Using Latent Class Analysis to Identify Different Risk Patterns for Patients With Masked Hypertension.

Abstract

Background: There is controversy whether masked hypertension (MHT) requires additional intervention. The aim of this study is to evaluate whether MHT accompanied with high-risk metabolic syndrome (MetS), as the subphenotype, will have a different prognosis from low-risk MetS. Methods: We applied latent class analysis to identify subphenotypes of MHT, using the clinical and biological information collected from High-risk Cardiovascular Factor Screening and Chronic Disease Management Programme. We modeled the data, examined the relationship between subphenotypes and clinical outcomes, and further explored the impact of antihypertensive medication. Results: We included a total of 140 patients with MHT for analysis. The latent class model showed that the two-class (high/low-risk MetS) model was most suitable for MHT classification. The high-risk MetS subphenotype was characterized by larger waist circumference, lower HDL-C, higher fasting blood glucose and triglycerides, and prevalence of diabetes. After four years of follow-up, participants in subphenotype 1 had a higher non-major adverse cardiovascular event (MACE) survival probability than those in subphenotype 2 (P = 0.016). There was no interaction between different subphenotypes and the use of antihypertensive medications affecting the occurrence of MACE. Conclusions: We have identified two subphenotypes in MHT that have different metabolic characteristics and prognosis, which could give a clue to the importance of tracing the clinical correlation between MHT and metabolic risk factors. For patients with MHT and high-risk MetS, antihypertensive therapy may be insufficient.

• Authors: Ming Fu, Xiangming Hu, Shixin Yi, Shuo Sun, Ying Zhang, Yingqing Feng, Qingshan Geng, Yingling Zhou, Haojian Dong
• Journal: Frontiers in cardiovascular medicine (Front Cardiovasc Med) Vol. 8 Pg. 680083 ( 2021) ISSN: 2297-055X [Print] Switzerland
• PMID: 34513942 (Publication Type: Journal Article)
• Copyright: Copyright © 2021 Fu, Hu, Yi, Sun, Zhang, Feng, Geng, Zhou and Dong.