Biological Versus Chronological Aging

Hamczyk, Magda R.; Nevado, Rosa M.; Barettino, Ana; Fuster, Valentín; Andrés, Vicente

doi:10.1016/j.jacc.2019.11.062

Cited by 266 publications

(271 citation statements)

References 113 publications

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“…To date, no standardized biomarkers of physiologic ageing are currently available. We, therefore, believe that our voice analysis with machine learning would provide a novel and advanced tool possibly helpful for quantifying the individual “biological” age of a single subject [ 81 , 82 ]. The objective voice analysis would also allow to better discriminate and monitor processes of physiological as well as pathological ageing.…”

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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“…The concept of biological aging comes from the fact that individuals do not age at the same pace. In particular, vascular aging involves arterial degeneration and hardening that impairs vascular function and ultimately causes end organ damage 27 . Concerning biomarkers of biological vascular aging, the most common arterial stiffness measure is pulse wave velocity (PWV), in our case the PPG waves, the velocity at which the blood pressure wave moves along the arterial tree.…”

Section: Discussionmentioning

confidence: 99%

Prediction of vascular aging based on smartphone acquired PPG signals

Dall’Olio

Curti

Remondini

et al. 2020

Sci Rep

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Photoplethysmography (PPG) measured by smartphone has the potential for a large scale, non-invasive, and easy-to-use screening tool. Vascular aging is linked to increased arterial stiffness, which can be measured by PPG. We investigate the feasibility of using PPG to predict healthy vascular aging (HVA) based on two approaches: machine learning (ML) and deep learning (DL). We performed data preprocessing, including detrending, demodulating, and denoising on the raw PPG signals. For ML, ridge penalized regression has been applied to 38 features extracted from PPG, whereas for DL several convolutional neural networks (CNNs) have been applied to the whole PPG signals as input. The analysis has been conducted using the crowd-sourced Heart for Heart data. The prediction performance of ML using two features (AUC of 94.7%) – the a wave of the second derivative PPG and tpr, including four covariates, sex, height, weight, and smoking – was similar to that of the best performing CNN, 12-layer ResNet (AUC of 95.3%). Without having the heavy computational cost of DL, ML might be advantageous in finding potential biomarkers for HVA prediction. The whole workflow of the procedure is clearly described, and open software has been made available to facilitate replication of the results.

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“…Atherosclerosis is a major contributor to adverse events and deaths from cardiovascular disease, and its onset and clinical manifestation are highly variable between normally-aging individuals [ 29 ]. Studying diseases that drastically accelerate atherogenesis in the absence of traditional cardiovascular risk factors, such as HGPS, may therefore help to identify unknown factors that speed up this process [ 30 ].…”

Section: Discussionmentioning

confidence: 99%

Premature Vascular Aging with Features of Plaque Vulnerability in an Atheroprone Mouse Model of Hutchinson–Gilford Progeria Syndrome with Ldlr Deficiency

Nevado

Hamczyk

Gonzalo

et al. 2020

Cells

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Hutchinson–Gilford progeria syndrome (HGPS) is among the most devastating of the laminopathies, rare genetic diseases caused by mutations in genes encoding nuclear lamina proteins. HGPS patients age prematurely and die in adolescence, typically of atherosclerosis-associated complications. The mechanisms of HGPS-related atherosclerosis are not fully understood due to the scarcity of patient-derived samples and the availability of only one atheroprone mouse model of the disease. Here, we generated a new atherosusceptible model of HGPS by crossing progeroid LmnaG609G/G609G mice, which carry a disease-causing mutation in the Lmna gene, with Ldlr−/− mice, a commonly used preclinical atherosclerosis model. Ldlr−/−LmnaG609G/G609G mice aged prematurely and had reduced body weight and survival. Compared with control mice, Ldlr−/−LmnaG609G/G609G mouse aortas showed a higher atherosclerosis burden and structural abnormalities typical of HGPS patients, including vascular smooth muscle cell depletion in the media, adventitial thickening, and elastin structure alterations. Atheromas of Ldlr−/−LmnaG609G/G609G mice had features of unstable plaques, including the presence of erythrocytes and iron deposits and reduced smooth muscle cell and collagen content. Ldlr−/−LmnaG609G/G609G mice faithfully recapitulate vascular features found in patients and thus provide a new tool for studying the mechanisms of HGPS-related atherosclerosis and for testing therapies.

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Biological Versus Chronological Aging

Cited by 266 publications

References 113 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

Prediction of vascular aging based on smartphone acquired PPG signals

Premature Vascular Aging with Features of Plaque Vulnerability in an Atheroprone Mouse Model of Hutchinson–Gilford Progeria Syndrome with Ldlr Deficiency

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