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Record W2119264914 · doi:10.4103/1673-5374.165222

Adipose-brain crosstalk: do adipokines have a role in neuroprotection?

2015· article· en· W2119264914 on OpenAlexaff
Jonathan P. Little, Adeel Safdar

Bibliographic record

VenueNeural Regeneration Research · 2015
Typearticle
Languageen
FieldMedicine
TopicAdipokines, Inflammation, and Metabolic Diseases
Canadian institutionsMcMaster UniversityUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsAdipokineAdipose tissueInflammationNeuroprotectionInsulin resistanceNeurodegenerationEndocrinologyCrosstalkMedicineLeptinInternal medicineNeuroscienceBiologyDiabetes mellitusObesityDisease

Abstract

fetched live from OpenAlex

Accumulating evidence from epidemiological and experimental studies indicate that obesity, and its related metabolic consequences of insulin resistance and type 2 diabetes, are associated with accelerated cognitive decline (Yates et al., 2012). The etiology of neurodegeneration in obesity is undoubtedly complex, with vascular, metabolic, inflammatory, and structural changes all likely to play a role (Yates et al., 2012). The discovery of leptin in 1994 and the subsequent advancement in our understanding that adipose tissue is an endocrine organ that can communicate with the brain to regulate appetite (Zhang et al., 1994) brings about the intriguing possibility that adipose-brain crosstalk can regulate aspects of neuronal physiology and pathology (Aguilar-Valles et al., 2015). Indeed neurons have been shown to express receptors for various adipokines, indicating that factors released from adipose tissue have the potential to communicate directly with the brain. Research in this area is relatively new, and while epidemiological data points towards the negative consequences of adipose-brain crosstalk (Whitmer et al., 2005), some intriguing new studies highlight that the secretory profile of adipose tissue might be involved in reduction in neurodegeneration via maintenance of neuronal viability (Tezapsidis et al., 2009; Wan et al., 2015). Obesity is accompanied by inflammation in adipose tissue. Much of this inflammation is driven by infiltration of macrophages and other immune cells, although adipocytes themselves can also secrete pro-inflammatory cytokines (termed “adipokines”). By virtue of its elevated mass, pro-inflammatory secretions from macrophage-infiltrated adipose tissue are believed to contribute to systemic low-grade inflammation, insulin resistance, and vascular dysfunction in obesity (Ouchi et al., 2011). Increased expression of pro-inflammatory cytokines and activation of inflammatory responses are prominent in brain of Alzheimer's disease (AD) and aging-related dementia patients. Rodent models of obesity demonstrate elevated markers of brain inflammation and oxidative stress (Pistell et al., 2010) and a recent study has supported that humans with obesity have elevated markers of brain inflammation in hypothalamus assessed in vivo (Thaler et al., 2012). On the other hand, both in vitro and in vivo studies have reported that adipokines like leptin can mitigate systemic and central nervous system molecular pathologies associated with AD (Greco et al., 2009; Chakrabarti et al. 2015). Taken together, these findings provide speculative support that secretions from adipose tissue may impact brain inflammation and subsequently alter the risk of neurodegeneration in obesity. This also warrants immediate attention to profile the secretome based on the source of adipose tissue (lean vs. obese mouse models or human subjects), which may determine positive or negative adipose-brain cross talk. Direct experimental evidence for adipose-brain crosstalk in vivo presents many technical challenges, particularly in humans. Recently, Wan et al. (2015) performed a series of experiments using an in vitro model of adipose-brain crosstalk in attempts to shed light on possible communication between human adipose tissue and neurons. Adipose tissue organ cultures, which retain in vivo secretory profile and intercellular communication between adipocytes and immune cells, were prepared from human donors. Adipose tissue organ culture (ATOC) conditioned media, containing the full ensemble of secretory products, was applied to human SH-SY5Y neuronal cells that were left untreated or treated with hydrogen peroxide (H2O2) to induce oxidative stress-induced cell death. ATOC conditioned media obtained from lean subjects had no effect on SH-SY5Y cell viability in the untreated condition but when neuronal cells were exposed to H2O2 they were almost completely protected from oxidative stress-induced cell death. However, ATOC conditioned media from obese donors lacked this protective effect. Since oxidative stress has been shown to drive neurodegenerative processes in AD and related dementias (Perry et al., 2002), the protective effect of lean ATOC conditioned media on SH-SY5Y neuronal cells suggests that some factor(s) secreted from lean adipose tissue may possess neuroprotective properties. Unfortunately the authors were unable to isolate what factor(s) were involved but they did show that when ATOC conditioned media was heated to denature proteins (10 minutes at 95°C) the neuroprotective effects were lost, implicating the peptide nature of the putative neuronal pro-survival adipokine(s). These preliminary findings suggest that adipokines can regulate adipose-brain crosstalk and can play a role in neuroprotection or neurodegeneration depending on the adiposity status of the individual. Particularly, the results indicate that lean adipose tissue may secrete certain adipokines that are protective towards neurons whereas in obesity, adipose tissue may lack this protective potential. It appears that proteins or peptides are involved but it remains to be determined which adipokine(s) may possesses these neuroprotective properties. It must be noted that these findings were obtained in an ex vivo-in vitro model system of adipose-brain crosstalk, so the results may not be entirely applicable to the much more complex system in vivo, where adipose secretions would interact with multiple different cell types and have to cross the blood-brain barrier prior to eliciting any effect on neurons. It seems clear that adipokines can cross the blood brain barrier, as shown by the classic findings involving leptin and appetite regulation (Zhang et al., 1994). However, the concentrations of adipokines reached in brain areas relevant to AD and dementia (e.g., hippocampus) are currently unclear. Future research confirming the potential benefits of lean adipose tissue secretions on neuronal cell viability is warranted. In this regard, the adipose tissue transplantation model developed by Kahn and colleagues (Tran et al., 2008) would seem to be an ideal model to test this in vivo. Much like leptin treatment, lean adipose tissue transplantation into obese mice has been shown to reverse metabolic defects and improve glucose tolerance (Tran et al., 2008); could transplantation of lean adipose tissue prevent brain inflammation and accelerated cognitive decline in obese animals? In addition, given the key role of glial cells in propagating brain inflammation in neurodegenerative diseases, the impact of adipokines on glial cell function warrants further investigation. An alternative line of research stemming from these findings could also include identifying the putative neuroprotective factor(s) secreted from lean adipose tissue for drug discovery purposes. In summary, the recent findings of Wan et al. (2015) provide intriguing preliminary evidence that lean adipose tissue may secrete factors that possess neuroprotective properties. Confirmation of these findings in vivo, and further exploration into their identity, may enhance our understanding of how adipokines mediate adipose-brain cross-talk and provide new therapeutic targets to help protect neurons from damage.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.449
Threshold uncertainty score0.651

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.081
GPT teacher head0.378
Teacher spread0.297 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations6
Published2015
Admission routes1
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