutrition Research Institute; and Schools of Medicine and Public Health, Department of Nutrition, Kannapolis, NC, United States
EPISTEMOLOGY AND THE INITIAL BUILDING BLOCKS OF KNOWLEDGE
Glossary Biomarker Measurable biological indicator of a state or outcome of interest, usually a molecule that can be analyzed in a sample. Nutritope Nutrition environment to which local groups are exposed to and which can shape their genome over time. Replication Implementation of a follow-up study or experiment based on the same design with the expectation of finding a similar result.
INTRODUCTION The rapid growth of research and practice makes it imperative to consider how we know what we think we know about nutrigenetics. We want to understand the foundations of our knowledge about genetically inherited dispositions affecting the impact of nutrition on health. Without going too much into depth about our ability to know anything at all, this discussion will focus on some practical aspects of nutrigenetic knowledge. In the end, we need to be prepared to answer the almost child-like question, “How do you know?” The reason we should ask ourselves and our peers that question is that rationally rooted nutrition knowledge has a better chance of holding up over time. We often find that the most worthwhile nutrition questions unavoidably involve genetic factors, although often in the negative. Distinguishing and enumerating the individual factors involved in seemingly simple nutritionehealth relations is a never-ending task. This goes for all involved variables, such as specific foods and food ingredients, metabolic and regulatory events, and functional circumstances at all levels, from molecular changes to physical conditions and population health.
Principles of Nutrigenetics and Nutrigenomics https://doi.org/10.1016/B978-0-12-804572-5.00014-8
The term “epistemology” (“making sense of knowledge”) was used by the Scottish philosopher Ferrier in his early exploration of the “laws of our knowing and of our thinking” (Ferrier, 1854). This branch of philosophy wants to find the nature of our knowledge, where it comes from, and whether we are justified in trusting it. We need to ask ourselves the same questions about nutrigenomics. When we read about typical nutrition guidelines, we rarely spend much time thinking about the myriad underlying assumptions we all bring to the table. It would be impossible to consider all of the different ways in which one individual differs from the next. We therefore ignore most of those differences and lump many others into broad categories. Current nutrition recommendations (Dietary Reference Intakes), for example, use just 22 groups defined by age, gender, pregnancy, and lactation status. Individuals in each group are given the same intake targets for the obvious reason that we cannot agree even about what the next level of subdivisions should be and then allocate sufficient resources to collect the necessary data. The first requirement in building knowledge is a precise statement of each constituent item. In practical terms, this means that we use terms with clearly defined meaning understood by stakeholders. Although this may appear needlessly reductionist, it promises a sustainable path toward steadily growing strength of knowledge about human biology and how to achieve desirable health outcomes. A common problem is that we find it hard to recognize our biases. After all, this is why we hold on
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14. EPISTEMOLOGY OF NUTRIGENETIC KNOWLEDGE
to them. Thus, ethnicity is too often assumed in practical terms as a deviation from a background norm without thinking much about it. For example, the ability of adults to digest lactose has been perceived as normal for the longest time. The absence of this digestive function in adults has been perceived as a loss of function and the resulting lactose intolerance as a health condition or disease. But who had this understanding as a matter of course and could not conceive of any other view? It was physicians and scientists in Central and Northern Europe and in the United States. Researchers in Asia would not have come to that conclusion because they knew otherwise from practical experience. Now we understand that adult lactose intolerance is the normal state present in a majority of the world’s populations and that specific mutations conferred the ability to tolerate lactose only a few hundred generations ago. It is now clear that people with lactose intolerance are the ones who are normal, and the milk aficionados are the mutants. But perhaps it is better not to assign such loaded terms as “normal” or “deviation” and just be neutrally descriptive. Another nutrigenetic example in which perspective matters relates to our definition of disease. Most will agree that the disease definition appropriately applies to celiac disease. But should we still consider it a disease even long after symptoms have been reversed by careful avoidance of gluten prompted by early diagnosis? Is it a disease because a sensitized individual is actually sick or because a health risk can be triggered at any time by exposure to gluten? If it is the latter, any nutrigenetic disposition that makes a nutrition exposure detrimental to the carrier’s health should possibly also be called disease. For all practical purposes, there are far too many variables to consider for any hypothesis, individual study, or intervention guideline. Therefore, the critical question becomes which we can ignore or group with others and what justifies our decisions. Most of the time, variable selection just happens without us even realizing that a choice has been made. An important benefit of exploring the epistemology of specific knowledge areas is that unjustified assumptions may become apparent and thus open to investigation. This is particularly true for most nutritional effects. We have to ask ourselves when genetic variation must be considered. Table 14.1 lists a few such commonly used variables that we use regularly, probably without giving much thought to the specific meaning we intend.
