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Mapping the healthy aging heart: a normative electrophysiological digital twin atlas for pathological remodeling assessment

This study establishes the first population-scale normative 3D digital twin atlas of the healthy aging heart by integrating MRI and electrocardiographic imaging from the MyoFit46 cohort to define age-specific electrophysiological parameters, thereby enabling the early detection of pathological remodeling before overt structural disease manifests.

Original authors: Jazmin Aguado-Sierra, Pablo Gonzalez-Martin, Maria Marin, Aida Jimenez, Jose Pozo, Constantine Butakoff, Alberto Zingaro, Franca Morselli, Matthew Webber, Debbie Falconer, Fiona Chan, Swapnanil De, Ch

Published 2026-08-05

📖 5 min read🧠 Deep dive

CC BY 4.0

Original authors: Jazmin Aguado-Sierra, Pablo Gonzalez-Martin, Maria Marin, Aida Jimenez, Jose Pozo, Constantine Butakoff, Alberto Zingaro, Franca Morselli, Matthew Webber, Debbie Falconer, Fiona Chan, Swapnanil De, Christos Charalambous, Lavinia Moro, Constantin-Cristian Topriceanu, Emma Martin, George Joy, Jonathan Bennett, Kanchani Makuloluwa, Hunain Shiwani, Rhodri Davies, Nish Chaturvedi, Michele Orini, James Moon, Pier Lambiase, Alun Hughes, Mariano Vazquez, Oscar Camara, Gabriella Captur

Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine your heart as a bustling city with millions of tiny workers (cells) that need to coordinate perfectly to keep the lights on and the traffic flowing. For these workers to do their job, they rely on a complex electrical grid. Sometimes, as a city ages, the wires get a bit frayed, or the workers slow down a little. This is "healthy aging." But here's the tricky part: sometimes, the city starts to develop real problems, like a short circuit or a blocked road, which we call "disease." The big challenge for doctors is telling the difference between a city that is just getting older and one that is actually getting sick. If you can't tell them apart, you might treat a healthy old person like they are sick, or worse, miss the early signs of trouble in someone who needs help. To solve this, scientists are building "digital twins"—super-accurate computer models that act like a perfect clone of a person's heart. These models let researchers test theories and see how the heart behaves without ever needing to cut anyone open.

This paper is about building the very first "map" of what a healthy, aging heart's electrical system should look like. The researchers took a group of 30 people who were all 75 years old and perfectly healthy (no heart disease, no high blood pressure) and created a digital twin for each of them. They used a special combination of MRI scans and a high-tech vest with 256 sensors to record the heart's electrical signals from the outside of the body. By feeding this data into their computer models, they created a "Normative Atlas"—a gold-standard reference guide for what a healthy 75-year-old heart looks like electrically.

Here is what they found, and why it's a game-changer:

The "Old Heart" Needs a New Rulebook
The biggest surprise was that the standard computer models scientists usually use (which are based on young, healthy hearts) simply didn't work for these older adults. It was like trying to drive a vintage car using a manual for a modern sports car; the instructions just didn't fit. The researchers discovered that to make their digital twins match the real hearts of 75-year-olds, they had to add a special "aging layer" to the math. This layer accounted for changes in how the heart cells recharge after beating. Without this specific adjustment, the models failed. This proves that a healthy 75-year-old heart isn't just a "worn-out" young heart; it has its own unique electrical personality that requires its own specific rules.

Mapping the "Spark Plugs"
Inside the heart, there are special pathways called Purkinje fibers that act like the spark plugs in a car engine, sending the electrical signal to start the heartbeat. The researchers mapped exactly where these "spark plugs" (called Purkinje-Myocardial Junctions, or PMJs) connect to the heart muscle in these older adults. They found that these connections follow a very specific, organized pattern that matches what we see in anatomy textbooks, but now they have a 3D map of how it looks in living, breathing 75-year-olds. They found that in the left ventricle (the main pumping chamber), the sparks usually start near the top and move down, while in the right ventricle, there are distinct clusters of sparks along the side walls. This map is the first of its kind for a healthy aging population.

