Introduction.

Modern medicine already uses a broad range of aging clocks — scientific models that estimate a person’s biological age, or the biological age of specific organs and physiological systems, using biological, physiological, molecular, or imaging data.

There are already epigenetic clocks based on DNA methylation, as well as proteomic, metabolic, physiological, and imaging-based models. Another important area is the assessment of the biological age of individual systems, including the brain, heart, vascular system, lungs, liver, kidneys, metabolic system, musculoskeletal system, and sleep. Multi-organ biological-age models are also becoming an active area of AI-based aging research.

For ACCO, the most relevant approaches are those for which the required input parameters are already available from ACCO data or can be supplemented with information entered by the user.

It is important to distinguish between two approaches: using established age-estimation methodologies and developing new AI Aging Clock models based on data processed by Accofrisk AI Health Intelligence.

What We Mean by “AI Aging Clock”.

In this article, AI Aging Clock refers to a class of artificial intelligence models that use a combination of physiological and biological data to estimate a person’s biological or functional age, either overall or for specific body systems.

Unlike specific scientific methodologies such as the Epigenetic Clock, Proteomic Clock, or Imaging Aging Clock, the term AI Aging Clock describes an approach rather than a single established methodology: a machine-learning model analyzes multiple parameters, their relationships, and changes over time to generate an age estimate.

In the context of ACCO, we use the term to describe a concept in which data from ACCO watches and Accofrisk AI Health Intelligence could support existing age-estimation methodologies or the development and subsequent scientific validation of proprietary AI Aging Clock models for different body systems.

Established Age-Estimation Methodologies Relevant to ACCO Data.

Framingham Heart Age is based on cardiovascular risk models developed from the long-running Framingham Heart Study, including the general cardiovascular risk profile published by Ralph B. D’Agostino and colleagues. The methodology uses age, sex, systolic blood pressure (SBP), antihypertensive treatment, smoking status, diabetes, total cholesterol and HDL cholesterol; a simpler version can use body mass index (BMI) instead of lipid measurements. The calculated cardiovascular risk can be expressed as a Heart/Vascular Age, translating an individual's risk profile into an age-equivalent measure.

ACCO applicability: High — potentially implementable, provided the complete required input set is available. ACCO provides several of the physiological and biochemical parameters, while additional information such as smoking status, diabetes status and medication use can be entered by the user.

Fitness Age / Cardiorespiratory Fitness Age is a class of models based primarily on cardiorespiratory fitness (CRF), with maximal oxygen uptake (VO₂max) as the key physiological measure. The principle is to compare an individual's cardiorespiratory fitness with age- and sex-specific reference values and express the result as an equivalent fitness age. Different implementations may additionally incorporate heart rate (HR), body mass index (BMI), physical activity and other fitness-related parameters.

Unlike Framingham Heart Age, Fitness Age is not one single model created by one research group. It represents a broader family of approaches built around the established relationship between CRF, age and health outcomes.

ACCO applicability: High — one of the most directly applicable external methodologies, because ACCO provides oxygen-consumption (VO₂) data together with heart-rate, physical-activity, BMI, body-composition, workload and recovery parameters.

KDM Biological Age was developed by Petr Klemera and Stanislav Doubal and published in 2006. The method mathematically integrates multiple biomarkers and their relationships with chronological age rather than relying on a single indicator. The original work was specifically designed to provide an integrated estimate of biological age and to quantify the precision of that estimate.

Depending on the implementation, KDM models can incorporate physiological and biochemical measures such as systolic blood pressure (SBP), lung function (FEV₁), creatinine (Creatinine), blood urea nitrogen (BUN), albumin (Albumin), alkaline phosphatase (ALP), glycated hemoglobin (HbA1c), C-reactive protein (CRP) and total cholesterol.

ACCO applicability: Moderate to high — the KDM methodology is highly relevant to ACCO's multidimensional data, but the exact published model must be matched to its required input variables. If all required variables for a selected implementation are available, the corresponding model can be applied; otherwise, KDM provides a methodological basis rather than a ready-to-use formula.

PhenoAge was developed by Morgan E. Levine and colleagues as a biological-age measure designed to capture differences in physiological aging and their relationship with mortality risk. The original model combines chronological age with a defined panel of nine blood biomarkers: albumin (Albumin), creatinine (Creatinine), glucose (Glucose), C-reactive protein (CRP), lymphocyte percentage (Lymphocyte %), mean corpuscular volume (MCV), red-cell distribution width (RDW), alkaline phosphatase (ALP) and white blood cell count (WBC)..

