The Crosstalk Between Non-Coding RNAs and Lipid Metabolism in Chronic Disease Progression
Bibliographic record
Abstract
In the last twenty years, an increasing volume of research has characterized lipids as dynamic signaling molecules that play essential roles in various physiological and pathological processes, especially concerning chronic diseases such as cardiovascular disorders, diabetes, liver disease, neurodegeneration, cancer, obesity, diabetic and chronic kidney diseases and atherosclerosis. Dysregulation of lipid synthesis and storage, lipolysis, fatty acid oxidation, lipid signaling pathways, and organelle-specific lipid modifications, including mitochondrial phospholipid remodeling and endoplasmic reticulum stress induced by saturated fatty acids, are recognized as contributors to the initiation and progression of this pathogenesis. Concurrently with the increasing comprehension of lipid metabolism, the last decade has seen progress in the understanding of genome control, especially with non-coding RNAs (ncRNAs). MicroRNAs, long non-coding RNAs, and circular RNAs, as ncRNAs, are essential modulators of gene expression at the epigenetic, transcriptional, and post-transcriptional levels that affect a number of lipid metabolism-related processes, such as fatty acid synthesis and oxidation, cholesterol homeostasis, and lipid droplet dynamics. Therapeutically, ncRNAs hold considerable promise owing to their tissue specificity and modularity, with antisense oligonucleotides and CRISPR-based editing currently under preclinical evaluation. In this context, we review recent studies exploring the interplay between ncRNAs and the regulatory networks governing lipid metabolism, and how disruptions in these networks contribute to chronic disease. This emerging paradigm underscores the role of ncRNA–lipid metabolism interactions as central nodes in metabolic and inflammatory pathways, highlighting the need for a holistic approach to therapeutic targeting.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".