From Obesity to Muscle Insulin Resistance: The Mediating Roles of Intramyocellular Lipids, Inflammation, and Oxidative Stress
Bibliographic record
Abstract
Obesity is highly correlated to muscle insulin resistance (IR), which significantly impacts metabolic health. One of the primary mechanisms connecting obesity to muscle IR is the accumulation of intramyocellular lipids (IMCL). Excessive lipid accumulation in muscle cells, that is, muscle lipotoxicity, leads to the formation of lipid metabolites, such as diacylglycerol (DAG) and ceramides, which disrupt insulin signalling pathways. These metabolites activate protein kinase C (PKC) and other kinases that inhibit insulin receptor substrate (IRS) proteins, subsequently impairing the insulin signalling cascade and reducing glucose uptake in skeletal muscle cells. This lipid-induced IR is a critical factor in the development of metabolic disorders associated with obesity. Furthermore, inflammation and oxidative stress may play significant roles in linking obesity with muscle IR. Obesity-induced inflammation is characterised by increased levels of pro-inflammatory cytokines, which activate signalling pathways, such as NF-κB and JNK. This transcription factor and stress protein further impair insulin signalling by promoting serine phosphorylation of IRS proteins. Concurrently, oxidative stress, resulting from an imbalance between reactive oxygen species (ROS) production and antioxidant defenses, exacerbates insulin resistance. Elevated ROS levels associated with damaging cellular components, including proteins, lipids, and DNA, may activate stress-sensitive signalling pathways, inhibiting insulin action. The current review analyses evidence on the interplay between IMCL accumulation, inflammation, and oxidative stress, establishing this interconnected triad as a vicious cycle: lipid metabolites activate inflammatory kinases, while inflammation and ROS further promote lipid deposition and mitochondrial inefficiency. This triad of mechanisms explains why muscle IR in obesity is both a cause and consequence of metabolic disease progression. Understanding these pathways is clinically urgent, as they represent actionable targets for therapies (e.g., peroxisome proliferator-activated receptor gamma [PPARγ] agonists to reduce ceramides, anti-inflammatory strategies to preserve insulin signalling). This synthesis of current evidence highlights how obesity-induced muscle IR propagates systemic metabolic risk, offering a framework for future translational research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".