RESUMEN
Autoimmune conditions have been reported among patients with cysteine-altering NOTCH3 variants and CADASIL. This study aimed to investigate the occurrence of autoimmune illnesses and markers of inflammation in such populations. Cases were identified who had a NOTCH3 cysteine-altering variant from the Geisinger MyCode® Community Health Initiative (MyCode®). We further performed external validation using the UK Biobank cohort. A cohort of 121 individuals with a NOTCH3 cysteine-altering variant from MyCode® was compared to a control group with no non-synonymous variation in NOTCH3 (n = 184). Medical records were evaluated for inflammatory markers and autoimmune conditions, which were grouped by the organ systems involved. A similar analysis was conducted using data from the UK Biobank (n~450,000). An overall increase in inflammatory markers among participants with a NOTCH3 cysteine-altering variant was observed when compared to an age- and sex-matched MyCode® control group (out of participants with laboratory testing: 50.9% versus 26.7%; p = 0.0047; out of total participants: 23.1% versus 10.9%; p = 0.004). Analysis of UK Biobank data indicated any autoimmune diagnosis (1.63 [1.14, 2.09], p= 2.665 × 10-3) and multiple sclerosis (3.42 [1.67, 6.02], p = 9.681 × 10-4) are associated with a NOTCH3 cysteine-altering variant in any domain. Our findings suggest a possible association between NOTCH3 cysteine-altering variants and autoimmune conditions.
RESUMEN
Introduction: The cut-point for defining the age of young ischemic stroke (IS) is clinically and epidemiologically important, yet it is arbitrary and differs across studies. In this study, we leveraged electronic health records (EHRs) and data science techniques to estimate an optimal cut-point for defining the age of young IS. Methods: Patient-level EHRs were extracted from 13 hospitals in Pennsylvania, and used in two parallel approaches. The first approach included ICD9/10, from IS patients to group comorbidities, and computed similarity scores between every patient pair. We determined the optimal age of young IS by analyzing the trend of patient similarity with respect to their clinical profile for different ages of index IS. The second approach used the IS cohort and control (without IS), and built three sets of machine-learning models-generalized linear regression (GLM), random forest (RF), and XGBoost (XGB)-to classify patients for seventeen age groups. After extracting feature importance from the models, we determined the optimal age of young IS by analyzing the pattern of comorbidity with respect to the age of index IS. Both approaches were completed separately for male and female patients. Results: The stroke cohort contained 7555 ISs, and the control included 31,067 patients. In the first approach, the optimal age of young stroke was 53.7 and 51.0 years in female and male patients, respectively. In the second approach, we created 102 models, based on three algorithms, 17 age brackets, and two sexes. The optimal age was 53 (GLM), 52 (RF), and 54 (XGB) for female, and 52 (GLM and RF) and 53 (RF) for male patients. Different age and sex groups exhibited different comorbidity patterns. Discussion: Using a data-driven approach, we determined the age of young stroke to be 54 years for women and 52 years for men in our mainly rural population, in central Pennsylvania. Future validation studies should include more diverse populations.
