Biological age--what is it and can it be measured?

Adding intrinsically disordered proteins to biological ageing clocks

Research into how the young and old differ, and which biomarkers reflect the diverse biological processes underlying ageing, is a current and fast-growing field. Biological clocks provide a means to evaluate whether a molecule, cell, tissue or even an entire organism is old or young. Here we summarize established and emerging molecular clocks as timepieces. We emphasize that intrinsically disordered proteins (IDPs) tend to transform into a β-sheet-rich aggregated state and accumulate in non-dividing or slowly dividing cells as they age. We hypothesize that understanding these protein-based molecular ageing mechanisms might provide a conceptual pathway to determining a cell's health age by probing the aggregation state of IDPs, which we term the IDP clock.

Gene-Environment Interactions and Gene-Gene Interactions on Two Biological Age Measures: Evidence from Taiwan Biobank Participants

PhenoAge and BioAge are two commonly used biological age (BA) measures. The author here searched for gene-environment interactions (GxE) and gene-gene interactions (GxG) on PhenoAgeAccel (age-adjusted PhenoAge) and BioAgeAccel (age-adjusted BioAge) of 111,996 Taiwan Biobank (TWB) participants, including a discovery set of 86,536 TWB2 individuals and a replication set of 25,460 TWB1 individuals. Searching for variance quantitative trait loci (vQTLs) provides a convenient way to evaluate GxE and GxG. A total of 4 nearly independent (linkage disequilibrium measure r2 < 0.01) PhenoAgeAccel-vQTLs are identified from 5,303,039 autosomal TWB2 SNPs (p < 5E-8), whereas no vQTLs are found from BioAgeAccel. These 4 PhenoAgeAccel-vQTLs (rs35276921, rs141927875, rs10903013, and rs76038336) are further replicated by TWB1 (p < 5E-8). They are located in the OR51B5, FAM234A, and AXIN1 genes. All 4 PhenoAgeAccel-vQTLs are significantly associated with PhenoAgeAccel (p < 5E-8). A phylogenetic heat map of the GxE analyses showed that smoking exacerbated the PhenoAgeAccel-vQTLs' aging effects, while higher educational attainment attenuated the PhenoAgeAccel-vQTLs' aging effects. Body mass index, chronological age, alcohol consumption, and sex do not prominently modulate PhenoAgeAccel-vQTLs' aging effects. Based on these vQTL results, rs141927875-rs35276921 interaction (p = 4.7E-61) and rs76038336-rs10903013 interaction (p = 3.3E-116) on PhenoAgeAccel are detected.

Development of a model for the prediction of biological age

BACKGROUND AND OBJECTIVE

Rates of aging vary markedly among individuals, and biological age serves as a more reliable predictor of current health status than does chronological age. As such, the ability to predict biological age can support appropriate and timely active interventions aimed at improving coping with the aging process. However, the aging process is highly complex and multifactorial. Therefore, it is more scientific to construct a prediction model for biological age from multiple dimensions systematically.

METHODS

Physiological and biochemical parameters were evaluated to gage individual health status. Then, age-related indices were screened for inclusion in a model capable of predicting biological age. For subsequent modeling analyses, samples were divided into training and validation sets for subsequent deep learning model-based analyses (e.g. linear regression, lasso model, ridge regression, bayesian ridge regression, elasticity network, k-nearest neighbor, linear support vector machine, support vector machine, and decision tree models, and so on), with the model exhibiting the best ability to predict biological age thereby being identified.

RESULTS

First, we defined the individual biological age according to the individual health status. Then, after 22 candidate indices (DNA methylation, leukocyte telomere length, and specific physiological and biochemical indicators) were screened for inclusion in a model capable of predicting biological age, 14 age-related indices and gender were used to construct a model via the Bagged Trees method, which was found to be the most reliable qualitative prediction model for biological age (accuracy=75.6%, AUC=0.84) by comparing 30 different classification algorithm models. The most reliable quantitative predictive model for biological age was found to be the model developed using the Rational Quadratic method (R2=0.85, RMSE=8.731 years) by comparing 24 regression algorithm models.

CONCLUSIONS

Both qualitative model and quantitative model of biological age were successfully constructed from a multi-dimensional and systematic perspective. The predictive performance of our models was similar in both smaller and larger datasets, making it well-suited to predicting a given individual's biological age.

Turning Back the Clock: A Retrospective Single-Blind Study on Brain Age Change in Response to Nutraceuticals Supplementation vs. Lifestyle Modifications

BACKGROUND

There is a growing consensus that chronological age (CA) is not an accurate indicator of the aging process and that biological age (BA) instead is a better measure of an individual's risk of age-related outcomes and a more accurate predictor of mortality than actual CA. In this context, BA measures the "true" age, which is an integrated result of an individual's level of damage accumulation across all levels of biological organization, along with preserved resources. The BA is plastic and depends upon epigenetics. Brain state is an important factor contributing to health- and lifespan.

METHODS AND OBJECTIVE

Quantitative electroencephalography (qEEG)-derived brain BA (BBA) is a suitable and promising measure of brain aging. In the present study, we aimed to show that BBA can be decelerated or even reversed in humans (N = 89) by using customized programs of nutraceutical compounds or lifestyle changes (mean duration = 13 months).

RESULTS

We observed that BBA was younger than CA in both groups at the end of the intervention. Furthermore, the BBA of the participants in the nutraceuticals group was 2.83 years younger at the endpoint of the intervention compared with their BBA score at the beginning of the intervention, while the BBA of the participants in the lifestyle group was only 0.02 years younger at the end of the intervention. These results were accompanied by improvements in mental-physical health comorbidities in both groups. The pre-intervention BBA score and the sex of the participants were considered confounding factors and analyzed separately.

CONCLUSIONS

Overall, the obtained results support the feasibility of the goal of this study and also provide the first robust evidence that halting and reversal of brain aging are possible in humans within a reasonable (practical) timeframe of approximately one year.

Epigenetic clocks provide clues to the mystery of uterine ageing

BACKGROUND

Rising maternal ages and age-related fertility decline are a global challenge for modern reproductive medicine. Clinicians and researchers pay specific attention to ovarian ageing and hormonal insufficiency in this regard. However, uterine ageing is often left out of the picture, with the majority of reproductive clinicians being close to unanimous on the absence of age-related functional decline in the uterine tissues. Therefore, most existing techniques to treat an age-related decline in implantation rates are based primarily on hormonal supplementation and oocyte donation. Solving the issue of uterine ageing might lead to an adjustment to these methods.

OBJECTIVE AND RATIONALE

A focus on uterine ageing and the possibility of slowing it emerged with the development of the information theory of ageing, which identifies genomic instability and erosion of the epigenetic landscape as important drivers of age-related decline in the functionality of most cells and tissues. Age-related smoothing of this landscape and a decline in tissue function can be assessed by measuring the ticking of epigenetic clocks. Within this review, we explore whether the uterus experiences age-related alterations using this elegant approach. We analyse existing data on epigenetic clocks in the endometrium, highlight approaches to improve the accuracy of the clocks in this cycling tissue, speculate on the endometrial pathologies whose progression might be predicted by the altered speed of epigenetic clocks and discuss the possibilities of slowing down the ticking of these clocks.

