Aging Out of the Blue: Region-Specific Epigenetic Clock Calibration for a Blue Zone with the DNAm SuperLearner

Apr 3, 2026medRxiv : the preprint server for health sciences

Adjusting biological age measures for specific body areas in a long-living population using DNA methylation

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Longevity & Aging on OpenScience ↗PubMed ↗DOI ↗OA ↗

Abstract

Residents of the Nicoya blue zone exhibit an estimated two-year advantage in biological age compared to other Costa Ricans.

Epigenetic clocks predict biological age using DNA methylation patterns, which correlate with health risks.
The study utilized data from 206 Nicoyans and 875 residents from other regions in Costa Rica.
An ensemble machine learning method, SuperLearner, was employed to optimize age predictions.
Before calibration, SuperLearner indicated a three-year reduction in biological age for Nicoyans.
After calibration with non-Nicoyan data, the estimated aging advantage for Nicoyans decreased to about two years.
This method could enhance understanding of biological aging in specific populations when relevant comparison groups are available.

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Epigenetic clocks estimate biological age from DNA methylation patterns at CpG sites, providing robust predictions of mortality and morbidity risk. "Blue zones"-regions of exceptional longevity-offer a unique opportunity to investigate how biological aging diverges from chronological age. However, standard clocks are typically trained on large, heterogeneous datasets, reflecting average population trends rather than region-specific dynamics. Using data from the Costa Rican Longevity and Healthy Aging Study (CRELES), we profiled DNA methylation from residents of the Nicoya blue zone (= 206) and a comparison population in other parts of Costa Rica (= 875). We propose training a, an ensemble machine learning approach, on the non-Nicoyan Costa Ricans to optimize predictive performance across existing clocks and flexible machine learners. Theoretically justified by its Oracle property,performs asymptotically as well as the best candidate predictor in the ensemble, resulting in a weighted combination of algorithms used to predict age. We then used this trained model to construct a calibrated hypothesis test comparing residual age distributions between the blue zone region and the comparison population. Comparing our approach to the five top-performing epigenetic clocks (ranked by MSE) in the Costa Rican cohort, only SuperLearner suggested age deceleration (an average of ∼ 1 year) in the non-Nicoyan reference group. Before calibration, SuperLearner showed the strongest evidence for slowed biological aging among blue zone Nicoyans, estimating a three-year reduction (= -3.05, 95% CI [-3.64, -2.46]) in epigenetic age. Calibrating with non-Nicoyan Costa Ricans improved consistency between estimates in all clocks, decreasing the estimated aging advantage in Nicoyans to about two years ( , 95% CI [-2.56, -1.37]). This approach provides a robust framework for estimating longevity in distinct regions when a relevant comparison population is available. n n SuperLearner SuperLearner R̄

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Aging Out of the Blue: Region-Specific Epigenetic Clock Calibration for a Blue Zone with the DNAm SuperLearner

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