Perspective: Application of N-of-1 Methods in Personalized Nutrition Research

Abstract

Personalized and precision nutrition aim to examine and improve health on an individual level, and this requires reconsideration of traditional dietary interventions or behavioral study designs. The limited frequency of measurements in group-level human nutrition trials cannot be used to infer individual responses to interventions, while in behavioral studies, retrospective data collection does not provide an accurate measure of how everyday behaviors affect individual health. This review introduces the concept of N-of-1 study designs, which involve the repeated measurement of a health outcome or behavior on an individual level. Observational designs can be used to monitor a participant's usual health or behavior in a naturalistic setting, with repeated measurements conducted in real time using an Ecological Momentary Assessment. Interventional designs can introduce a dietary or behavioral intervention with predictors and outcomes of interest measured repeatedly either during or after 1 or more intervention and control periods. Due to their flexibility, N-of-1 designs can be applied to both short-term physiological studies and longer-term studies of eating behaviors. As a growing number of disease markers can be measured outside of the clinic, with self-reported data delivered via electronic devices, it is now easier than ever to generate large amounts of data on an individual level. Statistical techniques can be utilized to analyze changes in an individual or to aggregate data from sets of N-of-1 trials, enabling hypotheses to be tested on a small number of heterogeneous individuals. Although their designs necessitate extra methodological and statistical considerations, N-of-1 studies could be used to investigate complex research questions and to study underrepresented groups. This may help to reveal novel associations between participant characteristics and health outcomes, with repeated measures providing power and precision to accurately determine an individual's health status.

Keywords: Ecological Momentary Assessment; N-of-1; personalized nutrition; precision nutrition; review; self-report measures; study design.

FIGURE 1

Distribution of difference in SBP between the start and end of a 12-wk intervention across 3 dietary intervention groups (n = 202): two wholegrain interventions [“whole wheat” (n = 71) and “whole wheat + oats” (n = 68)] and a control group not provided with whole grains [“refined” (n = 63)]. The dashed lines represent a mean reduction in SBP by intervention group. Data from a study by Tighe et al. (4) were obtained from Frank Thies (University of Aberdeen). SBP, systolic blood pressure.

FIGURE 2

Overview of an N-of-1 study. To examine a participant on an individual level, an N-of-1 study can be employed; this can take the form of an observational or interventional design. Both forms enable collection of multiple measurements to provide power for statistical analysis.

FIGURE 3

Schematic of a repeated-crossover trial with 2 different treatments (A and B). Each treatment is randomized within each cycle, over n cycles (at least 2). In this example, 8 different randomization sequences are possible.

FIGURE 4

Results from 2 hypothetical repeated-crossover N-of-1 trials using within-cycle randomization, to highlight hypothetical results of a study design with 3 cycles composed of 2 periods each (as shown in Figure 3). These graphs are a modification of those presented in Araujo et al. (1). (A) Triglyceride levels are plotted by cycle and period for 2 participants (labeled as 1 and 2). Note that the colors of the circles (black and white; representing assigned treatment) differ for both participants by treatment period, as each participant has been assigned to a different randomization sequence: results can still be aggregated and compared between individuals, as response to the 2 treatments can be compared by cycle. (B) Triglyceride levels after Treatment A and B for Participant 1, plotted by treatment cycle. Within each cycle, triglyceride levels are consistently lower after Treatment B than A, which suggests Treatment B is more effective for this participant. (C) Triglyceride levels after Treatment A and B for Participant 2, plotted by treatment cycle. Within each cycle, there is no clear association between treatment and triglyceride levels. This suggests neither treatment is effective for consistent triglyceride lowering for this participant.