SEARCH METHODS

Data for this review were identified by searches of Medline, PubMed and Google Scholar. References from relevant articles using the search terms 'ageing', 'maternal age', 'female reproduction', 'uterus', 'endometrium', 'implantation', 'decidualization', 'epigenetic clock', 'biological age', 'DNA methylation', 'fertility' and 'infertility' were selected. A total of 95 articles published in English between 1985 and 2022 were included, six of which describe the use of the epigenetic clock to evaluate uterine/endometrium ageing.

OUTCOMES

Application of the Horvath and DNAm PhenoAge epigenetic clocks demonstrated a poor correlation with chronological age in the endometrium. Several approaches were suggested to enhance the predictive power of epigenetic clocks for the endometrium. The first was to increase the number of samples in the training dataset, as for the Zang clock, or to use more sophisticated clock-building algorithms, as for the AltumAge clock. The second method is to adjust the clocks according to the dynamic nature of the endometrium. Using either approach revealed a strong correlation with chronological age in the endometrium, providing solid evidence for age-related functional decline in this tissue. Furthermore, age acceleration/deceleration, as estimated by epigenetic clocks, might be a promising tool to predict or to gain insights into the origin of various endometrial pathologies, including recurrent implantation failure, cancer and endometriosis. Finally, there are several strategies to slow down or even reverse epigenetic clocks that might be applied to reduce the risk of age-related uterine impairments.

WIDER IMPLICATIONS

The uterine factor should be considered, along with ovarian issues, to correct for the decline in female fertility with age. Epigenetic clocks can be tested to gain a deeper understanding of various endometrial disorders.

Modeling biological age and its link with the aging process

Differences in health status at older ages are a result of genetic predispositions and physiological responses to exposure accumulation over the lifespan. These vary across individuals and lead to health status heterogeneity as people age. Chronological age (CA) is a standard indicator that reflects overall risks of morbidity and mortality. However, CA is only a crude proxy for individuals' latent physiological deterioration. An alternative to CA is biological age (BA), an indicator of accumulated age-related biological change reflected in markers of major physiological systems. We propose and validate two BA estimators that improve upon existing ones. These estimators (i) are based on a structural equation model (SEM) that represents the relation between BA and CA, (ii) circumvent the need to impose arbitrary assumptions about the relation between CA and BA, and (iii) provide tools to empirically test the validity of assumptions the researcher may wish to invoke. We use the US National Health and Nutrition Examination Survey 1988-1994 and compare results with three commonly used methods to compute BA (principal components-PCA, multiple regression-MLR, and Klemera-Doubal's method-KD). We show that SEM-based estimates of BA differ significantly from those generated by PCA and MLR and are comparable to, but have better predictive power than KD's. The proposed estimators are flexible, allow testing of assumptions about functional forms relating BA and CA, and admit a rich interpretation as indicators of accelerated aging.

A Model for Estimating Biological Age From Physiological Biomarkers of Healthy Aging: Cross-sectional Study

BACKGROUND

Individual differences in the rate of aging and susceptibility to disease are not accounted for by chronological age alone. These individual differences are better explained by biological age, which may be estimated by biomarker prediction models. In the light of the aging demographics of the global population and the increase in lifestyle-related morbidities, it is interesting to invent a new biological age model to be used for health promotion.

OBJECTIVE

This study aims to develop a model that estimates biological age based on physiological biomarkers of healthy aging.

METHODS

Carefully selected physiological variables from a healthy study population of 100 women and men were used as biomarkers to establish an estimate of biological age. Principal component analysis was applied to the biomarkers and the first principal component was used to define the algorithm estimating biological age.

RESULTS

The first principal component accounted for 31% in women and 25% in men of the total variance in the biological age model combining mean arterial pressure, glycated hemoglobin, waist circumference, forced expiratory volume in 1 second, maximal oxygen consumption, adiponectin, high-density lipoprotein, total cholesterol, and soluble urokinase-type plasminogen activator receptor. The correlation between the corrected biological age and chronological age was r=0.86 (P<.001) and r=0.81 (P<.001) for women and men, respectively, and the agreement was high and unbiased. No difference was found between mean chronological age and mean biological age, and the slope of the regression line was near 1 for both sexes.

CONCLUSIONS

Estimating biological age from these 9 biomarkers of aging can be used to assess general health compared with the healthy aging trajectory. This may be useful to evaluate health interventions and as an aid to enhance awareness of individual health risks and behavior when deviating from this trajectory.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03680768; https://clinicaltrials.gov/ct2/show/NCT03680768.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.2196/19209.

Recalibrating the epigenetic clock after exposure to trauma: The role of risk and protective psychosocial factors

BACKGROUND

Epigenetic aging is associated with a plethora of negative health outcomes and increased mortality. Yet, the dynamicity of epigenetic age after exposure to trauma and the factors that influence epigenetic age are not fully understood. This research evaluated longitudinal changes in epigenetic age before and after exposure to work-related trauma among paramedicine students. We further investigated psychological and social risk (psychological distress, posttraumatic stress disorder/PTSD symptom severity, professional quality of life) and protective factors (social support and organisational membership) that drive epigenetic aging at both time points.

METHODS

The study comprised of 80 samples of University paramedicine students including 40 individuals at two time points - t0 (baseline) and t1 (post-trauma exposure). Epigenome-wide analysis was performed from t0 and t1 saliva using the Illumina EPIC arrays that cover >860k probes. Data analysis was performed using R via generalized regression models. The epigenetic age was calculated based on the Horvath algorithm, GrimAge and SkinBloodAge were calculated using the Horvath online calculator, and p-value for significance was corrected using the FDR method for multiple testing corrections.

RESULTS

The epigenetic age at t0 and t1 were highly correlated with chronological age and with each other (r = 0.84-0.94). Baseline epigenetic age and follow-up epigenetic age were significantly associated with risk factors of psychological distress and PTSD symptom severity. Among the protective factors, a sense of psychological organisational membership at the start of the paramedicine course as measured at baseline significantly reduced epigenetic age at baseline and post-trauma exposure. On the other hand, receiving social support acted as a protective factor only after exposure to trauma (follow-up), decreasing epigenetic aging at follow-up. GrimAge acceleration at follow-up was significantly associated with increased PTSD symptom severity at baseline and follow-up. Moreover, increased social support at baseline and follow-up was associated with reduced follow-up GrimAge acceleration.

CONCLUSION

These results demonstrate that epigenetic aging is dynamic and changes after exposure to trauma. Additionally, results demonstrate that different risk and protective factors influence epigenetic aging at different times. In conclusion, the research identified risk and protective factors associated with epigenetic aging pre- and post-trauma exposure, with implications for health and well-being among individuals exposed to trauma.

