C1q/TNF-related protein 6 (CTRP6) links obesity to adipose tissue inflammation and insulin resistance
Bibliographic record
Abstract
Obesity is associated with chronic low-grade inflammation, and metabolic regulators linking obesity to inflammation have therefore received much attention. Secreted C1q/TNF-related proteins (CTRPs) are one such group of regulators that regulate glucose and fat metabolism in peripheral tissues and modulate inflammation in adipose tissue. We have previously shown that expression of CTRP6 is up-regulated in leptin-deficient mice and, conversely, down-regulated by the anti-diabetic drug rosiglitazone. Here, we provide evidence for a novel role of CTRP6 in modulating both inflammation and insulin sensitivity. We found that in obese and diabetic humans and mouse models, CTRP6 expression was markedly up-regulated in adipose tissue and that stromal vascular cells, such as macrophages, are a major CTRP6 source. Overexpressing mouse or human CTRP6 impaired glucose disposal in peripheral tissues in response to glucose and insulin challenge in wild-type mice. Conversely, Ctrp6 gene deletion improved insulin action and increased metabolic rate and energy expenditure in diet-induced obese mice. Mechanistically, CTRP6 regulates local inflammation and glucose metabolism by targeting macrophages and adipocytes, respectively. In cultured macrophages, recombinant CTRP6 dose-dependently up-regulated the expression and production of TNF-α. Conversely, CTRP6 deficiency reduced circulating inflammatory cytokines and pro-inflammatory macrophages in adipose tissue. CTRP6-overexpressing mice or CTRP6-treated adipocytes had reduced insulin-stimulated Akt phosphorylation and glucose uptake. In contrast, loss of CTRP6 enhanced insulin-stimulated Akt activation in adipose tissue. Together, these results establish CTRP6 as a novel metabolic/immune regulator linking obesity to adipose tissue 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".