Absence of Tissue Inhibitor of Metalloproteinase-4 (TIMP4) ameliorates high fat diet-induced obesity in mice due to defective lipid absorption
Bibliographic record
Abstract
Abstract Tissue inhibitor of metalloproteases (TIMPs) are inhibitors of matrix metalloproteinases (MMPs) that regulate tissue extracellular matrix (ECM) turnover. TIMP4 is highly expressed in adipose tissue, its levels are further elevated following high-fat diet, but its role in obesity is unknown. Eight-week old wild-type (WT) and Timp4-knockout (Timp4−/−) mice received chow or high fat diet (HFD) for twelve weeks. Timp4−/− mice exhibited a higher food intake but lower body fat gain. Adipose tissue of Timp4 −/– -HFD mice showed reduced hypertrophy and fibrosis compared to WT-HFD mice. Timp4 −/– -HFD mice were also protected from HFD-induced liver and skeletal muscle triglyceride accumulation and dyslipidemia. Timp4−/−-HFD mice exhibited reduced basic metabolic rate and energy expenditure, but increased respiratory exchange ratio. Increased free fatty acid excretion was detected in Timp4−/−-HFD compared to WT-HFD mice. CD36 protein, the major fatty acid transporter in the small intestine, increased with HFD in WT but not in Timp4−/− mice, despite a similar rise in Cd36 mRNA in both genotypes. Consistently, HFD increased enterocyte lipid content only in WT but not in Timp4−/− mice. Our study reveals that absence of TIMP4 can impair lipid absorption and the high fat diet-induced obesity in mice possibly by regulating the proteolytic processing of CD36 protein in the intestinal enterocytes.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".