Published method
Klemera–Doubal Method and clinical PhenoAge are established published methods. Their original literature defines the mathematical and outcome-linked frameworks.
Public methods specification
Model specification.
Evidence boundaries.
This page separates published methods and supporting evidence from BioMIR-specific implementation choices. It documents what is computed, how the layers relate, what the quality indicators mean, and which claims require independent validation.
Interpretation boundary
Specification is not validation.
Published methods can provide a scientific foundation without validating a particular software implementation, biomarker panel, coefficient set, composite, classification system, or user population.
BioMIR therefore distinguishes four questions: what is measured, what the literature supports, what the software specifies, and what has been empirically validated. Evidence for component biomarkers does not automatically validate the composite.
Four evidence classes
Read each claim at the appropriate level.
Supporting evidence
Meta-analyses, pooled cohorts, trials, and measurement-validation studies provide context for individual biomarkers and constructs. Association does not establish an individual causal effect.
BioMIR specification
Input selection, reference handling, directionality, standardization, weighting, aggregation, Δ-years translation, classifications, persistence, and missing-data behavior are implementation choices.
Validation status
The exact daily composite, coefficient set, classifications, and changes in Δ-years require separate evaluation for calibration, reliability, subgroup performance, reproducibility, and clinically meaningful outcomes.
Canonical temporal architecture
Behavioral → Recovery →
Cardiometabolic → Clinical.
The ordering organizes characteristic measurement cadence and biological role. It is an interpretive architecture—not a deterministic causal chain.
01 / Behavioral Adaptation
Modifiable exposures and routines
Inputs include activity-related measures, dietary energy and carbohydrate, sodium, alcohol, daylight, meditation or mindful minutes, and supported related behavioral signals. Measurements are direction-aligned and combined with prespecified relative weights against defined reference context before translation into a domain Δ-years contribution.
Behavior can change within hours. The domain represents measured exposure and routine; it does not imply that one logged behavior causes a same-day physiological or clinical outcome.
02 / Recovery
Physiological response and reserve
Inputs include heart-rate variability, resting heart rate, total sleep, estimated deep sleep, and aerobic-fitness estimates. Their weighted standardized contributions provide complementary views of autonomic regulation, restoration, and functional reserve.
Some measures vary from day to day, while aerobic fitness integrates a longer adaptation window. Wearable estimates remain subject to device and algorithm measurement error.
03 / Cardiometabolic Anchors
Slower vascular and metabolic context
BMI, systolic blood pressure, and the selected glucose anchor are combined using age-regressed, sex-aware reference parameters in the daily cardiometabolic estimator. Each marker remains independently dated. Explicitly fasting clinical glucose is preferred when available; operational fallback glucose observations can support modeling but do not establish laboratory fasting plasma glucose.
When a new eligible observation is unavailable, the latest eligible value can be carried forward without changing its original observation date. CMA Freshness decreases as the supporting measurements age.
04 / Clinical Clocks
Episodic multivariate laboratory context
KDM and clinical PhenoAge are computed from their required clinical measurements and laboratory biomarkers when suitable dated records are available. They remain separate from Adaptive BioAge rather than being folded into the daily composite.
Their role is comparative clinical context across a longer horizon. Differences between clocks can reflect different biomarker panels, statistical objectives, collection dates, and reference assumptions.
Composite construction
Common reporting language does not imply common biology.
Adaptive BioAge
Adaptive BioAge combines the cardiometabolic age-equivalent anchor with Behavioral and Recovery Δ-year terms. The common unit allows decomposition into domains and biomarkers; it does not imply that the components share the same biology, timescale, or causal importance.
Relative weights
Weights are model parameters informed by construct design and published evidence. A hazard ratio, relative risk, or trial effect is not copied directly into a BioMIR coefficient. Exposure scale, covariance, nonlinearity, reference population, and calibration require separate treatment.
Δ-years
Δ-years expresses a model result relative to chronological age. It is not a direct measurement of biological aging rate, years of life gained or lost, disease probability, or treatment effect.
Separate laboratory models
KDM & clinical PhenoAge
BioMIR presents these as periodic laboratory perspectives rather than interchangeable substitutes for the daily wearable and behavioral composite.
Klemera–Doubal Method
KDM combines age-related information across a biomarker panel while accounting for statistical variation. BioMIR uses it as a multivariate physiological-age view relative to a reference population.
Original methodClinical PhenoAge
Clinical PhenoAge combines chronological age with nine laboratory biomarkers in an outcome-linked framework. It is distinct from DNAm PhenoAge, the related methylation model.
PhenoAge researchData quality & uncertainty
Support for an estimate is part of the result.
Data Confidence / Completeness
Data Confidence summarizes weighted availability of expected daily inputs; ABA Trends labels the corresponding series Completeness. It is not a probability that an estimate is correct, a measure of device accuracy, or diagnostic certainty.
CMA Freshness
Freshness summarizes the recency of the BMI, systolic pressure, and glucose observations supporting CMA. High overall Completeness can coexist with older cardiometabolic anchors.
Statistical uncertainty
Sample count, confidence intervals, aggregation method, missingness, irregular sampling, carried observations, device error, and reference-population mismatch can all affect interpretation. No single displayed quality metric captures all of these sources of uncertainty.
External validation
Publication-quality evaluation requires independent datasets, calibration, longitudinal reliability, sensitivity to measurement error and missingness, subgroup performance, reproducibility, and clinically meaningful endpoints. Scientific plausibility and transparent code paths are necessary but not sufficient.
Trace the evidence
Read the specification.
Read the sources.
Use Science for interpretation and Resources for curated literature and measurement context.