Delayed heart rate recovery after exercise predicts development of metabolic syndrome: A retrospective cohort study.

J Diabetes Investig

Division of Endocrinology and Metabolism, Department of Internal Medicine, Uijeongbu Eulji Medical Center, Eulji University School of Medicine, Uijeongbu, Korea.

Published: January 2022


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Article Abstract

Aims/introduction: Several cross-sectional studies have shown that delayed heart rate recovery (HRR) after exercise is associated with the development of metabolic syndrome (MetS). However, there has been a lack of comprehensively designed longitudinal studies. Therefore, our aim was to evaluate the longitudinal association of delayed HRR following a graded exercise treadmill test (GTX) with incident MetS.

Materials And Methods: This was a retrospective longitudinal cohort study of participants without MetS, diabetes, or cardiovascular diseases. The HRR was calculated as the peak heart rate minus the resting heart rate after a 1 min rest (HRR1), a 2 min rest (HRR2), and a 3 min rest (HRR3). Multivariate Cox proportional hazards analysis was performed to investigate the association between HRR and development of MetS.

Results: There were 676 (31.2%) incident cases of MetS identified during the follow-up period (9,683 person-years). The only statistically significant relationship was between HRR3 and the development of MetS. The hazard ratios (HRs) (95% confidence interval [CI]) of incident MetS comparing the first and second tertiles to the third tertile of HRR3 were 1.492 (1.146-1.943) and 1.277 (1.004-1.624) with P = 0.003 after adjustment for multiple risk factors. As a continuous variable, the HR (95% CI) of incident MetS associated with each one-beat decrease in HRR3 was 1.015 (1.005-1.026) with P = 0.004 after full adjustments. An HRR3 value ≤45 beats per minute (bpm) was associated with a higher risk of incident MetS compared with values >45 bpm, with an HR (95% CI) of 1.304 (1.061-1.602) and P = 0.001.

Conclusions: The slow phase of HRR, particularly HRR3, might be more sensitive at predicting the risk of MetS.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8756310PMC
http://dx.doi.org/10.1111/jdi.13637DOI Listing

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