How Should We Measure Aging?

Intrinsic capacity still lacks a common measuring stick

Featured paper: Existing validated tools for assessing intrinsic capacity and its components within a positive ageing framework: a scoping review and structured appraisal by the Multidisciplinary International Positive Ageing Group

The question

How consistently can intrinsic capacity actually be measured?

What the study found

Henrotin and colleagues reviewed the tools currently used to assess intrinsic capacity, the World Health Organization construct that brings together locomotion, cognition, psychological capacity, vitality, and sensory capacity.

They identified 61 studies describing 61 distinct assessment instruments.

The first problem was heterogeneity. Different studies used very different combinations of tests to describe the same construct. The second was imbalance. Locomotion, cognition, and psychological capacity were relatively well represented, while vitality and especially sensory capacity remained less consistently operationalized.

Few instruments covered all five domains, and very few had been developed or validated for adults younger than 60 years.

Why it matters

Intrinsic capacity was introduced to shift attention away from disease counts and toward what people are still physically and mentally able to do.

But that idea only becomes useful if different investigators mean roughly the same thing when they measure it.

At the moment, two papers can both report an “intrinsic capacity score” while using quite different indicators. That makes it harder to compare populations, follow trajectories, and determine whether an intervention has produced meaningful change.

The lack of measures before older age is equally important. If intrinsic capacity is meant to support prevention across the life course, we need tools capable of detecting change before major losses have already occurred.

Keep in mind

Intrinsic capacity is not another name for frailty. The two can overlap, but one focuses on retained capacities and the other primarily on vulnerability and deficit accumulation.

If the clock moves, did aging really change?

Featured paper: Responsiveness of epigenetic aging biomarkers to longevity interventions in humans

The question

Do epigenetic aging biomarkers actually respond when people undergo interventions intended to improve health or influence aging?

What the study found

Sehgal and colleagues built TranslAGE, bringing together 51 longitudinal intervention studies and 3,128 blood samples.

They recalculated the same panel of 16 epigenetic clocks across all studies, together with 94 other DNA methylation biomarkers.

The clocks behaved quite differently.

Newer measures designed around mortality risk or pace of aging were generally more responsive than early clocks developed mainly to reproduce chronological age. DunedinPACE showed some of the largest changes, while PCGrimAge produced particularly consistent signals.

The response also depended on the intervention. Pharmacological treatments produced larger average effects, while some lifestyle interventions showed consistent changes across several biomarkers.

Why it matters

This gets to the heart of why aging biomarkers are attractive in the first place.

If a marker can respond over months rather than decades, it could make trials of longevity interventions much more practical.

But a responsive biomarker is not automatically a meaningful surrogate. A clock can move without telling us whether disability, disease, or survival will change with it.

That is the next step the field has to solve.

Keep in mind

There is still no established clinically meaningful change for most epigenetic clocks. A younger biological-age estimate is not yet the same thing as a demonstrated improvement in healthspan.

A frailty score is more useful when we know what a change means

Featured paper: Outcome-specific thresholds and clinically relevant changes in frailty: evidence from a population-based longitudinal cohort

The question

Can a frailty score tell us more than whether someone crosses a diagnostic cut-off?

What the study found

Álvarez-Bustos and colleagues followed community-dwelling adults aged 65 years or older using the Frailty Trait Scale (FTS) and its shorter five-item version, the FTS-5.

The FTS was measured in 1,811 participants and repeated about five years later.

There was no single threshold that worked equally well for every outcome. On the 100-point FTS, risk began to rise at around 25 points for worsening disability, 40 for hospitalization, and 50 for mortality.

The authors also estimated changes associated with later outcomes. A reduction of at least 10.3 points was associated with lower hospitalization risk, while a reduction of at least 4.5 points was associated with lower mortality.

Why it matters

We often turn continuous aging measures into categories because categories are easy to use.

But “frail” and “not frail” can hide important information about severity and trajectory.

If frailty is going to be used as an outcome in trials or longitudinal care, we need to know whether a change in score is large enough to matter. This study is a useful attempt to move in that direction.

Keep in mind

These values belong to the FTS and FTS-5. They are not universal frailty thresholds and should not be applied to other instruments.

Measuring pathology is not the same as measuring resilience

Featured paper: Joint impact of pathological burden and cognitive resilience on Alzheimer’s disease risk

The question

Why do people with similar levels of Alzheimer pathology sometimes follow very different cognitive trajectories?