Lifestyle factors and genetic variants on two biological age measures: evidence from 94,443 Taiwan Biobank participants

BACKGROUND

Biological age (BA) can be estimated by phenotypes and is useful for predicting lifespan and healthspan. Levine et al. proposed a PhenoAge and a BioAge to measure BA. Although there have been studies investigating the genetic predisposition to BA acceleration in Europeans, little has been known regarding this topic in Asians.

METHODS

I here estimated PhenoAgeAccel (age-adjusted PhenoAge) and BioAgeAccel (age-adjusted BioAge) of 94,443 Taiwan Biobank (TWB) participants, wherein 25,460 TWB1 subjects formed a discovery cohort and 68,983 TWB2 individuals constructed a replication cohort. Lifestyle factors and genetic variants associated with PhenoAgeAccel and BioAgeAccel were investigated through regression analysis and a genome-wide association study (GWAS).

RESULTS

A unit (kg/m 2) increase of BMI was associated with a 0.177-year PhenoAgeAccel (95% C.I. = 0.163~0.191, p = 6.0×) and 0.171-year BioAgeAccel (95% C.I. = 0.165~0.177, p = 0). Smokers on average had a 1.134-year PhenoAgeAccel (95% C.I. = 0.966~1.303, p = 1.3×) compared with non-smokers. Drinkers on average had a 0.640-year PhenoAgeAccel (95% C.I. = 0.433~0.847, p = 1.3×) and 0.193-year BioAgeAccel (95% C.I. = 0.107~0.279, p = 1.1×) relative to non-drinkers. A total of 11 and 4 single-nucleotide polymorphisms (SNPs) were associated with PhenoAgeAccel and BioAgeAccel (p<5× in both TWB1 and TWB2), respectively.

CONCLUSIONS

A PhenoAgeAccel-associated SNP (rs1260326 in GCKR) and two BioAgeAccel-associated SNPs (rs7412 in APOE; rs16998073 near FGF5) were consistent with the finding from the UK Biobank. The lifestyle analysis shows that prevention from obesity, cigarette smoking, and alcohol consumption is associated with a slower rate of biological aging.

Low muscle strength and increased arterial stiffness go hand in hand

Low handgrip strength and increased arterial stiffness are both associated with poor health outcomes, but evidence on the relationship between handgrip strength and arterial stiffness is limited. In this cross-sectional analysis of combined baseline datasets from the LipidCardio and Berlin Aging Study II cohorts we aimed to examine whether handgrip strength (HGS) is associated with arterial stiffness. 1511 participants with a median age of 68.56 (IQR 63.13–73.08) years were included. Arterial stiffness was assessed by aortal pulse wave velocity (PWV) with the Mobil-O-Graph device. Handgrip strength was assessed with a handheld dynamometer.

The mean HGS was 39.05 ± 9.07 kg in men and 26.20 ± 7.47 kg in women. According to multivariable linear regression analysis per 5 kg decrease in handgrip strength there was a mean increase in PWV of 0.08 m/s after adjustment for the confounders age, sex, coronary artery disease, systolic blood pressure, body mass index, cohort, and smoking. Thus, there was evidence that low handgrip strength and increased arterial stiffness go hand in hand. Arterial stiffness can possibly create the missing link between low handgrip strength and increased cardiovascular morbidity and mortality. Causality and direction of causality remain to be determined.

Accelerated inflammatory aging in Alzheimer's disease and its relation to amyloid, tau, and cognition

It is unclear how pathological aging of the inflammatory system relates to Alzheimer's disease (AD). We tested whether age-related inflammatory changes in cerebrospinal fluid (CSF) and plasma exist across different stages of AD, and whether such changes related to AD pathology. Linear regression was first used model chronological age in amyloid-β negative, cognitively unimpaired individuals (Aβ- CU; n = 312) based on a collection of 73 inflammatory proteins measured in both CSF and plasma. Fitted models were then applied on protein levels from Aβ+ individuals with mild cognitive impairment (Aβ+ MCI; n = 150) or Alzheimer's disease dementia (Aβ+ AD; n = 139) to test whether the age predicted from proteins alone ("inflammatory age") differed significantly from true chronological age. Aβ- individuals with subjective cognitive decline (Aβ- SCD; n = 125) or MCI (Aβ- MCI; n = 104) were used as an independent contrast group. The difference between inflammatory age and chronological age (InflammAGE score) was then assessed in relation to core AD biomarkers of amyloid, tau, and cognition. Both CSF and plasma inflammatory proteins were significantly associated with age in Aβ- CU individuals, with CSF-based proteins predicting chronological age better than plasma-based counterparts. Meanwhile, the Aβ- SCD and validation Aβ- CU groups were not characterized by significant inflammatory aging, while there was increased inflammatory aging in Aβ- MCI patients for CSF but not plasma inflammatory markers. Both CSF and plasma inflammatory changes were seen in the Aβ+ MCI and Aβ+ AD groups, with varying degrees of change compared to Aβ- CU and Aβ- SCD groups. Finally, CSF inflammatory changes were highly correlated with amyloid, tau, general neurodegeneration, and cognition, while plasma changes were mostly associated with amyloid and cognition. Inflammatory pathways change during aging and are specifically altered in AD, tracking closely with pathological hallmarks. These results have implications for tracking AD progression and for suggesting possible pathways for drug targeting.

Independent factors associated with long-term functional outcomes in patients with a proximal femoral fracture: A systematic review

INTRODUCTION

The current understanding of prognostic factors of functional recovery after a proximal femoral fracture is limited, and enhancements could improve the prognostic accuracy and target subgroups for additional care strategies. This systematic review aims to identify all studied factors with an independent prognostic value for the long-term functional recovery of patients with a proximal femoral fracture.

MATERIALS AND METHODS

Observational studies with multivariate analyses on prognostic factors of long-term functional outcome after proximal femoral fractures were obtained through an electronic search performed on November 9, 2018.

RESULTS

In the 31 included articles, thirteen prognostic factors were studied by at least two independent studies and an additional ten by only one study. Age, comorbidity, functionality and cognition were factors for which the majority of studies indicated a significant effect. The majority of studies which included sex as a factor found no significant effect. The level of evidence for the remaining factors was deemed too low to be conclusive on their relevance for long-term functional outcome.

CONCLUSION

The identified factors showed overlap with prognostic factors of short-term functional outcomes and mortality. The validity and applicability of prognostic models based on these factors may be of interest for future research.

