Aging Clocks Catalog · How claims are assessed

How the evidence is judged

Reliable measurement, prediction, intervention response and better health decisions are different achievements. No single statistic establishes them all.

Foundation reviewed 21 September 2026; reliability and calibration evidence reviewed 22 September 2026. A bounded documentary review, not a diagnostic or treatment service.

The important question is not whether a clock is “validated” in the abstract. It is validated for which claim, in which setting, with which implementation? Consistent measurement, external prognosis, treatment response, clinical utility and surrogate validity are distinct. A research biomarker can be useful without meeting the last two standards. [M03; M05; M26]

A claim needs the right kind of evidence

Assessment standards informed by the inspected methods, controlled studies and FDA–NIH BEST definitions; not a claim that every paper was designed to satisfy every row. [M03; M05; M11; M20; M25; M26; SC1]

Three separate questions before interpreting a change

1. Does it exceed the relevant error?

   First match the output and complete pipeline. Identical numerical inputs test software reproducibility; a split specimen tests only steps after the split; separate collections include collection and intervening physiology. Paired version bridges estimate platform/pipeline disagreement. State bias and absolute difference variance for the relevant experiment rather than substitute calendar-age error or an ICC. [R03; R05]

2. Does it reflect the intended biological construct?

   A real inflammatory or cell-mixture change is not automatically sustained aging. Examine standardized repeated visits, untreated trajectories and independent functional or clinical measures. The inspected timing study’s dense series was one person and its additional paired data came from a stress experiment; it supports attention to context, not a universal daily age fluctuation. [R08; R09; R11]

3. Does using it improve a decision?

   Identify the action triggered by the score and compare benefits, harms and costs with a realistic no-test decision. Even a resolved, biologically relevant change does not establish clinical utility. Surrogacy separately requires treatment effects on the marker to predict clinical effects in a defined context. [M26]

No single reliability coefficient answers all three. Under a stable, approximately normal, unbiased error process, 1.96 × SD(error difference) is a statistical repeatability bound—not a clinically important difference. In general, Var(e₂ − e₁) = Var(e₂) + Var(e₁) − 2 Cov(e₁, e₂); the equal-independent-error shortcut is a special case. This is a measurement model, not an estimated threshold for any product. The matched-platform example estimates a mean version offset, not same-platform intervisit variation.

A group contrast is not an individual response classifier

For a hypothetical balanced trial with independent participants, n per arm and individual-change measurement-error SD sD, the measurement-error component of the difference-in-mean-changes has standard error sD × √(2/n). This excludes biological heterogeneity, clustering, missingness and systematic batch bias. More participants can reduce random error in a group contrast; they do not remove an arm-confounded shift. A statistically detectable group effect can therefore be smaller than an individual repeatability bound without contradiction.

The CALERIE control back-conversion illustrates why output definition also matters: its PCGrimAge summary is a group age-gap change standardized by baseline dispersion, not individual measurement error or raw-age change. [R09]

The missing uncertainty for clinical and proteomic scores

Clinical-laboratory composites: for a fixed linear score, input-error contribution is w′Σw: weights combined with the covariance matrix of input errors in the correct transformed units. The inspected clinical Phenotypic Age formula is linear in those transformed inputs, including log CRP, when calendar age is treated as known. A serial-use interval additionally requires collection/physiological and cross-visit covariance for the proposed timing. Those matrices were not established for the inspected services. Adding or averaging analyte CVs cannot recover them. [M03; R15]

Proteomic clocks: a linear score likewise needs weighted covariance; for a nonlinear clock, full-pipeline replicated predictions under the actual specimen, panel, preprocessing and model version are the more direct empirical check. Average protein ICCs or a count of reproducible proteins do not supply final-score error. A small preanalytical experiment found altered inflammatory-protein measurements despite analytical controls, but calculated no aging clock; it cannot establish error in ProtAge, PAC or rentosertib’s six outputs. [R10; R11]

Clinical-laboratory and proteomic individual-change thresholds therefore remain unresolved for the specific services and pipelines inspected. Neither methylation-platform variance nor external risk association fills the missing score-level evidence.

