Cathepsins in Human Obesity: Changes in Energy Balance Predominantly Affect Cathepsin S in Adipose Tissue and in Circulation
Bibliographic record
Abstract
CONTEXT: Recent studies in humans and mice suggest the implication of the cysteine proteases cathepsins S, L, and K in vascular and metabolic complications of obesity. OBJECTIVE: Our objective was to identify clinically relevant forms of cathepsin in human obesity. DESIGN AND SETTING: We conducted a prospective study on two independent cohorts. PARTICIPANTS AND INTERVENTIONS: The first cohort includes 45 obese women eligible for gastric surgery (age, 39 +/- 1.6 yr; body mass index, 47 +/- 0.99 kg/m(2)) and 17 nonobese women (age, 38 +/- 1.8 yr; body mass index, 21 +/- 0.44 kg/m(2)). The second cohort comprises 29 obese women (age, 57 +/- 0.8 yr; body mass index, 34 +/- 0.69 kg/m(2)) undergoing 6 months of medically supervised caloric restriction. MAIN OUTCOMES: Cathepsin S, L, and K mRNA levels were determined in surgical adipose tissue biopsies. The proteins were measured in conditioned medium of adipose tissue explants and in circulation. RESULTS: Obese subjects had a 2-fold increase in cathepsin S mRNA in adipose tissue as compared with normal-weight subjects and an increased rate (1.5-fold) of cathepsin S release in adipose tissue explants. Cathepsin S circulating concentrations were increased with obesity (+30%) and reduced after weight reduction (P < 0.05 for both). By contrast, cathepsin L was unaffected in adipose tissue and serum; cathepsin K was undetectable in circulation and unchanged in adipose tissue. CONCLUSION: In humans, cathepsin S is more influenced than cathepsins L and K by changes in energy balance in adipose tissue and circulation. This opens new avenues to explore whether selective inhibition of this protease could reduce cardiovascular risk and ameliorate metabolic status in obese subjects.
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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) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".