Improved Human Age Prediction by Using Gene Expression Profiles From Multiple Tissues

Studying transcriptome chronological change from tissues across the whole body can provide valuable information for understanding aging and longevity. Although there has been research on the effect of single-tissue transcriptomes on human aging or aging in mice across multiple tissues, the study of human body-wide multi-tissue transcriptomes on aging is not yet available. In this study, we propose a quantitative model to predict human age by using gene expression data from 46 tissues generated by the Genotype-Tissue Expression (GTEx) project. Specifically, the biological age of a person is first predicted via the gene expression profile of a single tissue. Then, we combine the gene expression profiles from two tissues and compare the predictive accuracy between single and two tissues. The best performance as measured by the root-mean-square error is 3.92 years for single tissue (pituitary), which deceased to 3.6 years when we combined two tissues (pituitary and muscle) together. Different tissues have different potential in predicting chronological age. The prediction accuracy is improved by combining multiple tissues, supporting that aging is a systemic process involving multiple tissues across the human body.

Association of Blood Chemistry Quantifications of Biological Aging With Disability and Mortality in Older Adults

Quantification of biological aging has been proposed for population surveillance of age-related decline in system integrity and evaluation of geroprotective therapies. However, methods of quantifying biological aging have been little studied in geriatric populations. We analyzed three clinical-biomarker-algorithm methods to quantify biological aging. Klemera-Doubal method Biological Age and homeostatic dysregulation algorithms were parameterized from analysis of U.S. National Health and Nutrition Examination Surveys (NHANES) data (N = 36,207) based on published methods. Levine method Biological Age was adapted from published analysis of NHANES data. Algorithms were applied to biomarker data from the Duke Established Populations for Epidemiologic Studies of the Elderly (Duke-EPESE) cohort of older adults (N = 1,374, aged 71-102 years, 35% male, 52% African American). We tested associations of biological aging measures with participant reported Activities of daily living (ADL), instrumental activities of daily living (IADL) dependencies, and mortality. We evaluated the sensitivity of results to the demographic composition of reference samples and biomarker sets used to develop biological aging algorithms. African American and white Duke-EPESE participants with more advanced biological aging reported dependence in more ADLs and IADLs and were at increased risk of death over follow-up through 2017. Effect sizes were similar across algorithms, but were strongest for Levine method Biological Age (per-quintile increase in ADL incidence rate ratio = 1.25, 95% confidence interval [1.17-1.37], IADL incidence rate ratio = 1.23 [1.15-1.32], mortality hazard ratio = 1.12 [1.08-1.16]). Results were insensitive to demographic composition of reference samples, but modestly sensitive to the biomarker sets used to develop biological aging algorithms. Blood-chemistry-based quantifications of biological aging show promise for evaluating the effectiveness of interventions to extend healthy life span in older adults.

Deep learning for biological age estimation

Modern machine learning techniques (such as deep learning) offer immense opportunities in the field of human biological aging research. Aging is a complex process, experienced by all living organisms. While traditional machine learning and data mining approaches are still popular in aging research, they typically need feature engineering or feature extraction for robust performance. Explicit feature engineering represents a major challenge, as it requires significant domain knowledge. The latest advances in deep learning provide a paradigm shift in eliciting meaningful knowledge from complex data without performing explicit feature engineering. In this article, we review the recent literature on applying deep learning in biological age estimation. We consider the current data modalities that have been used to study aging and the deep learning architectures that have been applied. We identify four broad classes of measures to quantify the performance of algorithms for biological age estimation and based on these evaluate the current approaches. The paper concludes with a brief discussion on possible future directions in biological aging research using deep learning. This study has significant potentials for improving our understanding of the health status of individuals, for instance, based on their physical activities, blood samples and body shapes. Thus, the results of the study could have implications in different health care settings, from palliative care to public health.

Calibrating Gompertz in reverse: What is your longevity-risk-adjusted global age?

This paper develops a computational framework for inverting Gompertz-Makeham mortality hazard rates, consistent with compensation laws of mortality for heterogeneous populations, to define a longevity-risk-adjusted global (L-RaG) age. To illustrate its salience and possible applications, the paper calibrates and presents L-RaG values using country data from the Human Mortality Database (HMD). Among other things, the author demonstrates that when properly benchmarked, the longevity-risk-adjusted global age of a 55-year-old Swedish male is 48, whereas a 55-year-old Russian male is closer in age to 67. The paper also discusses the connection between the proposed L-RaG age and the related concept of Biological age, from the medical and gerontology literature. Practically speaking, in a world of growing mortality heterogeneity, the L-RaG age could be used for pension and retirement policy. In the language of behavioral finance and economics, a salient metric that adjusts chronological age for longevity risk might help capture the public's attention, educate them about lifetime uncertainty and induce many of them to take action - such as working longer and/or retiring later.

Multimodality neuroimaging brain-age in UK biobank: relationship to biomedical, lifestyle, and cognitive factors

The brain-age paradigm is proving increasingly useful for exploring aging-related disease and can predict important future health outcomes. Most brain-age research uses structural neuroimaging to index brain volume. However, aging affects multiple aspects of brain structure and function, which can be examined using multimodality neuroimaging. Using UK Biobank, brain-age was modeled in n = 2205 healthy people with T1-weighted MRI, T2-FLAIR, T2∗, diffusion-MRI, task fMRI, and resting-state fMRI. In a held-out healthy validation set (n = 520), chronological age was accurately predicted (r = 0.78, mean absolute error = 3.55 years) using LASSO regression, higher than using any modality separately. Thirty-four neuroimaging phenotypes were deemed informative by the regression (after bootstrapping); predominantly gray-matter volume and white-matter microstructure measures. When applied to new individuals from UK Biobank (n = 14,701), significant associations with multimodality brain-predicted age difference (brain-PAD) were found for stroke history, diabetes diagnosis, smoking, alcohol intake and some, but not all, cognitive measures (corrected p < 0.05). Multimodality neuroimaging can improve brain-age prediction, and derived brain-PAD values are sensitive to biomedical and lifestyle factors that negatively impact brain and cognitive health.

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Brain-age was predicted from 6 different neuroimaging modalities.

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Combined multi-modality brain-age was more accurate than any single modality.

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Thirty-four neuroimaging measures were informative for brain-age prediction.

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Informative measures generally reflect brain volume and white-matter microstructure.

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Brain-age was associated with stroke, diabetes, smoking, alcohol and cognition.

Difference between the biologic and chronologic age as an individualized indicator for the skincare intensity selection: skin cell profile and age difference studies

Background

The present research addresses the issue of skin aging and corresponding skin treatment individualization. Particular research question was on the development of a simplified criterion supporting patient-specific decisions about the necessity and intensity of skin treatment. Basing on published results and a wide pool of our own experimental data, a hypothesis is formulated that a difference between biologic and chronologic age can be used as a powerful indicator of skin aging.

Methods

In the present paper, we report the results of studies with 80 volunteers between 15 and 65 years of age linking skin cell profile parameters to biologic and chronologic age. Biologic age was calculated using the empirical expressions based on the forced vital lung capacity, systolic blood pressure, urea concentration, and blood cholesterol level. Epidermis and derma cellular structures were studied using skin biopsy samples taken from the gluteal region.

