To characterize the spatial intersection of leptin signaling and neuroprotection within human hypothalamic nuclei as a framework for neuro-metabolic vulnerability.

While metabolic risk factors are known to modify autoimmune disease progression, the specific human neuro-anatomical hubs underlying this interaction remain understudied. Systemic leptin, a pro-inflammatory Type I cytokine, regulates the neuro-immune axis via hypothalamic signaling. We characterized the spatial distribution of the leptin receptor (LEPR) and the neuroprotective factor brain-derived neurotrophic factor (BDNF) within the human arcuate (ARC) and paraventricular (PVN) nuclei to identify potential sites of metabolic vulnerability.

We performed a high-resolution in silico analysis using the Allen Human Brain Atlas to map the innate transcriptomic architecture of the human diencephalon. Expression of LEPR and BDNF was quantified across N=6 clinically diverse donors. We utilized Z-score enrichment and log2 intensity data to characterize sub-nuclear signaling density within the ARC and PVN.

Mapping revealed a significant spatial divergence in neuro-metabolic signaling markers. The PVN and ARC demonstrated robust and consistent enrichment of LEPR mRNA, characterized by intense positive Z-scores across multiple probes and donors. In contrast, expression of BDNF within the same anatomical coordinates remained at or below baseline levels (Z-scores near zero; log2 intensity ~6.1–6.9), identifying a spatial mismatch where critical metabolic sensing hubs lack a corresponding enrichment of neurotrophic support.

Our findings provide a high-resolution human map of hypothalamic vulnerability. The high density of leptin receptors in regions with only baseline BDNF suggests that the ARC and PVN may be uniquely susceptible to systemic pro-inflammatory metabolic signals. This molecular architecture offers a potential mechanism for why patients with autoimmune conditions experience heightened hypothalamic dysfunction in the presence of metabolic risk factors. We are currently developing a new preclinical model and framework for bench-to-bedside clinical risk assessment.