Physical Frailty As a Clinical Marker of Biological Age and Aging

Vidal, Edison Iglesias de Oliveira; Mayoral, Vania F. S.; Boas, Paulo José Fortes Villas; Jacinto, Alessandro Ferrari; Fukushima, Fernanda Bono

doi:10.1111/jgs.13356

Cited by 8 publications

(5 citation statements)

References 6 publications

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“…Gerontologists have devoted themselves to looking for aging biomarkers for decades. 17 The existed aging markers mostly emerged from animal models with a complicated analytic method. 18 In this paper, we analyzed and compared how the humans' urinary 8-OHdG, 8-isoPGF2α and salivary AOPPs, MDA and DHEA-S levels were related to various factors using multiple regression analysis method and with the purpose of finding a convenient, appropriate, economical and practical aging marker.…”

Section: Discussionmentioning

confidence: 99%

Urinary 8-hydroxydeoxyguanosine is a better biomarker of aging in non-smokers

Zhao

Gong

Liu

et al. 2018

Tradit. Med. Mod. Med.

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Objective: The aim of this study is finding an optimal and convenient aging marker. Method: The information of participants’ occupation, family background, life style, physical condition, etc. was obtained by a self-administered questionnaire. Spot urine and saliva samples were collected during the early morning after fasting overnight. 8-hydroxydeoxygunaosine (8-OHdG), 8-iso-prostaglandin F2[Formula: see text] (8-isoPGF2[Formula: see text]), advanced oxidation protein products (AOPPs), malondialdehyde (MDA), and dehydroepian drosterone sulfate (DHEA-S) were measured by ELISA. Results: Around 112 non-smokers (males/females [Formula: see text] 61/51; mean age [Formula: see text] 44.9 years) were eligible and analyzed in this study. The mean urinary 8-OHdG level was [Formula: see text] (ng/mmol, creatinine) and mean 8-isoPGF2[Formula: see text] level was [Formula: see text] (pg/mmoL, creatinine). In saliva, the mean level of AOPPs, MDA, and DHEA-S was [Formula: see text] ([Formula: see text]mol/L), [Formula: see text] ([Formula: see text]mol/L) and [Formula: see text] (ng/mL), respectively. After analysis in multiple regression model, urinary 8-OHdG was influenced by age and tea consumption ([Formula: see text]); urinary 8-isoPGF2[Formula: see text] level was correlated with gender ([Formula: see text]); salivary DHEA-S was related to gender ([Formula: see text]) and marital status ([Formula: see text]); AOPPs and MDA in saliva presented no relationships with those factors. Conclusion: Among healthy non-smokers, urinary 8-OHdG is the best aging biomarker comparing to other markers.

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Section: Discussionmentioning

confidence: 99%

Urinary 8-hydroxydeoxyguanosine is a better biomarker of aging in non-smokers

Zhao

Gong

Liu

et al. 2018

Tradit. Med. Mod. Med.

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“…Existe un concepto emergente en que la fragilidad puede ser considerada como un marcador de edad biológica, tal como se observa en estudios donde el estrés oxidativo estuvo asociado con fragilidad, pero no con edad cronológica, probablemente debido a que la fragilidad física es un mejor indicador de edad biológica, por lo que prevenir o revertir el desarrollo de la fragilidad física por medio de la práctica de ejercicio físico y hábitos de vida saludables sería equivalente a mantenerse joven biológicamente (32,33) .…”

Section: Intervencionesunclassified

Fragilidad en el adulto mayor: detección, intervención en la comunidad y toma de decisiones en el manejo de enfermedades crónicas

Tello-Rodríguez

Varela-Pinedo

2016

Rev Peru Med Exp Salud Publica

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La fragilidad física constituye "un síndrome médico con múltiples causas y factores que contribuyen a su desarrollo, caracterizado por la disminución de la fuerza, resistencia y funciones fisiológicas reducidas, que aumentan la vulnerabilidad de un individuo en el desarrollo de la dependencia funcional y/o su muerte". Existen diferentes procesos fisiopatológicos multisistémicos involucrados en la patogénesis del síndrome de fragilidad, siendo uno de los más importantes la pérdida de masa muscular asociada al envejecimiento o sarcopenia. Diversos estudios indican que la fragilidad física se incrementa con la edad. El ejercicio físico constituye la mejor forma de intervención en la prevención y tratamiento de la fragilidad. En el adulto mayor con enfermedades crónicas identificar la fragilidad es muy importante para la toma de decisiones, individualizando el manejo y considerando sus valores y preferencias de cuidado.

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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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Physical Frailty As a Clinical Marker of Biological Age and Aging

Cited by 8 publications

References 6 publications

Urinary 8-hydroxydeoxyguanosine is a better biomarker of aging in non-smokers

Urinary 8-hydroxydeoxyguanosine is a better biomarker of aging in non-smokers

Fragilidad en el adulto mayor: detección, intervención en la comunidad y toma de decisiones en el manejo de enfermedades crónicas

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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