The "Recharge" Map
The heart cells have to recharge after every beat, a process called repolarization. The team found that this recharge isn't the same everywhere. In a healthy aging heart, the bottom tip (apex) of the heart recharges differently than the top (base). They also noticed that men and women have slightly different recharge patterns, likely because of how hormones change as we get older. The most important finding here is that the "aging layer" they had to add to the models wasn't random. It piled up in the exact same spots where the heart was already working the hardest to recharge. It's as if the heart's aging process is a smart system that adds extra support exactly where it's needed most, rather than breaking down randomly.

Testing the Map: The "Early Warning" System
To prove their map was useful, the researchers tested it against a group of people who had early-stage high blood pressure but no other heart damage yet. They wanted to see if the map could spot the very first signs of trouble before the heart actually changed shape or got sick. The results were promising. Even though these people looked healthy on standard tests, their digital twins showed a subtle shift: their heart cells were taking longer to recharge (a sign of stress) and the "spark plugs" were firing slightly earlier than they should. This suggests that this new atlas can act like a super-sensitive early warning system, spotting electrical changes that happen before the heart starts to physically enlarge or develop scars.

What This Means
This paper doesn't claim to have cured heart disease, but it has built the first reliable "ruler" for measuring the electrical health of an aging heart. Before this, doctors and scientists didn't have a clear picture of what "normal" looks like for a 75-year-old, so they often confused normal aging with early disease. Now, with this digital atlas, they can tell the difference. It shows that healthy aging is a structured, organized process, not just a slow decline. By understanding what a healthy aging heart looks like, we can better spot the tiny deviations that signal the start of real trouble, potentially allowing for earlier and more effective prevention strategies for older adults.

Technical Summary: Mapping the Healthy Aging Heart

Problem Statement

Distinguishing intrinsic electrophysiological (EP) remodeling associated with healthy cardiac aging from early pathological changes is a critical prerequisite for preventive cardiology in older adults. However, no population-scale, 3D normative reference exists for the healthy aging heart. Current standard adult cellular models fail to reproduce the repolarization dynamics of healthy aging hearts, leading to the risk that physiological aging is conflated with subclinical disease. Furthermore, existing digital twin (DT) frameworks often rely on 12-lead ECGs, limiting spatial richness, and lack a normative baseline derived from an age-matched, rigorously selected healthy cohort against which pathological remodeling can be systematically assessed.

Methodology

The study leveraged the MyoFit46 birth cohort, a prospective longitudinal study of adults born in the same week of March 1946. The methodology involved a deterministic, sequential mapping framework to construct subject-specific digital twins (DTs) from multimodal clinical data.

1. Data Acquisition and Cohort Selection

Cohort: From 505 participants in the MyoFit46 study (aged 75+), 30 were selected for the "healthy aging" reference group based on strict criteria: sinus rhythm, preserved LV ejection fraction (≥55%), minimal fibrosis (LGE burden <2%), normal systemic pressure, and absence of major comorbidities or cardiac devices.
Imaging: Participants underwent quasi-simultaneous 3 Tesla Cardiovascular Magnetic Resonance (CMR) and 256-lead high-resolution Electrocardiographic Imaging (ECGI).
Comparison Group: An early-stage hypertensive (eHTN) subgroup (n=47) was selected from the same cohort to serve as a proof-of-concept for pathological remodeling, meeting hypertension criteria but lacking overt target organ damage.

2. Digital Twin Personalization Pipeline

The pipeline translated clinical data into mechanistic DTs through three sequential stages:

Activation Personalization:
- Conduction Velocity (CV): Apparent CV was derived from ECGI activation maps using an inverse-gradient approach and translated into a homogeneous electrical diffusion tensor ( $D_f$ ) for the monodomain model.
- Activation Sequence: A reverse-propagation strategy was employed. Epicardial activation times from ECGI were inverted and propagated backward through the myocardium (incorporating fiber orientation and subject-specific CV) to approximate the endocardial activation map.
- Purkinje-Myocardial Junctions (PMJs): Self-activated regions were identified from the estimated endocardial map to determine PMJ locations and activation times. Supplementary PMJs were generated at endocardial borders to compensate for ECGI blind spots (e.g., septum).
Baseline Repolarization:
- Regional repolarization heterogeneities were calibrated by scaling the slow delayed rectifier potassium current conductance ($gKs$). This scaling was constrained (0.2x to 4.0x) based on gene expression data variability.
- The model used a modified O'Hara-Rudy cellular formulation with sex-specific ion channel expression patterns.
Aging Repolarization (AgR):
- A second calibration step was applied to regions where $gKs$ reached physiological bounds. An "aging repolarization" layer was introduced using the rapid delayed rectifier potassium current conductance ($gKr$) as a surrogate to encode regional action potential duration (APD) lengthening or shortening required to match observed ECGI data.

