# Lands cycle and skeletal muscle insulin action

> **NIH NIH R01** · UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH · 2022 · $489,341

## Abstract

Project Summary
Skeletal muscle insulin resistance is an early and fundamental defect in the development of type 2 diabetes.
Molecular mechanisms by which obesity promotes muscle insulin resistance remain incompletely understood,
but multiple lines of evidence suggest aberrant lipid metabolism as a likely contributor. Recently, our laboratory
published findings that accelerated lyso-phospholipid metabolism (Lands cycle) desensitizes skeletal muscle
insulin receptor to promote diabetes. In humans, obesity increased skeletal muscle lyso-phosphatidylcholine
(lyso-PC) acyltransferase-3 (LPCAT3) and decreased lyso-PC concomitant to a decrease in insulin sensitivity.
In mice, genetic or pharmacologic inhibition of LPCAT3 increased lyso-PC and enhanced skeletal muscle and
systemic insulin sensitivity. In this proposal, we will further exploit skeletal muscle Lands cycle to understand
its role in modulating insulin action. We hypothesize that: 1) Lands cycle modulates plasma membrane
microdomain clustering to amplify insulin signaling, 2) pharmacological inhibition of Lands cycle can ameliorate
hyperglycemia in the Zucker Diabetic Fatty rats, and 3) exercise training enhances skeletal muscle insulin
responsiveness by deceleration of Lands cycle.

## Key facts

- **NIH application ID:** 10516491
- **Project number:** 1R01DK127979-01A1
- **Recipient organization:** UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH
- **Principal Investigator:** Katsuhiko Funai
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $489,341
- **Award type:** 1
- **Project period:** 2022-08-01 → 2026-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10516491

## Citation

> US National Institutes of Health, RePORTER application 10516491, Lands cycle and skeletal muscle insulin action (1R01DK127979-01A1). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10516491. Licensed CC0.

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