# Microglial TREM2 and cognitive impairment: insights from Alzheimer’s disease with implications for spinal cord injury and AI-assisted therapeutics

## TL;DR

This paper explores how TREM2 in microglia may link inflammation in spinal cord injury to cognitive decline, suggesting new AI-driven treatment approaches.

## Contribution

The paper proposes TREM2 as a novel treatment target linking SCI-induced neuroinflammation to cognitive deficits, leveraging AI for precision medicine.

## Key findings

- TREM2 is critical in regulating neuroinflammation and cognitive outcomes in Alzheimer’s disease.
- TREM2’s role in spinal cord injury-related cognitive impairment remains unexplored but is hypothesized to be significant.
- AI can integrate diverse data types to identify TREM2-related disorders and guide personalized therapies.

## Abstract

Cognitive impairment is a frequent but underrecognized complication of neurodegenerative and traumatic central nervous system disorders. Although research on Alzheimer’s disease (AD) revealed that microglial triggering receptor expressed on myeloid cells 2 (TREM2) plays a critical role in inhibiting neuroinflammation and improving cognition, its contribution to cognitive impairment following spinal cord injury (SCI) is unclear. Evidence from AD shows that TREM2 drives microglial activation, promotes pathological protein clearance, and disease-associated microglia (DAM) formation. SCI patients also experience declines in attention, memory, and other functions, yet the specific mechanism of these processes remains unclear. In SCI, microglia and TREM2 are involved in inflammation and repair, but their relationship with higher cognitive functions has not been systematically examined. We infer that TREM2 might connect injury-induced neuroinflammation in the SCI with cognitive deficits, providing a new treatment target. Artificial intelligence (AI) offers an opportunity to accelerate this endeavor by incorporating single-cell transcriptomics, neuroimaging, and clinical data for the identification of TREM2-related disorders, prediction of cognitive trajectories, and applications to precision medicine. Novel approaches or modalities of AI-driven drug discovery and personalized rehabilitation (e.g., VR, brain–computer interface) can more precisely steer these interventions. The interface between lessons learned from AD and SCI for generating new hypotheses and opportunities for translation.