Genetic Test Reveals Which Weight Loss Drug Works Best for You

A new genetic test developed by Mayo Clinic can estimate how well a person will respond to weight loss medications like glucagon-like peptide 1 drugs by measuring how much food they need to feel full. Known as calories to satiation, this personalized approach connects biological traits to medication effectiveness, as detailed in Cell Metabolism (1^✔ ✔Trusted Source
Genetic and physiological insights into satiation variability predict responses to obesity treatment

Go to source). The test evaluates a person’s satiation level, how much food it takes to trigger a feeling of fullness and links it with potential treatment success, offering a major advancement in tailored obesity care. According to Dr. Andres Acosta, a gastroenterologist and senior author, the goal is to align treatments with an individual's biology rather than just using body size as the determining factor.

Beyond Body Mass Index and Toward Biology

More than 650 million adults globally are affected by obesity, a multifaceted condition influenced by behavioral, environmental, and genetic components. Despite this complexity, treatment typically relies on general metrics like body mass index instead of more accurate biological markers.

To address this, Dr. Acosta and his team studied satiation as a key signal controlling food intake. In 2021, they categorized different obesity phenotypes, noting that some individuals consume large meals ("hungry brain") while others graze frequently ("hungry gut"), even if their overall intake seems moderate.

How Satiation Varies Among Individuals

The researchers invited nearly 800 adults with obesity to eat a meal consisting of lasagna, pudding, and milk until they felt extremely full. The results showed a dramatic range: some felt full after consuming just 140 calories, while others exceeded 2,000. On average, men ate more than women, but common factors like body fat percentage, age, and hunger-related hormones offered limited explanations for the variation.

This led the researchers to examine genetic differences. They used machine learning to combine genetic variants from 10 genes involved in appetite regulation into a single measurement, called the calories to satiation genetic risk score. This score can be determined using either a blood or saliva sample and gives a customized estimate of how much a person needs to eat before feeling full.

Personalizing Medications Through Genetic Profiles

The team then applied this genetic score to clinical trials for two United States Food and Drug Administration-approved weight loss medications: phentermine-topiramate and liraglutide. They discovered that people with a high satiation threshold responded more effectively to phentermine-topiramate, which targets overeating during large meals, matching the "hungry brain" profile.

On the other hand, individuals with a low satiation threshold experienced better results with liraglutide, which is more effective at reducing overall hunger and controlling frequent eating, aligning with the "hungry gut" phenotype. This approach allows clinicians to better match a patient to the treatment most likely to work for them.

Looking Ahead to More Comprehensive Testing

According to Dr. Acosta, a single genetic test can help determine which of two medications a patient is most likely to succeed with, streamlining care and improving outcomes. Beyond phentermine-topiramate and liraglutide, the team is evaluating similar predictions for semaglutide, another glucagon-like peptide 1 medication sold under brand names like Ozempic and Wegovy.

Efforts are also underway to enhance the test by integrating data from the human microbiome and metabolome. Additionally, researchers aim to develop models that can predict common side effects such as nausea and vomiting, bringing even more precision to obesity treatment in the future.

Reference:

1. Genetic and physiological insights into satiation variability predict responses to obesity treatment - (https://www.cell.com/cell-metabolism/fulltext/S1550-4131(25)00264-5)

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