Hernández et al. (2026). SAGs-1.png

Science Advances

Science Advances - https://www.science.org/doi/10.1126/sciadv.aef9864

<strong>Abstract</strong>Biological aging clocks offer estimations of aging and dementia, yet scalability is limited. We introduce a large- scale, cross- national speech clock derived from 2928 individuals across five Latin American countries, spanning healthy controls (HCs), mild cognitive impairment (MCI), Alzheimer’s disease (AD), and non- language/language- dominant frontotemporal dementia (nldFTD/ldFTD). Multimodal acoustic and linguistic features were trained with supervised models to estimate chronological age, generating speech age gaps (SAGs) as cross- sectional markers of deviations from chronological age, with positive values interpreted as relatively older- appearing speech profiles. SAGs differentiated diagnostic groups (HCs&nbsp;&lt;&nbsp;patient groups, with AD &lt; nldFTD &lt; ldFTD). This pattern was associated with clinical/cognitive domains. SAGs correlated with phosphorylated tau (p- Tau217) in AD and social exposome in HCs and AD. Brain clocks (structural/functional/combined) were associated with SAG in AD, nldFTD, and ldFTD. Epigenetic age correlated with SAGs in HCs and AD across Hannum, Retroclock, and OMICmAge, whereas ldFTD associations were limited to Retroclock and OMICmAge. These results indicate that SAGs capture cross- sectional multilevel aging- related variation and may offer a scalable, culturally adaptable, low- cost biomarker candidate for research in underrepresented global settings.

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