Single-Subject Studies in Translational Nutrition Research

Abstract

There is a great deal of interest in personalized, individualized, or precision interventions for disease and health-risk mitigation. This is as true of nutrition-based intervention and prevention strategies as it is for pharmacotherapies and pharmaceutical-oriented prevention strategies. Essentially, technological breakthroughs have enabled researchers to probe an individual's unique genetic, biochemical, physiological, behavioral, and exposure profile, allowing them to identify very specific and often nuanced factors that an individual might possess, which may make it more or less likely that he or she responds favorably to a particular intervention (e.g., nutrient supplementation) or disease prevention strategy (e.g., specific diet). However, as compelling and intuitive as personalized nutrition might be in the current era in which data-intensive biomedical characterization of individuals is possible, appropriately and objectively vetting personalized nutrition strategies is not trivial and requires novel study designs and data analytical methods. These designs and methods must consider a very integrated use of the multiple contemporary biomedical assays and technologies that motivate them, which adds to their complexity. Single-subject or N-of-1 trials can be used to assess the utility of personalized interventions and, in addition, can be crafted in such a way as to accommodate the necessarily integrated use of many emerging biomedical technologies and assays. In this review, we consider the motivation, design, and implementation of N-of-1 trials in translational nutrition research that are meant to assess the utility of personalized nutritional strategies. We provide a number of example studies, discuss appropriate analytical methods given the complex data they generate and require, and consider how such studies could leverage integration of various biomarker assays and clinical end points. Importantly, we also consider the development of strategies and algorithms for matching nutritional needs to individual biomedical profiles and the issues surrounding them. Finally, we discuss the limitations of personalized nutrition studies, possible extensions of N-of-1 nutritional intervention studies, and areas of future research.

Keywords: clinical trials; genetic profiling; nutrigenomics; personalized nutrition; prediction modeling.

Figure 1.

Hypothetical depiction of 10 different N-of-1 study designs comparing two interventions, denoted 1 and 2. The left most panels name the different designs. The gray numbers and letters in each cell provide which intervention is being administered during 16 different measurement periods in addition to a baseline period, denoted B. The entries in the cells correspond to: 1: intervention 1; 2: intervention 2; W: Washout period; and X: termination of the study prior to completing all 16 periods. The dashed red line corresponds to values of a measure that are not associated with a favorable or unfavorable response to the interventions, but are ambiguous with respect to response. The solid red lines provide the values of hypothetical continuous measures made on an individual, with greater values than the red dashed line indicating a positive response and lesser values indicating a negative response.

Figure 2.

A graphical depiction of the result of aggregating the outcomes of 10 different N-of-1 studies. For the left panel, it is assumed that each individual underwent an N-of-1 trial with a similar design in which interventions were alternated after a baseline and washout periods. As with Figure 1, the dashed red line corresponds to values of a measure that are not associated with a favorable or unfavorable response to the interventions, but are ambiguous with respect to response. The solid red lines provide the values of hypothetical continuous measure made on an individual, with greater values than the red dashed line indicating a positive response and lesser values indicating a negative response. The right panel depicts the results of a clustering of the individual responses, with some individuals exhibiting a greater response to intervention 1 (upper set of curves), some individuals exhibiting a greater response to intervention 2 (middle set of curves) and some individuals exhibiting a lack of response to either intervention.

Figure 3.

Graphical depiction of the concept of ‘personalized thresholds’ for making claims about a health status change for an individual. 25 hypothetical individuals have undergone measurements on a phenotype measuring health (e.g., cholesterol level or other biomarker). Their values are ranked and are made at 10 different time points. A ‘population threshold’ (e.g., cholesterol level > 200 units) is depicted by the dashed black line. The rankings and values of a single individual, number 20, is highlighted in red. After enough measures are collected over time, one can calculate a ‘personal average’ for Individual 20, denoted by the solid red line as well as error bars representing variation in that individual’s values, depicted by the red shading. Based on the errors associated with Individual 20, a ‘personal threshold’ can be established for which any value beyond that limit has a low probability of occurring. This is depicted by the dashed red line. The dashed red circle indicates a value outside the personal threshold and at later time points two additional values, circled in black, get progressively higher. This deviation from historical or legacy values on the individual that have a low probability of occurring by chance could be an indication of a health status change despite being lower than the establish population threshold.