Results

The present study supports the conclusion that biologic and chronologic age difference is changing in the progress of life. Our studies are showing that time point when calculated biologic age becomes equal to the chronologic one reflecting the onset of specific changes in the age dependencies of experimentally measured skin cell profile parameters. Thus, it is feasible that a difference between chronologic and individually assessed biologic age indeed reflects the process of skin aging.

Conclusions

With all reservations to the relatively small number of study participants, it seems feasible that a difference between biologic and chronologic age can be used as an indicator of skin aging. Additional research linking blood immune profile and skin topography to the difference of biologic and chronologic age (reported in the following paper) provides further support for the formulated hypotheses. So, a difference between calculated biologic age and chronologic age can be used as an individualized criterion supporting decisions on skin treatment strategies. Further research involving larger numbers of participants aimed at optimizing the expressions for calculating biologic age could lead to reliable and easily available express criterion supporting the decision for the individualized skin treatment.

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

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.

Anticipatory measure: Alex Comfort, experimental gerontology and the measurement of senescence

Ageing is routinely measured by counting the number of years lived since the birth of an individual but at least since at least the 1930s, the validity, precision and sensitivity of chronological age as a measure has been criticised across the biological and behavioural sciences of ageing. This quest that has been reinforced by the contemporary investment in the possibility of technologically manipulating the rate of ageing to delay the onset the age-associated diseases. This paper explores the epistemic, institutional and political conditions that led to the formulation, at the turhn of the 1970s, of Alex Comfort's (1920-2000) seminal proposal to measure human biological ageing rate. Drawing on published and archival sources, I argue that Comfort's suggested measure of ageing can be understood as a form of 'anticipation work', and should be understood as an effort to evidence, and to make present, the technological and social promises that Comfort linked to experimental gerontology.

Deep Learning using Convolutional LSTM estimates Biological Age from Physical Activity

Human age estimation is an important and difficult challenge. Different biomarkers and numerous approaches have been studied for biological age estimation, each with its advantages and limitations. In this work, we investigate whether physical activity can be exploited for biological age estimation for adult humans. We introduce an approach based on deep convolutional long short term memory (ConvLSTM) to predict biological age, using human physical activity as recorded by a wearable device. We also demonstrate five deep biological age estimation models including the proposed approach and compare their performance on the NHANES physical activity dataset. Results on mortality hazard analysis using both the Cox proportional hazard model and Kaplan-Meier curves each show that the proposed method for estimating biological age outperforms other state-of-the-art approaches. This work has significant implications in combining wearable sensors and deep learning techniques for improved health monitoring, for instance, in a mobile health environment. Mobile health (mHealth) applications provide patients, caregivers, and administrators continuous information about a patient, even outside the hospital.

Centroid of Age Neighborhoods: A New Approach to Estimate Biological Age

Estimation of human biological age is an important and difficult challenge. Different biomarkers and numerous approaches have been studied for biological age prediction, each with its advantages and limitations. In this paper, we propose a new biological age estimation method, and investigate the performance of the new method. We introduce a centroid based approach, using the notion of age neighborhoods. Specifically, we develop a model, based on which we compute biological age using blood biomarkers, by considering the centroid or mediod of specially selected age neighborhoods. Experiments were performed on the National Health and Human Nutrition Examination Survey dataset with biomarkers (21 451 individuals). Compared with current popular methods for biological age prediction, our experiments show that the proposed age neighborhood model results in an improved performance in human biological age estimation.

A model for comprehensive oral biological age score with oral and systemic clinical parameters

BACKGROUND

Biological age reflects the functional status of an individual. The purpose of the study was to develop a model for estimating oral biological age with oral and systemic parameters.

METHODS

A total of 248 subjects who had a routine health check were assessed with oral and general clinical examination. Chi-square test was performed to screen oral clinical candidate indicators. General parameters were analyzed by Pearson correlation coefficient and principal component analysis to develop a general biological age score. A final comprehensive model of oral biological age score was established by combining oral and general biological age score.

RESULTS

A total of eight oral indicators (mucosal blood blister, mucosal dryness, impacted tooth, missing teeth, residual crowns, dental calculus, gingival hyperemia, and gingival recession) and 10 general clinical indicators (triglyceride, creatinine, blood urea nitrogen, glucose, total cholesterol, mean erythrocyte hemoglobin concentration, mean erythrocyte hemoglobin, uric acid, body weight, and systolic blood pressure) were selected for oral and general biological age score, respectively (r > 0.25, P < 0.05). A model of comprehensive oral biological age score was then formed by principal component analysis: 0.046 triglyceride + 0.010 creatinine + 0.141 blood urea nitrogen + 0.048 glucose + 0.068 total cholesterol + 0.014 mean erythrocyte hemoglobin concentration + 0.082 mean erythrocyte hemoglobin + 0.001 uric acid + 0.020 body weight + 0.005 systolic blood pressure + 0.037 oral biological age score -10.908. The score was increased accordingly with CA.

CONCLUSION

Oral biological age can be easily estimated clinically by the model of comprehensive oral biological age score using oral and systemic clinical parameters by general practitioners.

DNA Methylation Clocks in Aging: Categories, Causes, and Consequences

Age-associated changes to the mammalian DNA methylome are well documented and thought to promote diseases of aging, such as cancer. Recent studies have identified collections of individual methylation sites whose aggregate methylation status measures chronological age, referred to as the DNA methylation clock. DNA methylation may also have value as a biomarker of healthy versus unhealthy aging and disease risk; in other words, a biological clock. Here we consider the relationship between the chronological and biological clocks, their underlying mechanisms, potential consequences, and their utility as biomarkers and as targets for intervention to promote healthy aging and longevity.

Assessing biological aging following systemic administration of bFGF-supplemented adipose-derived stem cells with high efficacy in an experimental rat model

Biological aging (BA) is a tool for comprehensive assessment of individual health status. A rat model was developed for measuring BA by intravenously administering adipose-derived stem cells (ADSCs) into rats and evaluating several biochemical parameters. In addition, the effect of basic fibroblast growth factor (bFGF) on the differentiation potential of ADSCs was analyzed. A total of 12 male Sprague Dawley rats were divided into autologous ADSC administration (n=6) and saline administration (n=6) groups. The ADSC administration group was further divided into the bFGF supplemented (n=3) and bFGF non-supplemented (n=3) groups. Biochemical parameters and antioxidant potential were evaluated prior to fat harvest and ADSC administration, as well as 1, 3, and 5 weeks following ADSC administration. ADSC administration regulated inflammation, renal and hepatic functions, and levels of antioxidant enzymes. The cell doubling time of the bFGF-supplemented group was shorter (P=0.0001) than that of the bFGF non-supplemented group. Renal and hepatic functions were maintained with bFGF supplementation, which possibly enhanced the effect of ADSCs. The rat model developed in the present study may promote better understanding of BA in the context of bFGF-supplemented ADSC administration.

