Quantitative Trait of Neutrophil Count Is Influenced By Variants in the Region of <i>Gsdma</i> and <i>PSMD3-CSF3, a</i>ging, Cardiometabolic Comorbidities but Not By Chip
Bibliographic record
Abstract
Abstract Introduction Hematopoiesis ensures lifelong oxygenation, immune and hemostatic functions through the production of billions of blood cells on a daily basis. However, little is known about the factors regulating and influencing this highly coordinated process and the resulting peripheral blood cell quantitative traits during the aging process. In this study, we investigated germline and acquired factors associated with blood cell counts in a cohort of normal aging individuals. Methods Determinants underlying blood cell trait variability were assessed in a cohort of 2996 related and unrelated women of French-Canadian ancestry aged 55 to 101 years. All participants answered a medical questionnaire and provided a blood sample for complete blood count with differential (GenS, Beckman Coulter) and DNA analysis. Potential hereditary variants of significance were assessed using a genome-wide association study (GWAS). Genotyping was performed according to the manufacturer's specifications on the Illumina Infinium Global Array v3-MD (Illumina, San Diego, CA). Variants with a completion rate of ≥98%, minor allele frequency >1% and imputation probability of ≥0.80 were retained. Genome-wide association testing was conducted with SAIGE_0.43.3 to account for the family-based design of our study. Genome-wide significance threshold was at 5x10 -8. Potential acquired factors of importance such as chronic comorbidities and clonal hematopoiesis of indeterminate potential (CHIP) were also sought. CHIP status was assessed with targeted next-generation sequencing of polymorphonuclear cells using the Ampliseq AML panel. Germline and acquired factors were then integrated in generalized linear mixed models to identify factors associated with blood cell indices in multivariate analysis and to characterize their relative contribution. Statistical analyses were conducted with R. Results Mean age of study participants was 69.2 years (standard deviation 9.1 years). Mean values (and standard deviation) of blood cell indices were as follows: total white blood count (WBC) 6.43x10 9/L (1.63), absolute lymphocyte count 1.86x10 9/L (0.57), absolute monocyte count 0.46x10 9/L (0.16), absolute neutrophil count 3.93x10 9/L (1.29), hemoglobin 131.2 g/L (9.67), platelet count 249.2x10 9/L (57.6). Neutrophil and monocyte counts increased with age (b1 coefficient 0.02 (p-value 6.06E-08) and 0.004 (p-value 2.2E-16) respectively), while lymphocyte and platelet counts decreased with advancing age (b1 coefficient -0.01 (p-value 3.65E-07) and -0.29 (p-value 0.023) respectively). GWAS identified 13 variants in the region of GSDMA and PSMD3-CSF3 (chromosome 17) that met genome-wide requirements for WBC and neutrophil counts. Platelet count was significantly associated with a variant intronic to ARHGEF3 (chromosome 3). Among acquired factors, smoking was positively associated with neutrophil, monocyte, lymphocyte counts and hemoglobin levels. Distinctly, we document that cardiometabolic comorbidities (diabetes, coronary heart disease, hypertension and dyslipidemia) are associated with a statistically higher count of myeloid-derived cells (neutrophil count 4.1 vs 3.83 (95%CI 0.28-0.37, p-value <0.001), monocyte count 0.50 vs 0.45 (95%CI 0.02-0.05, p-value <0.001), and platelet count 259 vs 243 (95%CI 10-20, p-value <0.001). These results remained significant in multivariate analysis. In accordance with previous reports, CHIP, which was documented in 14% of the cohort, had no influence on blood counts. Conclusion We document that individual variation in blood counts in individuals is influenced by several factors: (i) germline variants related to GSDMA and PSMD3-CSF3 contribute to white blood cell and neutrophil counts and a variant intronic to ARHGEF3 is influential in determining platelet counts; (ii) aging is associated with increasing levels of neutrophils and monocytes, and reduced lymphocyte and platelet counts, indicating a shift towards myelopoiesis; (iii) this myeloid-biased skewing is further increased among individuals with cardiometabolic comorbidities; (iv) CHIP does not contribute to the age-associated myeloid shift. These findings support that chronic age-related diseases may promote myelopoiesis and contribute to population variability in peripheral blood traits, possibly through a state of low-grade inflammation. Figure 1 Figure 1. Disclosures Busque: Novartis: Consultancy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.003 | 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".