COLLECTING DATA These are golden days for genetic studies. It has become so much easier and less expensive to obtain
We Commonly Refer to Variables Without Defining Exactly What We Mean, Often Because We Do Not Know That Different Meanings Exist.
PERSONAL VARIABLES Gender (could be self-reported or anatomically confirmed or based on genetic markers, etc.) Ethnicity (could be race, self-reported ancestry, country of origin, genetic markers, etc.) Genotype (could be a single-nucleotide polymorphism (SNP), an SNP as one in a pair, an SNP within a long-range haplotype, etc.) EXPOSURE VARIABLES Food (could be defined by name, origin, varietal, quality grade, season, etc.) Ingredient (could be a natural or complex group with a shared name, natural plus added, etc.) OUTCOME VARIABLES Risk (could be acute events, morbidity, composite outcomes, all-cause mortality, etc.) Disease (could be ongoing ill health, a state of compensated vulnerability, etc.)
reliable genetic information. The quality of common genotyping platforms is commonly very high. Nonetheless, performance has to be carefully examined, particularly when using large-scale analyses with millions of genome positions interrogated, possibly even the full genome. The limiting factors will usually be nongenetic variables. Quantitative assessment of dietary intakes for many study participants is still a tough barrier involving high cost, high day-to-day variability, poor accuracy, and fluctuations over long periods. Although major efforts have been made to make assessment instruments comparable across different populations, cultural differences continue to cause major practical problems for nutrigenetic research. What too often gets lost when focusing on particular hypotheses are major differences in exposures outside the narrow research focus. For example, major items in the food supply in the United States and other countries are fortified, whereas this is not the case in Europe and elsewhere. This means that, for instance, the investigation of genotype-specific effects of folate on cardiovascular outcomes should not be expected to yield consistent results. Blood pressure data in an Irish population with different methylenetetrahydrofolate reductase (MTHFR) rs1801133 genotypes (MTHFR 677 C > T) were found to be consistently different (Wilson et al., 2012). However, this is not simply related to a difference in folate status, but to riboflavin intake. This would not be seen in an American population because with fortification, average riboflavin intake levels are much higher there than in European cohorts. A controlled feeding trial could demonstrate exactly that, with a sizable effect
I. THE BIOLOGICAL BASIS OF HERITABILITY AND DIVERSITY
of low-dose riboflavin (1.6 mg/day, about as much as the amount coming from fortification in Americans) on systolic and diastolic blood pressure (Wilson et al., 2013). This is one of the most obvious examples because we know the accurate amount of added vitamin coming from fortification. Another time, a discrepancy could arise from a different cultivar of a food item with the same name or dietary patterns between regions. This is not to say that analytical challenges are less important. It is just that measurement of standard biomarkers is getting much more attention and presents more finite problems. Analytical methodologies continue to present difficult questions, such as when deciding whether to determine folate concentration with a microbial assay or by mass spectrometry. These are obviously not trivial issues because each method captures different metabolites best and the resulting trade-offs are not easily reconciled. An easier question relates to analytical errors and what is tolerable. This is particularly important when wondering why a genotype-specific effect or interaction was observed in some studies and not in others.
TURNING DATA INTO KNOWLEDGE Some irreproducible reports are probably the result of coincidental findings that happen to reach statistical significance, coupled with publication bias. Another pitfall is overinterpretation of creative ‘hypothesis-generating’ experiments, which are designed to uncover new avenues of inquiry rather than to provide definitive proof for any single question. Collins and Tabak (2014).
