Key result
Diabetes was not significantly associated with lower wearable-derived heart rate variability when age was modeled using restricted cubic splines to account for nonlinear age effects.
Why the study?
To investigate whether modeling age influences the association between glycemic status and resting seated wearable-derived HRV, and whether corrected QT interval is independently associated with HRV.
Does nonlinear age modeling alter the association between glycemic status and resting seated wearable-derived heart rate variability in adults?
Cross-Sectional (n=72)
No
Does nonlinear age modeling alter the association between glycemic status and resting seated wearable-derived heart rate variability in adults?
Effect estimate: β = -0.56
p-value: p=0.09
Differences in wearable-derived heart rate variability across glycemic states are highly sensitive to age modeling and limited covariate overlap, suggesting caution in interpreting these differences as definitive evidence of diabetes-related autonomic dysfunction.
Do not interpret wearable HRV as a glycemic autonomic marker without nonlinear age adjustment; leaves open independent diabetes effects.
Heart rate variability (HRV) is an age-dependent autonomic biomarker increasingly derived from wearable devices. Across normal, prediabetes, and diabetes groups, we investigated whether modeling age influences the association between glycemic status and resting seated wearable-derived heart rate variability, and whether the corrected QT interval is independently associated with heart rate variability after multivariable adjustment. In this cross-sectional multimodal study, 72 adults across three glycemic states (normal, prediabetes, and diabetes) underwent wearable single-lead electrocardiography during stable seated resting-state recordings for heart rate variability assessment alongside standard 12-lead electrocardiography. Heart rate variability was quantified as the natural logarithm of the root mean square of successive differences (ln[RMSSD]). Multivariable linear regression models adjusted for age, sex, body mass index, mean heart rate, and electrocardiographic parameters, with restricted cubic splines used to account for nonlinear age effects. Unadjusted analyses showed significant differences in ln(RMSSD) across glycemic groups. In linear models, diabetes was associated with lower ln(RMSSD), whereas the corrected QT interval was not independently associated with heart rate variability. However, modeling age with restricted cubic splines attenuated the association between diabetes and ln(RMSSD) to non-significance. Predicted heart rate variability curves for normoglycemic and diabetic groups overlapped across the age range after adjustment. These findings suggest that differences in resting seated wearable-derived HRV across glycemic states are highly sensitive to age modeling and limited covariate overlap. Therefore, they should be interpreted cautiously as evidence of model-dependent biomarker interpretation rather than as definitive evidence for or against diabetes-related autonomic dysfunction.
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SUNG et al. (2026) conducted a cross-sectional in Diabetes and prediabetes (n=72). Diabetes vs. Normal glycemic status was evaluated on Natural logarithm of the root mean square of successive differences (ln[RMSSD]) (β = -0.56, p=0.09). Diabetes was not significantly associated with lower wearable-derived heart rate variability when age was modeled using restricted cubic splines to account for nonlinear age effects.
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