The frailty index outperforms DNA methylation age and its derivatives as an indicator of biological age

Joint association of frailty index and biological aging with all-cause and cause-specific mortality: a population-based longitudinal cohort study

BACKGROUND

The role of frailty in all-cause, cardiovascular, and cancer mortality is debatable, and the modification effect of biological aging remains unclear. Therefore, we aimed to evaluate the joint association of frailty index and biological aging with all-cause and cause-specific mortality.

METHODS

In this population-based cohort study, data were obtained from the National Health and Nutrition Examination Survey (NHANES) and National Death Index (NDI). Demographic variables were extracted, frailty index was constructed, and biological aging was calculated. All-cause deaths, cancer deaths, and cardiovascular disease (CVD) deaths were extracted as outcomes. Cox proportional hazards regression models were used to estimate the correlations, stratified subgroup analyses were used to figure out effect modifiers, and sensitivity analyses were used to confirm the robustness.

RESULTS

A total of 22,729 NHANES participants were included in this study, with 6786 all-cause deaths, 1830 CVD deaths, and 1396 cancer deaths occurred during an average follow-up of 8.5 years over a total of 192,601 person-years. The hazard ratios (HRs) of delayed aging group for all-cause mortality, CVD mortality, and cancer mortality were 0.45 (95 % CI: 0.41-0.49), 0.39 (95 % CI: 0.34-0.45), and 0.54 (95 % CI: 0.46-0.63), respectively, compared to accelerated aging group (P for all comparisons < 0.001). Likewise, the frailty index score was positively associated with all-cause mortality (HR, 1.06 [95 % CI, 1.06-1.06] per 0.01 increase in the frailty index), cardiovascular (CVD) mortality (HR, 1.07 [95 % CI, 1.06-1.07] per 0.01 increase in the frailty index), and cancer mortality (HR, 1.04 [95 % CI, 1.03-1.04] per 0.01 increase in the frailty index). The associations of frailty index with all-cause mortality and CVD mortality were modified by biological aging (P for interaction = 0.044), but cancer mortality was not (P for interaction = 0.482).

CONCLUSIONS

Accelerated biological aging is associated with higher frailty index, whereas delayed biological aging is inversely associated with risk of all-cause mortality, CVD mortality, and cancer mortality. Biological aging is effect modification among the associations of frailty index with all-cause mortality and CVD mortality, but not for cancer mortality. These findings suggest that for people with high frailty index and acceleration biological aging, to lower frailty degree and decrease biological aging acceleration by approaches such as lifestyle modifications might be beneficial for individual's longevity and lifespan.

Association between frailty index and epigenetic aging acceleration in older adults: Evidence from the health and retirement study

BACKGOUND

This study aimed to examine the associations between the frailty index and four epigenetic aging acceleration (EAA) estimators in cross-sectional and longitudinal settings.

METHODS

The frailty index in the older adults was measured according to a cumulative health-deficit model. Four different epigenetic age measures (Hannum, PhenoAge, GrimAge, and DunedinPoAm38) were regressed against chronological age, and the resulting standardized residuals were indicative of EAA. The longitudinal relationship between EAA at baseline and changes in the frailty index during the 4-year follow-up were examined using a mixed linear model.

RESULTS

A single standard deviation (SD) increment in the frailty index was associated with a faster EAA, as indicated by the four clocks in Hannum (b = 0.057; P = 0.015), PhenoAge (b = 0.096; P < 0.001), GrimAge (b = 0.120; P < 0.001), and DunedinPoAm38 (b = 0.062; P = 0.002) in the fully adjusted model. A 1-SD increment in the GrimAge EAA was associated with a 0.003 frailty index increase (b = 0.003; P = 0.002). A 1-SD increment in DunedinPoAm38 EAA was associated with a 0.002 frailty index increase (b = 0.002; P = 0.009).

CONCLUSIONS

The frailty index was cross-sectionally associated with EAA, while only GrimAge and DunedinPoAm38 EAA predicted changes in the frailty index. More research is needed to understand the interplay between pathways.

