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

• Published in: Diabetes, obesity & metabolism Vol. 25; no. 1; pp. 121 - 131
• Main Authors: Lumsden, Amanda L., Mulugeta, Anwar, Mäkinen, Ville‐Petteri, Hyppönen, Elina
• Format: Journal Article
• Language: English
• Published: Oxford, UK Blackwell Publishing Ltd 01.01.2023; Wiley Subscription Services, Inc
• Subjects: Apolipoprotein B; Atrophy; Biomarkers; Blood pressure; Body mass index; Brain - diagnostic imaging; brain iron; brain volume; Cholesterol; Cystatin C; Dementia; Dementia disorders; Hippocampus; Humans; Hypertension; Iron; metabolic profiling; Metabolic rate; Metabolism; Metabolome; Neural networks; Neuroimaging; Original; Risk factors; self‐organizing map; Substantia alba; Substantia grisea; Triglycerides; white matter hyperintensities; self-organizing map; brain iron; metabolic profiling; brain volume; white matter hyperintensities
• ISSN: 1462-8902; 1463-1326; 1463-1326
• DOI: 10.1111/dom.14853

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 (βstandardized −0.20, 95% confidence interval [CI] −0.24 to −0.16), HV (βstandardized −0.09, 95% CI −0.13 to −0.04), WMH volume (βstandardized 0.22, 95% CI 0.18 to 0.26), and caudate iron deposition (βstandardized 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 (βstandardized 0.23, 95% CI 0.22 to 0.24 per 1 SD increase), GMV (βstandardized −0.15, 95% CI −0.16 to −0.14) and HV (βstandardized −0.11, 95% CI −0.12 to −0.10), and between BP and WMH volume (βstandardized 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.