MAPPING POSTPRANDIAL RESPONSES SETS THE SCENE FOR TARGETED DIETARY ADVICE
A
new study finds that machine learning can predict differences between
people in how they respond to meals
If you are managing to stay lean in today’s obesogenic environment
(lucky you!), you might suspect that it’s your good genes and/or
disciplined adherence to a healthy diet and lifestyle. On the other
hand, if you’ve struggled with body fat and dieting for most of your
life, you live in hope there’s a specific type of diet (or perhaps
better still, a drug) that’s perfect for you… if only you could identify
which one. Enter Personalised Nutrition.
In the June issue of the prestigious journal Nature Medicine,
Sarah Berry and her colleagues took a major step in that direction.
They presented the findings of PREDICT (1), a large-scale study
involving ~1000 people, including twins and other adults from the US and
UK. Using machine learning, the goal was to use the data to derive
‘algorithms’ (mathematical formulas) that predict a person’s
postprandial (after-meal) responses, that is, the rise in glucose,
insulin and triglycerides (fats) in the blood after meals of varying
composition.
The end-game of this kind of research is
the ability to give scientifically valid ‘personalised’ dietary advice
based on factors such as age, body mass index (BMI), specific genes,
large bowel microbial flora (the “micobiome”) and postprandial
responses.
But the findings were not what they
expected. They found much more person-to-person variation than was
expected, but differences in genes, the gut microbiome and insulin
levels explained only a minor proportion of the differences.
By
contrast, they were surprised to find a person’s response to the same
foods was fairly predictable and reproducible. Food composition and
macronutrient (carbohydrate, fat and protein) distribution explained
some of the variation in post-meal blood glucose levels, but not in
triglyceride levels. And interestingly, blood glucose responses did not
predict triglyceride levels; indeed, they warned that advice based just
on glucose responses (such as flash glucose monitoring) alone would be
misleading.
From our point of view, the associations
between the carbohydrate content of meals, post-meal blood glucose
levels and other factors were among the most interesting findings. High
blood glucose levels after meals are a well-established predictor of
type 2 diabetes, the metabolic syndrome, fatty liver, and cardiovascular
disease (2).
We have known for a long time that people
vary widely in their ‘glucose tolerance’, i.e. the absolute blood
glucose response to a carbohydrate challenge. In a lean, active person,
the area under the curve (AUC) after a 50 g glucose challenge can be as
low as 50 units, but in a sedentary person with a family history of
type 2 diabetes, it can be 400 units, an 8-fold difference. Higher AUC
means the beta-cells (insulin producing) in the pancreas are working
hard. If you have a family history, your pancreas may not have what it
takes to do this without becoming dysfunctional over time.
We
know that glucose tolerance worsens (measured as higher AUC) with age,
increased body weight and sedentary lifestyle. We also know that the
background diet is important – low carbohydrate consumption is
associated with a higher glycemic response to a glucose challenge.
However, it’s reversible - just a day or so of higher carbohydrate
intake will improve glucose tolerance.
Is there an
optimal diet composition for your body? Is one diet better than another
for you but not me? Does human evolution play a role here? Yes! Many
different diets can reduce blood glucose responses on a day-to-day
basis. Indeed, we have argued that this is one of key mechanisms behind
the success of the Mediterranean diet, low GI diets, vegetarian diets
based on legumes and lower carbohydrate diets.
Logically,
reductions in blood glucose can also be achieved with carefully
planned, very-low-carbohydrate diets (50-100 g/day), with parallel
improvements in body weight and HbA1c (glycated haemoglobin) in people
with type 2 diabetes (3). However, it would be very easy to choose a
poor quality very-low-carbohydrate diet and it may be hard to sustain in
the longer-term. It may not be as effective (or as easy) as changing
the kind (quality) of carbohydrate.
For a given amount
of carbohydrate, the glycemic index of a food predicts the degree of
glycaemia relative to a standard reference food. Choosing a diet based
on low GI foods such as pasta, legumes, most fruit, milk, yogurt and
specific types of rice and bread can halve the AUC and reduce HbA1c in
individuals with diabetes. Furthermore, meta-analyses of observational
studies confirm that diets based on low GI food choices are associated
with reduced risk of type 2 diabetes (4) and cardiovascular disease (5).
The relative risk reduction is biologically significant, similar to
increasing the amount of exercise or dietary fibre.
In
our view, the potential of personalised nutritional guidance versus
standard advice (national dietary guidelines) to improve weight control
is far from proven. In many ways, the findings of PREDICT are important
because they challenge so much of the prevailing hype.
REFERENCES:
- Berry S, and colleagues. Decoding human postprandial responses to food and their potential for precision nutrition: the PREDICT 1 study.
- The DECODE group. European Diabetes Epidemiology Group. Glucose tolerance and mortality: comparison of WHO and American Diabetes Association diagnostic criteria.
- Wycherley TP, and colleagues. Effects of energy-restricted high-protein, low-fat compared with standard-protein, low-fat diets: a meta-analysis of randomized controlled trials.
- Livesey G, and colleagues. Dietary Glycemic Index and Load and the Risk of Type 2 Diabetes: A Systematic Review and Updated Meta-Analyses of Prospective Cohort Studies.
- Livesey G, and colleagues. Coronary Heart Disease and Dietary Carbohydrate, Glycemic Index, and Glycemic Load: Dose-Response Meta-analyses of Prospective Cohort Studies.
Professor Jennie Brand-Miller holds a Personal Chair in Human Nutrition in the Charles Perkins Centre and the School of Life and Environmental Sciences, at the University of Sydney. She is recognised around the world for her work on carbohydrates and the glycemic index (or GI) of foods, with over 300 scientific publications. Her books about the glycemic index have been bestsellers and made the GI a household word.