Abstract 610: Netrin-1 Promotes Macrophage Accumulation and Insulin Resistance in Obesity
Bibliographic record
Abstract
Obesity and its co-morbidities, type 2 diabetes and cardiovascular disease, continue to increase and are a major threat to global health. Studies in mice and humans have shown that expansion of adipose tissue mass is closely associated with the recruitment of cells of the myeloid and lymphoid lineage, which gives rise to a state of chronic inflammation that contributes to insulin resistance and type 2 diabetes. The factors that regulate the metabolic-dependent accrual of macrophages in adipose are not well understood. We show that the neuroimmune guidance cue netrin-1 is highly expressed in obese, but not lean adipose tissue of humans and mice, where it directs the retention of macrophages. In a mouse model of diet-induced obesity, we show that adipose tissue macrophages exhibit reduced migratory capacity ex vivo, which is reversed by blocking the effects of netrin-1. In vitro, expression of netrin-1 is induced in macrophages by the saturated fatty acid palmitate, and it acts by the receptor Unc5b to block macrophage migration to the chemokine CCL19, which directs the emigration of inflammatory macrophages from tissues. Using bone marrow transplantation, we show that hematopoietic deletion of Ntn1 facilitates adipose tissue macrophage emigration to the mesenteric lymph nodes, reduces inflammation, and improves insulin sensitivity and signaling in target tissues. Collectively, these findings identify netrin-1 as a macrophage retention signal that is induced in adipose tissue during obesity, which promotes chronic inflammation and insulin resistance.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".