I think we are all aware that
when we eat, our body breaks down carbohydrates to glucose and therefore our blood
sugar (glucose) level rises after eating. A really interesting paper about this
came out in Cell in November (1). The
paper is open access and can be found here. Here’s the summary:
“Elevated postprandial blood glucose levels constitute a global
epidemic and a major risk factor for prediabetes and type II diabetes, but
existing dietary methods for controlling them have limited efficacy. Here, we
continuously monitored week-long glucose levels in an 800-person cohort,
measured responses to 46,898 meals, and found high variability in the response
to identical meals, suggesting that universal dietary recommendations may have
limited utility. We devised a machine-learning algorithm that integrates blood
parameters, dietary habits, anthropometrics, physical activity, and gut
microbiota measured in this cohort and showed that it accurately predicts personalized
postprandial glycemic response to real-life meals. We validated these predictions
in an independent 100-person cohort. Finally, a blinded randomized controlled
dietary intervention based on this algorithm resulted in significantly lower
postprandial responses and consistent alterations to gut microbiota
configuration. Together, our results suggest that personalized diets may
successfully modify elevated postprandial blood glucose and its metabolic
consequences.”
Not very interesting or
informative is it? Nothing to interest those of us wading through the tsunami
of data in the scientific literature and searching for meaning about the state
of being human?
Well, I wouldn’t have hunted the
paper down at all if it wasn’t for an interview I heard with Eran Elinav (one
of the authors), on BBC Radio 4 (- I live in the UK), waxing lyrical about the
HUGE variability the team had found in Postprandial Glycemic Response (PPGR) in
humans. In particular he mentioned that humans had an unexpectedly massive
range in rise of blood glucose concentration in response to standardised meals
but ALSO to a range of foods including beer, ice cream, dark chocolate, bread
and even sushi!
Basically he was saying that,
when all outside factors are controlled for there was STILL an absolutely
unexplainable difference in response to the same foods.
In their introduction the authors
outline the problems that high postprandial glucose response (PPGR) can cause
and the methods previously used to estimate it:
“Blood glucose levels are rapidly increasing in the population, as evident
by the sharp incline in the prevalence of prediabetes and impaired glucose
tolerance.. Prediabetes, characterized by chronically impaired blood glucose
responses, is a significant risk factor for type II diabetes mellitus (TIIDM)..
It is also linked to other manifestations, collectively termed the metabolic
syndrome, including obesity, hypertension, non-alcoholic fatty liver disease,
hypertriglyceridemia, and cardiovascular disease. Thus, maintaining normal
blood glucose levels is considered critical for preventing and controlling the
metabolic syndrome.
Dietary intake is a central determinant of blood glucose levels, and
thus, in order to achieve normal glucose levels it is imperative to make food
choices that induce normal postprandial (postmeal) glycemic responses.
Postprandial hyperglycemia is an independent risk factor for the development of
TIIDM, cardiovascular disease, and liver cirrhosis and is associated with
obesity, and enhanced all-cause mortality in both TIIDM and cancer.
Despite their importance, no method exists for predicting PPGRs to
food. The current practice is to use the meal carbohydrate content even though
it is a poor predictor of the PPGR. Other methods aimed at estimating PPGRs are
the glycemic index, which quantifies PPGR to consumption of a single tested
food type, and the derived glycemic load. It thus has limited applicability in
assessing the PPGR to real-life meals consisting of arbitrary food combinations
and varying quantities, consumed at different times of the day and at different
proximity to physical activity and other meals.”
Frankly I found this absolutely ASTOUNDING! Basically doctors have NO
adequate tools for adequately assessing an individual patient’s response to
food in the REAL world! It would seem, to me, that we have at present, little
hope in dealing with this worldwide problem!
The current paper attempts to address
these linked problems.
Here is how they collected their
data:
“Each
participant was connected to a continuous glucose monitor (CGM), which measures
interstitial fluid glucose every 5 min for 7 full days (the ‘‘connection
week’’), using subcutaneous sensors (Figure 1D). CGMs estimate blood glucose
levels with high accuracy and previous studies found no significant differences
between PPGRs extracted from CGMs and those obtained from either venous or
capillary blood. We used blinded CGMs and thus participants were unaware of
their CGM levels during the connection week. Together, we recorded over 1.5
million glucose measurements from 5,435 days. While connected to the CGM, participants
were instructed to log their activities in real-time, including food intake,
exercise and sleep, using a smartphone-adjusted website.”
