1. Introduction
Aging is a systemic and progressive challenge of significant societal, medical, and scientific urgency. With global populations aging rapidly, age-related diseases, such as neurodegeneration and cardiovascular dysfunction, are accelerating, underscoring the urgent need to understand the mechanisms of aging and develop effective interventions. The trending organ-on-a-chip (OoC) technology offers a powerful solution by recapitulating organ- or tissue-level functions. However, the applications of OoC in aging research remain limited, since most current systems are confined to models that simulate endpoint phenotypes rather than the dynamic evolution of aging itself.
To fully unlock the potential of OoC systems for geroscience, these platforms should instead be redefined as dynamically programmable systems, modeling aging as a continuous and controllable biological trajectory. This requires the integration of multimodal data streams, real-time manipulation, and prediction of aging-related events. In this paper, we propose a transformative framework from three perspectives: ① redefining of “aging-on-a-chip” as a dynamically evolving, controllable process; ② enablement of aging-on-a-chip models to authentically reflect individual heterogeneity in aging processes and treatment responses; and ③ utilization of artificial intelligence (AI) and systems modeling to extract predictive insights from time-resolved chip data. These elements collectively establish a roadmap for the development of aging-on-a-chip systems as next-generation tools for precision geroscience (
Fig. 1).
2. From endpoint states to engineered aging trajectories
Current aging-on-a-chip systems are primarily categorized into two types: aging phenotype models and aging-associated disease models. The former focuses on simulating characteristic biological changes occurring at the cellular or tissue-level during aging, such as cell-cycle arrest (indicated by p16/p21-upregulation), telomere shortening, increased senescence-associated β
-galactosidase (SA-β-Gal) activity, and DNA damage [
1]. These phenotype models commonly employ aged donor cells, oxidative stress, radiation exposure, chemotherapeutic agents, or genetically modified progeroid cells. In contrast, aging-associated disease models target complex pathologies closely related to aging, such as Alzheimer’s disease, osteoporosis, heart failure, and atherosclerosis [
2]. These models often incorporate multi-organ interactions, immune senescence, chronic inflammation, and metabolic dysregulation, thus more pathologically replicating clinical realities and enabling detailed investigations into disease mechanisms and drug responses. In this context, aging-on-a-chip focuses on modeling aging as a primary biological process, rather than on-chip representations of specific age-related diseases.
Despite substantial progresses of these two categories of models, most existing approaches still replicate endpoint states rather than capturing the progressive, multidimensional nature of aging. Aging is not merely a disease state at a fixed time point; rather, it is a continuous life trajectory encompassing both temporal and spatial complexities. Such complexities make endpoint models insufficient to fully elucidate aging mechanisms or accurately identify optimal intervention windows. Consequently, advancing the aging-on-a-chip technology necessitates shifting from endpoint replications towards developing “engineered aging trajectory” platforms. These platforms would treat aging as a dynamic, adjustable, and manipulable experimental variable, enabling precise simulation of the progressive aging process. Unlike conventional dynamic OoC systems that apply short-term or cyclic perturbations, “engineered aging trajectories” aim to represent aging as a cumulative, time-dependent process across matrix, cellular, and tissue levels. The integration of AI-driven feedback, spatiotemporal aging cascades, and multi-organ coupling further distinguishes these platforms from traditional dynamic in vitro systems.
To achieve this transformative shift toward engineered aging trajectories, several technological advancements are required. From a materials engineering perspective, the development of smart extracellular matrices (ECMs) with temporally tunable material properties is essential. Such matrices should accurately replicate age-related physicochemical alterations [
3]. At the mechanical level, smart ECMs should incorporate aging tissue-matched stiffness ranges, spatial gradients, and strain-dependent responses. At the structural level, porosity and transport properties of smart ECMs should be regulated to reproduce the reduced nutrient and oxygen diffusion associated with tissue aging. At the biochemical level, aging cues may be introduced through the accumulation of glycation end-products, oxidative modifications, and modulation of collagen crosslink densities. These features can be implemented through programmable hydrogel systems with secondary crosslinking or controlled release strategies, allowing the smart ECMs to actively shape engineered aging trajectories. Natural matrices including gelatin, collagen, fibrin, and decellularized ECMs can be adapted into photocrosslinkable systems [
4], allowing precise control of stiffness, viscoelasticity, and spatial compositions, providing versatile platforms to integrate multiple aging features.
