Method of Determining Risk for Chronic Stress and Stroke

A method for determining a stress-risk index for the risk of developing chronic stress and ischemic heart disease related stroke, wherein said method comprises:

• i) performing a multiple stepwise linear regression of biomarkers and risk factors, both transformed to be normally distributed, of an adapted UCLA score, wherein said adapted UCLA score is a 10-year stroke risk composite score of the University of California, Los Angeles (UCLA), and wherein the adapted UCLA score includes as variables:
• individual's medical history regarding any cardiovascular disease, kidney disease, myocardial infarction, diabetes, and hypertension medication usage;
• demographic and lifestyle factors including age, race, sex, diabetes, smoking, alcohol use, and physical activity habits;
• systolic and diastolic blood pressure;
• fibrinogen;
• waist circumference;
• perfusion deficits including myocardial ischemia;
• electrocardiography atrial fibrillation;
• and electrocardiography left ventricular hypertrophy;
• wherein the biomarkers and risk factors have been determined by obtaining biological samples from individuals;
• measuring levels of the biomarkers in the biological samples obtained from the individuals and measuring levels of clinical markers of the individuals, wherein the biomarkers are serum cotinine values, gamma glutamyl transferase (γ-GT), lipids and high sensitivity c-reactive protein (CRP), whole blood EDTA glycated hemoglobin (HbA1C), citrate fibrinogen values, saliva cortisol, serum cortisol, adrenocorticotrophic hormone (ACTH), high sensitivity cardiac troponin, urinary norepinephrine, epinephrine, and creatinine, wherein the clinical markers are systolic blood pressure (SBP), diastolic blood pressure (DBP), silent myocardial ischemia (SMI) events or perfusion deficits, electrocardiography (ECG) atrial fibrillation, ECG left ventricular hypertrophy (ECG-LVH), retinal vessel calibers, intra-ocular pressure (IOP), and diastolic ocular perfusion pressure (DOPP) calculated from DBP minus IOP;
• ii) performing a receiver operating characteristic (ROC) analysis to assess a difference between the distribution of biomarkers and the distribution of risk factors at all classification thresholds;
• iii) determining a stress-risk index for three or more possible combinations of the biomarkers as an area under the curve (AUC) as a maximum of the ROC analysis, when discriminating positives and negatives of a composite dichotomous biomarker of the adapted UCLA score denoted as Y, wherein the stress-risk index AUC was 0.77, with a 95% confidence interval (CI) of 0.72, 0.82, for a positive prediction with 85% sensitivity and 48% specificity;
• iv) determining a stress-risk index cut-off value by a Youden index that maximizes correct classifications and/or minimizes incorrect classifications and denotes a combination of biomarkers at a determined optimal cut-off value as biomarker V;
• v) validating biomarker V by using Y as a dependent variable and V as a predicted probability of positives using a logistic regression model on input continuous biomarkers and confounding risk factors, thereby generating a validated stress-risk index;
• vi) discriminating the AUC between the positives and negatives of Y using the predicted probability of positives and using the sensitivity and specificity of correct predictions as diagnostic for predictive validity;
• vii) determining an optimal V cut-off value using ROC analysis;
• viii) using a non-linear regression model that includes neural networks as comparison to the logistic regression model;
• ix) determining the maximum of the Youden index using the ROC curves with the non-linear regression analysis substantiating a functional relationship between the models using multilayer perceptron with two layers and trained with Bayesian regularization, wherein hidden layers have tansig functions and an output layer is linear with ten bootstrap repetitions;
• x) optimizing the neural networks and extracting the functional relationship with analysis markers of a stroke risk profile of a patient;
• xi) diagnosing a risk of the patient of developing chronic stress and ischemic heart disease related stroke, utilizing the validated stress-risk index for a positive prediction of the stroke risk profile of the patient;
• and
• xii) facilitating a therapeutic decision by authorized personnel for the patient, based on the diagnosis of the risk of developing chronic stress and ischemic heart disease related stroke,
• wherein the step xii) of facilitating a therapeutic decision comprises generating, by authorized personnel and based on the diagnosed risk of the patient of developing chronic stress and ischemic heart disease related stroke utilizing the validated stress-risk index, one or more preventative recommendations tailored to the patient, the one or more preventative recommendations being provided to the patient for implementation to reduce the diagnosed risk of the patient of developing chronic stress and ischemic heart disease related stroke.