It is well established that the human brain undergoes natural changes with age, typically shrinking in both size and volume after the age of 30 to 40. However, in certain cases, it may age more rapidly than anticipated, potentially increasing the risk of early memory loss, cognitive decline, and various brain disorders.
Accelerated brain aging has been linked to a range of neurological and psychiatric conditions, as well as certain neurodegenerative diseases. Yet, the specific factors that influence the pace of brain aging have not been clearly or comprehensively understood until now.
Recently, researchers from Jilin University and China Medical University analyzed existing data from neuroimaging, genomics, and biology to gain deeper insights into how metabolic processes—the chemical reactions that convert food into energy—contribute to brain aging. Their findings, published in the journal Molecular Psychiatry, indicate that elevated blood glucose levels are associated with faster brain aging.
To explore the biological underpinnings of brain aging, the scientists examined data from the UK Biobank, a large-scale biomedical database containing health, genetic, and imaging information from thousands of individuals residing in the United Kingdom. By analyzing brain scans from these participants, they derived measurable brain characteristics, including the size of specific brain regions, tissue properties, and structural alterations.
Subsequently, they trained machine learning algorithms to predict individuals’ ages based on the identified brain features. They discovered that a specific statistical technique known as the Least Absolute Shrinkage and Selection Operator (LASSO) regression model was the most effective at estimating brain age, with an average margin of error of 3.26 years.
“We integrated multimodal neuroimaging data (MRI), plasma metabolomics, and genomic data from the UK Biobank to identify metabolic markers of brain aging and assess their causal relationships,” wrote Zhirong Li, Yating Miao, and their colleagues in their paper. “Using 1,079 imaging-derived phenotypes (IDPs) from 4,333 healthy participants, we trained and validated machine learning models to predict brain age, with the LASSO regression model performing best. Subsequently, the brain age gap (BAG) was evaluated in 37,458 participants.”
Utilizing the most effective LASSO model, the researchers calculated a value known as BAG for thousands of individuals in the UK Biobank dataset. Essentially, this value indicates whether a person’s predicted brain age is higher or lower than their chronological age, and by how many years.
Li, Miao, and their team then analyzed metabolomic data derived from blood samples of the same individuals. This allowed them to identify nine blood molecules that appeared to be significantly associated with BAG values.
Notably, glucose levels showed the strongest link to BAG values. Specifically, higher blood glucose levels were correlated with brains that displayed more pronounced signs of aging in scans, making them appear older than their actual age.
“Association analysis in 21,780 individuals identified nine plasma metabolites significantly associated with BAG after Bonferroni correction, with glucose showing the strongest effect (β = 0.32, P = 9.90 × 10⁻¹²),” wrote Li, Miao, and their colleagues. “Genome-wide association studies (GWAS) identified 392 BAG-associated single nucleotide polymorphisms (SNPs) (P < 5 × 10⁻⁸), and two-sample Mendelian randomization (MR) provided evidence supporting a potential causal role for glucose in accelerating brain aging.”
This study offers evidence that blood glucose may contribute to processes linked with accelerated brain aging. Interestingly, the researchers also found that higher blood glucose levels were associated with an increased risk of seven different diseases that affect brain function.
“In clinical practice, elevated plasma glucose levels were positively correlated with seven brain disorders, including all-cause dementia, Alzheimer’s disease, vascular dementia, Parkinson’s disease, stroke, depression, and anxiety, and negatively correlated with cognitive function, motor activity, and mental health indicators,” the authors wrote. “Higher glucose concentrations were also associated with reduced regional brain volumes in 80 cortical, subcortical, and cerebellar areas. These findings point to glucose metabolism as a modifiable mechanism of brain aging, with implications for early intervention strategies aimed at preserving brain health throughout life.”
Future research could build on the team’s results by delving deeper into the relationship between high glucose levels and brain aging, perhaps focusing on specific neurodegenerative or neuropsychiatric conditions. Ultimately, the recent work by Li, Miao, and their colleagues may help inform strategies for monitoring and preserving brain health.
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