The neuronal insulin receptor in its environment
Abstract
Insulin is known mainly for its effects in peripheral tissues, such as the liver, skeletal muscles and adipose tissue, where the activation of the insulin receptor (IR) has both short‐term and long‐term effects. Insulin and the IR are also present in the brain, and since there is evidence that neuronal insulin signaling regulates synaptic plasticity and that it is impaired in disease, this pathway might be the key to protection or reversal of symptoms, especially in Alzheimer's disease. However, there are controversies about the importance of the neuronal IR, partly because biophysical data on its activation and signaling are much less complete than for the peripheral IR. This review briefly summarizes the neuronal IR signaling in health and disease, and then focuses on known differences between the neuronal and peripheral IR with regard to alternative splicing and glycosylation, and lack of data with respect to phosphorylation and membrane subdomain localization. Particularities in the neuronal IR itself and its environment may have consequences for downstream signaling and impact synaptic plasticity. Furthermore, establishing the relative importance of insulin signaling through IR or through hybrids with its homolog, the insulin‐like growth factor 1 receptor, is crucial for evaluating the consequences of brain IR activation. An improved biophysical understanding of the neuronal IR may help predict the consequences of insulin‐targeted interventions. image
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Insulin in the brain contributes to diverse neuronal signalling mechanisms, including catecholamine release and uptake, ion channel trafficking and regulation of receptors for neurotransmitters (γ-GABA, NMDA and AMPA).
“Insulin in the brain contributes to diverse neuronal signalling mechanisms, including catecholamine release and uptake, ion channel trafficking and the regulation of receptors for neurotransmitters (ie, γ-GABA, NMDA and AMPA).117”
From Intranasal insulin (2021) · cites this paper as [117]
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Brain insulin resistance in type 2 diabetes and Alzheimer disease: concepts and conundrums
Arnold, S. E., Arvanitakis, Z., Macauley-Rambach, S. L., Koenig, A. M., Wang, H.-Y., Ahima, R. S., Craft, S., Gandy, S., Buettner, C., Stoeckel, L. E., et al. (2018) · Nature Reviews Neurology · doi:10.1038/nrneurol.2017.185 · Cited by 1,597 · see citing works
Brain insulin signalling in metabolic homeostasis and disease
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- Authors
- Gralle, M.
- Affiliations
- Universidade Federal do Rio de Janeiro (BR)
- Corresponding author
- Gralle, Matthias
- ORCID
- Gralle, Matthias
- Venue
- Journal of Neurochemistry
- Publisher
- Wiley
- Published
- 2016-12-27
- Volume
- 140
- Issue
- 3
- Pages
- 359-367
- ISSN
- 0022-3042 (ISSN-L)
- 1471-4159
- OpenAlex
- W2556540630
- Cited by
- 64
- References
- 115
- Citations by year
- 2026: 4 · 2025: 6 · 2024: 8 · 2023: 6 · 2022: 6 · 2021: 10 · 2020: 3 · 2019: 12 · 2018: 7 · 2017: 2
- FWCI
- 0.63
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Neurochemistry (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolism, Diabetes, and Cancer
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
- Keywords
- insulin signaling, synaptic plasticity, Alzheimer's disease, alternative splicing, receptor glycosylation, receptor phosphorylation, lipid rafts, IGF-1 receptor, brain insulin
- MeSH terms
- Alzheimer Disease, Animals, Brain, Humans, Insulin, Neuronal Plasticity, Neurons, Receptor, Insulin, Antigens, CD
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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