Serum Resistin Concentrations and Risk of New Onset Heart Failure in Older Persons
Bibliographic record
Abstract
OBJECTIVE: Resistin is associated with inflammation and insulin resistance and exerts direct effects on myocardial cells including hypertrophy and altered contraction. We investigated the association of serum resistin concentrations with risk for incident heart failure (HF) in humans. METHODS AND RESULTS: We studied 2902 older persons without prevalent HF (age, 73.6+/-2.9 years; 48.1% men; 58.8% white) enrolled in the Health, Aging, and Body Composition (Health ABC) Study. Correlation between baseline serum resistin concentrations (20.3+/-10.0 ng/mL) and clinical variables, biochemistry panel, markers of inflammation and insulin resistance, adipocytokines, and measures of adiposity was weak (all rho <0.25). During a median follow-up of 9.4 years, 341 participants (11.8%) developed HF. Resistin was strongly associated with risk for incident HF in Cox proportional hazards models controlling for clinical variables, biomarkers, and measures of adiposity (HR, 1.15 per 10.0 ng/mL in adjusted model; 95% CI, 1.05 to 1.27; P=0.003). Results were comparable across sex, race, diabetes mellitus, and prevalent and incident coronary heart disease subgroups. In participants with available left ventricular ejection fraction at HF diagnosis (265 of 341; 77.7%), association of resistin with HF risk was comparable for cases with reduced versus preserved ejection fraction. CONCLUSIONS: Serum resistin concentrations are independently associated with risk for incident HF in older persons.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".