Brain age and other bodily 'ages': implications for neuropsychiatry

As our brains age, we tend to experience cognitive decline and are at greater risk of neurodegenerative disease and dementia. Symptoms of chronic neuropsychiatric diseases are also exacerbated during ageing. However, the ageing process does not affect people uniformly; nor, in fact, does the ageing process appear to be uniform even within an individual. Here, we outline recent neuroimaging research into brain ageing and the use of other bodily ageing biomarkers, including telomere length, the epigenetic clock, and grip strength. Some of these techniques, using statistical approaches, have the ability to predict chronological age in healthy people. Moreover, they are now being applied to neurological and psychiatric disease groups to provide insights into how these diseases interact with the ageing process and to deliver individualised predictions about future brain and body health. We discuss the importance of integrating different types of biological measurements, from both the brain and the rest of the body, to build more comprehensive models of the biological ageing process. Finally, we propose seven steps for the field of brain-ageing research to take in coming years. This will help us reach the long-term goal of developing clinically applicable statistical models of biological processes to measure, track and predict brain and body health in ageing and disease.

Biological age as a health index for mortality and major age-related disease incidence in Koreans: National Health Insurance Service - Health screening 11-year follow-up study

Purpose

A comprehensive health index is needed to measure an individual's overall health and aging status and predict the risk of death and age-related disease incidence, and evaluate the effect of a health management program. The purpose of this study is to demonstrate the validity of estimated biological age (BA) in relation to all-cause mortality and age-related disease incidence based on National Sample Cohort database.

Patients and methods

This study was based on National Sample Cohort database of the National Health Insurance Service - Eligibility database and the National Health Insurance Service - Medical and Health Examination database of the year 2002 through 2013. BA model was developed based on the National Health Insurance Service - National Sample Cohort (NHIS - NSC) database and Cox proportional hazard analysis was done for mortality and major age-related disease incidence.

Results

For every 1 year increase of the calculated BA and chronological age difference, the hazard ratio for mortality significantly increased by 1.6% (1.5% in men and 2.0% in women) and also for hypertension, diabetes mellitus, heart disease, stroke, and cancer incidence by 2.5%, 4.2%, 1.3%, 1.6%, and 0.4%, respectively (p<0.001).

Conclusion

Estimated BA by the developed BA model based on NHIS - NSC database is expected to be used not only as an index for assessing health and aging status and predicting mortality and major age-related disease incidence, but can also be applied to various health care fields.

DNA methylation age and perceived age in elderly Danish twins

Perceived age is an easily accessible biomarker of aging. Here, we studied its relation to DNA methylation age (DNAm age) as introduced in (Horvath, 2013) in 180 elderly Danish twins. We found perceived age and DNAm age to be associated with chronological age (P=0.04 resp. P=2.2e-10) when correcting for gender, but did not see an association between perceived age and DNAm age (P=0.44). Intrapair-analysis showed that the proportion of pairs where the twin with the highest perceived age also had the highest DNAm age was not different from 0.5 (P=1), and we did not see a trend when dividing pairs according to their difference in perceived age (P=0.36). Hence, intrapair analysis did not reveal links between perceived age and DNAm age. Moreover, none of the 353 CpGs underlying DNAm age was individually associated with perceived age after correction for multiple-testing (P>6e-4, FDR>0.21). Finally, when constructing an epigenetic signature based on these CpGs to predict perceived age, we only found a correlation of 0.18 (95%CI: -0.06 to 0.40) and a mean square error of 13.6 years2 between observed and predicted values in the test dataset, indicating poor predictive strength. Altogether, our results suggest that perceived age and DNAm age capture different aging aspects.

Change in the Rate of Biological Aging in Response to Caloric Restriction: CALERIE Biobank Analysis

Biological aging measures have been proposed as proxies for extension of healthy life span in trials of geroprotective therapies that aim to slow aging. Several methods to measure biological aging show promise but it is not known if these methods are sensitive to changes caused by geroprotective therapy. We conducted analysis of two proposed methods to quantify biological aging using data from a recently concluded trial of an established geroprotector, caloric restriction. We obtained data from the National Institute on Aging CALERIE randomized trial through its public-access biobank (https://calerie.duke.edu/). The CALERIE trial randomized N = 220 nonobese adults to 25% caloric restriction (n = 145; 11.7% caloric restriction was achieved, on average) or to maintain current diet (n = 75) for 2 years. We analyzed biomarker data collected at baseline, 12-, and 24-month follow-up assessments. We applied published biomarker algorithms to these data to calculate two biological age measures, Klemera-Doubal Method Biological Age and homeostatic dysregulation. Intent-to-treat analysis using mixed-effects growth models of within-person change over time tested if caloric restriction slowed increase in measures of biological aging across follow-up. Analyses of both measures indicated caloric restriction slowed biological aging. Weight loss did not account for the observed effects. Results suggest future directions for testing of geroprotective therapies in humans.

Express Estimation of the Biological Age by the Parameters of Body Composition in Men and Women over 50 Years

Original formulas for rapid assessment of biological age in men and women over 50 were developed using factor analysis. The proposed technique is mainly based on the parameters of the body component composition (fat, musculoskeletal, and active cell mass, and specific metabolism) obtained using bioimpedance recording widely used in modern medicine and anthropology. The proposed formulas were tested on other samples (481 examined subjects). The developed method of express estimation of biological age differs from other models by its convenience, simplicity, low financial and time costs, and the possibility of using it in mass medico-anthropological examinations for identification of individuals/groups with accelerated rates of aging.

An empirical examination of subjective age in older adults

It has been observed that subjective age (SA) often trails chronological age, especially in older adults. In a previously published article, we argued that differences in individual's SA is a function of their level of activity on biological, mental, and social dimensions. This article empirically tests this proposition using a newly created Subjective Aging Index (SAI). The SAI is related to SA above the effect of age with differences existing across age groups and sex. The findings contribute to the literature on successful aging strategies with important implications for health care practitioners, marketers, and individuals heading towards older adult years.

The Longitudinal Study of Aging in Human Young Adults: Knowledge Gaps and Research Agenda

BACKGROUND

To prevent onset of age-related diseases and physical and cognitive decline, interventions to slow human aging and extend health span must eventually be applied to people while they are still young and healthy. Yet most human aging research examines older adults, many with chronic disease, and little is known about aging in healthy young humans.

METHOD

This article explains how this knowledge gap is a barrier to extending health span and puts forward the case that geroscience should invest in researching the pace of aging in young adults. As one illustrative example, we describe an initial effort to study the pace of aging in a young-adult birth cohort by using repeated waves of biomarkers collected across the third and fourth decades to quantify the pace of coordinated physiological deterioration across multiple organ systems (eg, pulmonary, periodontal, cardiovascular, renal, hepatic, metabolic, and immune function).

