Reducing a marker of biological age with high-intensity exercise Original paper

In this randomized controlled trial, 4 weeks of high-intensity interval training reduced a marker of biological age by almost 4 years in a group of middle-aged adults.

The effect of high-intensity interval training (HIIT) on estimated biological age measured using a “transcriptomic clock” based on messenger RNA. Messenger RNA is a class of biomolecule responsible for translating DNA into proteins.

The secondary study outcomes included self-perceived stress, sleep quality, depression symptoms, BMI, fat mass, visceral fat area, skeletal muscle mass, waist-to-hip ratio, blood pressure, and resting heart rate.

30 adults (average age of 50; 20 women, 10 men) who were characterized as nonexercisers.

The researchers conducted a 4-week randomized controlled study during which the participants completed 12 sessions of HIIT or a control intervention (no exercise).

HIIT was performed 3 times per week for a total of 23 minutes per session. The protocol involved ten 1-minute intervals at an intensity between 77% and 93% of the participants’ estimated maximal heart rate with a 2-minute warmup and cooldown. The participants completed one session per week on each of the following machines: a Concept2 rowing ergometer, a Concept2 bicycle ergometer, and a Noraxon PhysTread treadmill.

All of the study outcomes were measured before and after the 4-week intervention.

Compared to baseline, estimated biological age decreased in the HIIT group by 4 years and increased (nonsignificantly) in the control group by 3 years. Furthermore, age acceleration (the difference between the participants’ chronological age and estimated biological age) decreased by 4 years in the HIIT group and increased by 3 years in the control group. Estimated biological age and age acceleration were both lower in the HIIT group compared to the control group.

Depression symptoms and sleep quality improved, and body fat mass, BMI, and visceral fat area decreased in the HIIT group during the intervention. However, these outcomes were not different compared to the control group.

Chronological age is pretty easy to calculate. It’s the number of years that a person has been alive on this earth. Each calendar year, chronological age increases by one.

But chronological age may not be a true representation of how “old” a person is from a health perspective. In recent years, the development of genomic clocks such as the one used in this study has ushered in the era of biological age. Biological age is determined using several health indicators and biomarkers (e.g., blood glucose, inflammation, immune cell function) that are proposed to be reliable markers of aging. Some biological clocks use epigenetic changes (e.g., DNA methylation) to assess age and/or rate of aging. The utility of these biological clocks is their ability to change in response to health-promoting interventions.

Age acceleration is indicative of someone’s rate of aging, and its value can be either positive or negative. A positive age acceleration means that biological age is greater than chronological age and is indicative of “accelerated aging.” In contrast, a negative age acceleration means that biological age is lower than chronological age and is indicative of a slowed rate of aging. The latter is beneficial for health and what health interventions are designed to achieve.

The participants in the current study were estimated to be nearly 20 years older biologically (70 years old on average) than chronologically (50 years old on average). Positive age acceleration in this group of adults may have been the result of their inactive lifestyles (they were “nonexercisers”) and of having obesity (BMI was 30, on average). In fact, because the participants didn’t have diabetes or other known comorbidities, the acceleration may implicate body composition as a key factor in aging trajectory. This is further supported by the fact that the reduction in biological age was accompanied by a 0.2 decrease in BMI, a 1.5 lb weight loss, and a 4.25 cm³ reduction in visceral fat area.

1 month of HIIT reduces estimated biological age in middle-aged adults

None of the other study outcomes, including stress, depression symptoms, or sleep, were found to improve after HIIT. This lends more support to the idea that body composition may play a disproportionate role in aging.

Acute exercise is beneficial for preventing DNA damage, telomere shortening, and adverse DNA methylation changes, all of which are recognized biomarkers of aging.^[1] However, few studies have directly studied the chronic effects of exercise — particularly high-intensity exercise — on next-generation aging clocks, such as the transcriptomic clock used in this study.

A 2021 study in 219 postmenopausal women indicated that 24 months of improving dietary habits reduced participants’ DNA methylation age, whereas physical activity only reduced aging-associated biomarkers known as stochastic epigenetic mutations.^[2]

An 8-week study in a group of adult men found that a comprehensive lifestyle program comprising diet, sleep, exercise, and relaxation reduced biological age by 3 years compared to a nonintervention control group.^[3]

The problem with each of these studies is that multiple interventions were applied simultaneously, making it diificult to determine which particular factor may have been the most important.

1 month of HIIT reduces age acceleration in middle-aged adults

The current study was the first to investigate HIIT in isolation for reducing biological age. The impressive 4-year reduction in biological age over the 1-month study provides support for high-intensity exercise as a strategy for healthy aging. The participants exercised for a total of 275 minutes during the month-long study, far less than the current exercise guidelines of 150 minutes per week recommends.^[4]

Given the time-efficient nature of HIIT and the abovementioned effects on aging, high-intensity exercise may save time in more ways than one.

The authors noted that their model consistently overestimated the participants’ transcriptomic age (every participant's estimated biological age was considerably higher than their chronological age), perhaps due to the differences in their dataset and the dataset on which the model was trained. Indeed, although the participants’ average chronological age was 50, their average estimated biological age was 70. However, a consistent overestimation among groups likely avoided any introduction of bias into the changes in transcriptomic age that were observed.