Evidence map›Paper›PMID 42903342›Full record

ReviewFrontiers in endocrinology2026

Molecular signaling networks that connect tissue dysfunction to systemic insulin resistance.

Rashid Mir, Naseh A Algehainy, Mohammed M Jalal, Malik A Altayar, Reema M Almotairi, Faris J Tayeb, Ruqaiah I Bedaiwi, Faisal H Altemani, Kholoud S Almasoudi, Ibrahim Altedlawi Albalawi and 1 more

Abstract readReview
In one paragraph

Review in Frontiers in endocrinology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Rashid MirPrince Fahad Bin Sultan Chair for Biomedical Research, University of Tabuk, Tabuk, Saudi Arabia.
Naseh A AlgehainyDepartment of Medical Laboratory Technology, Faculty of Applied Medical Sciences, University of Tabuk, Tabuk, Saudi Arabia.
Mohammed M JalalDepartment of Medical Laboratory Technology, Faculty of Applied Medical Sciences, University of Tabuk, Tabuk, Saudi Arabia.
Malik A AltayarDepartment of Medical Laboratory Technology, Faculty of Applied Medical Sciences, University of Tabuk, Tabuk, Saudi Arabia.
Reema M AlmotairiPrince Fahad Bin Sultan Chair for Biomedical Research, University of Tabuk, Tabuk, Saudi Arabia.
Faris J TayebDepartment of Medical Laboratory Technology, Faculty of Applied Medical Sciences, University of Tabuk, Tabuk, Saudi Arabia.
Ruqaiah I BedaiwiDepartment of Medical Laboratory Technology, Faculty of Applied Medical Sciences, University of Tabuk, Tabuk, Saudi Arabia.
Faisal H AltemaniDepartment of Medical Laboratory Technology, Faculty of Applied Medical Sciences, University of Tabuk, Tabuk, Saudi Arabia.
Kholoud S AlmasoudiDepartment of Medical Laboratory Technology, Faculty of Applied Medical Sciences, University of Tabuk, Tabuk, Saudi Arabia.
Ibrahim Altedlawi AlbalawiDepartment of Surgical Oncology, Faculty of Medicine, University of Tabuk, Tabuk, Saudi Arabia.
Imadeldin ElfakiDepartment of Biochemistry, Faculty of Science, University of Tabuk, Tabuk, Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Insulin resistance is conventionally described as a single condition, but it is better understood as the point at which several tissue-specific signaling failures, each following its own molecular logic and its own timeline, converge into one measurable phenotype. This review treats insulin resistance as a network rather than a collection of independent organ pathologies. Because the components of this network rest on very different kinds of evidence, each node and edge is graded explicitly by study design and causal status, and the time-staged sequence is presented as a falsifiable proposal rather than an established progression. Put simply, insulin resistance is treated here not as one defect but as the shared endpoint of several tissue-specific failures that develop at different times and communicate with one another. Liver, skeletal muscle, adipose tissue, pancreatic islets, the gut, and the brain are each examined as nodes with characteristic signaling lesions built from a shared architecture of diacylglycerol- and ceramide-activated kinases, inhibitory serine phosphorylation of insulin receptor substrate proteins, and endoplasmic reticulum stress, while inter-organ messengers, incretins, adipokines, bile acids, and autonomic circuits, are treated as the edges that couple these nodes together. The pancreas is included not as a site of resistance but as the network's compensatory arm, and the brain is treated as an integrated hypothalamic-vagal node rather than a separate neurocognitive topic, closing a loop in which adipose-derived signals impair central insulin and leptin action, weakened central signaling relaxes hepatic glucose control, and reduced satiety sustains the intake that drove adipose dysfunction in the first place. A dedicated section addresses the post-translational and organelle-level machinery, shared serine kinases, ubiquitin-mediated IRS degradation, O-GlcNAcylation, and mitochondria-endoplasmic reticulum contact sites, that allows the same small set of molecular tools to produce different tissue-specific outputs. These mechanisms are then placed on an explicit time axis moving from silent compensation, in which adipose and hypothalamic changes precede any measurable defect elsewhere, through early decompensation in liver and muscle, to established decompensation marked by β-cell failure and self-sustaining feed-forward loops, a staging distinction that is largely absent from mechanistic studies conducted at a single, typically late, disease stage. This framing helps explain a pattern in current therapeutics: interventions that act on several network nodes at once, most notably incretin-based therapies, have outperformed mechanistically well-motivated single-node strategies, including direct inhibition of shared convergence kinases. The review closes by identifying where the network remains least understood, principally in longitudinal, tissue-resolved human data capable of testing the proposed staging sequence directly.

Indexed as

Insulin ResistanceSignal TransductionAdipose TissueAnimalsHumansLiverMuscle, Skeletaldiabetesinsulin receptor (INSR)metabolic disordersobesityprotein kinase Cβ-cell dysfunction

Identifiers

PMID42903342
PMCPMC13681193

What OpenQuestion holds

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Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.