Bisoniana 108. Seasonal changes in the white blood cell count and blood cell sedimentation rate in the European bison Bison bonasus

Increasing evidence shows that white blood cells are associated with the risk of coronavirus disease 2019 (COVID-19), but the direction and causality of this association are not clear. To evaluate the causal associations between various white blood cell traits and the COVID-19 susceptibility and severity, we conducted two-sample bidirectional Mendelian Randomization (MR) analyses with summary statistics from the largest and most recent genome-wide association studies. Our MR results indicated causal protective effects of higher basophil count, basophil percentage of white blood cells, and myeloid white blood cell count on severe COVID-19, with odds ratios (OR) per standard deviation increment of 0.75 (95% CI: 0.60–0.95), 0.70 (95% CI: 0.54–0.92), and 0.85 (95% CI: 0.73–0.98), respectively. Neither COVID-19 severity nor susceptibility was associated with white blood cell traits in our reverse MR results. Genetically predicted high basophil count, basophil percentage of white blood cells, and myeloid white blood cell count are associated with a lower risk of developing severe COVID-19. Individuals with a lower genetic capacity for basophils are likely at risk, while enhancing the production of basophils may be an effective therapeutic strategy.

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Identifying Top Predictors of Change in Noncalcified Coronary Burden in Psoriasis by Machine Learning Over 1-Year

Journal of Psoriasis and Psoriatic Arthritis ◽

10.1177/24755303211000757 ◽

2021 ◽

pp. 247553032110007

Author(s):

Eric Munger ◽

Amit K. Dey ◽

Justin Rodante ◽

Martin P. Playford ◽

Alexander V. Sorokin ◽

...

Keyword(s):

Machine Learning ◽

Blood Pressure ◽

Blood Cell ◽

Cell Count ◽

White Blood Cell Count ◽

White Blood Cell ◽

Severity Index ◽

Blood Cell Count ◽

Psoriasis Area Severity Index ◽

Severity Index Score

Background: Psoriasis is associated with accelerated non-calcified coronary plaque burden (NCB) by coronary computed tomography angiography (CCTA). Machine learning (ML) algorithms have been shown to effectively identify cardiometabolic variables with NCB in cross-sectional analysis. Objective: To use ML methods to characterize important predictors of change in NCB by CCTA in psoriasis over 1-year of observation. Methods: The analysis included 182 consecutive patients with 80 available variables from the Psoriasis Atherosclerosis Cardiometabolic Initiative, a prospective, observational cohort study at baseline and 1-year using the random forest regression algorithm. NCB was assessed at baseline and 1-year from CCTA. Results: Using ML, we identified variables of high importance in the context of predicting changes in NCB. For the cohort that worsened NCB (n = 102), top baseline variables were cholesterol (total and HDL), white blood cell count, psoriasis area severity index score, and diastolic blood pressure. Top predictors of 1-year change were change in visceral adiposity, white blood cell count, total cholesterol, c-reactive protein, and absolute lymphocyte count. For the cohort that improved NCB (n = 80), the top baseline variables were HDL cholesterol related including apolipoprotein A1, basophil count, and psoriasis area severity index score, and top predictors of 1-year change were change in apoA, apoB, and systolic blood pressure. Conclusion: ML methods ranked predictors of progression and regression of NCB in psoriasis over 1 year providing strong evidence to focus on treating LDL, blood pressure, and obesity; as well as the importance of controlling cutaneous disease in psoriasis.

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