Biomaterial Database

[Circadian variations of blood pressure]. 1996

X Chanudet, and N P Chau

Service de pathologie cardiovasculaire, Hôpital Begin, Saint-Mandé.

Blood pressure (BP) exhibit diurnal variations with low level during night time and high level during daytime. There is little evidence of an endogenous mechanism of BP variations whereas a lot of clues show the contribution of external factors such as activity, mood, postures. Different pathophysiological conditions may alter the pattern of BP circadian variations. This is the case of severe hypertension, hypertension in the elderly and some secondary forms of the disease. Other pathological conditions concerned by BP rhythm alterations are dysautonomia, complicated diabetes, Cushing syndrome, organ transplantations. Methods currently in use to handle data are reviewed.

UI	MeSH Term	Description	Entries
D006973	Hypertension	Persistently high systemic arterial BLOOD PRESSURE. Based on multiple readings (BLOOD PRESSURE DETERMINATION), hypertension is currently defined as when SYSTOLIC PRESSURE is consistently greater than 140 mm Hg or when DIASTOLIC PRESSURE is consistently 90 mm Hg or more.	Blood Pressure, High,Blood Pressures, High,High Blood Pressure,High Blood Pressures
D008297	Male		Males
D011237	Predictive Value of Tests	In screening and diagnostic tests, the probability that a person with a positive test is a true positive (i.e., has the disease), is referred to as the predictive value of a positive test; whereas, the predictive value of a negative test is the probability that the person with a negative test does not have the disease. Predictive value is related to the sensitivity and specificity of the test.	Negative Predictive Value,Positive Predictive Value,Predictive Value Of Test,Predictive Values Of Tests,Negative Predictive Values,Positive Predictive Values,Predictive Value, Negative,Predictive Value, Positive
D001794	Blood Pressure	PRESSURE of the BLOOD on the ARTERIES and other BLOOD VESSELS.	Systolic Pressure,Diastolic Pressure,Pulse Pressure,Pressure, Blood,Pressure, Diastolic,Pressure, Pulse,Pressure, Systolic,Pressures, Systolic
D002940	Circadian Rhythm	The regular recurrence, in cycles of about 24 hours, of biological processes or activities, such as sensitivity to drugs or environmental and physiological stimuli.	Diurnal Rhythm,Nyctohemeral Rhythm,Twenty-Four Hour Rhythm,Nycthemeral Rhythm,Circadian Rhythms,Diurnal Rhythms,Nycthemeral Rhythms,Nyctohemeral Rhythms,Rhythm, Circadian,Rhythm, Diurnal,Rhythm, Nycthemeral,Rhythm, Nyctohemeral,Rhythm, Twenty-Four Hour,Rhythms, Circadian,Rhythms, Diurnal,Rhythms, Nycthemeral,Rhythms, Nyctohemeral,Rhythms, Twenty-Four Hour,Twenty Four Hour Rhythm,Twenty-Four Hour Rhythms
D003627	Data Interpretation, Statistical	Application of statistical procedures to analyze specific observed or assumed facts from a particular study.	Data Analysis, Statistical,Data Interpretations, Statistical,Interpretation, Statistical Data,Statistical Data Analysis,Statistical Data Interpretation,Analyses, Statistical Data,Analysis, Statistical Data,Data Analyses, Statistical,Interpretations, Statistical Data,Statistical Data Analyses,Statistical Data Interpretations
D006801	Humans	Members of the species Homo sapiens.	Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D000368	Aged	A person 65 years of age or older. For a person older than 79 years, AGED, 80 AND OVER is available.	Elderly
D012307	Risk Factors	An aspect of personal behavior or lifestyle, environmental exposure, inborn or inherited characteristic, which, based on epidemiological evidence, is known to be associated with a health-related condition considered important to prevent.	Health Correlates,Risk Factor Scores,Risk Scores,Social Risk Factors,Population at Risk,Populations at Risk,Correlates, Health,Factor, Risk,Factor, Social Risk,Factors, Social Risk,Risk Factor,Risk Factor Score,Risk Factor, Social,Risk Factors, Social,Risk Score,Score, Risk,Score, Risk Factor,Social Risk Factor
D015233	Models, Statistical	Statistical formulations or analyses which, when applied to data and found to fit the data, are then used to verify the assumptions and parameters used in the analysis. Examples of statistical models are the linear model, binomial model, polynomial model, two-parameter model, etc.	Probabilistic Models,Statistical Models,Two-Parameter Models,Model, Statistical,Models, Binomial,Models, Polynomial,Statistical Model,Binomial Model,Binomial Models,Model, Binomial,Model, Polynomial,Model, Probabilistic,Model, Two-Parameter,Models, Probabilistic,Models, Two-Parameter,Polynomial Model,Polynomial Models,Probabilistic Model,Two Parameter Models,Two-Parameter Model