Feature Selection Algorithms Enhance the Accuracy of Frailty Indexes as Measures of Biological Age

Beyond hallmarks of aging - biological age and emergence of aging networks

The hallmarks of aging have contributed immensely to the systematization of research on aging and have influenced the emergence of geroscience. The developments that led to the concepts of the hallmarks and geroscience were first marked by the proliferation of 'theories' of aging, mostly based on the experimental predilections of practitioners of aging research. Deeper consideration of the concepts of hallmarks of aging and geroscience leads to the quandary of whether a biological aging process exists beyond disease itself. To address this difficulty, a metric of biological age as opposed to calendar age is necessary. Several examples of biological age measured using similar assumptions, but different methods, exist. One of these, the frailty index was the first to successfully characterize aging in terms of loss of integrated function, and it is simpler than and superior to other constructs for measuring biological age. Though relatively simple in construction, the frailty index is rich conceptually, however, pointing to a network model of the aging organism. This network functions as a nonlinear complex system that is governed by stochastic thermodynamics, in which loss of integration leads to increasing entropy. Its structure transcends all levels of biological organization, such that its parts form hierarchies that are self-similar (fractal). The hallmarks of aging are simply nodes in the aging network, which can be found repetitively in various locations of the network. Stochastic thermodynamics implies that the aging system with higher entropy can exist in a multitude of possible microstates that are tantamount to high disorder with a high probability to assume a certain state. This explains the observed variability among aging individuals.

Recent developments in frailty identification, management, risk factors and prevention: A narrative review of leading journals in geriatrics and gerontology

Frailty is an age-related clinical condition characterised by an increased susceptibility to stressors and an elevated risk of adverse outcomes such as mortality. In the light of global population ageing, the prevalence of frailty is expected to soar in coming decades. This narrative review provides critical insights into recent developments and emerging practices in frailty research regarding identification, management, risk factors, and prevention. We searched journals in the top two quartiles of geriatrics and gerontology (from Clarivate Journal Citation Reports) for articles published between 01 January 2018 and 20 December 2022. Several recent developments were identified, including new biomarkers and biomarker panels for frailty screening and diagnosis, using artificial intelligence to identify frailty, and investigating the altered response to medications by older adults with frailty. Other areas with novel developments included exercise (including technology-based exercise), multidimensional interventions, person-centred and integrated care, assistive technologies, analysis of frailty transitions, risk-factors, clinical guidelines, COVID-19, and potential future treatments. This review identified a strong need for the implementation and evaluation of cost-effective, community-based interventions to manage and prevent frailty. Our findings highlight the need to better identify and support older adults with frailty and involve those with frailty in shared decision-making regarding their care.

Height loss as an indicator of ageing through its association with frailty and sarcopenia: An observational cohort study

BACKGROUND

Height loss is associated with various health-related variables such as cardiovascular disease, osteoporosis, cognitive function, and mortality. We hypothesized that height loss can be used as an indicator of aging, and we assessed whether the degree of height loss for 2 years was associated with frailty and sarcopenia.

METHODS

This study was based on a longitudinal cohort, the Pyeongchang Rural Area cohort. The cohort included people aged 65 years or older, ambulatory, and living at home. We divided individuals according to the ratio of height change (height change for 2 years divided by height at 2 years from baseline): HL2 (<-2%), HL1 (-2%--1%), and REF (-1%≤). We compared the frailty index, diagnosis of sarcopenia after 2 years from baseline, and the incidence of a composite outcome (mortality and institutionalization).

RESULTS

In total, 59 (6.9%), 116 (13.5%), and 686 (79.7%) were included in the HL2, HL1, and REF groups, respectively. Compared with the REF group, groups HL2 and HL1 had a higher frailty index, and higher risks of sarcopenia and composite outcome. When groups HL2 and HL1 were merged, the merged group had higher frailty index (standardized B, 0.06; p = 0.049), a higher risk of sarcopenia (OR, 2.30; p = 0.006), and a higher risk of composite outcome (HR, 1.78; p = 0.017) after adjusting for age and sex.

