Abstract 4139115: Genomic, Phenomic, and Geographic Relationships with Leukocyte Telomere Length in the U.S. Yields New Insights for Neoplasms and Cardiometabolic Disease
Bibliographic record
Abstract
Background: Leukocyte telomere length (LTL) is an age-related marker strongly associated with neoplasms and cardiometabolic disease. LTL is also intimately linked with clonal hematopoiesis of indeterminate potential (CHIP), a recently described risk factor for cardiovascular disease (CVD). However, population-scale investigations of LTL in diverse backgrounds are limited, and geolocational variance has not been studied. Method: LTL and CHIP were derived using blood-derived whole genome sequencing data from 244,819 diverse U.S. residents in the NIH All of Us Research Program (AoU). We performed association analyses of LTL with CHIP and other available health-related traits, Phecode, as well as geolocation data by ZIP code. Genome-wide association study (GWAS) of LTL in AoU and meta-analysis with UK Biobank were performed. All association tests were adjusted for age, sex, sequencing site, and the first 10 genetic principal components with multiple test corrections. Mediation analysis was adjusted for additional risk factors for CVD. Result: Among 244,819 AoU participants, 146,717 (59.9%) were female with mean (standard deviation) age 51.8 (±16) years. Estimated LTL correlated strongly with age, sex, and genetic ancestry in AoU. Socioeconomic status, lifestyle variables, anthropometry, blood biomarkers (e.g., lipids and glucose), and vital signs were associated with LTL in AoU. Phecode analysis showed LTL is associated with neoplasms and age-related conditions, including CVDs. LTL mediated some of the observed increased coronary artery disease risk among individuals with hematologic malignancies ( P < 2 × 10 -16 ). Significantly longer LTL clustered in the West Coast and Central Midwest ( P = 0.001 and 0.033, respectively), while significantly shorter LTL clustered in the Southeast ( P = 0.001) (Figure). GWAS meta-analyses identified 197 loci, of which 36 (18%) were novel, including ancestry-specific loci. Rare variant aggregation test found 9 novel associated genes. Conclusions: LTL is a marker of biological age that is associated and interacts with neoplasms, CVDs, and its various risk factors, with notable geographic variability within the U.S. Germline genetic analyses of LTL yield new insights into drivers of LTL.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.005 | 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".