Yogesh M, Mody Mansi, Patel Jenish, Shah Samyak, Makwana Naresh, Nagda Jay
Department of Community Medicine, Shri M. P. Shah Government Medical College, Jamnagar, Gujarat, India.
Department of Internal Medicine Final Year Medical Student, Shri M. P. Shah Government Medical College, Jamnagar, Gujarat, India.
J Family Med Prim Care. 2024 Oct;13(10):4336-4342. doi: 10.4103/jfmpc.jfmpc_408_24. Epub 2024 Oct 18.
Hypertension prediction using anthropometry and bioimpedance offers practical advantages for screening. We aimed to analyze various anthropometric and bioelectrical impedance (BIA) estimates as predictive markers of prehypertension and hypertension.
This cross-sectional analysis included 432 adult participants recruited from the medicine outpatient department of a tertiary hospital. Blood pressure measurements; anthropometric measurements of weight, body mass index, waist circumference, and hip circumference; and BIA (Omron HBF 375) were performed for body fat%, resting metabolic rate, visceral fat level, and skeletal muscle percentage.
Of the 432 participants comprising 220 males and 212 females, 36.8% were normotensive, 42% were prehypertensive, and 21% were hypertensive. Visceral fat (r 0.662, 95% CI: 0.60-0.72, < 0.001) and resting metabolic rate (r 0.589, 95% CI: 0.52-0.65, < 0.001) had the highest positive correlation, while skeletal muscle percentage (r -0.551, 95% CI: -0.62 to -0.48, < 0.001) had a negative correlation with systolic blood pressure according to bivariate analysis. According to the receiver operating characteristic curve analysis for predicting hypertension, visceral fat volume had an area under curve (AUC) of 0.913, and resting metabolic rate had an AUC of 0.968, indicating the best predictive accuracy.
Multiple BIA estimates, including high visceral fat content, resting metabolic rate, and adipose marker levels combined with low skeletal muscle percentage, were strongly associated with hypertension. Our analysis suggested the superiority of bioimpedance predictors over anthropometry-based prediction modeling alone for screening for hypertension in clinical practice.
利用人体测量学和生物阻抗进行高血压预测为筛查提供了实际优势。我们旨在分析各种人体测量学和生物电阻抗(BIA)估计值作为高血压前期和高血压的预测指标。
这项横断面分析纳入了从一家三级医院内科门诊招募的432名成年参与者。进行了血压测量;体重、体重指数、腰围和臀围的人体测量;以及BIA(欧姆龙HBF 375)测量以获取体脂百分比、静息代谢率、内脏脂肪水平和骨骼肌百分比。
在432名参与者中,男性220名,女性212名,36.8%为血压正常者,42%为高血压前期患者,21%为高血压患者。双变量分析显示,内脏脂肪(r = 0.662,95%置信区间:0.60 - 0.72,P < 0.001)和静息代谢率(r = 0.589,95%置信区间:0.52 - 0.65,P < 0.001)与收缩压的正相关性最高,而骨骼肌百分比(r = -0.551,95%置信区间:-0.62至-0.48,P < 0.001)与收缩压呈负相关。根据预测高血压的受试者工作特征曲线分析,内脏脂肪体积的曲线下面积(AUC)为0.913,静息代谢率的AUC为0.968,表明预测准确性最佳。
包括高内脏脂肪含量、静息代谢率和脂肪标志物水平以及低骨骼肌百分比在内的多种BIA估计值与高血压密切相关。我们的分析表明,在临床实践中筛查高血压时,生物阻抗预测指标优于单纯基于人体测量学的预测模型。