Image credit: Zeevi et. al. 2015 (1).
Here is some of the detail on
what they found:
“As expected, the PDP of carbohydrates (Figure 4A) shows that as the
meal carbohydrate content increases, our algorithm predicts, on average, a
higher PPGR. We term this relation, of higher predicted PPGR with increasing
feature value, as non-beneficial (with respect to prediction), and the opposite
relation, of lower predicted
PPGR with increasing feature value, as beneficial (also with respect to
prediction; see PDP legend in Figure 4). However, since PDPs display the
overall contribution of each feature across the entire cohort, we asked whether
the relationship between carbohydrate amount and PPGRs varies across people. To
this end, for each participant we computed the slope of the linear regression
between the PPGR and carbohydrate amount of all his/her meals. As expected,
this slope was positive for nearly all (95.1%) participants, reflective of
higher PPGRs in meals richer in carbohydrates. However, the magnitude of this
slope varies greatly across the cohort, with the PPGR of some people correlating
well with the carbohydrate content (i.e., carbohydrates ‘‘sensitive’’) and that
of others exhibiting equally high PPGRs but little relationship to the amount of
carbohydrates (carbohydrate ‘‘insensitive’’; Figure 4B). This result suggests
that carbohydrate sensitivity is also person specific.”
“The large interpersonal differences in PPGRs are also evident in that
the type of meal that induced the highest PPGR differs across participants and
that different participants might have opposite PPGRs to pairs of different standardized
meals (Figures 2D and 2E).”
Here are some of their graphs:
Image credits: Zeevi et. al. 2015 (1).
Lastly the authors state that the
gut Microbiome data (the microbe faunal composition of an individual’s
digestive system) could be correlated with an individual’s PPGR. Essentially
some gut bacteria are associated with risk factors for high PPGR. Proteobacteria
and Enterobacteriaceae both exhibit positive associations with a few of the
standardized meals PPGR (Figure 2H). These taxa have reported associations with
poor glycemic control, and with components of the metabolic syndrome including
obesity, insulin resistance, and impaired lipid profile. RAs of Actinobacteria
are positively associated with the PPGR to both glucose and bread, which is intriguing
since high levels of this phylum were reported to associate with a high-fat, low-fiber
diet.
A good table showing the glycemic
index for some foods is shown in the supplemental Information:
Of particular note are all the
carbohydrate containing foods from bread onwards. Bread, Greek pastries,
wholemeal bread, rolls, and wholemeal rolls are all derived from wheat. Human
exposure to processed wheat and its associated gluten is extremely recent and
dating to the advent cereal growing in the Middle East ca. 10000 years ago. Other
foods on this list include Potatoes, Rice and Cereal (in this case Cornflakes
containing entirely maize flour). All these plants are also those which have
entered human diets over same time-span.
Interestingly Persimmons from
south china are also high in carbohydrates and soluble dietary fibre known as FODMAPs
which have been implicated causing digestive issues (2).
Some speculations
I find it quite startling and
also very suggestive that this variability in human response to carbohydrate is
SO varied.
Many of the foods causing the
greatest PPGR are foods that have entered human diets during an extremely
recent evolutionary interval. Indeed the great proportion of evolutionary
changes that have happened to make us humans (the HARs of our genome, see here)
date to just after our ancestors split from chimpanzees.
Thus my question is are all
humans as equally adapted to eating these grains/sources of carbohydrates and
their processed derivatives? Indeed the authors note genetic factors as a
likely source of PPGR in humans, in their introduction.
Therefore I find it extremely plausible that it is possible that some humans
carry an allele regulating PPGR similar to that responsible for Lactose Persistence
in Europeans.
References
1. Zeevi, D. et. al. 2015. Personalized
Nutrition by Prediction of Glycemic Responses. Cell 163, 1079–1094, (November
19).
2. Kris Gunnars at Authority Nutrition. Retrieved from http://authoritynutrition.com/foods/persimmons/
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