Clonal Hematopoiesis, Inflammation, Hemostasis, and Cardiometabolic Biomarkers: The Reasons for Geographic and Racial Differences in Stroke Study
Notice bibliographique
Résumé
CONCLUSIONS Clonal hematopoiesis (CH) is implicated in the pathogenesis of multiple age-associated disorders including malignancy, cardiovascular disease (CVD), chronic kidney disease, and mortality. Some of these adverse outcomes may be mediated by CH-driven inflammatory dysregulation, but the association of CH with biomarkers in other pathways related to these outcomes has not been studied. We studied the association between CH and biomarkers of inflammation, hemostasis and cardiometabolic risk in the REasons for Geographic and Racial Differences in Stroke (REGARDS) study. REGARDS is a prospective study that enrolled 30,239 Black and White individuals ≥45 years across United States between 2003 and 2007, and collected extensive health data by telephone and in-person exams. Biomarkers listed in Table 2, and CH were measured in baseline blood samples in an age-, sex-, and race-stratified cohort random sample (CRS). We excluded participants with prior history of CVD. Sequencing was performed at >500x depth to identify somatic mutations in 24 of the most common CH genes, capturing >95% of CH observed in the general population. CH was categorized based on variant allele frequency (VAF) of the dominant mutation as 0%, >0-<2%, 2-10% and >10%. Linear regression was used to calculate the age-, sex-, and race-adjusted difference in mean biomarker level for those with versus those without CH. The study included 781 participants (mean age at enrollment: 66 years [SD 12.1], 46% male, 50% Black individuals). The prevalence of CH at varying VAF was: VAF 0% - 43.3%; VAF >0 to <2% - 32.5%; 2 to 10% - 18.4%; >10% - 5.8%. Mutations were most prevalent in DNMT3A (60.3%), then TET2 (13.8%) and ASXL1 (10.1%). The mean age was higher in participants with CH (73 years, SD 10) than in those without CH (61 years, SD 11). Distributions of the most common CH mutations at VAF ≥2% by age, sex and race are shown in Table 1. Mutations in DNMT3A were overrepresented in Black participants and women, whereas mutations in TET2 and ASXL1 were more common in White participants. Associations of CH with inflammation, hemostasis and cardiometabolic biomarkers were presented in Table 2. Mean Log IL-8 was 0.23 (95% CI 0.05-0.41) units higher in those with high VAF CH (>10%) versus those without CH, with no association among other inflammation biomarkers.Among hemostasis biomarkers, mean log D-dimer was 0.26 (95% CI 0.01-0.52) units higher in those with high VAF CH versus those without CH. There was a non-significant trend towards an association between CH and lower Factor VIII and higher fibrinogen (Table 2). Among cardiometabolic biomarkers, mean log Adiponectin was 0.32 (95% CI 0.04-0.61) units higher in those with high VAF CH versus no CH. We did not identify a statistically significant association between CH and other cardiometabolic markers. CH is prevalent in the general population, especially at low VAF. In addition to inflammatory dysregulation, findings here suggest possible other pathways for deleterious effects of CH on health via hemostasis and cardiometabolic pathways. Since our study was cross-sectional, we could not ascertain if CH leads to abnormal biomarker levels or if pathways represented by the above biomarkers predispose to CH. Studying associations of CH with serial biomarkers in a larger population, by specific mutations, and by sex and race will provide mechanistic insights on CH associated adverse outcomes.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».