{"id":14880,"date":"2024-01-12T16:56:53","date_gmt":"2024-01-12T15:56:53","guid":{"rendered":"https:\/\/sano.science\/?post_type=research&#038;p=14880"},"modified":"2024-01-12T16:56:53","modified_gmt":"2024-01-12T15:56:53","slug":"vascular-phenotypes-in-early-hypertension","status":"publish","type":"research","link":"https:\/\/sano.science\/research\/vascular-phenotypes-in-early-hypertension\/","title":{"rendered":"Vascular phenotypes in early hypertension"},"content":{"rendered":"\n<h2 class=\"wp-block-heading eplus-wrapper\">Eleanor Murray, Christian Delles, Patryk Orzechowski, Pawel RENC, Arkadiusz SITEK, Joost Wagenaar, and Tomasz Guzik<\/h2>\n\n\n\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n\n\n\n<p class=\"eplus-wrapper wp-block-paragraph\">The study characterises vascular phenotypes of hypertensive patients utilising machine learning approaches. Newly diagnosed and treatment-na\u00efve primary hypertensive patients without co-morbidities (aged 18\u201355,\u00a0<em>n<\/em>\u2009=\u200973), and matched normotensive controls (<em>n<\/em>\u2009=\u200979) were recruited (NCT04015635). Blood pressure (BP) and BP variability were determined using 24\u2009h ambulatory monitoring. Vascular phenotyping included SphygmoCor\u00ae measurement of pulse wave velocity (PWV), pulse wave analysis-derived augmentation index (PWA-AIx), and central BP; EndoPAT\u2122-2000\u00ae provided reactive hyperaemia index (LnRHI) and augmentation index adjusted to heart rate of 75bpm. Ultrasound was used to analyse flow mediated dilatation and carotid intima-media thickness (CIMT). In addition to standard statistical methods to compare normotensive and hypertensive groups, machine learning techniques including biclustering explored hypertensive phenotypic subgroups. We report that arterial stiffness (PWV, PWA-AIx, EndoPAT-2000-derived AI@75) and central pressures were greater in incident hypertension than normotension. Endothelial function, percent nocturnal dip, and CIMT did not differ between groups. The vascular phenotype of white-coat hypertension imitated sustained hypertension with elevated arterial stiffness and central pressure; masked hypertension demonstrating values similar to normotension. Machine learning revealed three distinct hypertension clusters, representing \u2018arterially stiffened\u2019, \u2018vaso-protected\u2019, and \u2018non-dipper\u2019 patients. Key clustering features were nocturnal- and central-BP, percent dipping, and arterial stiffness\u00a0measures. We conclude that untreated patients with primary hypertension demonstrate early arterial stiffening rather than endothelial dysfunction or CIMT alterations. Phenotypic heterogeneity in nocturnal and central BP, percent dipping, and arterial stiffness observed early in the course of disease may have implications for risk stratification.<\/p>\n\n\n\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n\n\n\n\t\n    \n        \n\t\t\t<a href=\"https:\/\/www.nature.com\/articles\/s41371-022-00794-7\" target=\"_blank\" rel= \"noopener noreferrer nofollow\" class=\"button primary \">\n\n\t\t\t\t<span>\n\t\t\t\t\tREAD HERE\n\t\t\t\t<\/span>\n\n\t\t\t<\/a>\n\n        \n    \n","protected":false},"excerpt":{"rendered":"<p>In: Journal of Human Hypertension, 2022.<\/p>\n","protected":false},"featured_media":0,"template":"","research_type":[8],"research_team":[17],"class_list":["post-14880","research","type-research","status-publish","hentry","research_type-publications","research_team-health-informatics-group-higs"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.6 (Yoast SEO v27.6) - 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Newly diagnosed and treatment-na\u00efve primary hypertensive patients without co-morbidities (aged 18\u201355,\u00a0<em>n<\/em>\u2009=\u200973), and matched normotensive controls (<em>n<\/em>\u2009=\u200979) were recruited (NCT04015635). Blood pressure (BP) and BP variability were determined using 24\u2009h ambulatory monitoring. Vascular phenotyping included SphygmoCor\u00ae measurement of pulse wave velocity (PWV), pulse wave analysis-derived augmentation index (PWA-AIx), and central BP; EndoPAT\u2122-2000\u00ae provided reactive hyperaemia index (LnRHI) and augmentation index adjusted to heart rate of 75bpm. Ultrasound was used to analyse flow mediated dilatation and carotid intima-media thickness (CIMT). In addition to standard statistical methods to compare normotensive and hypertensive groups, machine learning techniques including biclustering explored hypertensive phenotypic subgroups. We report that