Data-driven identification and classification of nonlinear aging patterns reveals the landscape of associations between DNA methylation and aging

Okada, Daigo; Cheng, Jie; Zheng, Cheng; Kumaki, Tatsuro; Yamada, Ryūji

doi:10.1186/s40246-023-00453-z

Cited by 6 publications

(4 citation statements)

References 39 publications

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“…Okada et al (68) have employed mutual information to craft a DNA methylation aging clock, gauging the correlation between DNA methylation patterns and age and offering a means to estimate an individual's biological age.…”

Section: Mutual Informationmentioning

confidence: 99%

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Computational modeling of aging-related gene networks: a review

Freitas,

Bischof

2024

Front. Appl. Math. Stat.

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The aging process is a complex and multifaceted phenomenon affecting all living organisms. It involves a gradual deterioration of tissue and cellular function, leading to a higher risk of developing various age-related diseases (ARDs), including cancer, neurodegenerative, and cardiovascular diseases. The gene regulatory networks (GRNs) and their respective niches are crucial in determining the aging rate. Unveiling these GRNs holds promise for developing novel therapies and diagnostic tools to enhance healthspan and longevity. This review examines GRN modeling approaches in aging, encompassing differential equations, Boolean/fuzzy logic decision trees, Bayesian networks, mutual information, and regression clustering. These approaches provide nuanced insights into the intricate gene-protein interactions in aging, unveiling potential therapeutic targets and ARD biomarkers. Nevertheless, outstanding challenges persist, demanding more comprehensive datasets and advanced algorithms to comprehend and predict GRN behavior accurately. Despite these hurdles, identifying GRNs associated with aging bears immense potential and is poised to transform our comprehension of human health and aging. This review aspires to stimulate further research in aging, fostering the innovation of computational approaches for promoting healthspan and longevity.

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Section: Mutual Informationmentioning

confidence: 99%

“…The surge in nonlinear methods for GRN reconstruction is driven by the abundance of available data (19,30,68,(133)(134)(135), offers a versatile framework. This nonlinear description enables the synthesis of diverse findings in the literature (32, 51, 57, 58).…”

Section: Modeling Nonlinear Phenomenamentioning

confidence: 99%

Computational modeling of aging-related gene networks: a review

Freitas,

Bischof

2024

Front. Appl. Math. Stat.

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“…Previous works have suggested that the relationship between methylation state and age is nonlinear 14,28,[38][39][40][41] . Therefore, we decided to stratify our training data into three age groups and train separate predictors for "young", "middle-aged", and "old" subjects.…”

Section: A New Epigenetic Clock Compatible With the Epicv2 Arraymentioning

confidence: 99%

Applicability of epigenetic age models to next-generation methylation arrays

Garma,

Quintela-Fandino

2024

Preprint

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BackgroundEpigenetic clocks based on DNA methylation data are routinely used to obtain surrogate measures of biological age and estimate epigenetic age acceleration rates. These tools are mathematical models that rely on the methylation state of specific sets of CpG islands quantified using microarrays. The set of CpG islands probed in the microarrays differed between the models. Thus, as new methylation microarrays are developed and older models are discontinued, existing epigenetic clocks might become obsolete. Here, we explored the effects of the changes introduced in the new DNA methylation array from Illumina (EPICv2) on existing epigenetic clocks.MethodsWe compiled a whole-blood DNA methylation dataset of 10835 samples to test the performance of four epigenetic clocks on the probe set of the EPICv2 array. We then used the same data to train a new epigenetic age prediction model compatible across the 450k, EPICv1 and EPICv2 microarrays. We compiled a validation dataset of 2095 samples to compare our model with a state-of-the-art epigenetic clock. We used a dataset of cancer survivors who had undergone different types of therapy and a dataset of breast cancer patients and controls to test the ability of our model to detect alterations in epigenetic age acceleration.ResultsWe found that the results of the four epigenetic clocks tested are significantly distorted by the absence of specific probes in the EPICv2 microarray, causing an average drift of up to 25 years. We developed an epigenetic age prediction model compatible with the 450k, EPICv1 and EPICv2 microarrays. Our model produced highly accurate chronological age predictions that were comparable to those of a state-of-the-art epiclock. This finding reproduced previous results showing increased epigenetic age acceleration in cancer patients and in survivors who had been treated with radiation therapy.ConclusionOur work demonstrated that existing epigenetic clocks need to be updated to be applicable to data generated with the new EPICv2 microarray, which has phased out the 450k and EPICv1 models. To overcome this technical hurdle, we developed a new model that translates the capabilities of state-of-the-art epigenetic clocks to the new EPICv2 platform and is cross-compatible with the 450k and EPICv1 microarrays.

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Section: A New Epigenetic Clock Compatible With the Epicv2 Arraymentioning

confidence: 99%

Epigenetic age prediction drifts resulting from next-generation methylation arrays

Garma,

Quintela-Fandino

2024

Preprint

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Background Epigenetic clocks based on DNA methylation data are routinely used to obtain surrogate measures of biological age and estimate epigenetic age acceleration rates. These tools are mathematical models that rely on the methylation state of specific sets of CpG islands quantified using microarrays. The set of CpG islands probed in the microarrays differed between the models. Thus, as new methylation microarrays are developed and older models are discontinued, existing epigenetic clocks might become obsolete. Here, we explored the effects of the changes introduced in the new DNA methylation array from Illumina (EPICv2) on existing epigenetic clocks. Methods We compiled a whole-blood DNA methylation dataset of 10835 samples to test the performance of four epigenetic clocks on the probe set of the EPICv2 array. We then used the same data to train a new epigenetic age prediction model compatible across the 450k, EPICv1 and EPICv2 microarrays. We compiled a validation dataset of 2095 samples to compare our model with a state-of-the-art epigenetic clock. We used a dataset of cancer survivors who had undergone different types of therapy and a dataset of breast cancer patients and controls to test the ability of our model to detect alterations in epigenetic age acceleration. Results We found that the results of the four epigenetic clocks tested are significantly distorted by the absence of specific probes in the EPICv2 microarray, causing an average drift of up to 25 years. We developed an epigenetic age prediction model compatible with the 450k, EPICv1 and EPICv2 microarrays. Our model produced highly accurate chronological age predictions that were comparable to those of a state-of-the-art epiclock. This finding reproduced previous results showing increased epigenetic age acceleration in cancer patients and in survivors who had been treated with radiation therapy. Conclusion Our work demonstrated that existing epigenetic clocks need to be updated to be applicable to data generated with the new EPICv2 microarray, which has phased out the 450k and EPICv1 models. To overcome this technical hurdle, we developed a new model that translates the capabilities of state-of-the-art epigenetic clocks to the new EPICv2 platform and is cross-compatible with the 450k and EPICv1 microarrays.

show abstract

Data-driven identification and classification of nonlinear aging patterns reveals the landscape of associations between DNA methylation and aging

Cited by 6 publications

References 39 publications

Computational modeling of aging-related gene networks: a review

Computational modeling of aging-related gene networks: a review

Applicability of epigenetic age models to next-generation methylation arrays

Epigenetic age prediction drifts resulting from next-generation methylation arrays

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