# Metabolic coupling between Schwann cells and axons is functionally distinct from myelination and is disrupted in obesity, prediabetes, and diabetes

> **NIH NIH R01** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2024 · $605,984

## Abstract

ABSTRACT
Peripheral neuropathy (PN) is a common complication of type 2 diabetes (T2D), prediabetes, and obesity,
conditions that comprise aspects of the metabolic syndrome (MetS). Strict glycemic control does not treat PN
in MetS, and new clinical guidelines instead focus on improving metabolic health by modifying MetS
components through diet and exercise, though how lifestyle modifications improve PN is unknown. There is a
critical need to elucidate the mechanisms underlying PN pathophysiology in MetS to establish effective,
mechanism-based PN treatments. Metabolically active tissues like muscle and fat develop insulin resistance
(IR) in response to MetS; however, diet and/or exercise increase energy consumption and/or decrease IR,
reversing MetS. Like muscle and fat, nervous system cells develop IR under MetS conditions which is linked to
PN in multiple mouse models. We recently reported that dietary reversal (DR) in a high-fat diet (HFD) mouse
model of MetS improves PN and corrects PN-induced lipidome and transcriptome changes. Our new
preliminary data additionally confirms significant IR in sciatic nerves from this same animal model. Despite our
findings in mice and reports of beneficial effects of exercise in individuals with MetS and PN, the mechanisms
linking improved systemic metabolic health to improved nerve health remain poorly understood. Particularly,
the contribution of peripheral nervous system glia, Schwann cells (SCs), has not been investigated in
metabolically-acquired PN despite the non-cell autonomous nature of PN and a growing importance of SC-
axon metabolic crosstalk on nerve health. Our objective is to rigorously evaluate the effects of DR and high-
intensity interval training (HIIT) on nerve transcriptomics at a single cell level and on whole nerve
bioenergetics, metabolic flux, and function to define the role of neurometabolic coupling and SC-axon
metabolic crosstalk in PN. Our central hypothesis is that diet and exercise improve PN by normalizing MetS
and nerve insulin sensitivity, which restores critical SC-axon metabolic crosstalk and energy substrate transfer
from SCs to axons, normalizing peripheral nerve bioenergetics and function. Aim 1 will assess the effects of
DR and HIIT on global nerve bioenergetics and PN in HFD MetS mice by longitudinally assessing basic
metabolic parameters and PN phenotype, performing SC single cell RNA sequencing, and evaluating ex vivo
sciatic nerve bioenergetics, energy substrate fluxomics (glycolysis and fatty acid β-oxidation), and
metabolomics. Aim 2 will evaluate SC-axon metabolic crosstalk in in vitro models of MetS PN by characterizing
SC glycolysis and β-oxidation, neuron mitochondria dynamics, and global SC-axon bioenergetics. Aim 3 will
determine the impact of SC-restricted insulin signaling or energy transfer ablation on PN in HFD MetS mice by
using inducible SC-restricted insulin receptor/IGF-I receptor or monocarboxylate transporter 1 knockout mice.
This research will have ...

## Key facts

- **NIH application ID:** 10854948
- **Project number:** 5R01DK130913-03
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** Eva Lucille Feldman
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $605,984
- **Award type:** 5
- **Project period:** 2022-08-23 → 2026-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10854948

## Citation

> US National Institutes of Health, RePORTER application 10854948, Metabolic coupling between Schwann cells and axons is functionally distinct from myelination and is disrupted in obesity, prediabetes, and diabetes (5R01DK130913-03). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10854948. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*