Metabolic biomarkers and long-term blood pressure variability in military young male adults

Yu-Kai Lin; Pang-Yen Liu; Chia-Hao Fan; Kun-Zhe Tsai; Yen-Po Lin; Ju-Mi Lee; Jiunn-Tay Lee; Gen-Min Lin

doi:10.12998/wjcc.v8.i11.2246

Metabolic biomarkers and long-term blood pressure variability in military young male adults

World J Clin Cases. 2020 Jun 6;8(11):2246-2254. doi: 10.12998/wjcc.v8.i11.2246.

Authors

Yu-Kai Lin¹, Pang-Yen Liu², Chia-Hao Fan¹, Kun-Zhe Tsai¹, Yen-Po Lin³, Ju-Mi Lee⁴, Jiunn-Tay Lee⁵, Gen-Min Lin¹

Affiliations

¹ Department of Medicine, Hualien Armed Forces General Hospital, Hualien 970, Taiwan.
² Department of Medicine, Tri-Service General Hospital and National Defense Medical Center, Taipei 114, Taiwan.
³ Department of Critical Care Medicine, Taipei Tzu-Chi General Hospital, New Taipei City 231, Taiwan.
⁴ Department of Preventive Medicine, Eulji College of Medicine, Daejeon 34824, South Korea.
⁵ Departments of Neurology, Tri-Service General Hospital, and Graduate Institute of Medical Sciences, National Defense Medical Center, Taipei 114, Taiwan.

Abstract

Background: Metabolic syndrome is a cluster of cardiovascular risk factors, including central obesity, high blood pressure, elevated plasma glucose, reduced high-density lipoprotein and elevated triglyceride levels.

Aim: To investigate the relationship between metabolic biomarkers and long-term blood pressure variability (BPV) in young males.

Methods: A cohort of 1112 healthy military males aged 18-40 years from the cardiorespiratory fitness and hospitalization events in armed forces study in eastern Taiwan was prospectively included. The following metabolic biomarkers were used: Waist circumference, serum uric acid (SUA), triglycerides, high density lipoprotein, triglycerides, and fasting glycose. BPV was assessed by average real variability (ARV) and standard deviation (SD) across 4 clinic visits during the study period (2012-14, 2014-15, 2015-16, and 2016-18). Multivariable linear regression analysis was used to determine the association after adjusting for age, body mass index, systolic and diastolic blood pressure (SBP and DBP), lipid profiles, physical activity, alcohol intake and tobacco smoking status.

Results: In the unadjusted model, waist circumference was significantly and positively correlated with ARV_DBP and SD_DBP [β (standard errors) = 0.16 (0.049) and 0.22 (0.065), respectively], as was SUA [β = 0.022 (0.009) and 0.038 (0.012), respectively]. High-density lipoprotein was negatively correlated with ARV_SBP [β = -0.13 (0.063)]. There were no associations with the other metabolic biomarkers. In contrast, only SUA was significantly correlated with SD_SBP and SD_DBP [β = 0.019 (0.011) and 0.027 (0.010), respectively] in the adjusted model.

Conclusion: Our findings showed that of traditional metabolic biomarkers, SUA had the strongest positive correlation with long-term systolic and diastolic BPV in young male adults, and the clinical relevance needs further investigation.

Keywords: Blood pressure variability; Metabolic syndrome; Serum uric acid; Young males.