A Longitudinal Study of the Bidirectional Temporal Dynamics Between Body Mass Index and Biological Aging

BACKGROUND

Obesity and aging share biological processes, but their relationship remains unclear, especially in late life. Understanding how body mass index (BMI) and biological aging influence each other can guide strategies to reduce age- and obesity-related health risks. We examined the bidirectional, longitudinal association between changes in BMI and biological aging, measured by frailty index (FI) and functional aging index (FAI), across late life.

METHODS

This longitudinal cohort study used data from the Swedish Twin Registry substudies, GENDER, OCTO-Twin and SATSA, collected via in-person assessments from 1986 to 2014 at 2- to 4-year intervals. We analysed 6216-6512 evaluations from 1902 to 1976 Swedish twins. Dual change score models were applied to assess the bidirectional, longitudinal association between BMI and FI or FAI from ages 60.0-91.9. FI measured physiological aging, while FAI assessed functional aging through a composite score of functional abilities.

RESULTS

At first measurement, mean age was 74 ± 8, and 41% were males. BMI-FI relationship was bidirectional (p value ≤ 0.001): Higher BMI predicted a greater increase in FI over time (coupling effect [γ] = 0.86, 95% confidence interval [CI] = 0.65-1.06, p value ≤ 0.001), and higher FI predicted steeper decline in BMI (γ = -0.04, 95% CI = -0.05 to -0.03, p value ≤ 0.001). When including coupling from FI, BMI showed a nonlinear trajectory with a mean intercept of 26.32 kg/m2 (95% CI = 25.76-26.88), declining more rapidly after age 75. When including BMI coupling, FI increased from a mean intercept of 7.91% (95% CI = 6.41-9.42), with steeper growth from ages 60-75. BMI-FAI relationship was unidirectional (p value ≤ 0.001): Higher FAI predicted a steeper BMI decline (γ = -0.02, 95% CI = -0.02 to -0.01, p value ≤ 0.001). By including FAI coupling, BMI had a mean intercept of 26.10 kg/m2 (95% CI = 25.47-26.74), declining rapidly after age 75. FAI increased exponentially from a mean intercept of 36.49 (95% CI = 34.54-38.43).

CONCLUSIONS

Higher BMI predicted a steeper increase in FI, substantiating the hypothesis that obesity accelerates biological aging. Higher biological aging, measured as FI and FAI, drove a steeper BMI decline in late life, signalling that late-life weight loss may result from accelerated aging. Higher BMI may accelerate aspects of the aging process, and the aging process, in turn, accelerates late-life BMI decline, necessitating an integrated approach to manage both obesity and unintentional weight loss among older adults.

How do we age? A decomposition of Gompertz law

A strong regularity of human life is Gompertz's law, which predicts a near-perfect exponential increase in mortality with age. In this paper, we take into account that chronological age is not a cause of death and decompose Gompertz's law into two equally strong laws: (i) an exponential increase in health deficits as measured by the frailty index, and (ii) a power law association between the frailty index and the mortality rate. We show how the increase in the frailty index can be derived from the feature of self-productivity of health deficits. We explore the robustness of the Gompertz decomposition across countries, sex, and over time and show how information about mortality rates can be used to infer the state of health of an age-structured population. Finally, we use this method to infer the biological ages of past populations, such as Australians in 1940 and Swedes in 1770.

