Normal Changes of Ageing

At a biological level, normal ageing or ‘senescence’ results in limitation of function, increased risk of disease and ultimately death. The pattern, onset and rate vary between individuals and appear not to be the result of a single overarching mechanism but instead that of a complex interplay between intrinsic and extrinsic factors. As such ‘chronological age’ and ‘biological age’ often widely differ. The effects of the ageing process are becoming more important to consider as each generation passes, due to increased life expectancy and other demographic changes.

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Can You be Biologically Younger than Your Chronological Age? An Overview of Biological Ageing

Biomedicine ◽

10.51248/.v41i3.682 ◽

2021 ◽

Vol 41 (3) ◽

pp. 508-514

Author(s):

Sumit Kumar ◽

Shailaja Moodithaya ◽

Shruthi Suvarna H I ◽

Amrit Mirajkar

Keyword(s):

Life Expectancy ◽

Biological Age ◽

Cochrane Library ◽

Chronological Age ◽

Free Text ◽

Biological Ageing ◽

Current Review ◽

Normal Ageing ◽

The Cochrane Library ◽

Expansion Of Morbidity

The ageing of the population is rapidly escalating worldwide irrespective of unpredictable health challenges like climate change, emerging infectious disease, a microbe that develops drug resistance. India is also experiencing rapid socioeconomic progress and urbanization and the result of this demographic transition is population ageing. Even though there is an increase in life expectancy, there is no increase in health span, and thus increased life expectancy leads to ‘expansion of morbidity'. Longer life expectancy with the expansion of morbidity could enforce a challenge to geroscience as well as a substantial health burden and a threat to the national economy. In normal ageing, chronological age equates to biological age but certain disease conditions accelerate biological age. Similarly, intervention with physical activity, anti-ageing nutraceuticals would slow down the rate ageing process and provide powerful benefits for longevity. The current review article is based on MeSH and free-text terms in databases such as PubMed, the Cochrane Library, and Science Direct. This article aims to provide an overview of the concept of biological ageing with emphasis on the pathophysiology of ageing, quantification of biological ageing and the anti-ageing strategies.

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The AgeGuess database, an open online resource on chronological and perceived ages of people aged 5–100

Scientific Data ◽

10.1038/s41597-019-0245-9 ◽

2019 ◽

Vol 6 (1) ◽

Cited By ~ 1

Author(s):

Julia A. Barthold Jones ◽

Ulrik W. Nash ◽

Julien Vieillefont ◽

Kaare Christensen ◽

Dusan Misevic ◽

...

Keyword(s):

Life Expectancy ◽

Human Life ◽

Developed Countries ◽

Biological Age ◽

Ageing Process ◽

Birth Cohorts ◽

Online Resource ◽

Human Ageing ◽

Perceived Age ◽

Citizen Science Project

Abstract In many developed countries, human life expectancy has doubled over the last 180 years. Underlying this higher life expectancy is a change in how we age. Biomarkers of ageing are used to quantify changes in the aging process and to determine biological age. Perceived age is such a biomarker that correlates with biological age. Here we present a unique database rich with possibilities to study the human ageing process. Using perceived age enables us to collect large amounts of data on biological age through a citizen science project, where people upload facial pictures and guess the ages of other people at www.ageguess.org. The data on perceived age we present here span birth cohorts from the years 1877 to 2012. The database currently contains around 220,000 perceived age guesses. Almost 4500 citizen scientists from over 120 countries of origin have uploaded ~4700 facial photographs. Beyond studying the ageing process, the data present a wealth of possibilities to study how humans guess ages and who is better at guessing ages.

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MACHINE LEARNING ANALYSIS OF MOUSE FRAILTY FOR PREDICTION OF BIOLOGICAL AGE AND LIFE EXPECTANCY

Innovation in Aging ◽

10.1093/geroni/igz038.3299 ◽

2019 ◽

Vol 3 (Supplement_1) ◽

pp. S903-S903 ◽

Cited By ~ 1

Author(s):

Alice Kane ◽

Michael B Schultz ◽

Sarah Mitchell ◽

Michael MacArthur ◽

James Mitchell ◽

...