Precise and programmable regulation of the cellular microenvironment is equally critical for simulating the progressive and multifactorial nature of aging. This can be achieved through advanced microfluidic chip systems capable of dynamically controlling perfusion rate, oxygen tension, nutrient availability, and temporal stimuli. These capabilities enable the gradual induction of critical aging-related pathophysiological conditions, such as nutritional deficiencies, hypoxia, metabolic disturbances, and vascular aging [
5]. Such gradual modulation of the microenvironments allows investigating age-related diseases, including but not limited to ischemic conditions, muscle atrophy, and vascular dysfunction. For example, dynamic loading can replicate the mechanical stress decline observed in aging skeletal muscle or cardiac tissue, while integrated heart-on-a-chip platforms that combine electrical and mechanical inputs help elucidate the interplay between biomechanical signals and aging phenotypes [
6], [
7]. Together, these technologies offer powerful platforms for investigating aging mechanisms and developing targeted interventions to mitigate functional decline and age-related diseases. Importantly, engineered aging trajectories must be distinguished from artificial stress responses. While both may induce senescence markers, physiological aging is characterized by coordinated, time-dependent changes across cellular, molecular, and functional levels. Aging-on-a-chip systems should therefore rely on validation approaches that integrate cellular-, molecular-, and tissue-level readouts over defined timelines. Consistent trends across these levels can serve as a practical indicator of physiological aging, helping distinguish it from acute stress-induced damage and improving the reproducibility and biological relevance of these models.
The core potential of these engineered aging trajectory platforms lies in converting aging from an uncontrollable natural phenomenon into a tunable experimental variable. This approach empowers researchers to precisely manipulate the rate, trajectory, and temporal coordination of aging-related biological events. Consequently, these platforms offer powerful tools for elucidating causative mechanisms and defining optimal windows for intervention. Such control facilitates the identification of clearer molecular and cellular targets, as well as the timing for preventive and therapeutic strategies. For instance, systematically adjusting inflammatory factors, nutrient levels, or ECM stiffness over defined time intervals could reveal critical transition points from reversible functional decline to irreversible pathological damage.
3. Personalizable aging trajectories—Towards precision geroscience
Aging is not a singular or uniform process but rather a complex biological phenomenon shaped by numerous intertwined factors, including genetic background, environmental exposure, metabolic status, immune-inflammatory conditions, and comorbidities. This multifactorial nature leads to substantial inter-individual variability in aging rates, functional declines, and susceptibility to age-related diseases, even among individuals of similar chronological ages. Consequently, developing aging-on-a-chip models that incorporate individualized characteristics is crucial for advancing precision geroscience research.
Achieving this level of individualized modeling relies heavily on advancements in induced pluripotent stem cell (iPSC) techniques. By reprogramming somatic cells from donors of different ages, ethnicities, and disease backgrounds into iPSCs, OoC models that precisely recapitulate individual-specific genetic and epigenetic profiles can be generated. When subjected to identical aging-inducing conditions, these iPSC-derived tissue models exhibit distinctly different cellular responses, stress resilience, and molecular expression profiles, clearly highlighting intrinsic anti-aging capabilities and sensitivities unique to each individual [
8].
Building on advances in iPSC-based aging models, it is further crucial to move beyond modeling isolated tissues towards interconnected organ systems, which would be essential to faithfully replicate the systemic nature of aging. To comprehensively capture such variability, multi-organ interconnected chip systems are being developed, such as the gut-liver-brain axis or liver-kidney-skin multi-organ platforms [
9]. These sophisticated models utilize microfluidic channels to connect various tissue compartments, recreating physiologically relevant inter-organ communication networks to simulate the complex clinical realities of multimorbidity and polypharmacy commonly observed in aging populations. Such integrated multi-organ platforms demonstrate considerable advantages for evaluating drug interactions, metabolic side effects, and therapeutic outcomes, aligning closely with real-world clinical geriatric pharmacology requirements and providing essential tools for designing personalize medical interventions.