CONCLUSIONS

Individuals with greater height loss were frailer, more likely to be diagnosed with sarcopenia and had worse outcomes regardless of age and sex.

All-Subset Analysis Improves the Predictive Accuracy of Biological Age for All-Cause Mortality in Chinese and U.S. Populations

BACKGROUND

Klemera-Doubal's method (KDM) is an advanced and widely applied algorithm for estimating biological age (BA), but it has no uniform paradigm for biomarker processing. This article proposed all subsets of biomarkers for estimating BAs and assessed their association with mortality to determine the most predictive subset and BA.

METHODS

Clinical biomarkers, including those from physical examinations and blood assays, were assessed in the China Health and Nutrition Survey (CHNS) 2009 wave. Those correlated with chronological age (CA) were combined to produce complete subsets, and BA was estimated by KDM from each subset of biomarkers. A Cox proportional hazards regression model was used to examine and compare each BA's effect size and predictive capacity for all-cause mortality. Validation analysis was performed in the Chinese Longitudinal Healthy Longevity Survey (CLHLS) and National Health and Nutrition Examination Survey (NHANES). KD-BA and Levine's BA were compared in all cohorts.

RESULTS

A total of 130 918 panels of BAs were estimated from complete subsets comprising 3-17 biomarkers, whose Pearson coefficients with CA varied from 0.39 to 1. The most predictive subset consisted of 5 biomarkers, whose estimated KD-BA had the most predictive accuracy for all-cause mortality. Compared with Levine's BA, the accuracy of the best-fitting KD-BA in predicting death varied among specific populations.

CONCLUSION

All-subset analysis could effectively reduce the number of redundant biomarkers and significantly improve the accuracy of KD-BA in predicting all-cause mortality.

Pan-Tissue Aging Clock Genes That Have Intimate Connections with the Immune System and Age-Related Disease

In our recent transcriptomic meta-analysis, we used random forest machine learning to accurately predict age in human blood, bone, brain, heart, and retina tissues given gene inputs. Although each tissue-specific model utilized a unique number of genes for age prediction, we found that the following six genes were prioritized in all five tissues: CHI3L2, CIDEC, FCGR3A, RPS4Y1, SLC11A1, and VTCN1. Since being selected for age prediction in multiple tissues is unique, we decided to explore these pan-tissue clock genes in greater detail. In the present study, we began by performing over-representation and network topology-based enrichment analyses in the Gene Ontology Biological Process database. These analyses revealed that the immunological terms "response to protozoan," "immune response," and "positive regulation of immune system process" were significantly enriched by these clock inputs. Expression analyses in mouse and human tissues identified that these inputs are frequently upregulated or downregulated with age. A detailed literature search showed that all six genes had noteworthy connections to age-related disease. For example, mice deficient in Cidec are protected against various metabolic defects, while suppressing VTCN1 inhibits age-related cancers in mouse models. Using a large multitissue transcriptomic dataset, we additionally generate a novel, minimalistic aging clock that can predict human age using just these six genes as inputs. Taken all together, these six genes are connected to diverse aspects of aging.

Frailty and Biological Age

A reliable model of biological age is instrumental in the field of geriatrics and gerontology. This model should account for the heterogeneity and plasticity of aging and also accurately predict aging-related adverse outcomes. Epigenetic age models are based on DNA methylation levels at selected genomic sites and can be significant predictors of mortality and healthy/unhealthy aging. However, the biological function of DNA methylation at selected sites is yet to be determined. Frailty is a syndrome resulting from decreased physiological reserves and resilience. The frailty index is a probability-based extension of the concept of frailty. Defined as the proportion of health deficits, the frailty index quantifies the progression of unhealthy aging. The frailty index is currently the best predictor of mortality. It is associated with various biological factors and provides insight into the biological processes of aging. Investigation of the multi-omics factors associated with the frailty index will provide further insight.