Source: University of South Australia
Every three seconds, someone in the world is diagnosed with dementia. And while there is no known cure, changes in the brain can occur years before a dementia diagnosis.
Now, a global study from the University of South Australia’s Australian Center for Precision Health has found a link between metabolism and brain measures related to dementia, providing valuable insights into the disease.
Analyzing data from 26,239 people in the UK Biobank, the researchers found that those with obesity linked to liver stress, or inflammation and kidney stress, had the most adverse brain findings.
The study measured associations of six diverse metabolic profiles and 39 cardiometabolic markers with MRI brain scan measures of brain volume, brain injury and iron accumulation, to identify early risk factors for dementia.
Individuals with obesity-related metabolic profiles were more likely to have adverse MRI profiles showing lower hippocampal and gray matter volumes, higher brain injury burden, and higher iron accumulation.
UniSA researcher Dr Amanda Lumsden says the research adds a new layer of understanding to brain health.
“Dementia is a debilitating disease that affects more than 55 million people worldwide,” says Dr. Lumsden.
“Understanding the metabolic factors and profiles associated with dementia-related brain changes may help identify early risk factors for dementia.
“In this research, we found that adverse neuroimaging patterns were more common among people who had obesity-related metabolic types.
“These people also had the highest basal metabolic rate (BMR)—how much energy your body needs when you’re at rest to support its basic functions—but, interestingly, BMR seemed to contribute to adverse brain markers beyond the effects of l ‘obesity’.
Senior researcher UniSA Professor Elina Hyppönen says the finding presents a new avenue for understanding brain health.
“This study indicates that metabolic profiles are associated with aspects of brain health. We also found associations with many individual biomarkers that may provide clues about the processes leading to dementia,” says Professor Hyppönen.
“The human body is complex, and more work is now needed to figure out exactly why and how these associations arise.”
About this research news in metabolism and neurology
Author: Press Office
Source: University of South Australia
Contact: Press Office – University of South Australia
Image: The image is in the public domain
Original Research: Open access
“Subgroups Based on Metabolic Profiling Can Identify Differences in Brain Volumes and Brain Iron Deposition” by Amanda L. Lumsden et al. Diabetes, obesity and metabolism
Subgroups based on metabolic profiling may identify differences in brain volumes and brain iron deposition
To evaluate associations of metabolic profiles and biomarkers with brain atrophy, injury and iron deposition to understand early risk factors associated with dementia.
Materials and methods
Using data from 26,239 UK biobank participants free of dementia and stroke, we assessed associations of metabolic subgroups, derived using an artificial neural network (self-organizing map) approach, and 39 individual biomarkers with brain MRI measurements: total brain volume (TBV). ), gray matter volume (GMV), white matter volume (WMV), hippocampal volume (HV), white matter hyperintensity volume (WMH) and caudate iron deposition.
In metabolic subgroup analyses, participants characterized by high triglycerides and high liver enzymes showed the most adverse brain outcomes compared to the healthy reference subgroup with high-density lipoprotein cholesterol and low body mass index (BMI), including partnerships with GMV.bstandardized −0.20, 95% confidence interval. [CI] −0.24 to −0.16), HV (bstandardized -0.09, 95% CI: -0.13 to -0.04), WMH volume (bstandardized 0.22, 95% CI 0.18 to 0.26) and caudate iron deposition (bstandardized 0.30, 95% CI 0.25 to 0.34), with similar adverse associations for the subgroup with high BMI, C-reactive protein and cystatin C, and the subgroup with high blood pressure (BP) and apolipoprotein B. Among the biomarkers, surprising associations were found. observed between basal metabolic rate (BMR) and caudate iron deposition (bstandardized 0.23, 95% CI 0.22 to 0.24 per 1 SD increase), GMV (bstandardized -0.15, 95% CI -0.16 to -0.14) and HV (bstandardized -0.11, 95% CI -0.12 to -0.10) and between PA volume and WMH (bstandardized 0.13, 95% CI 0.12 to 0.14 for diastolic BP).
Metabolic profiles were differentially associated with brain neuroimaging features. Associations of BMR, BP, and other individual biomarkers may provide insight into the actionable mechanisms driving these brain associations.