Lipocalin-2 Is an Inflammatory Marker Closely Associated with Obesity, Insulin Resistance, and Hyperglycemia in Humans
Bibliographic record
Abstract
BACKGROUND: Lipocalin-2, a 25-kDa secreted glycoprotein, is a useful biomarker for early detection of various renal injuries. Because lipocalin-2 is abundantly expressed in adipose tissue and liver, we investigated its relevance to obesity-related pathologies. METHODS: We used real-time PCR and in-house immunoassays to quantify the mRNA and serum concentrations of lipocalin-2 in C57BL/KsJ db/db obese mice and their age- and sex-matched lean littermates. We analyzed the association between serum lipocalin-2 concentrations and various metabolic and inflammatory variables in 229 persons (121 men and 108 women) recruited from a previous cross-sectional study, and we evaluated the effect of the insulin-sensitizing drug rosiglitazone on serum lipocalin-2 concentrations in 32 diabetic patients (21 men and 11 women). RESULTS: Compared with the lean littermates, lipocalin-2 mRNA expression in adipose tissue and liver and its circulating concentrations were significantly increased in db/db diabetic/obese mice (P <0.001). These changes were normalized after rosiglitazone treatment. In humans, circulating lipocalin-2 concentrations were positively correlated (P <0.005) with adiposity, hypertriglyceridemia, hyperglycemia, and the insulin resistance index, but negatively correlated (P = 0.002) with HDL cholesterol. There was also a strong positive association between lipocalin-2 concentrations and high sensitivity C-reactive protein (hs-CRP), independent of age, sex, and adiposity (P = 0.007). Furthermore, rosiglitazone-mediated decreases in lipocalin-2 concentrations correlated significantly with increases in insulin sensitivity (r = 0.527; P = 0.002) and decreases in hs-CRP concentrations (r = 0.509; P = 0.003). CONCLUSIONS: Lipocalin-2 is an inflammatory marker closely related to obesity and its metabolic complications. Measurement of serum lipocalin-2 might be useful for evaluating the outcomes of various clinical interventions for obesity-related metabolic and cardiovascular diseases.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".