Maternal Obesity Linked to Early Changes in Infant Gut Microbiome, Study Suggests
Key Takeaways:
- Infants born to mothers with obesity show distinct differences in gut bacteria during the first six months of life, including reduced microbial diversity.
- These early microbial changes are associated with pathways linked to fat metabolism, particularly in the first three months after birth.
- Researchers suggest that early-life interventions targeting the gut microbiome may help reduce longer-term metabolic risks for children.
Maternal obesity and the infant gut microbiome
Babies born to mothers with obesity may begin life with a markedly different gut microbiome, a factor that could influence their metabolism and long-term health, according to new research from Nazarbayev University (NU).
The study, led by researchers Almagul Kushugulova and Samat Kozhakhmetov, explored how maternal obesity may shape the early development of the gut microbiome in infants. The research team followed 24 mothers and their babies from birth to six months of age, analysing stool samples using advanced DNA sequencing techniques.
By comparing infants born to mothers with obesity with those born to mothers without obesity, the researchers identified clear differences in the composition and diversity of gut bacteria during early life.
Reduced microbial diversity and altered metabolic pathways
The analysis showed that infants of mothers with obesity had significantly lower gut microbial diversity. In addition, these infants had a higher abundance of bacterial species associated with fat metabolism.
These differences were most pronounced during the first three months of life, a period widely recognised as critical for the establishment of the gut microbiome and for metabolic programming.
“During the first three months of life, we observed what appears to be a shift in how gut bacteria process nutrients – with a tendency toward fat storage pathways rather than breaking down carbohydrates for energy,” Kozhakhmetov explained.
He noted that this early metabolic pattern may have implications for how energy balance is regulated later in life.
Opportunities for early intervention
The researchers suggest that their findings open the door to preventive strategies during infancy. Kozhakhmetov highlighted that understanding these early microbial shifts could inform interventions aimed at promoting healthier metabolic outcomes.
This discovery, he said, “opens up possibilities for early intervention”, including approaches such as targeted probiotics or tailored dietary guidance designed to support a more balanced gut microbiome and potentially reduce future metabolic risk.
Beyond metabolism – immune and appetite regulation
The implications of the findings may extend beyond metabolism alone. The researchers propose that maternal obesity could also influence immune system development and appetite regulation in children through microbial transmission.
“We tend to think that we only pass on our genes to our children. But our research suggests that we may also pass on our bacteria – and the type of bacteria a child inherits could have important effects on their long-term health, potentially influencing their health trajectory as they grow,” Kushugulova said.
This perspective reinforces the idea that early-life exposures play a significant role in shaping health across the life course.
Placing the findings in context
The study, published in the journal Biomedicines, adds to a growing body of research highlighting the importance of the early-life microbiome. Previous studies have linked maternal weight status and gut dysbiosis to disrupted nutrient metabolism, inflammation, and changes in immune, metabolic, or neurodevelopmental outcomes in children.
As obesity during pregnancy becomes increasingly common worldwide, the authors argue that maternal health should be viewed as a key determinant not only of pregnancy outcomes, but also of a child’s longer-term metabolic health.
Implications for future research and practice
The researchers conclude that interventions targeting the gut microbiota during early infancy may represent a promising avenue for reducing health risks associated with maternal obesity. Further research will be needed to determine which strategies are most effective, when they should be implemented, and how they can be integrated into routine maternal and child healthcare.
Taken together, the findings underline the importance of addressing obesity before and during pregnancy, while also highlighting the potential of microbiome-focused approaches to support healthier outcomes for future generations.
CCH insight:
Evidence for the role of the gut microbiome in obesity and metabolic health continues to grow. This study is ground-breaking in demonstrating that maternal obesity influences the new-born child’s microbiome, potentially priming the child for health challenges later in life right from their first few weeks of life. On the positive side, this offers the potential to identify babies at risk of metabolic diseases from a very early stage of life, and also the opportunity for early interventions though diet, pre- and probiotics.
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AI Model Could One Day Help Prevent Childhood Obesity by Counting Bites
Key Takeaways:
- Researchers at Penn State have developed an artificial intelligence (AI) system capable of counting how many bites a child takes during a meal, achieving around 70% accuracy compared to human observers.
- Eating too quickly increases the risk of obesity in children because the body has less time to register fullness, leading to overeating.
- The AI system, named ByteTrack, may in future help parents, clinicians, and researchers monitor and guide children’s eating habits in real-world environments.
