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Cardiovascular variability, sociodemographics, and biomarkers of disease: the MIDUS study
Introduction: Like heart rate, blood pressure (BP) is not steady but varies over intervals as long as months to as short as consecutive cardiac cycles. This blood pressure variability (BPV) consists of regularly occurring oscillations as well as less well-organized changes and typically is computed...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10484414/ https://www.ncbi.nlm.nih.gov/pubmed/37693005 http://dx.doi.org/10.3389/fphys.2023.1234427 |
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author | Gruenewald, Tara Seeman, Teresa E. Choo, Tse-Hwei Scodes, Jennifer Snyder, Clayton Pavlicova, Martina Weinstein, Maxine Schwartz, Joseph E. Mukkamala, Ramakrishna Sloan, Richard P. |
author_facet | Gruenewald, Tara Seeman, Teresa E. Choo, Tse-Hwei Scodes, Jennifer Snyder, Clayton Pavlicova, Martina Weinstein, Maxine Schwartz, Joseph E. Mukkamala, Ramakrishna Sloan, Richard P. |
author_sort | Gruenewald, Tara |
collection | PubMed |
description | Introduction: Like heart rate, blood pressure (BP) is not steady but varies over intervals as long as months to as short as consecutive cardiac cycles. This blood pressure variability (BPV) consists of regularly occurring oscillations as well as less well-organized changes and typically is computed as the standard deviation of multiple clinic visit-to-visit (VVV-BP) measures or from 24-h ambulatory BP recordings (ABPV). BP also varies on a beat-to-beat basis, quantified by methods that parse variation into discrete bins, e.g., low frequency (0.04–0.15 Hz, LF). However, beat-to-beat BPV requires continuous recordings that are not easily acquired. As a result, we know little about the relationship between LF-BPV and basic sociodemographic characteristics such as age, sex, and race and clinical conditions. Methods: We computed LF-BPV during an 11-min resting period in 2,118 participants in the Midlife in the US (MIDUS) study. Results: LF-BPV was negatively associated with age, greater in men than women, and unrelated to race or socioeconomic status. It was greater in participants with hypertension but unrelated to hyperlipidemia, hypertriglyceridemia, diabetes, elevated CRP, or obesity. LF-diastolic BPV (DBPV), but not-systolic BPV (SBPV), was negatively correlated with IL-6 and s-ICAM and positively correlated with urinary epinephrine and cortisol. Finally, LF-DBPV was negatively associated with mortality, an effect was rendered nonsignificant by adjustment by age but not other sociodemographic characteristics. Discussion: These findings, the first from a large, national sample, suggest that LF-BPV differs significantly from VVV-BP and ABPV. Confirming its relationship to sociodemographic risk factors and clinical outcomes requires further study with large and representative samples. |
format | Online Article Text |
id | pubmed-10484414 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-104844142023-09-08 Cardiovascular variability, sociodemographics, and biomarkers of disease: the MIDUS study Gruenewald, Tara Seeman, Teresa E. Choo, Tse-Hwei Scodes, Jennifer Snyder, Clayton Pavlicova, Martina Weinstein, Maxine Schwartz, Joseph E. Mukkamala, Ramakrishna Sloan, Richard P. Front Physiol Physiology Introduction: Like heart rate, blood pressure (BP) is not steady but varies over intervals as long as months to as short as consecutive cardiac cycles. This blood pressure variability (BPV) consists of regularly occurring oscillations as well as less well-organized changes and typically is computed as the standard deviation of multiple clinic visit-to-visit (VVV-BP) measures or from 24-h ambulatory BP recordings (ABPV). BP also varies on a beat-to-beat basis, quantified by methods that parse variation into discrete bins, e.g., low frequency (0.04–0.15 Hz, LF). However, beat-to-beat BPV requires continuous recordings that are not easily acquired. As a result, we know little about the relationship between LF-BPV and basic sociodemographic characteristics such as age, sex, and race and clinical conditions. Methods: We computed LF-BPV during an 11-min resting period in 2,118 participants in the Midlife in the US (MIDUS) study. Results: LF-BPV was negatively associated with age, greater in men than women, and unrelated to race or socioeconomic status. It was greater in participants with hypertension but unrelated to hyperlipidemia, hypertriglyceridemia, diabetes, elevated CRP, or obesity. LF-diastolic BPV (DBPV), but not-systolic BPV (SBPV), was negatively correlated with IL-6 and s-ICAM and positively correlated with urinary epinephrine and cortisol. Finally, LF-DBPV was negatively associated with mortality, an effect was rendered nonsignificant by adjustment by age but not other sociodemographic characteristics. Discussion: These findings, the first from a large, national sample, suggest that LF-BPV differs significantly from VVV-BP and ABPV. Confirming its relationship to sociodemographic risk factors and clinical outcomes requires further study with large and representative samples. Frontiers Media S.A. 2023-08-24 /pmc/articles/PMC10484414/ /pubmed/37693005 http://dx.doi.org/10.3389/fphys.2023.1234427 Text en Copyright © 2023 Gruenewald, Seeman, Choo, Scodes, Snyder, Pavlicova, Weinstein, Schwartz, Mukkamala and Sloan. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Physiology Gruenewald, Tara Seeman, Teresa E. Choo, Tse-Hwei Scodes, Jennifer Snyder, Clayton Pavlicova, Martina Weinstein, Maxine Schwartz, Joseph E. Mukkamala, Ramakrishna Sloan, Richard P. Cardiovascular variability, sociodemographics, and biomarkers of disease: the MIDUS study |
title | Cardiovascular variability, sociodemographics, and biomarkers of disease: the MIDUS study |
title_full | Cardiovascular variability, sociodemographics, and biomarkers of disease: the MIDUS study |
title_fullStr | Cardiovascular variability, sociodemographics, and biomarkers of disease: the MIDUS study |
title_full_unstemmed | Cardiovascular variability, sociodemographics, and biomarkers of disease: the MIDUS study |
title_short | Cardiovascular variability, sociodemographics, and biomarkers of disease: the MIDUS study |
title_sort | cardiovascular variability, sociodemographics, and biomarkers of disease: the midus study |
topic | Physiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10484414/ https://www.ncbi.nlm.nih.gov/pubmed/37693005 http://dx.doi.org/10.3389/fphys.2023.1234427 |
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