Journal of the American Heart Association · 2026
Sublingual Sidestream Dark Field (SDF) Microscopy With GlycoCheck Analysis in Individuals With and Without Cardiovascular Risk Factors and Disease.
СтатьяПоддержка NIH: внешняя программаНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Haller Matthew D, Xia Yuhe, McGowan Natalie G, Garshick Michael S, Heffron Sean P, Berger Jeffrey S, Smilowitz Nathaniel R
Аннотация
BACKGROUND: Microvascular density and endothelial glycocalyx function may provide insights into early atherosclerosis and cardiovascular disease (CVD) risk. Sublingual sidestream darkfield (SDF) microscopy permits imaging of red blood cells (RBC) to assess the microcirculation. We sought to define reference ranges for SDF microscopy parameters in healthy populations and individuals with coronary artery disease (CAD) and to assess factors correlated with microcirculatory abnormalities.
METHODS: Adults with and without CVD risk factors and epicardial CAD underwent SDF microscopy using a CapiScope Handheld Video Capillaroscopy System and Glycocheck analytic software to measure the perfused boundary region (a measure of RBC glycocalyx penetration), percent RBC filling (%RBC, a measure of microvascular perfusion) and microvascular density.
RESULTS: A total of 413 individuals underwent SDF microscopy, including 86 healthy adults, 273 adults with ≥1 CVD risk factors without established obstructive CAD, and 54 with obstructive CAD. Among healthy adults, median perfused boundary region was 1.89 μm [interquartile range 1.71-2.06], median %RBC was 74.4% [71.2-77.6], and median microvascular density was 411 μm/mm2 [331-480]. Compared with healthy adults, participants with obstructive CAD had higher median perfused boundary region (2.02 [1.89-2.15], P=0.004), lower %RBC (71.1% [67.2-74.6], P=0.002), and lower microvascular density (298 [250-384], P<0.0001), though values overlapped substantially; after adjustment for demographics and CVD risk factors, obstructive CAD was not independently associated with sublingual microvascular parameters.
CONCLUSIONS: Obstructive CAD was not associated with sublingual microvascular parameters after accounting for demographics and CVD risk factors. The overlap of microvascular parameters in patients with and without CAD limits the clinical utility of SDF microscopy to identify traditional CVD.
METHODS: Adults with and without CVD risk factors and epicardial CAD underwent SDF microscopy using a CapiScope Handheld Video Capillaroscopy System and Glycocheck analytic software to measure the perfused boundary region (a measure of RBC glycocalyx penetration), percent RBC filling (%RBC, a measure of microvascular perfusion) and microvascular density.
RESULTS: A total of 413 individuals underwent SDF microscopy, including 86 healthy adults, 273 adults with ≥1 CVD risk factors without established obstructive CAD, and 54 with obstructive CAD. Among healthy adults, median perfused boundary region was 1.89 μm [interquartile range 1.71-2.06], median %RBC was 74.4% [71.2-77.6], and median microvascular density was 411 μm/mm2 [331-480]. Compared with healthy adults, participants with obstructive CAD had higher median perfused boundary region (2.02 [1.89-2.15], P=0.004), lower %RBC (71.1% [67.2-74.6], P=0.002), and lower microvascular density (298 [250-384], P<0.0001), though values overlapped substantially; after adjustment for demographics and CVD risk factors, obstructive CAD was not independently associated with sublingual microvascular parameters.
CONCLUSIONS: Obstructive CAD was not associated with sublingual microvascular parameters after accounting for demographics and CVD risk factors. The overlap of microvascular parameters in patients with and without CAD limits the clinical utility of SDF microscopy to identify traditional CVD.