The study, published yesterday (June 11) in Nature Medicine, studied postprandial metabolic responses to a series of mixed-nutrient dietary challenges using a sample of 1,000 individuals from the UK (unrelated, identical and non-identical twins) and 100 unrelated individuals from the US.
On studying blood triglyceride, glucose and insulin responses, the researchers discovered that person-specific factors, such as gut microbiome, had a greater influence (7.1% of variance) than did meal macronutrients (3.6%) for postprandial lipemia, but not for postprandial glycemia (6.0% and 15.4%, respectively) and they found that genetic variants had a modest impact on predictions (9.5% for glucose, 0.8% for triglyceride, 0.2% for C-peptide).
The report states: "In many cases, we observed responses that contrast with those reported in traditional clinic-based studies, thereby reshaping conclusions about the key factors influencing responses to foods.
"For example, genetic influence was lower than expected, especially for triglyceride, whereas modifiable factors such as meal timing conveyed larger effects than anticipated."
In a preview of the results in an ASN online conference last week, Dr Tim Spector, professor of genetic epidemiology and director of the TwinsUK Registry at King’s College London, described the study as the largest ongoing programme to measure postprandial responses to food in nutritional science.
The ultimate aim of this programme is to develop algorithms that predict an individual’s postprandial metabolic responses to specific foods. The team then aims to create a home test and app that will allow anyone to understand their personal nutritional response and choose foods that optimise their metabolism, improve long-term health and manage weight more easily.
Helpfully for their cause, the team confirmed that, although postprandial triglyceride and glucose responses were highly variable between individuals, a person’s response to the same meals was often similar and therefore predictable.
The researchers also point out that the low correlation between triglyceride and glucose suggests that prediction algorithms relying solely on glucose would be insufficient for the detection of dysregulated triglyceride responses.
They also argue that the minimal influence of genetics means that, "even in an unrealistically optimistic scenario in which most of this trait variance is explained by known DNA variants, it is unlikely that prediction algorithms using DNA variant data alone, which many direct-to-consumer nutrigenomics companies advocate, would succeed."
Method
During a controlled clinic day, the team assessed postprandial (0-6h) metabolic responses to sequential mixed-nutrient dietary challenges. This was followed by a two week at-home phase.
Glycemic and lipaemic responses to multiple duplicate isocaloric meals of different macronutrient content and self-selected meals (>100,000), were tested at home using a continuous glucose monitor (CGM) and dry blood spots. Baseline factors included metabolomics, genomics, gut metagenomics and body composition.
Dietary data was collected using an app and dashboard combining weighed food records, photographs, bar coding and live nutritional support. Sleep and activity were monitored using wearable devices.
Throughout the study, over 32,000 muffins were consumed, 28,000 TAG readings taken, 132,000 meals were logged and over 2 million CGM glucose readings were taken.
Genetic Factors
The heritability of postprandial responses was examined by recruiting 230 twin pairs from the TwinsUK registry.
Data showed that additive genetic factors explained 48% of the variance in glucose, but 0% of the variance in triglyceride and only 9% of the variance in insulin. The estimated genetic variances in insulin and C-peptide were close to 0.
In a subgroup of participants who had genome-wide genotyping previously done and had available genome-wide association study (GWAS) data (n = 241), the team tested whether 32 SNPs were associated with the postprandial variables studied.
They found that several SNPs were significantly (P < 0.05) associated with these variables, but they collectively explained only about 9% of observed variation in glucose, and less than 1% of variation for postprandial triglyceride and postprandial C-peptide.
Gut microbiome (16S ribosomal RNA)
To estimate the contribution of the gut-microbiome, the team used relative bacterial taxonomic abundances and measures of community diversity and richness, derived from 16S rRNA high-throughput sequencing of baseline stool specimens.
They found that, without adjusting for any other individual characteristics, gut-microbiome composition explained 7.5% of postprandial triglyceride, 6.4% of postprandial glucose and 5.8% of postprandial C-peptide.
Meal composition, habitual diet and meal context
To determine the impact of the macronutrient composition of meals, they measured triglyceride and C-peptide for two standardised home-phase meals of differing macronutrient compositions in subsets of participants. Glucose was measured for seven standardised meals, totalling 9,102 meals in 920 individuals.
Results revealed that glucose readings were significantly reduced by 79, 142 and 185 for every 1 g fat, fiber and protein, respectively, after adjustment for carbohydrate consumption.
The team also found that meal context had a surprisingly large influence, with individuals showing a far higher glycemic response to the same meal when consumed at lunch, compared to when consumed at breakfast, but this variability was strongly influenced by age.
In their preview of the results, Dr Sarah Berry, senior lecturer at King’s College London, explained: "We looked at the size of this effect and the variability between individuals to find out whether some are better off eating high carb meals in the morning and other in the evening.
"We found the time of day affect was particularly pronounced in young people but for people over the age of 60 there was far less influence, it may not matter so much what time of day you eat."
Future studies
Noting the study's strengths and weaknesses, the authors note: "food frequency questionnaires have well-known limitations, and other objective approaches may be considerably less biased and less error prone. Pairing this with short-term assessments, such as the weighed dietary record included in the PREDICT study app, may help mitigate these limitations.
"More comprehensive challenge tests might also reveal new aspects of postprandial metabolism; here, we used a 6-h test meal challenge, as this was deemed the maximum duration that most participants were likely to accept. Data from challenge tests of longer durations (up to 8 h) may provide valuable information on both glucose and triglyceride responses."
The authors point out that their prediction models derived in the UK cohort performed almost as well in the independent US validation cohort, which is reassuring given the differences in environmental factors.
However, they point out that both cohorts comprised younger healthy adults of European ancestry.
"Thus, the generalisation of our findings would require validation in people of non-European ancestry, older adults and people with diseases that affect metabolism, such as diabetes. The clinical implications of our predictions will require appropriately powered longitudinal studies."
Source: Nature Medicine
Berry, S.E., Valdes, A.M., Drew, D.A. et al.
"Human postprandial responses to food and potential for precision nutrition"
https://doi.org/10.1038/s41591-020-0934-0
