In Nature Aging, researchers have published the creation of a new clock that uses multiple metrics to evaluate biological aging.
What’s worth measuring?
Multiple metrics have been used to measure aging. The most commonly known in the literature are the epigenetic clocks, such as GrimAge and PhenoAge, but those are not the only sources of information. For many decades, people have been attempting to build clocks based on physical analysis in rodents [1] and people [2], an effort that continues to this day [3].
Some clocks are meant to estimate biological age [4], while others are built around determining how likely it is that an organism will die within a certain timeframe [5]. The latter are often correlated with markers of specific risks, such as cardiovascular risks, but are more geared towards predicting all-cause mortality.
Many methylation-based clocks are built around clinical features, but these researchers have decided to go straight to the source instead, focusing on clinical clocks, which directly measure clinical metrics intead of epigenetic one. Becaue the number of metrics that can be derived from any one individual is very large, this team prioritized focusing on combining them into principal components (PCs), which are often used to analyze large data sets.
Proving the concept
After removing incomplete information, this study used data from just under 1,800 people in the 1999-2000 cohort of the widely referenced NHANES database, and they tested it on just over 2,000 people in the 2001-2002 NHANES cohort to demonstrate its validity. Starting with 165 clinical parameters, the team was able to use an algorithm to compress them into 18 PCs that the team could use to predict all-cause mortality, calculating men and women separately. They used this prediction as their basis for a clock that estimates biological age (PCAge), which, unsurprisingly, was closely correlated with chronological age.
People with lower PCAge estimates had longer telomeres, faster walking speed, and better cognitive performance than people with higher estimates but the same chronological age. Compared to the ASCVD, a widely used measurement of estimating cardiovascular risk, PCAge was found to be a better predictor of mortality and was less sensitive to noise in the data. The researchers also found that PCAge was more useful in predicting survival than the PhenoAge clock.
The researchers were also able to group people into five broad categories based on this data: healthy agers, people with three distinct severity levels of metabolic disorders, and people with multimorbidities. As expected, the healthy aging group had the lowest PCAge compared to chronological age. People who lived to be centenarians, also as expected, had lower PCAges than other people in their cohort who did not live that long.
One of the PCs used in this study, PC2, was found to be the most correlated with healthy aging. When they unpacked this PC back into its components, they found that its strongest elements involved metrics related to fat mass, leading the researchers to suggest that healthy weight maintenance and healthy aging are strongly linked.
PC4 was also found to be very strongly significant, and this PC was comprised of such factors as kidney function, glucose metabolism, and inflammation. People with untreated kidney disease, as measured by the albumin-to-creatinine ratio, also scored worse on PC4’s other components; people with treated disease had much better outcomes than the untreated group. This finding, according to the researchers, underscores the need for early detection and proper prescription of drugs that treat this particular ailment.
The researchers also used their methodology to analyze the effects of caloric restriction, as conducted by the CALERIE trial. Unsurprisingly, they found that caloric restriction was associated with reduced biological age.
An easier clock
Being made of so many measurements, PCAge is hard to derive in the clinic. Therefore, the researchers used the same cohorts to develop a simpler clock, LinAge, which uses standard blood biomarkers along with basic information about patients that is readily available in any medical setting. LinAge was found to be a better predictor of mortality than chronological age, ASCVD, and the chronic frailty scale, and it performed slightly better than PhenoAge in predicting mortalty as well. Despite being trained on 20-year follow-up data, LinAge was found to be effective in determining mortality 25 years away in an earlier NHANES cohort.
The researchers note that they cannot determine the causality of the interventions they suspect to be effective; confounding factors may be at play. However, the clock they have created appears to be accurate and can be quickly derived from simple blood tests and clinical data. They see their tool as being “to geroscience what clinical risk scores are to traditional primary prevention.”
Aging clocks are not replacements for disease-specific risk markers or differential diagnosis. They differentiate subjects who are aging well from those who are aging poorly, helping us to define the former and pointing to interventions to help the latter.
Literature
[1] Ingram, D. K. (1983). Toward the behavioral assessment of biological aging in the laboratory mouse: concepts, terminology, and objectives. Experimental aging research, 9(4), 225-238.
[2] Comfort, A. (1969). Test-battery to measure ageing-rate in man. The Lancet, 294(7635), 1411-1415.
[3] Ferrucci, L., Gonzalez‐Freire, M., Fabbri, E., Simonsick, E., Tanaka, T., Moore, Z., … & de Cabo, R. (2020). Measuring biological aging in humans: A quest. Aging cell, 19(2), e13080.
[4] Horvath, S. (2013). DNA methylation age of human tissues and cell types. Genome biology, 14, 1-20.
[5] Lu, A. T., Binder, A. M., Zhang, J., Yan, Q., Reiner, A. P., Cox, S. R., … & Horvath, S. (2022). DNA methylation GrimAge version 2. Aging (Albany NY), 14(23), 9484.