arterial stiffness (PWV, PWA-AIx, EndoPAT-2000-derived AI@75) and central pressures were greater in incident hypertension than normotension. Endothelial function, percent nocturnal dip, and CIMT did not differ between groups. The vascular phenotype of white-coat hypertension imitated sustained hypertension with elevated arterial stiffness and central pressure; masked hypertension demonstrating values similar to normotension. Machine learning revealed three distinct hypertension clusters, representing \u2018arterially stiffened\u2019, \u2018vaso-protected\u2019, and \u2018non-dipper\u2019 patients. Key clustering features were nocturnal- and central-BP, percent dipping, and arterial stiffness\u00a0measures. We conclude that untreated patients with primary hypertension demonstrate early arterial stiffening rather than endothelial dysfunction or CIMT alterations. Phenotypic heterogeneity in nocturnal and central BP, percent dipping, and arterial stiffness observed early in the course of disease may have implications for risk stratification.<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">The study characterises vascular phenotypes of hypertensive patients utilising machine learning approaches. Newly diagnosed and treatment-na\u00efve primary hypertensive patients without co-morbidities (aged 18\u201355,\u00a0<em>n<\/em>\u2009=\u200973), and matched normotensive controls (<em>n<\/em>\u2009=\u200979) were recruited (NCT04015635). Blood pressure (BP) and BP variability were determined using 24\u2009h ambulatory monitoring. Vascular phenotyping included SphygmoCor\u00ae measurement of pulse wave velocity (PWV), pulse wave analysis-derived augmentation index (PWA-AIx), and central BP; EndoPAT\u2122-2000\u00ae provided reactive hyperaemia index (LnRHI) and augmentation index adjusted to heart rate of 75bpm. Ultrasound was used to analyse flow mediated dilatation and carotid intima-media thickness (CIMT). In addition to standard statistical methods to compare normotensive and hypertensive groups, machine learning techniques including biclustering explored hypertensive phenotypic subgroups. We report that arterial stiffness (PWV, PWA-AIx, EndoPAT-2000-derived AI@75) and central pressures were greater in incident hypertension than normotension. Endothelial function, percent nocturnal dip, and CIMT did not differ between groups. The vascular phenotype of white-coat hypertension imitated sustained hypertension with elevated arterial stiffness and central pressure; masked hypertension demonstrating values similar to normotension. Machine learning revealed three distinct hypertension clusters, representing \u2018arterially stiffened\u2019, \u2018vaso-protected\u2019, and \u2018non-dipper\u2019 patients. Key clustering features were nocturnal- and central-BP, percent dipping, and arterial stiffness\u00a0measures. We conclude that untreated patients with primary hypertension demonstrate early arterial stiffening rather than endothelial dysfunction or CIMT alterations. Phenotypic heterogeneity in nocturnal and central BP, percent dipping, and arterial stiffness observed early in the course of disease may have implications for risk stratification.<\/p>\n"]},{"blockName":"core\/spacer","attrs":{"height":"50px","epAnimationGeneratedClass":"edplus_anim-mDL8y1","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n"]},{"blockName":"acf\/button","attrs":{"title":"READ HERE","button_type":"link","url":"https:\/\/www.nature.com\/articles\/s41371-022-00794-7","button_style":"primary","target":"_blank","button_extra_classes":""},"innerBlocks":[],"innerHTML":"","innerContent":[]}],"meta_data":{"is_automatically_other_posts":true,"number_of_posts":"3","is_automatically_check_also_posts":true},"_links":{"self":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research\/14880","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research"}],"about":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/types\/research"}],"version-history":[{"count":2,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research\/14880\/revisions"}],"predecessor-version":[{"id":14882,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research\/14880\/revisions\/14882"}],"wp:attachment":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/media?parent=14880"}],"wp:term":[{"taxonomy":"research_type","embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research_type?post=14880"},{"taxonomy":"research_team","embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research_team?post=14880"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}