Metabolism related to brain health

Summary: Researchers have found a link between metabolism and brain measures associated with dementia. Obesity associated with inflammation, kidney stress, or liver stress has the greatest impact on adverse brain health.

source: University of South Australia

Every three seconds, someone in the world is diagnosed with dementia. And although there is no known cure, brain changes can occur years before a diagnosis of dementia.

Now, a world-first study from the University of South Australia’s Australian Center for Precision Health has found a link between metabolism and brain measures associated with dementia, providing valuable insight into the disease.

Analyzing data from 26,239 people in the UK Biobank, the researchers found that those with obesity associated with liver stress or inflammation and kidney stress had the most adverse brain findings.

The study measured the associations of six different metabolic profiles and 39 cardiometabolic markers with MRI brain scan measures of brain volume, brain lesions and iron accumulation to identify early risk factors for dementia.

Individuals with metabolic profiles associated with obesity were more likely to have unfavorable MRI profiles, showing lower hippocampal and gray matter volumes, a greater burden of brain lesions, and higher iron accumulation.

UniSA researcher Dr Amanda Lumsden says the research adds a new level of understanding of brain health.

“Dementia is a debilitating disease that affects more than 55 million people worldwide,” says Dr Lumsden.

“Understanding metabolic factors and profiles associated with dementia-related brain changes may help identify early risk factors for dementia.”

Individuals with metabolic profiles associated with obesity were more likely to have unfavorable MRI profiles, showing lower hippocampal and gray matter volumes, a greater burden of brain lesions, and higher iron accumulation. Image is in the public domain

“In this study, we found that adverse neuroimaging patterns are more prevalent among people who have metabolic types associated with obesity.

“These people also had the highest basal metabolic rate (BMR) – how much energy your body needs when it’s resting to maintain its basic functions – but curiously, BMR appears to contribute to adverse brain markers above the effects of obesity. “

Senior researcher Professor Elina Hyppönen of UniSA says the discovery represents a new avenue for understanding brain health.

“This study shows that metabolic profiles are associated with aspects of brain health. We also found associations with many individual biomarkers that may provide clues to the processes leading to dementia,” says Prof. Hyppönen.

“The human body is complex, and more work is now needed to understand exactly why and how these associations occur.”

For this metabolic and neuroscience research news

Author: Press office
source: University of South Australia
Contact: Press Office – University of South Australia
Image: Image is in the public domain

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Original research: Free access.
“Metabolic Profile-Based Subgroups Can Identify Differences in Brain Volumes and Brain Iron Deposition” by Amanda L. Lumsden et al. Diabetes, obesity and metabolism


Summary

Metabolic profile-based subgroups can identify differences in brain volumes and brain iron deposition

Aims

To assess the associations of metabolic profiles and biomarkers with brain atrophy, lesions 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 obtained using an artificial neural network (self-organizing map) approach and 39 individual biomarkers with brain MRI measurements: total volume brain volume (TBV), gray matter volume (GMV), white matter volume (WMV), hippocampal volume (HV), white matter hyperintensity volume (WMH) and tail iron deposition.

Results

In metabolic subgroup analyses, participants characterized by high triglycerides and 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 associations with GMV (bstandardized −0.20, 95% confidence interval [CI] from −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 coccyx 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, striking associations have been observed between basal metabolic rate (BMR) and tailbone 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 BP and WMH volume (bstandardized 0.13, 95% CI 0.12 to 0.14 for diastolic BP).

Conclusions

Metabolic profiles are differentially associated with brain neuroimaging features. Associations of BMR, BP, and other individual biomarkers may provide insight into the mechanisms at work driving these brain associations.

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