Back

Ketone bodies mitigate against systemic inflammation-induced changes in brain energy metabolism and delirium-like deficits in aged mice

Hollier, P.-L.; Chui, K. M. K.; Cuitavi, J.; Denver, P.; Delaney, H. J.; Newman, J. C.; Cunningham, C.

2026-01-24 neuroscience
10.64898/2026.01.22.701114 bioRxiv
Show abstract

Acute systemic inflammation affects brain function, with detrimental consequences in aged individuals. These include delirium, an acute neuropsychiatric syndrome characterized by fluctuating disturbances in attention, perception and cognition. Delirium is associated with disrupted brain energy metabolism but our understanding of this during acute systemic inflammation is limited. Here we hypothesized that LPS-induced systemic inflammation would disrupt brain energy metabolism in aged C57BL6J mice and that the consequent functional impairments would be mitigated by ketone body utilization. We investigated ketone body effects in sickness behaviour, inflammation, energy metabolism and cognitive function. Real-time changes in utilisation of energy sources were quantified by indirect calorimetry and administration of radioisotope-labelled glucose and betahydroxybutyrate. Mass-spectrometry metabolomics was used to index severity of behavioural distrurbances to changes in hippocampal energy metabolism. LPS precipitated hypoglycemia and induced a whole-body switch from carbohydrate to lipid utilisation. Despite this, hippocampal insulin resistance and preserved brain glucose was observed while alternative carbohydrates, mannose and fructose, became depleted. Ketone ester treatment reversed insulin resistance, mitigated sickness behaviour and prevented delirium-like cognitive dysfunction without altering pro-inflammatory responses. Our results show that promoting ketone body usage mitigates systemic inflammation-induced brain energy disruption and prevents delirium-like cognitive deficits in aged mice.

Matching journals

The top 9 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.