Department of Internal Medicine III, University Hospital Carl Gustav Carus at Technische Universität Dresden, 01307 Dresden, Germany.
Centre for Vascular Medicine, Clinic of Angiology, St.-Josefs-Hospital, Katholische Krankenhaus Hagen gem. GmbH, 58097 Hagen, Germany.
Sensors (Basel). 2024 Mar 5;24(5):1673. doi: 10.3390/s24051673.
Body mass index (BMI) is seen as a predictor of cardiovascular disease (CVD) in lipedema patients. A valid predictor of CVD is increased aortic stiffness (IAS), and previous research described IAS in lipedema. However, it is not known if this applies to all patients. In this cross-sectional single-center cohort study, peripheral pulse wave velocity (PWV) as a non-invasive indicator of aortic stiffness was measured in 41 patients with lipedema, irrespective of stage and without pre-existing cardiovascular conditions or a history of smoking and a maximum body mass index (BMI) of 35 kg/m. Automatically electrocardiogram-triggered oscillometric sensor technology by the Gesenius-Keller method was used. Regardless of the stage of lipedema disease, there was no significant difference in PWV compared to published standard values adjusted to age and blood pressure. BMI alone is not a predictor of cardiovascular risk in lipedema patients. Measuring other anthropometric factors, such as the waist-hip ratio or waist-height ratio, should be included, and the existing cardiovascular risk factors, comorbidities, and adipose tissue distribution for accurate risk stratification should be taken into account. Automated sensor technology recording the PWV represents a valid and reliable method for health monitoring and early detection of cardiovascular risks.
体重指数 (BMI) 被视为脂肪水肿患者心血管疾病 (CVD) 的预测指标。CVD 的一个有效预测指标是主动脉僵硬度 (IAS) 增加,先前的研究描述了脂肪水肿患者的 IAS。然而,目前尚不清楚这是否适用于所有患者。在这项横断面单中心队列研究中,测量了 41 名脂肪水肿患者的外周脉搏波速度 (PWV),作为主动脉僵硬度的无创指标,无论疾病分期如何,且不考虑存在心血管疾病或吸烟史以及最大 BMI(体重指数)为 35 kg/m。使用 Gesenius-Keller 方法的自动心电图触发的振荡传感器技术。无论脂肪水肿疾病的阶段如何,与根据年龄和血压调整后的公布标准值相比,PWV 均无显着差异。BMI 本身并不是脂肪水肿患者心血管风险的预测指标。应该包括测量其他人体测量因素,如腰臀比或腰高比,并且应该考虑现有的心血管危险因素、合并症和脂肪组织分布,以进行准确的风险分层。记录 PWV 的自动传感器技术代表了一种有效的、可靠的健康监测和早期心血管风险检测方法。