Inconsistent results from studies that attempt to define the cardiometabolic health characteristics of different macronutrient intake patterns may result from the inherent intra- and inter-individual variability in response to specific dietary factors. Our ability to provide individuals with personalized nutritional recommendations is contingent upon understanding the impact of genetic diversity, variations in diet characteristics, environment, and physical activities on individual responses to a particular dietary intervention. One approach to achieving this goal is to conduct research with an n-of-1 study design. Ma and colleagues conducted a study to determine the impact of type and quantities of macronutrients on individual postprandial glycemic responses. They report their results in the October 2021 issue of The Journal of Nutrition.
Participants in the study included healthy young Chinese adults (n=28) between 22 and 34 years of age and with a BMI between 17.2 and 31.9 kg/m2. The individuals were provided diets designed to be either high-fat, low-carbohydrate (60-70% fat, 15-25% carbohydrate, and 15% protein) or low-fat, high-carbohydrate (10-20% fat, 65-75% carbohydrate, and 15% protein). The experimental diets were consumed for 6 days, followed by a 6 day wash out period, and then an additional 6 day experimental diet period. The diets were provided in a randomized sequence and three cycles of this testing regime were conducted in which a continuous glucose monitoring system was used. Maximum postprandial glucose (MPG), mean amplitude of glycemic excursions (MAGE) and 24 hour AUC between intervention periods were the primary outcomes measured.
The probability of reaching a clinically meaningful difference between the low-fat, high-carbohydrate diet and the high-fat, low-carbohydrate diet for MPG, MAGE, and AUC varied among the participants. Those with a posterior probability >80% were high-carbohydrate responders (n=9) or high-fat responders (n=6). When the data were aggregated for analyses, a relatively low posterior probability was obtained for a clinically meaningful difference for any of the three primary outcomes. The authors concluded that n-of-1 trials are feasible to conduct using automated systems to monitor glucose levels in order to characterize individual responses to dietary interventions.
In an editorial, Kaput describes the need for new strategies to understand interindividual variability in response to nutrients if we are to move from population-based health recommendations to more personalized recommendations. He notes that the individual responses detected by Ma and colleagues would have been masked if average diet responses across all participants were used. Kaput suggests that all n-of-1 studies should include biomarkers derived from deep phenotyping, and genetic information should be included as this would allow not only individual analyses, but also allow groups with similar metabotypes to be formed for analyses beyond a n-of-1 study design.
References
Ma Y, Fu Y, Tian Y, Gou W, Miao Z, Yang M, Ordovas JM, Zheng J-S. Individual postprandial glycemic responses to diet in n-of-1 trials: Westlake N-of-1 trials for macronutrient intake (WE-MACNUTR). The Journal of Nutrition, Volume 151, Issue 10, October 2021, Pages 3158–3167, https://doi.org/10.1093/jn/nxab227
Kaput J. Developing the pathway to personalized health: The potential of N-of-1 studies for personalizing nutrition. The Journal of Nutrition, Volume 151, Issue 10, October 2021, Pages 2863–2864, https://doi.org/10.1093/jn/nxab243.
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