Multimodal contrastive regression for organ-resolved biological age prediction

Veronika Ecker, Tim Förster, Sergios Gatidis, Thomas Küstner, Bin Yang

Keywords: Multimodal Data, Contrastive Regression, Biological Age, Multi-organ, Magnetic Resonance Imaging

Abstract: Aging is complex and heterogeneous. A person's chronological age (CA) cannot reflect individual aging trajectories which prompted investigations for biological age (BA). Existing BA models often rely on single organs or limited biomarkers, restricting holistic assessment. Overcoming these constraints requires multimodal models that better reflect the distributed nature of aging. Accurate estimation of BA therefore requires models that capture the combined influence of structural, functional, and physiological factors on the aging process. BA progresses heterogeneously across organs, motivating models that integrate multiple data types to capture this complexity. We propose a multimodal contrastive regression framework that jointly learns from MRI and structured clinical variables to estimate organ-specific BA. A contrastive regression loss structures the latent space to reflect continuous age differences both within and across modalities. Applied to a population cohort, this approach produces well-structured embeddings, improves age estimation relative to unimodal systems, and reveals characteristic age-gap patterns in healthy and diseased subgroups. The results demonstrate that combining MRI with complementary tabular features strengthens BA estimation and supports a comprehensive multi-organ view of aging.

Primary Subject Area: Integration of Imaging and Clinical Data

Secondary Subject Area: Unsupervised Learning and Representation Learning

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Submission Number: 144