The basis of the method is therefore a specific laboratory biomarker profile, rather than a general combination of any available physiological parameters..

ACCO applicability: Limited for the original model — PhenoAge can be considered if the required laboratory biomarkers are available, but it should not be presented as directly implementable from ACCO data alone.

PCA-Based Biological Age is a class of biological-age methodologies based on principal component analysis (PCA). An early influential implementation was published by E. Nakamura, K. Miyao and T. Ozeki in 1988. The researchers examined multiple physiological variables and used PCA to identify the dominant pattern across them, creating an integrated biological-age measure..

The key principle is particularly relevant to ACCO: instead of assigning age based on a single parameter, multiple physiological measurements are mathematically combined into a composite indicator of biological aging..

ACCO applicability: High as a methodology, but not as a direct transfer of a published formula. PCA can be applied to the multidimensional ACCO dataset to develop an integrated physiological-age model, but the resulting ACCO-specific model would need to be trained, calibrated and scientifically validated..

Concept of AI Aging Clock models based on data from Accofrisk AI Health Intelligence.

The next level is to use the multidimensional physiological data processed by Accofrisk AI Health Intelligence to develop proprietary models for estimating biological age.

This is not simply a matter of applying someone else's published formula. The concept is to combine established scientific knowledge about age-related changes in organs and physiological systems with data from Accofrisk AI Health Intelligence and machine-learning methods, and then train and validate new models using appropriate clinical and age-stratified datasets.

Brain Age — biological age of the brain. ACCO includes a broad range of parameters related to cerebral hemodynamics and oxygen metabolism, including cerebral blood flow (CBF), cerebral blood volume (CBV), cerebral perfusion pressure (CPP), cerebrovascular reactivity (CVR), intracranial pressure (ICP), cerebral metabolic rate of oxygen (CMRO₂), jugular venous oxygen saturation (SjvO₂), and other parameters. These data could provide a basis for developing a proprietary Brain Aging Clock. However, a published model cannot automatically be transferred if, for example, its CBF measurements were obtained using a specific technique such as ASL-MRI.

Cardiac Age — biological age of the heart. ACCO includes cardiac output (CO), cardiac index (CI), global ejection fraction (GEF), cardiac power output (CPO), stroke work (SW), myocardial oxygen consumption (MVO₂), and other indicators of cardiac function. Their combined use could provide the basis for a proprietary Cardiac Age model.

Vascular Age — biological age of the vascular system. In addition to pulse-wave data, ACCO includes blood pressure, blood-flow, perfusion, microcirculation, blood-viscosity, fibrinogen, and hematocrit parameters. Together, these measurements could support the development of a dedicated Vascular Aging Clock.

Kidney Age — biological age of the kidneys. ACCO includes renal blood flow (RBF), renal filtration fraction (RFF), renal perfusion pressure (RPP), serum creatinine, blood urea nitrogen (BUN), and uric acid. These parameters could be used to develop a model of functional kidney age.

Liver Age — biological age of the liver. ACCO includes alanine aminotransferase (ALT), aspartate aminotransferase (AST), the AST/ALT ratio, ammonia (NH₃), lactate, and metabolic parameters. These measurements could form the basis of a proprietary Liver Aging Clock.

Metabolic Age — metabolic age. This is one of the areas with the richest set of potentially relevant ACCO parameters. These include glucose, glycated hemoglobin (HbA1c), HOMA-IR, HOMA-B, the triglyceride-glucose index (TyG), visceral adiposity index (VAI), lipid accumulation product (LAP), uric acid-to-HDL ratio (UHR), BMI, body-fat percentage, muscle mass, resting metabolic rate (RMR), and other indicators. Metabolic Age could therefore become one of the first proprietary AI Aging Clocks developed using data from Accofrisk AI Health Intelligence.

Lung Age — functional age of the respiratory system. ACCO includes oxygen consumption (VO₂), minute ventilation (VE), alveolar ventilation (VA), respiratory quotient (RQ), arterial oxygen partial pressure (PaO₂), arterial carbon dioxide partial pressure (PaCO₂), physiological dead space (VD), rapid shallow breathing index (RSBI), pulmonary vascular resistance (PVR), mean pulmonary artery pressure (MPAP), and other respiratory and gas-exchange parameters. These data could support a proprietary model of respiratory-system age. However, it should not automatically be called the classical Spirometric Lung Age if the original methodology requires forced expiratory volume in one second (FEV₁).

