Abstract MP17: Galectin-3 and Subsequent Risk of Lower-extremity Peripheral Artery Disease: The Atherosclerosis Risk in Communities (ARIC) Study
Bibliographic record
Abstract
Background: Galectin-3 is involved in the regulation of inflammation and the formation of fibrosis and has been liked to atherosclerosis. However, there are no studies investigating prospective associations of galectin-3 with incidence of lower-extremity peripheral artery disease (PAD). Methods: Among 9,827 ARIC participants without a history of PAD, we investigated whether galectin-3 (measured at visit 4 [1996-98]) was associated with incident clinical PAD through 2013, defined as hospitalizations with PAD diagnosis or leg revascularization. We defined PAD cases with rest pain or tissue loss as critical limb ischemia (CLI). We constructed Cox models with galectin-3 modeled categorically (quartiles) and continuously (log transformed). Results: During a median follow-up of 15.8 years, 287 participants developed PAD (105 incident CLI cases). In demographically adjusted models, galectin-3 demonstrated a dose-response association with incident PAD: hazard ratios (HRs) 2.55 (95% CI 1.80-3.61) and 1.69 (1.18-2.41) for the highest and second highest quartiles, as compared to the lowest quartile (Table; Model 1). Additional adjustment for traditional cardiovascular risk factors attenuated the associations, although the highest quartile remained borderline significant (HR 1.44 [0.99-2.07], p=0.051, Table: Model 2) and galectin-3 as a continuous variable remained significant (1.15 [1.02-1.29]). Similar results were observed for the association of galectin-3 with CLI. Conclusions: Galectin-3 was modestly associated with future risk of clinical PAD events in a community-based cohort, supporting the involvement of inflammation and fibrosis in the development of clinical PAD.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".