{"doi":"10.1002/ctm2.70558","title":"Expotype–phenotype resilience and multimodal aging clocks","abstract":"Aging trajectories vary widely across individuals, even under comparable biological and environmental pressures, yet most biomedical frameworks prioritize vulnerability over protection. This perspective proposes a shift towards identifying resilient expotype–phenotypes, defined by combinations of exposures and individual adaptive responses that support unexpectedly healthy aging. We propose multimodal aging clocks (focusing on delayed agers) to address resilience and its phenotypic and expotype contributions. Building on recent evidence from global exposome analyses1-4, multimodal aging clocks1, 5-8 and neuroecological frameworks2, 9, we argue that resilience offers an essential dimension for precision brain health. The exposome1, 10 captures the totality of physical, social and sociopolitical exposures across the lifespan, exerting marked influences on biological, systemic and cognitive health. This multidimensional construct provides a foundation for defining expotypes11, the characteristic combinations of exposures that shape individual risk or protection12. Aging clocks (epigenetic clocks, proteomic or multi-omic clocks, brain clocks and biobehavioural clocks) quantify biological aging relative to chronological time, enabling direct assessment of how exposures modulate aging trajectories. These tools reveal that diverse exposures, from pollution and temperature peaks to structural inequalities and political instability, accelerate biological aging,1, 2, 6 whereas enriching environments, cognitive stimulation and social cohesion may delay it. Together, they provide a framework for evaluating how cumulative exposures influence aging across datasets, populations and biological systems12, 13. Thus, the exposome and aging clocks may jointly enable a more mechanistic assessments of how protective and adverse exposures shape biological aging across systems. Our recent Nature Medicine study illustrates how biobehavioural age gaps (BBAGs) – the discrepancy between predicted age from protective/risk factors and chronological age – capture delayed or accelerated aging across 40 countries1. BBAGs were estimated using a Gradient Boosting Regressor with 10-fold cross-validation to predict age from biobehavioural factors (risk and protective) in >160 000 participants. The age gap was computed as predicted minus chronological age, with negative values indicating delayed and positive values indicating accelerated aging. To correct regression-to-the-mean, gaps were residualized against chronological age using coefficients from the training set and applied to the test set. Delayed BBAGs were linked to favourable exposomes: cleaner air, inclusive migration contexts, structural and gender equality, and democratic stability. These findings illustrate a neurosyndemic, neuroecological processes, where environmental, behavioural and political stressors converge to shape vulnerability or resilience2, 13. They also demonstrate the feasibility of connecting macrostructural features, such as governance, income distribution and collective stress, to individual-level metrics of aging13-15. Integrating exposome dimensions with individual clocks therefore allows the tracing of multiple pathways through which global conditions embed into biological trajectories16. Most exposome and aging-clock studies focus on accelerated aging, disease risk and vulnerability. Yet across populations (particularly those enduring adversity), some individuals exhibit unexpectedly delayed aging or preserved function. Such phenotypes cannot be explained by individual risk factors alone; they point to resilience mechanisms capable of counteracting adverse exposomal conditions. These individuals reveal protective expotypes: interactions of exposures, behaviours and biological processes that sustain healthy trajectories beyond what risk-based models predict. Understanding these high-resilient individuals is critical for precision medicine. They provide evidence about protective me","journal":"Clinical and Translational Medicine","year":2025,"id":537728,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9475,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":126688,"name":"Agustin Ibanez","orcid":null,"position":1,"is_corresponding":false},{"id":338058,"name":"Hernan Hernandez","orcid":null,"position":0,"is_corresponding":true}],"reference_count":8,"raw_metadata":null,"created_at":"2026-07-19T02:52:16.891390Z","pmid":"41454487","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}