What the study found

Wang and colleagues followed 3,119 older adults for a median of 13.7 years.

They measured two separate dimensions.

Pathological burden was represented by the cerebrospinal fluid p-tau181/Aβ42 ratio.

Cognitive resilience was estimated from the portion of a person’s cognitive trajectory that remained unexplained after accounting for pathology, age, and sex.

Both independently predicted Alzheimer dementia. Each standard-deviation increase in pathological burden was associated with a hazard ratio of 2.50, while greater cognitive resilience was associated with substantially lower risk, HR 0.51.

People with high pathology and high resilience did better than those with comparable pathology but low resilience.

Why it matters

Disease biomarkers tell us how much pathology is present.

They do not tell us the whole story of how that pathology is expressed in a person.

Resilience asks a different question: how well is function being maintained despite biological burden?

That distinction is useful well beyond dementia. In aging research, measuring damage and measuring the ability to withstand damage are not the same task.

Keep in mind

Cognitive resilience here was estimated statistically from longitudinal data. It is a useful research construct, not a clinical test that can currently be ordered for an individual patient.

Menopause may mark a biological transition that chronological age misses

Featured paper: Blood proteomics of menopause map to brain aging and dementia risk

The question

Can the biological changes of menopause be distinguished from the effects of simply getting older?

What the study found

A study published in Nature Medicine on September 22 used blood proteomics to characterize the menopause transition.

The discovery sample included 80 women aged 43 to 58 years who were rigorously classified as premenopausal, perimenopausal, or postmenopausal. The investigators identified a proteomic signature involving inflammatory, synaptic, metabolic, and Alzheimer-related pathways.

Importantly, these signals tracked more strongly with menopause stage and hormone levels than with chronological age.

The pattern was then tested in 2,814 age-matched women from UK Biobank, where broad proteomic changes again distinguished postmenopausal from pre- and perimenopausal women. Across four additional cohorts totaling 11,925 older women, stronger menopause-related proteomic signatures were associated with less favorable cognitive aging and dementia risk.

Why it matters

Age is usually treated as a smooth, continuous variable.

Biology is not always that tidy.

Transitions such as menopause may reorganize physiology in ways that chronological age alone cannot capture. That makes them potentially important anchors for understanding when and how aging trajectories change.

Keep in mind

The study identifies a biological signature associated with menopause and later cognitive outcomes. It does not show that the proteomic changes themselves cause dementia.

What do we lose when many deficits become one number?

Featured paper: Frailty index deficit interchangeability: an empirical test using random survival forests in adults with cardiovascular disease

One of the strengths of a frailty index is also what makes it slightly counterintuitive.

The idea is that if we count enough health deficits, exactly which deficits are included should matter less than the proportion accumulated.

Quach and colleagues tested that assumption in 3,669 adults with cardiovascular disease, using a 46-item frailty index.

When all 46 individual deficits were kept separate, they predicted mortality better than when they were compressed into a single score. The C-index was 0.71 for the individual-item model compared with roughly 0.64 to 0.67 for the composite approaches.

But as the number of items in the frailty index increased, the importance of choosing the “right” deficits faded.

Randomly assembled indices approached the performance of carefully selected ones at about 35 deficits for all-cause mortality and 20 for cardiovascular mortality.

There is a useful lesson here.

A summary measure always throws away information. If we know that someone has anemia, cognitive impairment, difficulty walking, diabetes, weight loss, and twenty other specific problems, we know more than a single number can tell us.

But the single number buys something in return: portability.

The frailty index can be reconstructed in different cohorts and clinical datasets precisely because it does not depend on one exact list of variables.

Good measurement is often a compromise between precision and usefulness.

Can routine health data reveal an aging trajectory?

Most aging biomarkers require a specialized assay, scan, or research visit.

A new study in npj Aging took the opposite approach: what can we learn from measurements already collected during routine health examinations?

The investigators assembled 94,055,326 observations from 99 biomarkers in 189,095 adults, organizing them into ten physiological domains.

Different biomarkers followed different trajectories with age. The researchers then summarized how far individuals deviated from age- and sex-specific reference patterns. Greater overall physiological burden was associated with mortality, and longitudinal instability also appeared informative.

The team used these repeated data to build models predicting future clinical abnormalities, reaching a macro AUROC of 0.802 in held-out participants.

I like the direction more than the idea of another standalone “aging clock”.

Repeated routine measurements may eventually tell us more about how an individual is changing than a single sophisticated assay obtained once.

That possibility is worth following.