3. Verification and Analysis

Verification: The pipeline was verified against six synthetic ground-truth DTs, demonstrating activation time root mean square errors (AT-RMSE) of ~8–10 ms and repolarization time RMSE (RT-RMSE) of ~17 ms.
Statistical Analysis: Population-level analysis involved mapping PMJ distributions, calculating principal component analysis (PCA) for repolarization modes, and comparing cohorts using Mann-Whitney U and Wilcoxon signed-rank tests.

Key Results

1. Necessity of Aging-Specific Modeling

Standard adult cellular models were insufficient to reproduce the EP dynamics of the healthy aging cohort. An explicit aging repolarization (AgR) layer was required in all 30 healthy participants, confirming that age-related ionic remodeling is systematic and distinct from young adult physiology.

2. Activation and PMJ Atlas

PMJ Distribution: The study mapped 1,034 PMJs across the cohort. The distribution showed structured hotspots consistent with classical anatomical landmarks (e.g., Durrer et al.).
- LV: High prevalence in the mid-anterolateral wall and basal anterior regions.
- RV: Distinct base-to-apex activation driven by five structured hotspots, particularly on the lateral and anterolateral walls, expanding upon previous ex vivo descriptions.
Activation Phases: Early activation was concentrated in the LV anterior wall and RV base, while late activation was distributed homogeneously in the LV and apically in the RV.

3. Repolarization Heterogeneities

Baseline ($gKs$): A dominant apico-basal gradient was observed, with higher $gKs$ values at the apex decaying toward the base in both ventricles. PCA revealed secondary transverse gradients (e.g., RV anterolateral to LV inferolateral).
Aging Repolarization (AgR):
- Neutral ($AgR=$): Dominated the surface (median 61.4%), particularly in the LV.
- Shortening ( $AgR\downarrow$ ): Concentrated at the RV apex and mid-apical regions, where baseline $gKs$ was highest.
- Prolongation ( $AgR\uparrow$ ): Concentrated at the basal regions, particularly in the RV lateral wall and LV inferior/anterior walls.
Sex Differences: Females exhibited significantly higher $gKs$ and AgR scaling factors than males, reflecting intrinsic sex differences compounded by age-related hormonal decline.

4. Proof-of-Concept: Early Hypertension (eHTN)

Comparison with the eHTN cohort revealed:

Structural Preservation: No significant differences in PMJ distribution or global structural metrics (e.g., LV mass index) were found, confirming the group was in an early, non-fibrotic stage.
Functional Remodeling: A directional shift was observed from $AgR\downarrow$ (shortening) toward $AgR\uparrow$ (prolongation) in the eHTN group, particularly in basal regions. This indicates an early action potential prolongation before overt structural disease.
Activation Shift: A significant reduction in the "Late-phase" fraction of PMJ activation was observed in eHTN, shifting toward "Mid-early" phases.

Significance and Claims

The paper establishes the first 3D normative electrophysiological digital twin atlas of the healthy aging heart. Its primary contributions and claims are:

Normative Reference: It provides a principled baseline for assessing pathological remodeling in older adults, distinguishing healthy aging trajectories from early disease.
Model Validation: It demonstrates that standard adult cellular models are inadequate for the 75+ population, necessitating explicit age-related ionic remodeling parameters for accurate in silico modeling.
Subclinical Detection: The atlas successfully identified coherent, subclinical electrophysiological deviations (specifically APD prolongation and activation shifts) in an early-stage hypertensive cohort in the absence of structural remodeling.
Clinical Utility: The framework offers a non-invasive tool for preventive cardiology, enabling the differentiation of age-related changes from pathology. It also serves as a generative resource for synthetic aging populations in virtual clinical trials and as an informed prior for DT personalization in elderly patients (e.g., for pacing or ablation strategies).

The authors conclude that this work provides a quantitative normative reference for the aging heart, grounded in a rigorously selected cohort and a deterministic multimodal mapping framework, setting the stage for future life-course risk stratification.