1. The nutritional geometry of liver disease including NAFLD
Amany Saleh Elyamany, MD
Assistant professor of Internal Medicine
Subdivision of Hepatology
Faculty of Medicine
Alexandria University

2. The intimate relationship between diet and physiology confers nutrition with considerable potential for the primary prevention, management and treatment of human disease.

3. Geometric Framework for Nutrition (GFN)
The interactions among nutrients within foods and diets, maps the consequences of different dietary compositions on multiple measures of physiology and health. .

4. Foods contain mixtures of nutrients along with other substances, and have physicochemical properties that are greater than the sum of their nutrient parts.
we usually do not eat foods singly
instead we combine them into meals and, along with snack foods, meals accumulate over time to form our habitual diet

5. A nutritionally balanced diet does more than deliver a requisite number of calories; it provides an optimal mix of multiple nutrients and fibre as well as energy to meet the needs of the individual.
It follows that

6. The evidence base for determining nutritional guidelines has mainly derived from studies that have considered single nutrients and link it to health outcomes
For example

7. While this approach has had considerable success in dealing with diseases associated with micronutrient deficiencies, it has not been effective when considering chronic diseases associated with over-nutrition.

8. Geometric Framework for Nutrition (GFN)
integrates key aspects
of nutritional systems to
map the relationships
between nutrients, physiology
and health.

9. The basis of GFN models is a graph called a nutrient space, in which each nutrient of interest is included as a dimension. in which the
x-axis represents amount of protein eaten, and the y-axis intake of non-protein energy (carbohydrate and fat). A nutritionally balanced food (red line) intersects the intake target, The two green lines are nutritionally imbalanced foods, Zig-zag arrows indicate the change in cumulative intakes of protein and nonprotein
energy as the two foods are ingested one after the other to attain the target.

10. Geometric Framework for Nutrition based on percentage of total energy from dietary macronutrients
here the nutrient space represents the percentage of total energy in foods, meals and diets from protein, carbohydrate
and fat (which together sum to 100%).
An illustration of how to read the graph is seen for the composition of rice, which contains 5% protein, 10% fat and 85% carbohydrate.
here are two foods, a meal (m) can be constructed that falls anywhere along the line joining the two in the nutrient space. Hence, for
steak and peas, m1 and 2 are possible but not m3-7. Where there are three foods, this opens a triangular region of possibilities
– adding rice to steak and peas now allows m1-3 but not m4-7 to be constructed. At the next level in the hierarchy, meals can
be combined into diets (stars), such that a combination of meals 1–7 allows diets 1 and 2 but not d3

11. function of macronutrient ratio, plotted as a response surface with maximal
elevation in the deep red and minimal intake in the dark blue regions.
The predominant pattern is increasing energy intake with declining percent protein in the diet, irrespective of whether the decline results from increasing the percentage of fat and/ or carbohydrate,
The model is consistent with the association between weight loss and high-percent protein diets,

12. A total of 858 animals were placed from weaning onto one of 25 diets varying in protein, carbohydrate, fat and energy content.
study was done to explore the
consequences of dietary macronutrient composition
on lifespan and late-life cardio-metabolic
health.

15. have been undertaken in mice.
The only studies employing GFN to elucidate the association between nutrition and liver disease
have been undertaken in mice.
There have yet to be GFN studies undertaken in human liver disease.
unfort

16. The probability of having NO fatty liver increases once protein intake exceedes 10 kJ/day
while the probability decreases once carbohydrate intake exceedes 25 kJ/day

17. While diet-induced weight loss is a standard approach for the treatment of fatty liver, these results in mice suggest that the ratio (and quality) of the macronutrients may be as important as the energy content of the weight-loss diets.

18. In general, these suggest that weight loss is a critical determinant of histological improvement, with greater weight loss leading to greater degrees of NASH resolution and fibrosis reversal.
In the largest trial to date, patients with biopsy-proven NASH were assigned to a low fat hypocaloric diet.
To date, Only a handful of studies has examined the role of lifestyle intervention on liver histology in NAFLD.

19. In addition, patients were encouraged to walk at least 200 min per week, to record habitual physical activity and to attend individual behavioural sessions to promote adherence.
Following the intervention for 52 weeks, at second biopsy, 25% (72) had resolved histological steatohepatitis, and 19% (56) had regression in liver fibrosis.

20. Even less is known about the impact of nutrient composition on liver histology over time.
What is known in relation to carbohydrate and fat intake suggests that on hypocaloric diets, both high-fat, low-carbohydrate and low-fat, high-carbohydrate diets are equally effective in reducing liver lipid

21. Nutrients can reshape the gut microbiome.
Holmes et al. have recently shown that the source of nitrogen,
host-derived or dietary is critical for
beneficially or detrimentally reshaping gut microbial composition, respectively.

22. Increased gut permeability and thus the
translocation of bacteria and bacterial products
has been reported to contribute to liver inflammation in patients with NASH.
Whether this increased gut permeability is a cause or a consequence of the disease has not been fully elucidated, but the methionine choline deficient diet animal model of NASH suggests that gut permeability is a consequence.

23. The GFN provides a modelling framework within which relationships between the multiple dimensions of diet, nutrition and health can be integrated.
These models can be applied to population, to visualise nutritional relationships and to generate predictors for nutritional interventions for the prevention and treatment of disease.