Lipid Tests Revolutionize Predictions of Childhood Diseases
Researchers at King’s College London have developed a new blood test using lipids that can more effectively predict risks of metabolic diseases in children, such as type two diabetes and liver disease.
Using mass spectrometry and an intervention model, they’ve demonstrated that lipid levels can indicate health risks unrelated to a child’s weight, suggesting a more complex role for lipids in health and disease.
Lipid-Based Blood Testing
A new type of blood test using lipids could make it easier to identify children at risk of complications around obesity including type two diabetes, liver and heart disease, say scientists.
A new study from King’s College London published today (September 20) in Nature Medicine reveals a new relationship between lipids and diseases impacting metabolism in children, which could serve as an early warning system for conditions like liver disease.
Advancements in Early Disease Detection
Using machines that test blood plasma in babies that already exist in hospitals, the researchers suggest this could help doctors spot early signs of disease in children quicker and help them access the right treatment.
The findings also contest the common idea that cholesterol is a leading cause of complications around obesity in children, identifying new lipid molecules that contribute to health risks like blood pressure but are not only correlated with a child’s weight.
Complexities in Lipid Functionality
Lipids have traditionally been thought to be fatty acids in the body, either good or bad types of cholesterol or triglycerides, fats found in the bloodstream that is the most common in the human body. Recent studies from the same group of scientists have suggested that the picture is more complex.
Using a technique associated with chemistry called mass spectrometry, current evidence puts the types of different lipid present in the body in the thousands, each with separate functions.
Efficacy of HOLBAEK-Model Intervention
Taking a control sample of 1,300 children with obesity, the team assessed their lipids in blood. Afterward, 200 of them were put on the HOLBAEK-model for a year, a lifestyle intervention for people with obesity popular in Denmark.
Subsequent readings showed that among the intervention group, counts of lipids tied to diabetes risk, insulin resistance, and blood pressure decreased, despite limited improvements in some children’s BMI.
Implications for Future Healthcare
Dr. Cristina Legido-Quigley, a group leader in Systems Medicine at King’s College London, Head of Systems Medicine at the Steno Diabetes Centre Copenhagen (SDCC) and principal author, said: “For decades, scientists have relied on a classification system for lipids that have split them into good and bad cholesterol, but now with a simple blood test we can assess a much broader range of lipid molecules that could serve as vital early warning signs for illness. In the future, this has the potential to be an entirely new way to evaluate someone’s personal risk of disease and by studying how to change lipid molecules in the body, we could even prevent metabolic diseases like diabetes altogether.”
Obesity continues to be a risk factor for conditions like fatty liver disease, but the team hope that doctors can use these measurements to treat children when they are at risk and not just a little larger than their peers.
Perspectives on Obesity Management
Dr. Karolina Sulek, who was part of the study and performed analysis at the SDCC, said: “Early recognition of children at risk for these life-threatening diseases is crucial. The study provides strong evidence of the great need for obesity management and gives parents confidence to intervene in their children’s life more compassionately, helping them to lose weight.”
The next step for the researchers is to help understand how genetics affects lipids and what this means for metabolic diseases, as well as how these lipids can be changed to improve health.
Reference: 20 September 2024, Nature Medicine.
DOI: 10.1038/s41591-024-03279-x
