Biomarkers of Aging

Bai, Xia

doi:10.1007/978-981-13-1117-8_14

Cited by 25 publications

(24 citation statements)

References 76 publications

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“…A final comment concerns how relevant is the objective evaluation of ageing processes in humans [ 78 ]. Age can be classified into “chronological” and “biological” components [ 79 ], the former referring to the actual amount of years of a subject, whereas the latter reflects the amount of age-related changes in various physiological functions in the same subject.…”

Section: Discussionmentioning

confidence: 99%

Machine-Learning Analysis of Voice Samples Recorded through Smartphones: The Combined Effect of Ageing and Gender

Asci

Costantini

Leo

et al. 2020

Sensors

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Background: Experimental studies using qualitative or quantitative analysis have demonstrated that the human voice progressively worsens with ageing. These studies, however, have mostly focused on specific voice features without examining their dynamic interaction. To examine the complexity of age-related changes in voice, more advanced techniques based on machine learning have been recently applied to voice recordings but only in a laboratory setting. We here recorded voice samples in a large sample of healthy subjects. To improve the ecological value of our analysis, we collected voice samples directly at home using smartphones. Methods: 138 younger adults (65 males and 73 females, age range: 15–30) and 123 older adults (47 males and 76 females, age range: 40–85) produced a sustained emission of a vowel and a sentence. The recorded voice samples underwent a machine learning analysis through a support vector machine algorithm. Results: The machine learning analysis of voice samples from both speech tasks discriminated between younger and older adults, and between males and females, with high statistical accuracy. Conclusions: By recording voice samples through smartphones in an ecological setting, we demonstrated the combined effect of age and gender on voice. Our machine learning analysis demonstrates the effect of ageing on voice.

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Section: Discussionmentioning

confidence: 99%

Machine-Learning Analysis of Voice Samples Recorded through Smartphones: The Combined Effect of Ageing and Gender

Asci

Costantini

Leo

et al. 2020

Sensors

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“…It is generally acknowledged that chronological age is not an accurate indicator for the aging process, making it difficult to identify and validate effective measures to promote longevity and healthy aging. Thus, the concept of biological age has been proposed to accurately predict the aging status of an organ or person [ 23 ]. Enormous progress has been made in recent years in the development of several potential biological age estimators, of which DNA methylation level is the most promising [ 19 ].…”

Section: Dna Methylation In Agingmentioning

confidence: 99%

Epigenetic Clock: DNA Methylation in Aging

Jiang

Guo

2020

Stem Cells International

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Aging, which is accompanied by decreased organ function and increased disease incidence, limits human lifespan and has attracted investigators for thousands of years. In recent decades, with the rapid development of biology, scientists have shown that epigenetic modifications, especially DNA methylation, are key regulators involved in this process. Regular fluctuations in global DNA methylation levels have been shown to accurately estimate biological age and disease prognosis. In this review, we discuss recent findings regarding the relationship between variations in DNA methylation level patterns and aging. In addition, we introduce the known mechanisms by which DNA methylation regulators affect aging and related diseases. As more studies uncover the mechanisms by which DNA methylation regulates aging, antiaging interventions and treatments for related diseases may be developed that enable human life extension.

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“…Several aging biomarkers such as telomere length [36][37][38][39][40][41][42][43][44] have already been identified [45][46][47][48][49][50][51][52][53][54][55][56][57][58][59][60][61][62][63][64] , and biological age predictors such as the DNA methylation clock [65][66][67][68] have been built from them. We discuss these existing biological age predictors more in detail in the supplemental.…”

Section: Introductionmentioning

confidence: 99%

Analyzing the multidimensionality of biological aging with the tools of deep learning across diverse image-based and physiological indicators yields robust age predictors

Collin

Diai

et al. 2021

Preprint

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It is hypothesized that there are inter-individual differences in biological aging; however, differences in aging among (heart images vs. electrophysiology) and across (e.g., brain vs heart) physiological dimensions have not been systematically evaluated and compared. We analyzed 676,787 samples from 502,211 UK Biobank participants aged 37-82 years with deep learning approaches to build a total of 331 chronological age predictors on different data modalities such as videos (e.g. heart magnetic resonance imaging [MRI]), images (e.g. brain, liver and pancreas MRIs, full body X-rays, eye fundus and optical coherence tomography [OCT] images, carotid ultrasound images), time-series (e.g. electrocardiograms [ECGs], pulse wave analysis, wrist accelerometer data) and scalar data (e.g. blood biomarkers, anthropometric measures) to characterize the multiple dimensions of aging. We combined these age predictors into 11 main aging dimensions, 31 subdimensions and 84 sub-subdimensions ensemble models based on specific organ systems. Some of the organ systems were highly predictive of age. For example, heart features predict chronological age with a testing root mean squared error (RMSE) and standard error of 2.83+/-0.04 years, and musculoskeletal features predict age with a RMSE of 2.65+/-0.04 years. We defined "accelerated" agers as participants whose predicted age was greater than their chronological age and computed the correlation between these different definitions of accelerated aging. We found that most aging dimensions are largely uncorrelated (average correlation=.139+/-.090) but that dimensions that are biologically related tend to be more positively correlated. For example, we found that heart anatomical (from MRI) accelerated aging and heart electrical (from ECG) accelerated aging are correlated (average Pearson of .249+/-.005). Likewise, we found that most aging dimensions are genetically largely uncorrelated (average correlation=.104+/-.149). For example, heart anatomical and electrical accelerated aging are genetically .508+/-.089 correlated (r_g). We identified 9,697 SNPs in 3,318 genes associated with accelerated aging in at least one aging dimension and found an average GWAS-based heritability for accelerated aging of 26.1+/-7.42% (e.g. heart aging: 35.2+/-1.6%). Finally, we identified biomarkers, clinical phenotypes, diseases, family history, environmental variables and socioeconomic variables associated with accelerated aging in each aging dimension and computed the correlation between the different aging dimensions in terms of these associations. We found that environmental and socioeconomic variables are similarly associated with accelerated aging across aging dimensions (average correlations of respectively .639+/-.180 and .607+/-.309). We also built multivariate models to predict accelerated aging as a function of these same variables. For example, we predicted accelerated pulse wave analysis-measured arterial aging from diet with a R2 of 9.1%. Overall, most dimensions of aging are complex traits with both genetic and non-genetic correlates. These dimensions are weakly correlated with each other, highlighting the multidimensionality of the aging process and the need for multi-dimensional biological age predictors. Our results can be interactively explored on the following website: https://www.multidimensionality-of-aging.net/

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Biomarkers of Aging

Cited by 25 publications

References 76 publications

Machine-Learning Analysis of Voice Samples Recorded through Smartphones: The Combined Effect of Ageing and Gender

Machine-Learning Analysis of Voice Samples Recorded through Smartphones: The Combined Effect of Ageing and Gender

Epigenetic Clock: DNA Methylation in Aging

Analyzing the multidimensionality of biological aging with the tools of deep learning across diverse image-based and physiological indicators yields robust age predictors

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