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Prediabetes is associated with abnormal circadian blood pressure variability

Research output: Contribution to journalArticlepeer-review

Abstract

Blood pressure (BP) exhibits a circadian variation characterized by a morning increase, followed by a small postprandial valley and a deeper descent during nocturnal rest. Although abnormal 24-h variability (abnormal circadian variability (ACV)) predicts adverse cardiovascular disease (CVD) outcomes, a 7-day automatic ambulatory BP monitoring (ABPM) and subsequent chronobiologic analysis of the gathered data, permits identification of consistency of any abnormal circadian variation. To test whether normal overweight healthy men and women with prediabetes differed from subjects with normoglycemia in having ACV with a 7-day ABPM. Consent for a 7-day ABPM was obtained from subjects with family history of diabetes mellitus, who were participating in the screening phase for a randomized, double blind, placebo-controlled weight loss trial in prediabetics to prevent progression to diabetes mellitus. The automatic 7-day ABPM device recorded BP and heart rate every 30 min during the day and every 60 min during the night. Normoglycemic and prediabetic subjects matched for age, sex, race, BP, BMI, waist circumference and glycemic control, differed statistically significantly only in their fasting and/or 2-h postprandial serum glucose concentrations. Chronobiologically-interpreted 7-day ABPM uncovered no abnormalities in normoglycemics, whereas prediabetics had a statistically significantly higher incidence of high mean BP (MESOR-hypertension), excessive pulse pressure and/or circadian hyper-amplitude-tension (CHAT) (P<0.001). ACV detected with 7-day ABPM may account for the enhanced CVD risk in prediabetes. These findings provide a basis for larger-scale studies to assess the predictive value of 7-day ABPM over the long term.

Original languageEnglish (US)
Pages (from-to)627-633
Number of pages7
JournalJournal of Human Hypertension
Volume22
Issue number9
DOIs
StatePublished - 2008

Bibliographical note

Funding Information:
This study was supported by grants from NIH (GM-13981) (FH) and University of Minnesota Supercomputing Institute (GC and FH).

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

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