Keyword(s):

Life Expectancy ◽

Dietary Intervention ◽

Enzyme Inhibitor ◽

Strong Predictor ◽

Frailty Index ◽

Biological Age ◽

Chronological Age ◽

Aging Research ◽

Non Invasive ◽

Median Error

Abstract In mammals, the lack of accurate biomarkers for biological age is a current limitation to identifying novel aging interventions. Molecular biomarkers including DNA methylation hold promise but are invasive and currently expensive. The Frailty Index (FI) quantifies the accumulation of health-related deficits and is fast, cheap, and non-invasive. Studies have demonstrated that FI correlates with age and mortality risk in mice and humans. However, the FI has not been modelled to directly predict biological age or life expectancy. We tracked aging male C57BL/6 mice until their natural deaths, scoring them longitudinally with the FI. We find that FI score correlates with and is predictive of age and that some but not all parameters of the FI are individually well-correlated with age. To better predict chronological age, we performed an elastic net regression on the FI termed FRIGHT (Frailty Inferred Geriatric Health Timeline) Age. FRIGHT Age is a strong predictor of age (r2=0.73, median error=47.5 days), but is not superior to chronological age at predicting life expectancy. To better predict mortality, we built a random forest model termed the AFRAID (Analysis of Frailty and Death) score, which predicted survival at multiple ages (r2=0.375, median error = 46.4 days). The FRIGHT and AFRAID models were responsive to chronic treatment with enalapril (30mg/kg/day), an angiotensin converting enzyme inhibitor that extends healthspan, and methionine restriction, a dietary intervention that extends healthspan and lifespan. Our findings underscore the value of assessing non-invasive biomarkers for aging research and may help speed the identification of aging interventions.

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Cell-Extrinsic Differentiation Block Mediated by EphA3 in Pre-Leukaemic Thymus Contributes to Disease Progression

Cancers ◽

10.3390/cancers13153858 ◽

2021 ◽

Vol 13 (15) ◽

pp. 3858

Author(s):

Adriana C. Pliego Zamora ◽

Hansini Ranasinghe ◽

Jessica E. Lisle ◽

Chun Ki Ng ◽

Stephen Huang ◽

...

Keyword(s):

T Cell ◽

Wild Type ◽

Developmental Pathway ◽

Complex Interplay ◽

Cell Competition ◽

Extrinsic Factors ◽

Self Renewal ◽

Abnormal Accumulation ◽

Differentiation Block ◽

Intrinsic And Extrinsic Factors

We recently characterised the NUP98-HOXD13 (NHD13) mouse as a model of T-cell pre-leukaemia, featuring thymocytes that can engraft in recipient animals and progress to T-cell acute lymphoblastic leukaemia (T-ALL). However, loss of this engraftment ability by deletion of Lyl1 did not result in any loss of leukemogenesis activity. In the present study, we observe that NHD13 thymocytes overexpress EPHA3, and we characterise thymocyte behaviour in NHD13 mice with deletion of EphA3, which show a markedly reduced incidence of T-ALL. Deletion of EphA3 from the NHD13 mice does not prevent the abnormal accumulation or transplantation ability of these thymocytes. However, upon transplantation, these cells are unable to block the normal progression of recipient wild type (WT) progenitor cells through the normal developmental pathway. This is in contrast to the EphA3+/+ NHD13 thymocytes, which block the progression of incoming WT progenitors past the DN1 stage. Therefore, EphA3 is not critical for classical self-renewal, but is essential for mediating an interaction between the abnormally self-renewing cells and healthy progenitors—an interaction that results in a failure of the healthy cells to differentiate normally. We speculate that this may orchestrate a loss of healthy cell competition, which in itself has been demonstrated to be oncogenic, and that this may explain the decrease in T-ALL incidence in the absence of EphA3. We suggest that pre-leukaemic self-renewal in this model is a complex interplay of cell-intrinsic and -extrinsic factors, and that multiple redundant pathways to leukaemogenesis are active.

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Prediction of biological age and all-cause mortality by 12-lead electrocardiogram in patients without structural heart disease

10.21203/rs.3.rs-27441/v1 ◽

2020 ◽

Author(s):

Naomi Hirota ◽

Shinya Suzuki ◽

Takuto Arita ◽

Naoharu Yagi ◽

Takayuki Otsuka ◽

...

Keyword(s):

Principal Component Analysis ◽

Heart Disease ◽

Ventricular Tachyarrhythmia ◽

Principal Component ◽

Structural Heart Disease ◽

Biological Age ◽

Chronological Age ◽

Increased Risk ◽

All Cause Mortality ◽

Electrocardiogram Ecg

Abstract Background: The 12-lead electrocardiogram (ECG) is affected by not only the cardiovascular but also the non-cardiovascular status. Whether ECG can be the determinant of biological age (BA) and the gap between chronological age (CA) and ECG-predicted BA reflect differences in prognosis are unclear. Methods: In the Shinken Database 2010 – 2017 (n = 19170), 12-lead ECG was analyzed in 13005 patients excluding those with structural heart disease or having pacing beats, atrial or ventricular tachyarrhythmia, and indeterminate axis (R axis > 180˚) on index ECG. The prediction model of BA was developed by principal component analysis with 438 ECG parameters. The gap between ECG-predicted BA and CA was calculated (AgeDiff = ECG-predicted BA − CA). Results: The ECG-predicted BA was significantly correlated with CA (r = 0.967). Patients with a positively wide AgeDiff had a higher incidence of all-cause mortality compared to those with a narrow AgeDiff or those with negative AgeDiff. The risk of AgeDiff > 0 for all-cause mortality compared with AgeDiff ≤ 0 was 1.78 (95%CI: 1.00 − 3.16), which increased according to the aging and became the highest in patients with CA of 71 − 80 years. Conclusion: Our data suggested that 12-lead ECG can be a tool to estimate BA. The gap between ECG-predicted BA and CA allowed estimation of increased risk of all-cause mortality in patients without structural heart disease.

