ISSN: 2940-3243

Review

Yoga, Biomarker, and the Current Role of AI

by Pascal Büttiker¹ and Tobias Esch¹

Abstract
Yoga-based mind-body practices are increasingly examined through multi-domain biomarker frameworks spanning inflammation, neuroendocrine regulation, gene expression, epigenetics, and cellular aging. Evidence suggests yoga engages distributed regulatory networks rather than isolated molecular targets, though effect sizes remain heterogeneous. Advances in composite biomarker design reflect a systems-level maturation of the field. Artificial intelligence (AI) currently contributes most robustly through enhanced measurement fidelity, posture classification, physiologic phenotyping, and exposure-response modeling. Future AI-enabled health systems should adopt a human-in-the-loop augmented intelligence framework that strengthens rigor, personalization, and scalability while preserving clinical judgment, ethical oversight, and the experiential dimensions intrinsic to yoga practice.

Keywords: Yoga, Biomarkers, Artificial Intelligence, Mind-Body Medicine, Augmented Intelligence

Opinion
A substantial clinical and mechanistic literature links yoga, commonly delivered through combinations of postural practice, breath regulation, and meditation, to measurable changes in biomarkers spanning inflammation, stress physiology, and molecular regulation. Across both chronic disease populations and healthy cohorts, systematic reviews consistently identify inflammatory mediators such as interleukin-6, C-reactive protein, and tumor necrosis factor-α as the most frequently assessed biomarkers, with many studies reporting pre-post improvements but marked heterogeneity in intervention design, comparator conditions, and methodological quality (Djalilova et al. 2019). More recent meta-analytic work reinforces the biological plausibility of yoga-related immunomodulation while underscoring that effects are neither uniform across biomarkers nor robust across all study designs (Mishra 2024). This pattern aligns with broader mind-body medicine frameworks in which therapeutic benefit emerges from integrative regulatory networks rather than single molecular targets (Esch & Stefano 2022, Owino et al. 2019).

At a more mechanism-proximal level, randomized controlled trials provide molecular correlates of mind-body effects. A brief daily yogic meditation practice has been shown to shift leukocyte gene-expression profiles, including reduced activity of pro-inflammatory transcriptional signaling pathways and altered antiviral response dynamics, offering direct evidence that contemplative practices can engage conserved stress-immune regulatory programs (Black et al. 2013). Complementary epigenetic findings further support this systems-level view: in chronically stressed women, an eight-week yoga intervention was associated with reduced DNA methylation within a tumor necrosis factor–related genomic region, alongside exploratory associations between inflammatory markers and psychosocial outcomes (Harkess et al. 2016). Extending beyond inflammation and gene regulation, biomarkers of cellular aging are now incorporated into randomized designs. A two-arm trial in obese adults demonstrated that a twelve-week yoga-based lifestyle intervention could influence telomere length and telomerase activity, signaling a shift toward integrative, multi-domain endpoints (Sharma et al. 2022). This evolution toward composite and systems-level biomarkers parallels long-standing arguments that biological resilience and adaptation reflect coordinated regulation of energy metabolism, immunity, and stress responsiveness (Cipolla Neto et al. 2014).

Within this expanding biomarker landscape, AI and machine-learning approaches are presently strongest as tools for measurement, phenotyping, and adherence or quality control, and more limited, though evolving, as predictors of biomarker response. Computer-vision systems can now classify and grade yoga postures using skeletal key points and contrastive learning architectures, enabling scalable quantification of movement fidelity and practice consistency, an essential advance for trials in which dose and execution quality materially affect outcomes (Wu et al. 2022). Wearable sensor platforms integrating electrodermal activity, temperature, motion, and heart-rate-derived features have likewise been paired with interpretable machine-learning models to classify autonomic and physiologic states during yoga practice, demonstrating feasibility for capturing high-frequency physiologic signatures that can be temporally aligned with biomarker sampling (Anumula et al. 2025).