Home Blood Pressure Compared With Office Blood Pressure in Relation to Dysglycemia.


Journal

American journal of hypertension
ISSN: 1941-7225
Titre abrégé: Am J Hypertens
Pays: United States
ID NLM: 8803676

Informations de publication

Date de publication:
01 09 2022
Historique:
received: 04 04 2022
revised: 01 06 2022
accepted: 28 06 2022
pubmed: 19 7 2022
medline: 9 9 2022
entrez: 18 7 2022
Statut: ppublish

Résumé

Masked hypertension is more common in individuals with type 2 diabetes than in individuals with normoglycemia. We aimed to explore if there is a discrepancy between office blood pressure (office BP) and home blood pressure monitoring (HBPM) in relation to HbA1c as well as glycemic status in 5,029 middle-aged individuals. HBPM was measured in a subsample of 5,029 participants in The Swedish CardioPulmonary BioImage Study (SCAPIS), a population-based cohort of 50-64 years old participants. Both office BP and HBPM were obtained after 5 minutes' rest using the semiautomatic Omron M10-IT oscillometric device. White coat effect was calculated by subtracting systolic HBPM from systolic office BP. Participants were classified according to glycemic status: Normoglycemia, prediabetes, or diabetes based on fasting glucose, HbA1c value, and self-reported diabetes diagnosis. Of the included 5,025 participants, 947 (18.8%) had sustained hypertension, 907 (18.0%) reported taking antihypertensive treatment, and 370 (7.4%) had diabetes mellitus. Both systolic office BP and HBPM increased according to worsened glycemic status (P for trend 0.002 and 0.002, respectively). Masked hypertension was more prevalent in participants with dysglycemia compared with normoglycemia (P = 0.036). The systolic white coat effect was reversely associated with HbA1c (P = 0.012). The systolic white coat effect was reversely associated with HbA1c, and the prevalence of masked hypertension increased with dysglycemia.

Sections du résumé

BACKGROUND
Masked hypertension is more common in individuals with type 2 diabetes than in individuals with normoglycemia. We aimed to explore if there is a discrepancy between office blood pressure (office BP) and home blood pressure monitoring (HBPM) in relation to HbA1c as well as glycemic status in 5,029 middle-aged individuals.
METHODS
HBPM was measured in a subsample of 5,029 participants in The Swedish CardioPulmonary BioImage Study (SCAPIS), a population-based cohort of 50-64 years old participants. Both office BP and HBPM were obtained after 5 minutes' rest using the semiautomatic Omron M10-IT oscillometric device. White coat effect was calculated by subtracting systolic HBPM from systolic office BP. Participants were classified according to glycemic status: Normoglycemia, prediabetes, or diabetes based on fasting glucose, HbA1c value, and self-reported diabetes diagnosis.
RESULTS
Of the included 5,025 participants, 947 (18.8%) had sustained hypertension, 907 (18.0%) reported taking antihypertensive treatment, and 370 (7.4%) had diabetes mellitus. Both systolic office BP and HBPM increased according to worsened glycemic status (P for trend 0.002 and 0.002, respectively). Masked hypertension was more prevalent in participants with dysglycemia compared with normoglycemia (P = 0.036). The systolic white coat effect was reversely associated with HbA1c (P = 0.012).
CONCLUSIONS
The systolic white coat effect was reversely associated with HbA1c, and the prevalence of masked hypertension increased with dysglycemia.

Identifiants

pubmed: 35849046
pii: 6645154
doi: 10.1093/ajh/hpac082
pmc: PMC9434242
doi:

Substances chimiques

Glycated Hemoglobin A 0

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

810-819

Informations de copyright

© The Author(s) 2022. Published by Oxford University Press on behalf of American Journal of Hypertension, Ltd.

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Auteurs

Peder Af Geijerstam (P)

Department of Health, Medicine and Caring Sciences, Faculty of Medicine and Health Sciences, Linköping University, Linköping, Sweden.

Jan Engvall (J)

Department of Health, Medicine and Caring Sciences, Faculty of Medicine and Health Sciences, Linköping University, Linköping, Sweden.
Center of Medical Image Science and Visualization, Linköping University, Linköping, Sweden.
Department of Clinical Physiology, Linköping University, Linköping, Sweden.

Carl Johan Östgren (CJ)

Department of Health, Medicine and Caring Sciences, Faculty of Medicine and Health Sciences, Linköping University, Linköping, Sweden.
Center of Medical Image Science and Visualization, Linköping University, Linköping, Sweden.

Fredrik H Nyström (FH)

Department of Health, Medicine and Caring Sciences, Faculty of Medicine and Health Sciences, Linköping University, Linköping, Sweden.

Karin Rådholm (K)

Department of Health, Medicine and Caring Sciences, Faculty of Medicine and Health Sciences, Linköping University, Linköping, Sweden.
The George Institute for Global Health, University of New South Wales, Sydney, Australia.

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Classifications MeSH