# A Gut-Systemic Perspective for Metabolic Disease

> **NIH NIH R13** · KEYSTONE SYMPOSIA · 2020 · $12,000

## Abstract

ABSTRACT
Support is requested for a Keystone Symposia conference entitled A Gut-Systemic Perspective for Metabolic
Disease, organized by Drs. Tony K.T. Lam, Nancy A. Thornberry and Fredrik Bäckhed. The conference will be
held in Santa Fe, New Mexico from March 22-26, 2019
Changes of the microbiome and nutrient sensing within the gut alter whole-body glucose and energy
homeostasis. The underlying mechanism and the therapeutic potential of such metabolic regulation in
diabetes, obesity, and related disorders remain largely unexplored. This Keystone Symposia conference aims
to progressively highlight a spectrum of basic and translational investigations and discoveries that first focus on
microbial-host interaction and nutrient sensing mechanisms. Other sessions focus on the interaction between
the gut microbiome and nutrient sensing pathways as well as how these pathways could be targeted by small
molecules and/or immunological responses within the gut to impact systemic organs and whole-body metabolic
homeostasis. In summary, we anticipate this conference will foster interactions between the basic,
translational, clinical and pharmaceutical researchers and promote collaborative work aimed at realizing the
therapeutic potential of targeting the gut microbiota-nutrient sensing axis for the treatment of metabolic
disorders.

## Key facts

- **NIH application ID:** 9993764
- **Project number:** 1R13DK125010-01
- **Recipient organization:** KEYSTONE SYMPOSIA
- **Principal Investigator:** Thale Cross Jarvis
- **Activity code:** R13 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $12,000
- **Award type:** 1
- **Project period:** 2020-02-19 → 2021-02-18

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9993764

## Citation

> US National Institutes of Health, RePORTER application 9993764, A Gut-Systemic Perspective for Metabolic Disease (1R13DK125010-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9993764. Licensed CC0.

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