Unsupervised cluster analysis reveals distinct subgroups in healthy population with different exercise responses of cardiorespiratory fitness

Abstract

Background/Objective: Considerable attention has been paid to interindividual differences in the cardiorespiratory fitness (CRF) response to exercise. However, the complex multifactorial nature of CRF response variability poses a significant challenge to our understanding of this issue. We aimed to explore whether unsupervised clustering can take advantage of large amounts of clinical data and identify latent subgroups with different CRF exercise responses within a healthy population. Methods: 252 healthy participants (99 men, 153 women; 36.8 ± 13.4 yr) completed moderate endurance training on 3 days/week for 4 months, with exercise intensity prescribed based on anaerobic threshold (AT). Detailed clinical measures, including resting vital signs, ECG, cardiorespiratory parameters, echocardiography, heart rate variability, spirometry and laboratory data, were obtained before and after the exercise intervention. Baseline phenotypic variables that were significantly correlated with CRF exercise response were identified and subjected to selection steps, leaving 10 minimally redundant variables, including age, BMI, maximal oxygen uptake (VO2max), maximal heart rate, VO2 at AT as a percentage of VO2max, minute ventilation at AT, interventricular septal thickness of end-systole, E velocity, root mean square of heart rate variability, and hematocrit. Agglomerative hierarchical clustering was performed on these variables to detect latent subgroups that may be associated with different CRF exercise responses. Results: Unsupervised clustering revealed two mutually exclusive groups with distinct baseline phenotypes and CRF exercise responses. The two groups differed markedly in baseline characteristics, initial fitness, echocardiographic measurements, laboratory values, and heart rate variability parameters. A significant improvement in CRF following the 16-week endurance training, expressed by the absolute change in VO2max, was observed only in one of the two groups (3.42 ± 0.4 vs 0.58 ± 0.65 ml⋅kg−1⋅min−1, P = 0.002). Assuming a minimal clinically important difference of 3.5 ml⋅kg−1⋅min−1 in VO2max, the proportion of population response was 56.1% and 13.9% for group 1 and group 2, respectively (P<0.001). Although group 1 exhibited no significant improvement in CRF at group level, a significant decrease in diastolic blood pressure (70.4 ± 7.8 vs 68.7 ± 7.2 mm Hg, P = 0.027) was observed. Conclusions: Unsupervised learning based on dense phenotypic characteristics identified meaningful subgroups within a healthy population with different CRF responses following standardized aerobic training. Our model could serve as a useful tool for clinicians to develop personalized exercise prescriptions and optimize training effects.

Keywords

• Interindividual variability
• Oxygen uptake
• Training responsiveness
• Unsupervised learning