Circulating IGFBP-2 levels are incrementally linked to correlates of the metabolic syndrome and independently associated with VLDL triglycerides
Bibliographic record
Abstract
OBJECTIVE: To assess whether plasma IGFBP-2 is independently associated with components of the lipoprotein-lipid profile and to suggest a cutoff value that could identify subjects with the features of the metabolic syndrome. METHODS: In this cross-sectional study, 379 Caucasian men from the general population and covering a wide range of BMI were recruited through the media. Subjects with type 2 diabetes, BMI values > 40 kg/m(2), or taking medication targeting glucose or lipid metabolism or blood pressure were excluded. Anthropometric data were collected and plasma IGFBP-2 concentrations, glucose tolerance and an extensive plasma lipid profile were determined after an overnight fast. RESULTS: Subjects with low IGFBP-2 levels were characterized by increased fat mass (p < 0.0001), impaired insulin sensitivity (p < 0.0001) and higher plasma triglyceride (TG) levels (p < 0.0001). When divided into 6 quantiles, only subjects with the highest IGFBP-2 levels (>221.5 ng/mL) did not meet the NCEP ATP III criteria for the clinical diagnosis of the metabolic syndrome. In addition, circulating IGFBP-2 levels were significantly associated with VLDL-TG (r = -0.51, p < 0.0001) and HDL-C (r = -0.27, p < 0.0001) levels. After adjustments, plasma IGFBP-2 was found to be independently associated with VLDL-TG levels but not with HDL-C concentrations. CONCLUSIONS: In our cohort, IGFBP-2 levels <221.5 ng/mL are incrementally associated with a detrimental plasma lipoprotein-lipid profile. After adjustment for covariates, IGFBP-2 remained independently associated with VLDL-TG but not HDL-C levels. This study supports further investigations in other populations and validation of IGFBP-2 as a biomarker of early dyslipidemia.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".