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

Amanda L Lumsden; Anwar Mulugeta; Ville-Petteri Mäkinen; Elina Hyppönen

doi:10.1111/dom.14853

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

Diabetes Obes Metab. 2023 Jan;25(1):121-131. doi: 10.1111/dom.14853. Epub 2022 Sep 21.

Authors

Amanda L Lumsden^{1

2}, Anwar Mulugeta^{1

2

3}, Ville-Petteri Mäkinen^{2

4}, Elina Hyppönen^{1

2}

Affiliations

¹ Australian Centre for Precision Health, Unit of Clinical and Health Sciences, University of South Australia, Adelaide, Australia.
² South Australian Health and Medical Research Institute, Adelaide, Australia.
³ Department of Pharmacology and Clinical Pharmacy, College of Health Sciences, Addis Ababa, Ethiopia.
⁴ Computational Systems Biology Program, Precision Medicine Theme, South Australian Health and Medical Research Institute, Adelaide, Australia.

Abstract

Aims: To evaluate associations of metabolic profiles and biomarkers with brain atrophy, lesions, and iron deposition to understand the early risk factors associated with dementia.

Materials and methods: Using data from 26 239 UK Biobank participants free from dementia and stroke, we assessed the associations of metabolic subgroups, derived using an artificial neural network approach (self-organizing map), and 39 individual biomarkers with brain MRI measures: total brain volume (TBV), grey matter volume (GMV), white matter volume (WMV), hippocampal volume (HV), white matter hyperintensity (WMH) volume, and caudate 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 (β_standardized -0.20, 95% confidence interval [CI] -0.24 to -0.16), HV (β_standardized -0.09, 95% CI -0.13 to -0.04), WMH volume (β_standardized 0.22, 95% CI 0.18 to 0.26), and caudate iron deposition (β_standardized 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 were seen between basal metabolic rate (BMR) and caudate iron deposition (β_standardized 0.23, 95% CI 0.22 to 0.24 per 1 SD increase), GMV (β_standardized -0.15, 95% CI -0.16 to -0.14) and HV (β_standardized -0.11, 95% CI -0.12 to -0.10), and between BP and WMH volume (β_standardized 0.13, 95% CI 0.12 to 0.14 for diastolic BP).

Conclusions: Metabolic profiles were associated differentially with brain neuroimaging characteristics. Associations of BMR, BP and other individual biomarkers may provide insights into actionable mechanisms driving these brain associations.

Keywords: brain iron; brain volume; metabolic profiling; self-organizing map; white matter hyperintensities.

MeSH terms

Brain / diagnostic imaging
Dementia*
Humans
Iron
Metabolome*

Substances

Iron

Abstract

MeSH terms

Substances

Grants and funding