RESULTS

Findings provided proof of principle that it is possible to quantify individual variation in the pace of aging in young adults still free of age-related diseases.

CONCLUSIONS

This article articulates research needs to improve longitudinal measurement of the pace of aging in young people, to pinpoint factors that slow or speed the pace of aging, to compare pace of aging against genomic clocks, to explain slow-aging young adults, and to apply pace of aging in preventive clinical trials of antiaging therapies. This article puts forward a research agenda to fill the knowledge gap concerning lifelong causes of aging.

Protein profiling reveals consequences of lifestyle choices on predicted biological aging

Ageing is linked to a number of changes in how the body and its organs function. On a molecular level, ageing is associated with a reduction of telomere length, changes in metabolic and gene-transcription profiles and an altered DNA-methylation pattern. Lifestyle factors such as smoking or stress can impact some of these molecular processes and thereby affect the ageing of an individual. Here we demonstrate by analysis of 77 plasma proteins in 976 individuals, that the abundance of circulating proteins accurately predicts chronological age, as well as anthropometrical measurements such as weight, height and hip circumference. The plasma protein profile can also be used to identify lifestyle factors that accelerate and decelerate ageing. We found smoking, high BMI and consumption of sugar-sweetened beverages to increase the predicted chronological age by 2–6 years, while consumption of fatty fish, drinking moderate amounts of coffee and exercising reduced the predicted age by approximately the same amount. This method can be applied to dried blood spots and may thus be useful in forensic medicine to provide basic anthropometrical measures for an individual based on a biological evidence sample.

Integration of 'omics' data in aging research: from biomarkers to systems biology

Age is the strongest risk factor for many diseases including neurodegenerative disorders, coronary heart disease, type 2 diabetes and cancer. Due to increasing life expectancy and low birth rates, the incidence of age-related diseases is increasing in industrialized countries. Therefore, understanding the relationship between diseases and aging and facilitating healthy aging are major goals in medical research. In the last decades, the dimension of biological data has drastically increased with high-throughput technologies now measuring thousands of (epi) genetic, expression and metabolic variables. The most common and so far successful approach to the analysis of these data is the so-called reductionist approach. It consists of separately testing each variable for association with the phenotype of interest such as age or age-related disease. However, a large portion of the observed phenotypic variance remains unexplained and a comprehensive understanding of most complex phenotypes is lacking. Systems biology aims to integrate data from different experiments to gain an understanding of the system as a whole rather than focusing on individual factors. It thus allows deeper insights into the mechanisms of complex traits, which are caused by the joint influence of several, interacting changes in the biological system. In this review, we look at the current progress of applying omics technologies to identify biomarkers of aging. We then survey existing systems biology approaches that allow for an integration of different types of data and highlight the need for further developments in this area to improve epidemiologic investigations.

Quantification of biological aging in young adults

Antiaging therapies show promise in model organism research. Translation to humans is needed to address the challenges of an aging global population. Interventions to slow human aging will need to be applied to still-young individuals. However, most human aging research examines older adults, many with chronic disease. As a result, little is known about aging in young humans. We studied aging in 954 young humans, the Dunedin Study birth cohort, tracking multiple biomarkers across three time points spanning their third and fourth decades of life. We developed and validated two methods by which aging can be measured in young adults, one cross-sectional and one longitudinal. Our longitudinal measure allows quantification of the pace of coordinated physiological deterioration across multiple organ systems (e.g., pulmonary, periodontal, cardiovascular, renal, hepatic, and immune function). We applied these methods to assess biological aging in young humans who had not yet developed age-related diseases. Young individuals of the same chronological age varied in their "biological aging" (declining integrity of multiple organ systems). Already, before midlife, individuals who were aging more rapidly were less physically able, showed cognitive decline and brain aging, self-reported worse health, and looked older. Measured biological aging in young adults can be used to identify causes of aging and evaluate rejuvenation therapies.

Deriving a cardiac ageing signature to reveal MMP-9-dependent inflammatory signalling in senescence

AIMS

Cardiac ageing involves the progressive development of cardiac fibrosis and diastolic dysfunction coordinated by MMP-9. Here, we report a cardiac ageing signature that encompasses macrophage pro-inflammatory signalling in the left ventricle (LV) and distinguishes biological from chronological ageing.

METHODS AND RESULTS

Young (6-9 months), middle-aged (12-15 months), old (18-24 months), and senescent (26-34 months) mice of both C57BL/6J wild type (WT) and MMP-9 null were evaluated. Using an identified inflammatory pattern, we were able to define individual mice based on their biological, rather than chronological, age. Bcl6, Ccl24, and Il4 were the strongest inflammatory markers of the cardiac ageing signature. The decline in early-to-late LV filling ratio was most strongly predicted by Bcl6, Il1r1, Ccl24, Crp, and Cxcl13 patterns, whereas LV wall thickness was most predicted by Abcf1, Tollip, Scye1, and Mif patterns. With age, there was a linear increase in cardiac M1 macrophages and a decrease in cardiac M2 macrophages in WT mice; of which, both were prevented by MMP-9 deletion. In vitro, MMP-9 directly activated young macrophage polarization to an M1/M2 mid-transition state.

CONCLUSION

Our results define the cardiac ageing inflammatory signature and assign MMP-9 roles in mediating the inflammaging profile by indirectly and directly modifying macrophage polarization. Our results explain early mechanisms that stimulate ageing-induced cardiac fibrosis and diastolic dysfunction.

Lung transplantation: a treatment option in end-stage lung disease

BACKGROUND

Lung transplantation is the final treatment option in the end stage of certain lung diseases, once all possible conservative treatments have been exhausted. Depending on the indication for which lung transplantation is performed, it can improve the patient's quality of life (e.g., in emphysema) and/ or prolong life expectancy (e.g., in cystic fibrosis, pulmonary fibrosis, and pulmonary arterial hypertension). The main selection criteria for transplant candidates, aside from the underlying pulmonary or cardiopulmonary disease, are age, degree of mobility, nutritional and muscular condition, and concurrent extrapulmonary disease. The pool of willing organ donors is shrinking, and every sixth candidate for lung transplantation now dies while on the waiting list.

METHOD

We reviewed pertinent articles (up to October 2013) retrieved by a selective search in Medline and other German and international databases, including those of the International Society for Heart and Lung Transplantation (ISHLT), Eurotransplant, the German Institute for Applied Quality Promotion and Research in Health-Care (Institut für angewandte Qualitätsförderung und Forschung im Gesundheitswesen, AQUA-Institut), and the German Foundation for Organ Transplantation (Deutsche Stiftung Organtransplantation, DSO).

RESULTS

The short- and long-term results have markedly improved in recent years: the 1-year survival rate has risen from 70.9% to 82.9%, and the 5-year survival rate from 46.9% to 59.6%. The 90-day mortality is 10.0%. The postoperative complications include acute (3.4%) and chronic (29.0%) transplant rejection, infections (38.0%), transplant failure (24.7%), airway complications (15.0%), malignant tumors (15.0%), cardiovascular events (10.9%), and other secondary extrapulmonary diseases (29.8%). Bilateral lung transplantation is superior to unilateral transplantation (5-year survival rate 57.3% versus 47.4%).