Numbers only mean something with their comparator

Mean absolute error for calendar age does not measure longitudinal reliability or outcome prediction. A hazard ratio describes conditional association under a fitted model. A C-statistic measures ranking; it does not establish that a predicted 10% risk occurs 10% of the time. That last question concerns calibration. Clinical utility additionally asks what happens when a decision changes because of the result.

The NMR example reports five-year C-statistics of 0.837 versus 0.772, and ten-year values of 0.830 versus 0.790, in FINRISK. It has a real conventional-risk comparison, but cohort-based scaling limits immediate individual classification and the models were not simply the same base with one extra predictor. The worked application preserves that boundary. [M11]

What a favorable summary must not hide

What this review did

This is a purposive, claim-driven documentary review with foundation evidence/access observations dated 21 September 2026 and a reliability/calibration follow-up dated 22 September 2026. It follows selected original methods and validations; separates parent and ancillary trials; inspects public product/legal documents; and reads code where it changes feasibility. It is not a systematic review, market census, pooled meta-analysis or product certification. Search visibility was not treated as popularity or market share.

“Inspected” means the stated relevant text was read—not every citation, figure or supplement. Some emerging-family entries and the 2025 benchmark rely only on abstracts/metadata. The foundation used publisher-provided ProtAge and CALERIE text delivered on ResearchGate; unrelated citing-paper snippets were excluded. The follow-up also inspected CALERIE’s university-hosted publisher supplement and Tay’s Supplementary Table S4 visually. Source access depth remains specific to each entry, not an assertion that all supplements were read.

All study numbers are author-reported unless explicitly identified as arithmetic. No participant-level dataset was obtained, clock package run, restricted model acquired, product purchased or seller contacted. The follow-up reconstructed agreement limits for all 18 selected specimen/model rows in Tay’s rounded S4 aggregates, keeping age and pace units separate, and checked consistency with reported rounding. It added conditional normal-theory intervals, back-converted rounded CALERIE group summaries and checked fixed synthetic clinical/calibration examples. It did not reproduce raw-pair nonparametric tests, methylation processing, imputation or clock fitting. [R05; R09; R15]

How to read the local source labels

M identifies scientific methods/validation or the official terminology framework; SC identifies the six-clock case, parent trial and model/access sources; C identifies seller/contract documents; T identifies implementation documentation; added R labels identify the reliability/calibration evidence review. They are source identifiers, not quality scores. A local source note tells you the inspected depth and relevant conflicts. A source’s availability does not give another source’s claims its authority.

Original papers support scientific claims; sellers describe their own offerings and terms; current code documentation describes access, not what an older trial necessarily ran. A “latest” documentation address is mutable. The date attached to the review is not a new publication date, a purchase guarantee or a claim that every underlying experiment was recent.

The paired-platform reconstruction assumes independent participants and approximately normal, homogeneous differences within each specimen/model. Rounded aggregates cannot test those assumptions, outliers, proportional bias or subgroup behavior. The intervals are pointwise rather than simultaneous over all rows. S4 and main-text SDs disagree in some rows, and a normalization name/reference inconsistency remains; S4 was used without inventing unrounded data or silently resolving preprocessing. [R05]

What would materially strengthen the conclusions?

For an individual retest: independent, matched collection-to-report repeatability, untreated longitudinal variation, exact report-version correspondence and a demonstrated decision advantage. An unrelated age-error statistic would not resolve the gap.

For the IPF case: reconciled registry histories, dated protocol/analysis plan, ancillary arm denominators, numerical S2–S5 tables and exact study-specific model artifacts. Reproducing tests from published predictions would be useful, but would still not equal reproducing all predictions from raw proteins.

For clinical risk use: external calibration and threshold/net-benefit evaluation against a realistic current comparator, followed by evidence on the decisions and outcomes produced by using the score. One more adjusted association would not by itself settle utility.

Next: Inspect the three worked evidence chains or separate public code from reproducible access.