One in four adults has metabolic syndrome – and it may be ageing their brains
One in four adults has metabolic syndrome – and it may be ageing their brains
Publish Date: 2026-07-14 09:49:00
Source Domain: theconversation.com
An estimated one in four adults worldwide has metabolic syndrome. While metabolic syndrome is most often thought of as a warning sign that diabetes or cardiovascular disease may be on the horizon, my team’s new study suggests that its consequences reach further – and it actually may be accelerating the ageing of the brain.
Metabolic syndrome is not a single disease. Rather, it’s a cluster of interrelated risk factors, including high blood sugar, high blood pressure, excess belly fat, low levels of HDL (“good”) cholesterol and high triglycerides (blood fats). A diagnosis of metabolic syndrome means having at least three of the five components. Each of these risk factors raises health risks on its own, but together they compound one another.
Previous studies have linked metabolic syndrome to increased risk of neurological disorders such as stroke, dementia and even Parkinson’s disease. But what metabolic syndrome actually does to the brain, and how, has remained unclear.
My colleagues and I set out to answer this using data from 27,375 study participants from the UK Biobank, a large research database that tracks the health of UK adults between the ages of 40 and 70 as they age. At the centre of our analysis was a concept called the brain-age gap.
This concept reflects the idea that while we all grow older at the same pace, our brains can age faster or slower than the years alone would suggest. Advances in brain imaging and artificial intelligence now allow researchers to estimate how old a person’s brain looks based on patterns in magnetic resonance imaging (MRI) scans – including loss of brain tissue, deterioration of the fibres that connect different brain regions and damage to blood vessels.
In our study, brain age was estimated using more than 1,000 different imaging markers from brain MRI scans. We first trained a machine learning model on the scans of the healthiest participants, whose brain age should have closely matched their…
