OR WAIT null SECS
A new analysis suggests an association between elevated heart rate and increased risk of developing new-onset T2D, but the effects were largely restricted to younger individuals.
High heart rate and heart rate variability may be significantly associated with an increased risk of developing type 2 diabetes (T2D), particularly among younger people, according to a longitudinal analysis of the Rotterdam Study.1
However, inconsistent with the longitudinal findings, bidirectional Mendelian randomization analyses suggested a lack of causal association between heart rate variability and incident T2D.
“At first sight, our findings suggest an association between elevated heart rate and increased risk of developing T2D, consistent with previous studies," investigators wrote. “However, the effects were restricted to younger individuals.”
Led by Maryam Kavousi, PhD, Erasmus University Medical Center, the team investigated the prospective association of evolution of heart rate variability, as a proxy for autonomic function, with T2D incidence. As well, investigators conducted a bidirectional Mendelian randomization (MR) analysis using summary-level data to explore the causality of the association. Using the population-based Rotterdam Study, Kavousi and colleagues included a total of 7630 participants (mean age, 62.1 years; 58% women) without a history of T2D and atrial fibrillation and had repeated HRV assessments at baseline and follow-up.
For the purpose of analysis, joint models were used to assess the association between the longitudinal evolution of heart rate and different heart rate variability metrics (heart rate corrected standard deviation of the normal-to-normal RR intervals [SDNNc] and root mean square of successive RR-interval differences [RMSSDc]) with incident T2D. The models were adjusted for age and sex, as well as body mass index (BMI), smoking status, use of blood-pressure medications, and heart rate variability metrics.
Over a median follow-up of 8.6 years, 871 study participants developed incident T2D. The joint model analysis revealed heart rate and heart rate variability metrics were positively associated with incident T2D. Regarding heart rate, one standard deviation (SD) increase in heart rate was associated with the risk of developing T2D (hazard ratio [HR], 1.20; 95% CI, 1.09 - 1.33).
For heart rate variability, both metrics showed positive associations with T2D development, with statistically significant associations found for RMSSDc (HR, 1.16; 95% CI, 1.01 - 1.33). However, the association of SDNNc with incident T2D was not found to be statistically significant (HR, 1.10; 95% CI, 0.94 - 1.29) in the full model.
Investigators noted a significant interaction between age and heart rate (P for interaction <.001), with the association stronger among younger participants. Data showed the HRs were 1.54 (95% CI, 1.08 - 2.06) for those <62 years and 1.15 (95% CI, 1.01 - 1.31) for those >62 years. Despite significant associations being restricted to men, the analysis indicated the interaction term for sex was not statistically significant.
Similar associations between heart rate and different heart rate variability metrics with incident T2D were observed in sensitivity analyses, after adjusting for relevant cardiovascular variables. Moreover, the results from the bidirectional MR analyses suggested no causal association between HRV and incident T2D (OR, 0.94 [95% CI, 0.75 - 1.18] per one unit log increment for SDNN; OR, 1.04 [95% CI, 0.82 - 1.32] per one unit log increment for RMSSD).
Kavousi and colleagues noted the inconsistent findings may be a result of limited study power, or the use of heart-rate uncorrected heart rate variables due to the lack of available SNPs. Together, it may partly explain the observed heterogeneity from the analysis. The team indicated future genome-wide association study with a larger sample size and individual level data could potentially identify more genetic variants used to assess the association.
“Given that the more novel MR approaches can check the potential nonlinear association between exposure and outcomes using individual-level data, future studies with more detailed data and using comprehensive methods are needed to validate our findings,” investigators wrote.