Accurate Epigenetic Aging in Bottlenose Dolphins (Tursiops Truncatus), an Essential Step in the Conservation of at-Risk Dolphins.

Epigenetics, specifically DNA methylation, allows for estimation of animal age from blood or remotely sampled skin. This multi tissue epigenetic aging clock uses 110 longitudinal samples from 34 Navy bottlenose dolphins (Tursiops truncatus), identifying 195 cytosine-phosphate-guanine sites associated with chronological aging via leave-one-individual-out-cross-validation (R2=0.95). With a median absolute error of 2.5 years this clock improves age estimation capacity in wild dolphins, expanding conservation efforts, enabling better understanding of population demographics.

Download Full-text

Accurate Epigenetic Aging in Bottlenose Dolphins (Tursiops truncatus), an Essential Step in the Conservation of at-Risk Dolphins

Journal of Zoological and Botanical Gardens ◽

10.3390/jzbg2030030 ◽

2021 ◽

Vol 2 (3) ◽

pp. 416-420

Author(s):

Ashley Barratclough ◽

Cynthia R. Smith ◽

Forrest M. Gomez ◽

Theoni Photopoulou ◽

Ryan Takeshita ◽

...

Keyword(s):

Age Estimation ◽

Tursiops Truncatus ◽

Cross Validation ◽

Bottlenose Dolphins ◽

Absolute Error ◽

Population Demographics ◽

Chronological Aging ◽

Epigenetic Aging ◽

Epigenetic Age ◽

Estimation Capacity

Epigenetics, specifically DNA methylation, allows for the estimation of animal age from blood or remotely sampled skin. This multi-tissue epigenetic age estimation clock uses 110 longitudinal samples from 34 Navy bottlenose dolphins (Tursiops truncatus), identifying 195 cytosine-phosphate-guanine sites associated with chronological aging via cross-validation with one individual left out in each fold (R2 = 0.95). With a median absolute error of 2.5 years, this clock improves age estimation capacity in wild dolphins, helping conservation efforts and enabling a better understanding of population demographics.

Download Full-text

A machine vision based frailty index for mice

10.1101/2021.09.27.462066 ◽

2021 ◽

Author(s):

Leinani E. Hession ◽

Gautam S. Sabnis ◽

Gary A. Churchill ◽

Vivek Kumar

Keyword(s):

Health Status ◽

Open Field ◽

Large Scale ◽

Frailty Index ◽

Absolute Error ◽

Video Data ◽

Biological Aging ◽

Chronological Aging ◽

Field Assay ◽

Median Absolute Error

1AbstractChronological aging is uniform, but biological aging is heterogeneous. Clinically, this heterogeneity manifests itself in health status and mortality, and it distinguishes healthy from unhealthy aging. Clinical frailty indexes (FIs) serve as an important tool in gerontology to capture health status. FIs have been adapted for use in mice and are an effective predictor of mortality risk. To accelerate our understanding of biological aging, high-throughput approaches to pre-clinical studies are necessary. Currently, however, mouse frailty indexing is manual and relies on trained scorers, which imposes limits on scalability and reliability. Here, we introduce a machine learning based visual frailty index (vFI) for mice that operates on video data from an open field assay. We generate a large mouse FI datasets comprising 256 males and 195 females. From video data on these same mice, we use neural networks to extract morphometric, gait, and other behavioral features that correlate with manual FI score and age. We use these features to train a regression model that accurately predicts frailty within 1.03 ± 0.08 (3.9% ± 0.3%) of the pre-normalized FI score in terms of median absolute error. We show that features of biological aging are encoded in open-field video data and can be used to construct a vFI that can complement or replace current manual FI methods. We use the vFI data to examine sex-specific aspects of aging in mice. This vFI provides increased accuracy, reproducibility, and scalability, that will enable large scale mechanistic and interventional studies of aging in mice.

Download Full-text