Anorexia and fat aversion induced by vertical sleeve gastrectomy is attenuated in neurotensin receptor 1–Deficient mice

Publikation: Bidrag til tidsskriftTidsskriftartikelfagfællebedømt

Neurotensin (NT) is an anorexic gut hormone and neuropeptide that increases in circulation following bariatric surgery in humans and rodents. We sought to determine the contribution of NT to the metabolic efficacy of vertical sleeve gastrectomy (VSG). To explore a potential mechanistic role of NT in VSG, we performed sham or VSG surgeries in diet-induced obese NT receptor 1 (NTSR1) wild-type and knockout (ko) mice and compared their weight and fat mass loss, glucose tolerance, food intake, and food preference after surgery. NTSR1 ko mice had reduced initial anorexia and body fat loss. Additionally, NTSR1 ko mice had an attenuated reduction in fat preference following VSG. Results from this study suggest that NTSR1 signaling contributes to the potent effect of VSG to initially reduce food intake following VSG surgeries and potentially also on the effects on macronutrient selection induced by VSG. However, maintenance of long-term weight loss after VSG requires signals in addition to NT.

OriginalsprogEngelsk
Artikelnummerbqab130
TidsskriftEndocrinology
Vol/bind162
Udgave nummer9
Antal sider12
ISSN0013-7227
DOI
StatusUdgivet - 2021

Bibliografisk note

Funding Information:
This work was supported by the Project grants in Endocrinology and Metabolism?Nordic Region 2019 (#0057417) and Novo Nordisk Foundation NNF15OC0013655. In addition, it was funded from The Novo Nordisk Foundation Center for Basic Metabolic Research (www.metabol.ku.dk), which is supported by an unconditional grant (NNF10CC1016515) from the Novo Nordisk Foundation to University of Copenhagen. C.R. was financially supported by a postdoctoral grant (R231-2016-3031) from the Lundbeck Foundation. This work was also supported by NIH grants P30 DK089503, P01 DK117821, R01 DK119188 to R.J.S. The computations for microbiota analyses were performed on resources provided by SNIC through Uppsala Multidisciplinary Center for Advanced Computational Science (UPPMAX) under Project SNIC 2018-3-350.

Publisher Copyright:
© The Author(s) 2021.

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