Moreover, aging-on-a-chip models should integrate precise assessments of “biological age” through quantitative biomarkers such as epigenetic clocks and senescence-specific markers, including p16 and SA-β-Gal. These biomarkers not only serve to validate the biological accuracy of aging models but also hold promise as practical metrics for evaluating individualized therapeutic interventions. Compared to traditional chronological age-based assessments, such precise biological metrics provide a more accurate distinction of an individual’s physiological aging state that could present interpersonal variations, thereby providing a robust theoretical and practical foundation for the development of personalized geroscience strategies.
The advancement of personalized aging-on-a-chip models greatly enhances their translational potential by enabling tailored therapies for specific subpopulations or individuals, while also serving as
in vitro platforms for early drug screening in elderly clinical trials [
10]. For example, patient-derived aging-on-a-chip platforms can be used to screen senolytic drug and identify individualized therapeutic windows. This not only accelerates drug development but also reduces risks and costs by predicting therapeutic outcomes before human testing. However, the path towards clinical translation remains constrained by practical challenges, including insufficient throughput, fabrication variability, and the lack of standardized operating protocols. Addressing these issues will likely require modular chip fabrication technologies, and partially or fully automated monitoring systems, thereby providing a realistic pathway for integrating aging-on-a-chip technologies into translational and clinical geroscience workflows. The growing complexity of these systems and their associated datasets further underscores the need for a robust “digital layer” within aging-on-a-chip technologies.
4. Building the “digital layer”: AI and systems modeling for aging-on-a-chip
As aging-on-a-chip systems evolve from endpoint models toward dynamic and personalized multi-organ trajectory platforms, the volume and complexity of experimental data increase exponentially. Continuous monitoring and recording of cellular morphology, gene-expression dynamics, metabolic pathway shifts, and functional behaviors are now possible through advanced imaging and real-time biosensors [
11]. Identifying effective strategies to extract meaningful information from these high-dimensional, time-series, and multimodal datasets, and to construct interpretable and predictive aging models, has emerged as a pivotal challenge advancing the intelligent evolution of aging-on-a-chip technologies.
In this context, AI and systems modeling technologies are emerging as indispensable digital layer strategies, providing the analytical capacities needed to interpret complex datasets. Among these methods, deep learning and machine learning algorithms empower us to identify aging-associated morphological alterations from vast numbers of cellular images. They also enable the discovery of hidden relations between molecular expression profiles and specific aging stages or phenotypes within omics datasets [
5], [
12]. To ensure that such predictions remain biologically meaningful and clinically relevant, the predictive power of these AI systems should be anchored to clinically validated aging biomarkers, while algorithmic transparency and interpretability are maintained to reduce overfitting and enhance translational credibility. For example, previous studies have demonstrated that aging-associated secretory patterns are closely related to immunological aging (inflammaging) [
13]. Inflammaging is an important characteristic and driving force of immunosenescence. The two interact during the aging process and jointly accelerate the decline of immune system functions. Future applications of clustering and classification algorithms to analyze these secretomic datasets in depth hold promise for accurately detecting pre-disease states associated with aging.
A more advanced exploration involves creating “digital twins” of tissues or organs, virtual models that operate in real-time synchronization with their physical counterparts on aging chips. These digital twins not only mirror the current (patho)physiological status of the tissue but also leverage mathematical modeling and biological principles to predict long-term dynamic responses and evolutionary trajectories under various interventions. For example, by developing machine learning-driven cardiac digital twin populations, it is possible to simulate and evaluate individual-specific electrophysiological changes, such as conduction and repolarization,
in silico. This approach enables precise prediction of cardiac functional alterations along specific aging trajectories and supports evaluations of therapeutic interventions [
14]. This digital twin concept, which has already shown promise in modeling neurodegenerative and cardiovascular diseases, is now being increasingly integrated into aging-on-a-chip (and in general OoC) platforms to enhance predictive capability and advance personalizable medicine.