The Mouse Social Frailty Index (mSFI): A Standardized Protocol

The advent of geroscience engendered the development of approaches to quantify the aging process and estimate biological age on an individual level. Recognizing that declines observed in aging are not only physical but also social led us to develop a mouse Social Frailty Index (mSFI) designed to quantify age-related impairments of social functioning in mice. The mSFI consists of seven behavioral assays that measure essential facets of social behavioral functioning in mice: social communication, social interaction, and social functional ability. The assays that comprise the mSFI are all minimally disruptive, relatively simple to execute, and optimized for compatibility with longitudinal studies utilizing experimental interventions relevant to geroscience. The mSFI is conducted over AM and PM sessions spanning a maximum of 3.5 days, using materials common to most animal facilities. The data for all assays is obtained observationally, manually recorded, and entered into predefined template sheets that automate the computation of the mSFI. We have demonstrated the validity and applicability of the mSFI across multiple laboratory sites and experiments. This index has proven to discriminate between differential trajectories of biological aging driven by sex, progeria, or social stress-relevant contexts. The mSFI represents a novel index to quantify trajectories of biological aging in mice, and its application may help elucidate the social dimensions of the aging process. Key features • The mSFI is a comprehensive assessment for the evaluation of impairment in social behavioral functioning related to aging in mice. • Minimally disruptive, relatively simple, commonly used high-throughput assays of spontaneous social behavior that are optimized for compatibility with longitudinal studies of aging. • The protocol spans AM and PM sessions over 3.5 days maximum; the sequence of individual assays is flexible by design. • The mSFI requires materials common to most animal research facilities. • mSFI application is compatible with most experimental treatments, social behavioral paradigms, longitudinal within-subject designs, and genetically modified mouse models.

Complementary value of molecular, phenotypic, and functional aging biomarkers in dementia prediction

DNA methylation age (MA), brain age (BA), and frailty index (FI) are putative aging biomarkers linked to dementia risk. We investigated their relationship and combined potential for prediction of cognitive impairment and future dementia risk using the ADNI database. Of several MA algorithms, DunedinPACE and GrimAge2, associated with memory, were combined in a composite MA alongside BA and a data-driven FI in predictive analyses. Pairwise correlations between age- and sex-adjusted measures for MA (aMA), aBA, and aFI were low. FI outperformed BA and MA in all diagnostic tasks. A model including age, sex, and aFI achieved an area under the curve (AUC) of 0.94 for differentiating cognitively normal controls (CN) from dementia patients in a held-out test set. When combined with clinical biomarkers (apolipoprotein E ε4 allele count, memory, executive function), a model including aBA and aFI predicted 5-year dementia risk among MCI patients with an out-of-sample AUC of 0.88. In the prognostic model, BA and FI offered complementary value (both βs 0.50). The tested MAs did not improve predictions. Results were consistent across FI algorithms, with data-driven health deficit selection yielding the best performance. FI had a stronger adverse effect on prognosis in males, while BA's impact was greater in females. Our findings highlight the complementary value of BA and FI in dementia prediction. The results support a multidimensional view of dementia, including an intertwined relationship between the biomarkers, sex, and prognosis. The tested MA's limited contribution suggests caution in their use for individual risk assessment of dementia.

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.

Predicting cardiovascular morbidity and mortality with SCORE2 (OP) and Framingham risk estimates in combination with indicators of biological ageing

BACKGROUND AND OBJECTIVE

Previous research assessing whether biological ageing (BA) indicators can enhance the risk assessment of cardiovascular disease (CVD) outcomes beyond established CVD risk indicators, such as Framingham Risk Score (FRS) and Systematic Coronary Risk Evaluation (SCORE2)/SCORE2-Older Persons (OP), is scarce. We explored whether BA indicators, namely the Rockwood Frailty Index (FI) and leukocyte telomere length (TL), improve predictive accuracy of CVD outcomes beyond the traditional CVD risk indicators in general population of middle-aged and older CVD-free individuals.

METHODS

Data included 14 118 individuals from three population-based cohorts: TwinGene, Health 2000 (H2000), and the Helsinki Birth Cohort Study, grouped by baseline age (<70, 70+). The outcomes were incident CVD and CVD mortality with 10-year follow-up. Risk estimations were assessed using Cox regression and predictive accuracies with Harrell's C-index.

RESULTS

Across the three study cohorts and age groups: (i) a higher FI, but not TL, was associated with a higher occurrence of incident CVD (P < .05), (ii) also when considering simultaneously the baseline CVD risk according to FRS or SCORE2/SCORE2-OP (P < .05) (iii) adding FI to the FRS or SCORE2/SCORE2-OP model improved the predictive accuracy of incident CVD. Similar findings were seen for CVD mortality, but less consistently across the cohorts.

CONCLUSIONS

We show robust evidence that a higher FI value at baseline is associated with an increased risk of incident CVD in middle-aged and older CVD-free individuals, also when simultaneously considering the risk according to the FRS or SCORE2/SCORE2-OP. The FI improved the predictive accuracy of CVD outcomes beyond the traditional CVD risk indicators and demonstrated satisfactory predictive accuracy even when used independently.