AI and eating behaviours: A new frontier in obesity prevention
The faster a child eats, the greater their risk of developing obesity, according to researchers from the Penn State Department of Nutritional Sciences. However, accurately measuring bite rate—the number of bites taken during a meal—has long posed a challenge. Traditionally, this requires a researcher to watch and manually record each bite from hours of video footage, limiting most studies to small, controlled laboratory environments.
In a collaborative effort between Penn State’s Departments of Nutritional Sciences and Human Development and Family Studies, researchers have created an AI model designed to automate this process. Their pilot study, published in Frontiers in Nutrition, shows that the system is currently around 70% as effective as a human observer in counting bites. Although still under development, the researchers believe the technology could eventually help identify when a child needs to slow their eating rate or adjust their eating behaviour.
The link between eating speed and obesity
“When we eat quickly, we do not give our digestive tract time to sense the calories,” explained Professor Kathleen Keller, the Helen A. Guthrie Chair of Nutritional Sciences at Penn State and co-author of the study. “The faster you eat, the faster it goes through your stomach, and the body cannot release hormones in time to let you know you are full. Later, you may feel like you have overeaten, but when this behaviour repeats, faster eaters are at greater risk for developing obesity.”
Keller’s research group has previously demonstrated that a faster bite rate, especially when combined with larger bite size, correlates with a higher likelihood of obesity in children. Other studies have also linked larger bite size to an increased risk of choking.
“Bite rate is often the target behaviour for interventions aimed at slowing eating rate,” noted Dr Alaina Pearce, research data management librarian at Penn State and co-author of the study. “This is because bite rate is a stable characteristic of children’s eating style that can be targeted to reduce their eating rate, intake, and ultimately risk for obesity.”
Manually recording bite rate, however, is both labour-intensive and costly. As Keller pointed out, “Measuring bite rate is tedious, labour-intensive work, meaning it is expensive, which often limits the amount of data considered in bite rate studies.”
Using AI to support healthier habits
To overcome these limitations, Yashaswini Bhat, a doctoral candidate in nutritional sciences and lead author of the study, set out to develop the first AI-powered bite counter designed specifically for studying children’s eating behaviours.
“I have an interest in AI and data science, but I had never developed a system like this one,” Bhat explained.
She partnered with Associate Professor Timothy Brick, from Penn State’s Department of Human Development and Family Studies, to create a system capable of detecting children’s faces within videos and identifying when a child takes a bite.
“An experienced and knowledgeable collaborator like Dr Brick was invaluable to this project,” Bhat added.
The team trained the system using 1,440 minutes of video footage from Keller’s Food and Brain Study, funded by the National Institute of Diabetes and Digestive and Kidney Diseases. The footage featured 94 children aged seven to nine, each consuming four meals with identical foods on different occasions.
Researchers manually identified bites in 242 videos to train the AI. Once the system had been trained to recognise what a bite looks like, it was tested on an additional 51 videos. The AI’s results were then compared to those of human researchers.
Promising early results
“The system we developed was very successful at identifying the children’s faces,” Bhat said. “It also did an excellent job identifying bites when it had a clear, unobstructed view of a child’s face.”
While the AI was 97% as effective as a human observer at recognising faces, it achieved about 70% accuracy in counting bites. Bhat noted that challenges arose when children were partially obscured, turned away from the camera, or engaged in behaviours such as chewing on their spoons or playing with their food—actions common among younger participants.
“The system was less accurate when a child’s face was not in full view of the camera or when a child chewed on their spoon or played with their food, as often happens toward the end of a meal,” Bhat said. “Chewing on a utensil sometimes appeared to be a bite, and this complicated the task for the AI model.”
Next steps for the ByteTrack system
Although still in its early stages, the researchers view the pilot as an important step toward automating bite rate analysis. The system, called ByteTrack, will continue to be refined so it can distinguish between bites and similar movements such as sipping a drink.
“The eventual goal is to develop a robust system that can function in the real world,” Bhat said. “One day, we might be able to offer a smartphone app that warns children when they need to slow their eating so they can develop healthy habits that last a lifetime.”
The research was supported by the National Institute of Diabetes and Digestive and Kidney Diseases, the National Institute of General Medical Sciences, the Penn State Institute for Computational and Data Sciences, and the Penn State Clinical and Translational Science Institute.
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