- Recent research suggests that the composition of the gut microbiome can predict an individual’s likelihood of obesity.
- A new study reports that differences in the functional profile of the gut microbiome are also associated with the body’s response to weight loss interventions.
- The study found that gut microbiome genes associated with bacterial replication and the breakdown of carbohydrates and proteins predicted weight loss response.
- This research could lead to the development of diagnostic techniques and individualized treatments for people looking to lose weight.
The gut microbiome consists of various bacteria, fungi, and other microorganisms that reside in the digestive tract, with its composition varying among individuals.
Some experts believe that the gut microbiome can significantly influence an individual’s overall well-being by modulating metabolism, immune function, and mental health.
These gut microbes influence energy metabolism by regulating glucose metabolism, appetite, and fat storage.
Consistent with the role of gut microbiota in energy metabolism, animal and human studies have shown that changes in the composition and function of gut microorganisms are associated with obesity and diabetes.
Researchers at the Institute for Systems Biology in Seattle recently investigated how differences in gut microbiota composition may influence the body’s response to weight loss interventions.
The researchers identified the genes that were most abundant in the gut microbiota of individuals before they participated in a weight loss program.
Based on the biological functions that these genes perform, the researchers were able to infer the functional profile of the entire gut microbiome.
They found that the functional profile of gut microbiota genes at the onset of the weight loss program predicted an individual’s ability to lose weight.
Notably, between the individuals who lost weight and those resistant to weight loss, there was a difference in the abundance of microbiome genes that scientists know to influence human metabolism.
Medical News Today spoke with Prof. Eran Elinav, who is a microbiome expert at the Weizmann Institute of Science and the National German Cancer Research Center (DKFZ) and was not involved with the study. He said:
“While a causative role for gut microbiome features has been suggested by a number of preclinical trials in animal models, human data remain associative to date. The current study by Diener et al. contributes to our understanding of human microbiome contributions to dietary responses by identifying a set of baseline microbiome features that were associated with dietary weight loss in humans.”
The study’s lead author, Dr. Sean Gibbons, assistant professor at the Institute for Systems Biology, told MNT that “this work may lead to diagnostics for identifying individuals likely to respond to mild lifestyle interventions or those who may need more drastic interventions to lose weight.”
“Beyond that,” Dr. Gibbons continued, “these results hint at the organisms and genes responsible for weight loss success or resistance, which may guide future interventions aimed at engineering weight loss-resistant microbiomes into weight loss-permissive microbiomes.”
The study appears in the journal mSystems.
In the present study, the researchers analyzed data from 105 individuals who had enrolled in a commercial behavioral wellness program.
The researchers collected information on the participants, including their weight and body mass index (BMI) — a value that
The researchers also collected dietary information and stool samples at the onset of the wellness program.
They used the blood samples to evaluate the levels of various metabolites and proteins and used the stool samples to determine gut microbiota composition and function.
The researchers also assessed differences in the function of the gut microbiota using metagenomic analysis. Instead of characterizing the genome of individual microorganism species, a metagenomic analysis involves identifying genes that are most abundant in the whole community of microorganisms that constitute the gut microbiota.
The identification of the most abundant genes can help predict the function of the entire gut microbiome.
Among the 105 participants, 48 individuals lost at least 1% of their weight per month, whereas the remaining 57 did not lose any weight.
The researchers identified the 15 individuals who lost the greatest amount of weight and the 10 people in the no-weight-loss group who showed the least significant change in their weight.
They then determined gut microbiome composition and function using samples from this subgroup of 25 individuals. They used samples from all 105 individuals to examine the association between weight loss and certain variables, such as dietary patterns and blood metabolites and proteins.
Upon examining the data from all of the participants, the researchers found that individuals with a higher BMI at onset lost more weight.
The association between high BMI and weight loss is well-known, and the researchers wanted to determine the factors that predicted weight loss independent of BMI. The reason for this is that the initial BMI could distort or mask the potential association between weight loss and other baseline factors.
Hence, the researchers conducted their subsequent analysis after controlling for the effect of BMI.
Using the blood samples that they collected before and after the weight loss intervention, the researchers compared changes in the levels of metabolic markers in the weight loss and no-weight-loss groups.
They found that the weight loss group, in comparison with the stable weight group, showed an increase in adiponectin levels.