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Pulmonary Complications of Cystic Fibrosis

Seminars in Respiratory and Critical Care Medicine ◽

10.1055/s-0039-1697639 ◽

2019 ◽

Vol 40 (06) ◽

pp. 804-809 ◽

Cited By ~ 2

Author(s):

Bryan Garcia ◽

Patrick A. Flume

Keyword(s):

Cystic Fibrosis ◽

Respiratory Failure ◽

Lung Disease ◽

Airflow Obstruction ◽

Pulmonary Complications ◽

Extrinsic Factors ◽

Hypercapnic Respiratory Failure ◽

Increased Risk ◽

Impaired Lung Function ◽

Intrinsic And Extrinsic Factors

AbstractCystic fibrosis (CF) lung disease is characterized by the development of progressive bronchiectasis and impaired lung function with severe airflow obstruction. CF patients suffer from shortened life expectancy, primarily driven by respiratory failure. The mechanism by which CF lung disease develops is the result of an interplay of multiple intrinsic and extrinsic factors including genotype, abnormalities in mucus composition and movement, chronic inflammation, and chronic airway infection. Although all CF patients are at increased risk for pulmonary complications including hemoptysis, pneumothorax, pulmonary hypertension, and chronic hypoxic and hypercapnic respiratory failure, the risk of developing these complications increases with progression of lung disease. The focus of this article is to summarize the pathophysiology, epidemiology, and management of these key pulmonary complications.

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Team-Betreuung von Kindern und Jugendlichen – Vor dem Spiel…

Sports & Exercise Medicine Switzerland ◽

10.34045/sems/2021/5 ◽

2021 ◽

Keyword(s):

Risk Profile ◽

Medical Examination ◽

Extrinsic Factors ◽

Effective Prevention ◽

Joint Injuries ◽

Increased Risk ◽

Intrinsic And Extrinsic Factors

Team coverage of adolescents in sports includes preventive tasks parallel to competition. In addition to the sports medical examination, regular evaluations of the sport’s risk profile should be recorded. To achieve efficient and effective prevention, intrinsic and extrinsic factors have to be considered. Especially during puberty, there is an increased risk for epiphyseal and apophyseal joint injuries.

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Construction of a Biological Age Score to Predict Tooth Loss over 10 Years

Journal of Dental Research ◽

10.1177/0022034519861037 ◽

2019 ◽

Vol 98 (10) ◽

pp. 1096-1102

Author(s):

P. Meisel ◽

C. Pink ◽

M. Nauck ◽

H. Völzke ◽

T. Kocher

Keyword(s):

Tooth Loss ◽

Population Based ◽

Outcome Data ◽

Biological Age ◽

Chronological Age ◽

Population Based Study ◽

Receiver Operating Characteristic Curves ◽

Increased Risk ◽

The Impact

The aim of the present study was to construct a biological age score reflecting one’s physiologic capability and aging condition with respect to tooth loss over 10 y. From the follow-up to the population-based Study of Health in Pomerania (i.e., SHIP-2), 2,049 participants were studied for their baseline biomarker measures 10 y before (i.e., in SHIP-0). Metabolic and periodontal data were regressed onto chronological age to construct a score designated as “biological age.” For either sex separately, the impact of this individualized score was used to predict tooth loss in the follow-up cohort in comparison with each participant’s chronological age. Outcome data after 10 y with respect to tooth loss, periodontitis, obesity, and inflammation were shown to be better for biologically younger subjects than as expected by their chronological age, whereas for the older subjects, data were worse. Especially for tooth loss, a striking increase was observed in subjects whose biological age at baseline appeared to be higher than their chronological age. Biological age produced significantly better tooth loss predictions than chronological age ( P < 0.001). Areas under receiver operating characteristic curves for tooth loss of ≥3 teeth in men during follow-up were 0.811 and 0.745 for biological and chronological age, respectively. For women, these figures were 0.788 and 0.724. For total tooth loss, areas under the curve were 0.890 and 0.749 in men and 0.872 and 0.752 in women. Biological age combines various measures into a single score and allows identifying individuals at increased risk of tooth loss.

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