Interplay between hereditary and acquired factors determines the neutrophil counts in older individuals
Bibliographic record
Abstract
Blood cell production is a complex process, partly genetically determined and influenced by acquired factors. However, there is a paucity of data on how these factors interplay in the context of aging, which is associated with a myeloid proliferation bias, clonal hematopoiesis (CH), and an increased incidence of myeloid cancers. We investigated hereditary and acquired factors underlying blood cell trait variability in a cohort of 2996 related and unrelated women from Quebec aged from 55 to 101 years. We performed a genome-wide association study, evaluated the impact of chronic diseases, and performed targeted deep sequencing of CH driver genes and X-chromosome inactivation (XCI)-based clonality analyses. Multivariable analyses were conducted using generalized linear mixed models. We document that aging is associated with increasing neutrophil and monocyte counts and decreasing lymphocyte counts. Neutrophil counts were influenced by the variants in the region of GSDMA and PSMD3-CSF3, but this association decreased with age; in parallel, older individuals with cardiometabolic comorbidities exhibited significantly higher neutrophil counts (4.1 × 109/L vs 3.83 × 109/L; P < .001) than younger individuals. These age-related diseases were also associated with an increase in other myeloid-derived cells. Neither CH nor XCI clonality correlated with neutrophil counts. In conclusion, we show that neutrophil counts are genetically influenced, but as individuals age, this contribution decreases in favor of acquired factors. Aging is associated with a myeloid proliferation bias which is greater in the presence of cardiometabolic comorbidities but not of CH. These findings support that cell-extrinsic factors may contribute to the myeloid shift possibly through low-grade inflammation.
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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 | 0.000 | 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".