Musculoskeletal / Functional Age — functional age. ACCO includes muscle mass, body-fat percentage, BMI, resting metabolic rate (RMR), physical activity, workload, and recovery. Together, these parameters could support a model of physical and functional age.

Remember the phrase “sleeps like a baby”? It reflects an intuitive idea that the quality and characteristics of sleep can tell us something about a person's physiological state. ACCO collects multiple sleep-related parameters, including sleep duration, sleep stages, sleep disturbances, snoring and apnea-related characteristics. These data could potentially be used to develop an AI model that evaluates the physiological age of sleep in relation to age-specific patterns and individual dynamic

Sleep Physiological Age — estimated age of sleep characteristics. ACCO includes sleep duration, sleep stages, apnea, snoring, and other sleep-related characteristics. These data could potentially be used to develop a proprietary Sleep Age model that estimates how closely an individual's sleep profile corresponds to patterns observed at different chronological ages.

What cannot simply be transferred from existing models.

Some aging clocks require completely different types of data. Epigenetic clocks rely on DNA methylation patterns. Proteomic clocks require measurements of specific proteins. MRI-based aging clocks use medical images and quantitative characteristics of tissues and organs.

Therefore, having a physiologically similar parameter does not mean that a published formula can simply be applied to ACCO data. For example, if a Brain Age model was trained using cerebral blood flow (CBF) measured by ASL-MRI, the presence of CBF in ACCO data does not automatically make the original formula applicable. In such a case, the appropriate approach would be to develop and validate a new model using data from Accofrisk AI Health Intelligence.

Overall AI Aging Clock concept.

ACCO could therefore become more than a device for measuring individual physiological parameters. It could serve as a source of multidimensional physiological data for a system of biological-age assessment.

The first level would be the integration of established methodologies:

Framingham Heart Age → Fitness Age → AI-VascularAge

The second level would be the development of proprietary models based on data from Accofrisk AI Health Intelligence:

Brain Age → Cardiac Age → Vascular Age → Kidney Age → Liver Age → Metabolic Age → Lung Age → Musculoskeletal / Functional Age → Sleep Age

These individual indicators could then potentially be integrated into an Overall Biological Age.

Instead of providing only one generalized number — for example, “biological age: 52” — the system could generate an individual map of physiological aging:

This approach would show not only how old the body appears to be overall, but also which physiological systems may be functionally older or younger than the person's chronological age.

That is the core concept of an AI Aging Clock based on ACCO watches and Accofrisk AI Health Intelligence: ACCO watches capture the pulse wave and other physiological parameters; Accofrisk AI Health Intelligence processes these data, analyzes them longitudinally, and uses them to generate individualized indicators of physiological status and functional age across different body systems.

Established scientific methodologies can be used where the ACCO data are sufficiently compatible, while new models can be developed and validated for areas where direct transfer of existing methodologies is not scientifically justified.

Conclusion: What Makes the ACCO AI Aging Clock Concept Different.

Existing age-estimation methodologies provide established scientific approaches for assessing specific aspects of biological, physiological, cardiovascular or functional aging. The ACCO concept builds on this foundation but proposes a broader AI framework based on multidimensional and longitudinal physiological data.

The concept focuses not only on the estimated age at a particular moment, but also on how physiological characteristics change over time.

The analysis could potentially identify whether a person's physiological aging profile is changing faster, slower, or more steadily than expected for their chronological age.

Instead of applying separate age calculators independently, ACCO data could be analyzed within a common AI framework covering multiple physiological domains.

AI could analyze patterns and relationships between different physiological parameters rather than treating each parameter or system in isolatio

The concept moves beyond the idea that one number should represent the biological age of the entire body. It proposes a more detailed individual Aging Profile, where different physiological dimensions can be evaluated and interpreted together.

Existing approaches such as Framingham Heart Age, Fitness Age, KDM Biological Age, PhenoAge and PCA-based Biological Age can provide scientific reference points and validation frameworks. At the same time, ACCO data could serve as the basis for developing new AI Aging Clock models specifically designed around its multidimensional and longitudinal dataset.

The central concept is therefore not simply another age calculator, but an AI framework for analyzing physiological aging as a dynamic, multidimensional process — connecting established age-estimation methodologies with the development of new ACCO-based models.