CONCLUSION

Seamless integration of the various components of treatment will be essential for further improvements in outcome. In particular, the follow-up care of transplant recipients should always be provided in close cooperation with the transplant center.

Time benders: a model of subjective aging in aging adults

This article develops a new model for understanding the aging experience. Drawing upon aging literature from the chronological, biological, mental, and social aging perspectives, the model offered is an integrated perspective that provides better understanding of the relationship between chronological age and an individual's perceived age. The article provides evidence of ways that consumers are trying to "time bend" and change today's perceived reality of aging. The article concludes with a discussion of implications for the health care industry and provides examples of how some businesses seem to already be looking at aging and health related issues through this lens.

How pleiotropic genetics of the musculoskeletal system can inform genomics and phenomics of aging

Genetic study can provide insight into the biologic mechanisms underlying inter-individual differences in susceptibility to (or resistance to) organisms' aging. Recent advances in molecular genetics and genetic epidemiology provide the necessary tools to perform a study of the genetic sources of biological aging. However, to be successful, the genetic study of a complex condition requires a heritable phenotype to be developed and validated. Genome-wide association studies offer an unbiased approach to identify new candidate genes for human diseases. It is hypothesized that convergent results from multiple aging-related traits will point out the genes responsible for the general aging of the organism. This perspective focuses on the musculoskeletal aging as an example of an approach to identify a downstream common pathway that summarizes aging processes. Since the musculoskeletal traits are linked to the state of many vital functions, disability, and ultimately survival rates, we postulate that there is significance in studying musculoskeletal aging. Construction of an integrated phenotype of aging can be achieved based on shared genetics among multiple musculoskeletal biomarkers. Valid biomarkers from other systems of the organism should be similarly explored. The new composite aging score needs to be validated by determining whether it predicts all-cause mortality, incidences of major chronic diseases, and disability late in life. Comprehensive databases on biomarkers of musculoskeletal aging in multiple large cohort studies, along with information on various health outcomes, are needed to validate the proposed measure of biological aging.

1H NMR metabolomics study of age profiling in children

Metabolic profiling of urine provides a fingerprint of personalized endogenous metabolite markers that correlate to a number of factors such as gender, disease, diet, toxicity, medication, and age. It is important to study these factors individually, if possible to unravel their unique contributions. In this study, age-related metabolic changes in children of age 12 years and below were analyzed by (1)H NMR spectroscopy of urine. The effect of age on the urinary metabolite profile was observed as a distinct age-dependent clustering even from the unsupervised principal component analysis. Further analysis, using partial least squares with orthogonal signal correction regression with respect to age, resulted in the identification of an age-related metabolic profile. Metabolites that correlated with age included creatinine, creatine, glycine, betaine/TMAO, citrate, succinate, and acetone. Although creatinine increased with age, all the other metabolites decreased. These results may be potentially useful in assessing the biological age (as opposed to chronological) of young humans as well as in providing a deeper understanding of the confounding factors in the application of metabolomics.

Biomarkers by gender

Regressions were determined for age-related human biological functions containing information for both genders. Their intercept T(0) on the age axis (x) was used as a measure of the aging rate. The peak of the frequency distribution of T(0) was consistent with earlier estimates. The frequency distribution of the ratio R of T(0)(women)/T(0)(men) peaked at unity. However, when the T(0)-values were divided into two groups, namely those relating to functions involving musculature vs. the rest, respectively, the ratio of R for musculature was <1 and that for the latter significantly >1. This suggests that men are the stronger gender when musculature is involved, but, more broadly, women are "biologically stronger".

The metal ion theory of ageing: dietary target hazard quotients beyond radicals

Numerous theories of ageing exist and many are interconnected when viewed through a modern integrative biology perspective. Diet provides a link to a large number of the theories that prevail at the molecular levels. In particular, metal ions form key elements of the radical theory along with having established roles in several age-related neurodegenerative disorders. Lifetime exposure to metals has been linked to ageing by contributions to oxidative stress and neurodegenerative disorders. As many foodstuffs contain high levels and diverse profiles of metals, their cumulative effect on ageing warrants investigation. The cumulative level of concern from environmental exposure can be expressed as a dimensionless index of target hazard quotient (THQ) or for known carcinogens, the target cancer risk (TR). This paper posits that a quantifiable relationship exists between ageing and level of concern resulting from cumulated metal exposure; and that this relationship can be used to develop an ageing-related index of concern from chronic metal ion exposure. As individual differences may facilitate or moderate this cumulated exposure, the potential influence on ageing or on the development of neurodegenerative disorders should be included into the model.

Relative Fitness and Frailty of Elderly Men and Women in Developed Countries and Their Relationship with Mortality

OBJECTIVES

To investigate the relationship between accumulated health-related problems (deficits), which define a frailty index in older adults, and mortality in population-based and clinical/institutional-based samples.

DESIGN

Cross-sectional and cohort studies.

SETTING

Seven population-based and four clinical/institutional surveys in four developed countries.

PARTICIPANTS

Thirty-six thousand four hundred twenty-four people (58.5% women) aged 65 and older.

MEASUREMENTS

A frailty index was constructed as a proportion of all potential deficits (symptoms, signs, laboratory abnormalities, disabilities) expressed in a given individual. Relative frailty is defined as a proportion of deficits greater than average for age. Measures of deficits differed across the countries but included common elements.

RESULTS

In each country, community-dwelling elderly people accumulated deficits at about 3% per year. By contrast, people from clinical/institutional samples showed no relationship between frailty and age. Relative fitness/frailty in both sexes was highly correlated (correlation coefficient >0.95, P<.001) with mortality, although women, at any given age, were frailer and had lower mortality. On average, each unit increase in deficits increased by 4% the hazard rate for mortality (95% confidence interval=0.02-0.06).

CONCLUSION

Relative fitness and frailty can be defined in relation to deficit accumulation. In population studies from developed countries, deficit accumulation is robustly associated with mortality and with age. In samples (e.g., clinical/institutional) in which most people are frail, there is no relationship with age, suggesting that there are maximal values of deficit accumulation beyond which survival is unlikely.

Disentangling the genetic determinants of human aging: biological age as an alternative to the use of survival measures

The choice of a phenotype is critical for the study of a complex genetically regulated process, such as aging. To date, most of the twin and family studies have focused on broad survival measures, primarily age at death or exceptional longevity. However, on the basis of recent studies of twins and families, biological age has also been shown to have a strong genetic component, with heritability estimates ranging from 27% to 57%. The aim of this review is twofold: first, to summarize growing consensus on reliable methods of biological age assessment, and second, to demonstrate validity of this phenotype for research in the genetics of aging in humans.