Table 3.

Integration of hereditary and acquired factors associated with the neutrophil and platelet counts in multivariable GLMM

PredictorsLogarithm (base 10) of neutrophil countInverse normal transformation of platelet count
EstimateSEPR2EstimateSEPR2
(Intercept) 0.27 0.04 < .001  0.73 0.17 < .001  
Age (y) 0.003 0.001 < .001 0.028 −0.01 0.002 < .001 0.004 
Smoker 0.06 0.009 < .001 0.012     
Cardiometabolic comorbidities 0.03 0.006 < .001 0.014 0.22 0.05 < .001 0.007 
rs60134943 (GSDMA) (T vs G) 0.1 0.03 .003 0.014    0.014 
CHIP 0.002 0.008 .780 0.012    0.012 
Interaction variable of age and rs60134943 (GSDMA) −0.001 0.000 .013      
rs1354034 (ARHGEF3) (C vs T)     −0.18 0.03 < .001 0.015 
Cancer     −0.13 0.06 .03 0.002 
Full model R2 0.054    0.031    
PredictorsLogarithm (base 10) of neutrophil countInverse normal transformation of platelet count
EstimateSEPR2EstimateSEPR2
(Intercept) 0.27 0.04 < .001  0.73 0.17 < .001  
Age (y) 0.003 0.001 < .001 0.028 −0.01 0.002 < .001 0.004 
Smoker 0.06 0.009 < .001 0.012     
Cardiometabolic comorbidities 0.03 0.006 < .001 0.014 0.22 0.05 < .001 0.007 
rs60134943 (GSDMA) (T vs G) 0.1 0.03 .003 0.014    0.014 
CHIP 0.002 0.008 .780 0.012    0.012 
Interaction variable of age and rs60134943 (GSDMA) −0.001 0.000 .013      
rs1354034 (ARHGEF3) (C vs T)     −0.18 0.03 < .001 0.015 
Cancer     −0.13 0.06 .03 0.002 
Full model R2 0.054    0.031    

Because continuous traits were not normally distributed, logarithmic and inverse normal transformation were performed for neutrophil counts and platelet counts, respectively.

Family structure was accounted for as a random effect. Combined cardiometabolic comorbidities include coronary artery disease, dyslipidemia, hypertension, or diabetes.

SE, standard error.

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