The Geometric Framework for Nutrition as a tool in precision medicine

Abstract

Fundamental questions in nutrition include, "What constitutes a nutritionally balanced diet?", "What are the consequences of failing to achieve diet balance?", and "How does diet balance change across the lifecourse and with individual circumstances?". Answering these questions requires coming to grips with the multidimensionality and dynamic nature of nutritional requirements, foods and diets, and the complex relationships between nutrition and health, while at the same time avoiding becoming overwhelmed by complexity. Here we illustrate the use of an integrating framework for taming the complexity of nutrition, the Geometric Framework for Nutrition (GFN), and show how this might be used to untap the full potential for nutrition to provide targeted primary interventions and treatments for the chronic diseases of aging. We first briefly introduce the concepts behind GFN, then provide an example of how GFN has been used to relate nutrition to various behavioural, physiological and health outcomes in a large mouse experiment, and end by suggesting a translational pathway to human health.

Keywords: Geometric Framework for Nutrition; healthy aging; macronutrients; micronutrients; precision medicine.

Figures

Fig.1

Core concepts of the Geometric Framework for Nutrition. The intake target represents the optimal amount and balance of the nutrients required by the animal. Radial lines are “rails” showing the ratio of the nutrients in foods, and grey circles represent hypothetical nutrient intakes (I1 –I5). As the animal eats it “moves” along a trajectory at an angle equal to the angle of the rail for the food it is eating (arrows), with sequential arrows representing intake trajectories. A) Food 1 is balanced with respect to the protein:fat ratio (P:F): it passes through the Intake Target, and thus enables the animal to directly reach the target (solid arrow). In contrast, Foods 2 and 3 are imbalanced (excess P and F respectively) and do not enable the animal to reach the target. It can, however, reach the target by mixing its intake from these “nutritionally complementary” foods (dotted arrows): for example, by first feeding on Food 3 to point I1, then switching to Food 2 at I2 switching back to Food3. B) If restricted to a single imbalanced food, the animal faces a trade-off between over-eating one nutrient and under-eating another. At I3 it meets its requirements for fat but suffers a protein deficit, at I5 it has optimal protein intake but excess fat, and at I4 it has both a moderate excess of fat and deficit of protein.

Fig.2

A selection of surface plots from an experiment in which male and female mice were confined to one of 25 diets differing in protein, carbohydrate and fat content from weaning until either being culled at 15 month for cardio-metabolic phenotyping or until death by natural causes (see text for references). Thin-plate spline-fitted response surfaces are shown for protein vs carbohydrate (cut as a slice through the median for fat) from the full, 3-nutrient response topologies. Each full surface has its highest elevation for a given variable in the dark red area and its foothills in the dark blue regions. Plots A) and B) indicate intakes of food and total energy as a function of the concentrations of protein and carbohydrate in the diet. The food intake surface shows compensatory feeding responses for both protein and carbohydrate, with the former somewhat stronger than the latter, thereby driving elevated energy intake on low percent protein, high carbohydrate diets (P:C). [This effect, termed protein leverage, was more marked when protein was diluted by fat in the diet than with carbohydrate – see 10.] All other surfaces are mapped onto absolute average daily intakes for protein and carbohydrate, rather than dietary concentrations as in A) and B). Plots C) and D) show how body lean mass and percent fat mass were greatest on high P:C and low P:C diets, respectively, the latter reflecting increased energy intake driven by protein leverage. E) and F) indicate that dietary P:C has contrasting effects on two key life-history traits – lifespan and reproduction – with mice living longest on low P:C diets (as distinct from low P, high F diets, where they lived less long), but having highest values for indicators of reproductive function (in this case male testes mass) on higher P:C diets. Plots G) and H) map microbial relative abundances and illustrate the two major functional response types found across microbial taxa. Surfaces I) – L) map metabolic outcomes in mid-late life (15 months) and show how the longevity surface (E) accords with various cardio-metabolic health markers, including improved mitochondrial function, better glucose tolerance, low blood pressure and low triglycerides. Plots M) – P) next map components of nutrient signalling pathways, illustrating how associations can be made seamlessly across scales of response from life history and behaviour through to underlying molecular mechanisms.