You will be shown an abstract from a biomedical research paper. Given this abstract, your task is to extract all unique entities of the following types: ["HealthCareActivity", "InjuryOrPoisoning", "BodySubstance", "IntellectualProduct", "AnatomicalStructure", "SpatialConcept", "Chemical", "Bacterium", "MedicalDevice", "Organization", "BiomedicalOccupationOrDiscipline", "Finding", "BiologicFunction", "Virus", "ResearchActivity", "ClinicalAttribute", "PopulationGroup", "Eukaryote", "BodySystem", "Food", "ProfessionalOrOccupationalGroup"].

Please return the output as a JSON object of the format: {"Virus": ["HIV", ...], "Bacterium": ["MRSA", ...], "AnatomicalStructure": ["Lung", ...], "BodySystem": ["CNS", ...], "BodySubstance": ["Serum", ...], "Finding": ["Headache", ...], "InjuryOrPoisoning": ["Fracture", ...], "BiologicFunction": ["Death", ...], "HealthCareActivity": ["Biopsy", ...], "ResearchActivity": ["Clinical trial", ...], "MedicalDevice": ["Lenses", ...], "SpatialConcept": ["Camps", ...], "BiomedicalOccupationOrDiscipline": ["Forensic medicine", ...], "Organization": ["WHO", ...], "ProfessionalOrOccupationalGroup": ["Provider", ...], "PopulationGroup": ["Swimmers", ...], "Chemical": ["Gold", ...], "Food": ["Rice", ...], "IntellectualProduct": ["RPAM", ...], "ClinicalAttribute": ["Biomarker", ...], "Eukaryote": ["Dogs", ...]}. The keys should be entity types and values should be lists of extracted entities belonging to the corresponding type. If you cannot find entities belonging to a specific type, the value should be [].

Only output the JSON object and do not include any additional text.

Abstract:

Pre-hypertension, pre-diabetes or both: which is best at predicting cardiovascular events in the long term? The present study aimed to assess the value of pre-diabetes and pre-hypertension in predicting cardiovascular events. A population -based, cross-sectional survey was conducted, representing a large sample of the general Iranian population aged 35 years and older from the Isfahan Province and determined using a random, multistage cluster-sampling 10-year cohort. The five end points considered as study outcome were unstable angina (UA), acute occurrence of myocardial infarction (MI), sudden cardiac death (SCD), brain stroke and cardiovascular disease (CVD). Of the 6323 subjects scheduled for assessment of diabetes state 617 were diabetics and 712 were pre-diabetic. In addition, of these subjects, 1754 had hypertension and 2500 had pre-hypertension. Analysing only pre-hypertension, pre-diabetes and its combination and adjusted for gender and age variables, pre-hypertension and pre-diabetes status together, could only effectively predict occurrence of MI (hazard ratio (HR)=3.21, 95% confidence interval (CI): 1.06-9.76, P=0.04). In the same COX regression models, pre-hypertension status could predict UA and CVD occurrence (HR =2.94, 95% CI: 1.68-5.14, P<0.001 and HR =1.74, 95% CI: 1.23-2.47, P=0.002, respectively). However, pre-diabetes status could not predict any of these events after adjustment for gender and age. Our data provide valuable evidence of the triggering role of pre-hypertension and pre-diabetes together, on appearance and progression of MI even in healthy individuals and the significant predicting value of pre-hypertension on the occurrence of UA and CVD. In this regard, the value of pre-hypertension and pre-diabetes together, and the pre-hypertension state alone, are clearly superior to pre-diabetes state alone in predicting cardiovascular events.
{"AnatomicalStructure": [], "Bacterium": [], "BiologicFunction": ["CVD", "Pre-hypertension", "pre-hypertension", "unstable angina", "progression", "cardiovascular disease", "UA", "hypertension", "myocardial infarction", "diabetes state", "pre-diabetes", "SCD", "sudden cardiac death", "MI"], "BiomedicalOccupationOrDiscipline": [], "BodySubstance": [], "BodySystem": [], "Chemical": [], "ClinicalAttribute": [], "Eukaryote": [], "Finding": ["pre-diabetic", "cardiovascular events", "diabetics"], "Food": [], "HealthCareActivity": ["assessment"], "InjuryOrPoisoning": [], "IntellectualProduct": ["COX regression models"], "MedicalDevice": [], "Organization": [], "PopulationGroup": ["population", "healthy individuals", "Iranian", "cohort"], "ProfessionalOrOccupationalGroup": [], "ResearchActivity": ["study", "random, multistage cluster-sampling", "cross-sectional survey"], "SpatialConcept": ["Isfahan Province"], "Virus": []}