Fat issue secretes the hormone adiponectin, and an increase in the levels of this protein is associated with weight loss.
The weight loss group also exhibited a decrease in the level of six proteins, which scientists have previously shown to be associated with inflammation, obesity, and other metabolic disorders.
Thus, weight loss was associated with an improvement in the metabolic and immune profiles of the individuals.
The researchers then analyzed the association between weight loss and various features measured at baseline, after controlling for baseline BMI, age, and sex.
These baseline features included dietary patterns, blood protein and metabolite levels, and gut microbiome composition and function.
The researchers found that the extent of weight loss was not correlated with baseline dietary patterns or blood metabolite levels. The levels of only one obesity-associated protein in the blood, the KIT ligand, were positively associated with resistance to weight loss.
In contrast, a number of baseline features were associated with the initial BMI of the participants.
Although the researchers found no association between microbiome composition and weight loss, the levels of 31 microbiome genes were associated with weight loss.
In other words, the microbiome gene profile was a better predictor of weight loss than baseline dietary patterns or blood metabolite and protein levels. Overall, lead author Christian Diener, Ph.D., concludes:
“The gut microbiome is a major player in modulating whether a weight loss intervention will have success or not.”
The class of microbiome genes most abundant in the weight loss group was that associated with the synthesis of bacterial cell walls.
Increased synthesis of cell walls occurs during bacterial replication. The researchers found that the bacterial replication rates were indeed higher in the weight loss group than in the no-weight-loss group.
Furthermore, bacteria belonging to the genus Prevotella were responsible to a large extent for the increased replication rate in the weight loss group.
These short-chain fatty acids are less energy-dense than the consumed carbohydrates and can reduce inflammation. This is especially noteworthy since experts believe that obesity is
In contrast, microbiome genes associated with the breakdown of complex carbohydrates and proteins and those involved in the stress response and cellular respiration were enriched in individuals resistant to weight loss.
To be precise, genes enriched in the no-weight-loss group included those coding for enzymes that degrade complex carbohydrates into simple sugars.
At the same time, the lower levels of bacteria with the ability to transform these simple sugars into fermentation products in individuals resistant to weight loss may result in greater absorption of simple sugars by the host, i.e., the human body.
Thus, the authors hypothesize that the lower replication rates of bacteria involved in fermentation and high levels of carbohydrate-degrading enzymes may be responsible for the lack of response to weight loss interventions.
“Microbiomes that were permissive to weight loss were primed for the rapid growth of strictly anaerobic fermenters, while microbiomes resistant to weight loss showed enrichment in starch-degrading genes, combined with more sluggish growth,” explained Dr. Gibbons. He continued:
“Overall, this suggests that weight loss resistance is driven by the host outcompeting the microbiota for the simple sugars cleaved from dietary fibers/starches. The host absorbs these sugars if the microbiome isn’t growing rapidly to consume them. Thus, the microbiome appears to modulate the efficiency by which the host extracts calories from the diet.”
Describing the strengths of the study, Dr. Gibbons told MNT: “Prior work often conflates BMI and weight loss. These factors are highly correlated because people with higher BMIs tend to lose more weight in response to an intervention.”
“This is a major problem because many phenotypic factors are correlated with BMI, even if they may not be relevant to weight loss responses. Therefore, we corrected for baseline BMI when looking for associations with weight loss. The results reported here are features associated with weight loss that are fully independent of baseline BMI.”
The authors acknowledged that the study had certain limitations. They observed that “[t]he current study only looked at baseline dietary patterns and did not track detailed dietary records throughout the full duration of this personalized intervention study.”
They hope that future studies will “capture this longitudinal dietary data in order to better delineate between the influence of dietary variation and baseline gut microbiomes in predicting weight loss responses.”
Dr. Gibbons also noted, “our cohort size is rather modest, and these results should be considered rather preliminary.”
To address the small sample size in the present study, the authors intend to replicate the research with larger groups of participants.
Discussing future research directions, Dr. Gibbons said, “Ultimately, we hope to develop diagnostics and personalized interventions that help people lose weight. Personalized interventions will require predictive models for how an individual’s microbiome responds to dietary inputs.”
“We are currently building these models — for example, we recently built a community-scale metabolic model of the gut microbiome that can be personalized to an individual, called MICOM.”