Now it gets even harder because data do not speak for themselves. Probably the most important requirement is diligent replication of studies, because, as Collins and Tabak pointed out, coincidental findings are the bane of nutrigenetic studies. Correcting the statistical significance for testing multiple contrasts is an insufficient solution, because with so many different investigators looking for statistically significant associations, some will get lucky and submit for publication an interesting association with apparently legitimate numbers (Goodman et al., 2016). Proper replication should help, but before considering what this means, it must be recognized that few original reports of nutrigenetic interactions are replicated in a timely manner (Hirschhorn et al., 2002). Replication studies need an adequate size and comparable study design. The possibility of publication bias is a serious concern. Many investigations that do not find a previously reported result will often never be published because the earlier finding has already become dominant. The investigators may be too discouraged to complete all of the work needed to submit a manuscript for
publication. If they do, they may find that reviewers are more critical than they would be with a confirmatory report, or editors are less interested and are worried about an imperfect replication. Goodman and colleagues highlighted the difficulty of even using a consistent conceptual framework to achieve comparability. They pleaded for reproducing research as an “imperfect surrogate for scientific truth” rather than an end in itself (Goodman et al., 2016). The MTHFR story mentioned earlier should be enough reason to pay close attention to studies that fail to find a comparable result. Although this is to be expected when the original finding was strictly coincidental, real interactions are easily missed because one or more critical variables were different without the authors knowing about it. Avoiding over interpretations is closely related to having too much confidence in the findings absent their replication. Another even more important issue is the unsupported use of extrapolations to another gender or other age groups, regions, ethnicities, or health status. A lot can and often will go wrong when assuming that people with different characteristics will show the same genotype-specific response found in a particular study. The integration of diverse types of study data, including from cross-sectional surveys, prospective cohorts, and targeted intervention trials, requires a fine balance between forced bundling and thoughtful alignment of the collected information (Ioannidis et al., 2008). The experience with MTHFR, folate and riboflavin intake, and blood pressure outcomes shows what can be done with less challenging hypotheses. It may also serve as a reminder that it makes a lot of sense to focus on proximal effects and interactions first, such as blood pressure, which tends to respond fairly quickly. The question about a much more complex disease group such as stroke could then build on that prior knowledge base. Another weakness, though often unavoidable, is the expectation that findings from cross-sectional population studies can be used to predict response to an intervention. The understandable argument is that in most cases it is impractical to demonstrate a genotype-specific response to a nutrition intervention for which it will take many years or even decades to yield results, such as with the prevention of diabetes, cardiovascular disease, or cancer. There are no easy answers, but only with full awareness of the involved issues can riskebenefit assessments of predictive uses of genetics data even be discussed, much less effectively used in practice.
FURTHER CONSIDERATIONS Knowledge should always be considered tentative, even after multiple confirmatory observations. There is
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14. EPISTEMOLOGY OF NUTRIGENETIC KNOWLEDGE
always the possibility that important variables have not been considered and that underlying mechanisms have been misunderstood. Many times, the science has been considered final, only to give way to better understanding. A key criterion for accepting new knowledge items is improved ability to predict effects and improve outcomes. Consistent agreement of observations with the expected behavior increases confidence but does not establish certainty.
References Collins, F.S., Tabak, L.A., 2014. NIH plans to enhance reproducibility. Nature 505 (7485), 612e613. Ferrier, J.F., 1854. Institutes of Metaphysics. The Theory of Knowing and Being. W. Blackwood and Sons, Edinburgh. x55, p. 44ff. Goodman, S.N., Fanelli, D., Ioannidis, J.P., 2016. What does research reproducibility mean? Sci Transl Med 8 (341), 341ps12.
Hirschhorn, J.N., Lohmueller, K., Byrne, E., Hirschhorn, K., 2002. A comprehensive review of genetic association studies. Genet Med 4 (2), 45e61. Ioannidis, J.P., Boffetta, P., Little, J., O’Brien, T.R., Uitterlinden, A.G., Vineis, P., Balding, D.J., Chokkalingam, A., Dolan, S.M., Flanders, W.D., Higgins, J.P., McCarthy, M.I., McDermott, D.H., Page, G.P., Rebbeck, T.R., Seminara, D., Khoury, M.J., 2008. Assessment of cumulative evidence on genetic associations: interim guidelines. Int J Epidemiol 37 (1), 120e132. Wilson, C.P., Ward, M., McNulty, H., Strain, J.J., Trouton, T.G., Horigan, G., Purvis, J., Scott, J.M., 2012. Riboflavin offers a targeted strategy for managing hypertension in patients with the MTHFR 677TT genotype: a 4-y follow-up. Am J Clin Nutr 95 (3), 766e772. Wilson, C.P., McNulty, H., Ward, M., Strain, J.J., Trouton, T.G., Hoeft, B.A., Weber, P., Roos, F.F., Horigan, G., McAnena, L., Scott, J.M., 2013. Blood pressure in treated hypertensive individuals with the MTHFR 677TT genotype is responsive to intervention with riboflavin: findings of a targeted randomized trial. Hypertension 61 (6), 1302e1308.
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