Furthermore, the integration of closed-loop control systems into aging-on-a-chip platforms allows for real-time sensing, evaluation, and adaptive response to dynamic microenvironmental changes. Coupled with micro-scale sensors (e.g., those monitoring pH, oxygen, electrophysiology, and secreted biomarkers) and real-time analytic algorithms, these systems can automatically trigger interventions in response to critical alterations. This ability creates an adaptive, self-regulating platform [
15]. For example, cardiac chip models integrated with nanoelectrode arrays and electrical stimulation modules have enabled real-time regulation of cardiac electrical activity, demonstrating the capability to model and intervene in electrophysiological abnormalities such as arrhythmias [
16]. Beyond sensing and data extraction, AI can guide experimental design by identifying informative conditions, reducing variability, and supporting protocol standardization for more robust and scalable aging-on-a-chip studies.
The AI and digitized systems have enabled novel approaches towards investigating the process of aging. In addition to extracting insights into aging, properly designing interventions, which may consist of therapeutics, lifestyle medicine, and other regimens, to enable healthy aging, is also critically important. For example, an AI-driven platform, IDentif.AI, has been used to design combination therapies for diseases like SARS-CoV-2 [
17] and heart disorders, including sex-specific drug combinations for aortic valve stenosis [
18]. This technology can also identify therapeutic and supplement combinations to improve healthspan [
19] and personalize treatments using patient-specific samples. After selecting interventions, CURATE.AI builds personalized dose-response profiles to optimize outcomes. These platforms, originally applied in cancer and hypertension, are mechanism-agnostic [
20], [
21] and can be adapted for longevity strategies, including supplements and lifestyle interventions.
The integration of this digital layer transforms aging-on-a-chip systems from passive observational tools into predictive and intervention-capable research engines. This advancement facilitates not only hypothesis generation and validation but also provides a highly flexible, system-level framework for drug screening, toxicity assessment, and mechanistic investigations. Importantly, it further refines precision geroscience by integrating individual-specific variables, including genetic backgrounds, lifestyle factors, and pathological conditions, into digital twin models. The integration enables the construction of highly personalizable “digital aging trajectories,” allowing to model unique aging pathways and identify individual-specific risk factors and windows [
22]. Future anti-aging strategies will likely transition from universal treatments to integrated personalized decisions based on a combination of digital biomarkers, physiological traits, and comprehensive personal life histories. Through the support of specialized algorithms and tailored chip models, individuals will be empowered to precisely manage their unique aging patterns and intervention windows, thereby achieving personalized and precise management of the aging process. With the continued development of intelligent modeling and biosensing technologies, we are entering an era where aging processes can be biologically replicated and precisely predicted at the individual level, paving the way for personalized aging trajectory management.
5. Participatory health
The reach of OoC models for aging can further be extended through participatory health wherein participants are reframed to being active agents in understanding aging and optimizing their own health and aging. Using data itself from these platforms and frameworks as an intervention, the continuous data output does not just reflect the health and aging status of the participant, but can additionally actively drive behavior change through feedback and data insights. By visualizing how interventions, lifestyle choices, or therapeutics influence their personalized OoC and digital twin systems, participants can actively and iteratively refine their lifestyle habits and health regimens. This participatory engagement can transform longevity research into a dynamic process where the awareness of data itself can extend healthspan.
6. Conclusions and outlook
Study of aging requires a paradigm shift from traditional endpoint observations towards dynamic, controllable, and individualized investigation. Although current aging-on-a-chip technologies have achieved notable advancements, they remain largely limited to replicating aging phenotypes or disease endpoints, thus failing to accurately represent the dynamic, evolving nature of aging processes.
By developing engineered aging trajectories, it is envisioned that we will be able to transform aging into a controllable experimental variable. This approach involves dynamically tunable ECMs, precisely regulated microenvironmental gradients, and programmable activations of biological signaling pathways, enabling rigorous investigation of aging mechanisms and intervention windows. Furthermore, combining iPSCs with multi-OoC systems empowers personalized aging trajectory modeling. Accurate assessment of biological age, in turn, facilitates the better identification of individual-specific aging states and the optimization of personalized therapeutic interventions. Concurrently, the implementation of a robust AI-driven digital layer can further enhance the efficient handling of vast datasets generated by these systems, creating predictive aging models and enabling real-time platform adjustments.
Looking forward, the integration of dynamic, personalized, and digital approaches holds the potential to revolutionize both the study of aging mechanisms and the design of clinical trials, as well as the development of personalized therapeutics. Ultimately, this integrated strategy will drive aging research towards precise, individualized interventions, significantly enhancing human health span and overall quality of life.
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