Association of Physical Activity with Phenotypic Age among Populations with Different Breakfast Habits

Abstract

Background: The global aging situation has reached a serious stage, and healthy lifestyles, like regular physical activity and eating breakfast, could slow the process. Phenotypic age (PhenoAge) is regarded as a novel measure of aging. Therefore, our study aimed to quantify the impact of physical activity and eating breakfast on aging via PhenoAge and phenotypic age acceleration (PhenoAgeAccel).

Methods: A total of 3719 adults who participated in the National Health and Nutrition Examination Survey were involved in this study. Physical activity was divided into an active group and an inactive group. According to the number of reported breakfast recalls, eating breakfast was divided into the no recalls group, one recall group, and both recalls group. Sensitivity analysis was performed by stratified analysis.

Results: Active physical activity was a protective factor for PhenoAge and PhenoAgeAccel. Compared to the inactive group, the β values of the active group were -8.36 (-10.09, -6.62) for PhenoAge and -1.67 (-2.21, -1.14) for PhenoAgeAccel. The stratified analysis results showed that in the groups reporting breakfast in both recalls, one recall, and no recalls, the β values of the active group were -8.84 (-10.70, -6.98), -8.17 (-12.34, -4.00), and -3.46 (-7.74, 0.82), respectively, compared to the inactive group.

Conclusions: Active physical activity was strongly correlated with lower values of PhenoAge and PhenoAgeAccel, but the association was no longer statistically significant when combined with not regularly eating breakfast.

Keywords: aging; eating breakfast; inflammation; phenotypic age; physical activity.

Figure 1

Flowchart of the study design and participants.

Figure 2

Forest plot of stratified analyses of the associations between physical activity and PhenoAge. Note: the control group was physically inactive. Adjusted for reported breakfast, gender, race, education status, marital status, income status, BMI, DII, energy intake, smoking status, drinking status, and sleep disorder. Bold indicates the value of the P-interaction for the grouping variable.