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Developing the Pathway to Personalized Health: The Potential of N-of-1 Studies for Personalizing Nutrition.

Personalization is one of the dominant zeitgeists of the new millennium, driven by individuals who demand the right information or product or service at the right time for their needs and wants. The commercial world has taken note because the personalized nutrition market is expected to be worth $16.4 billion by 2025 (1), a business opportunity that includes direct-to-consumer genetic testing, digital health care, supplements, and foods for specialized physiological needs (e.g., pregnancy, athletics, health conditions). The prime impediment in creating personalized recommendations, or products, is the “tyranny of the average” (2): that is, that the majority of research studies analyze (in the simplest terms) differences between cases and controls—also known as the population attributable factor (PAF). The PAF is the proportion of disease cases that would be prevented in the population after elimination of the exposure, assuming that the exposure is causal (2, 3). The PAF is used as an individual factor. Although population-based research and analytical methods have obviously contributed significantly to improvements in personal and public health, new strategies are needed to understand interindividual variability in response to nutrients. In this issue, Ma et al. (4) investigated individual responses to macronutrients using an n-of-1 experimental study design. Twenty-eight individuals participated in 3 sets of four 6-d periods with the following experimental sequence: 1) a washout diet, 2) experimental diet 1, 3) a washout diet, and 4) experimental diet 2. The experimental diets were low fat– high carbohydrate (LF-HC) or high fat–low carbohydrate (HFLC) and their order was changed between the 3 sets (4). The outcomes were maximum postprandial glucose (MPG) within 3 h after starting a meal, mean amplitude of glycemic excursion (MAGE—the arithmetic mean of the differences between consecutive peaks and nadirs), and AUC for a 24-h period (which proved to be uninformative) with the data obtained every 15 min from a continuous glucose monitoring system. Bayesian methods were used to assess group-level and individual-level treatment effects. An MPG >0.167 mmol/L was identified in 7 HC-LF responders but also in 3 HF-LC diet responders. A MAGE >0.072 mmol/L was identified in 5