Machine vision-based frailty assessment for genetically diverse mice

Frailty indexes (FIs) capture health status in humans and model organisms. To accelerate our understanding of biological aging and carry out scalable interventional studies, high-throughput approaches are necessary. We previously introduced a machine vision-based visual frailty index (vFI) that uses mouse behavior in the open field to assess frailty using C57BL/6J (B6J) data. Aging trajectories are highly genetic and are frequently modeled in genetically diverse animals. In order to extend the vFI to genetically diverse mouse populations, we collect frailty and behavior data on a large cohort of aged Diversity Outbred (DO) mice. Combined with previous data, this represents one of the largest video-based aging behavior datasets to date. Using these data, we build accurate predictive models of frailty, chronological age, and even the proportion of life lived. The extension of automated and objective frailty assessment tools to genetically diverse mice will enable better modeling of aging mechanisms and enable high-throughput interventional aging studies.

Analysis of the Correlation between Frailty Index, Clinical Characteristics, Use of Anti-Epileptic Drug, and Prognosis in Elderly Patients with Epilepsy

BACKGROUND

Epilepsy is a common neurological disorder among the elderly, often leading to significant morbidity. Therefore, it is necessary to study the correlation between the frailty index, clinical characteristics of epilepsy, use of anti-epileptic drug, and the prognosis of elderly patients with epilepsy.

METHODS

This retrospective study included 106 elderly patients with epilepsy who were treated at the Affiliated Mindong Hospital, Fujian Medical University, China, between January 2018 and December 2022. Based on the severity of the prognosis, the seizures were classified into the major seizure group (tonic-clonic), minor seizure group (absence, myoclonus, clonus, tonic, atonic and partial seizures), and no seizure group. Furthermore, the relationship between the frailty index, clinical characteristics, use of epilepsy drugs, and the degree of epileptic seizures was assessed using the Logistic regression analysis.

RESULTS

Univariate analysis indicated that older age (p < 0.001), longer disease duration (p = 0.009), and the presence of comorbid conditions such as diabetes (p = 0.002) and coronary heart disease (p < 0.001) were all associated with seizure severity. Additionally, frailty was significantly related to seizure severity, with the non-frailty group having fewer major seizures compared to the pre-frailty and frailty groups (p < 0.001). Similarly, regular medication use (p < 0.001) and the number of drugs taken (p < 0.001) were significant factors, with irregular medication use and single-drug regimens being more common in patients with more severe seizures. Multivariate Logistic regression analysis indicated that a higher frailty index (p = 0.033), age over 70 years (p = 0.015), longer disease duration (p = 0.003), the presence of coronary heart disease (p < 0.001), and regular medication use (p = 0.022) were all significantly associated with more severe seizures.

CONCLUSION

Frailty index, age, disease duration, coronary heart disease, and regular medication are related to the prognosis of elderly patients with epilepsy. These findings highlight the significance of comprehensive management strategies to improve clinical outcomes in this group of patients.

Roadmap for alleviating the manifestations of ageing in the cardiovascular system

Ageing of the cardiovascular system is associated with frailty and various life-threatening diseases. As global populations grow older, age-related conditions increasingly determine healthspan and lifespan. The circulatory system not only supplies nutrients and oxygen to all tissues of the human body and removes by-products but also builds the largest interorgan communication network, thereby serving as a gatekeeper for healthy ageing. Therefore, elucidating organ-specific and cell-specific ageing mechanisms that compromise circulatory system functions could have the potential to prevent or ameliorate age-related cardiovascular diseases. In support of this concept, emerging evidence suggests that targeting the circulatory system might restore organ function. In this Roadmap, we delve into the organ-specific and cell-specific mechanisms that underlie ageing-related changes in the cardiovascular system. We raise unanswered questions regarding the optimal design of clinical trials, in which markers of biological ageing in humans could be assessed. We provide guidance for the development of gerotherapeutics, which will rely on the technological progress of the diagnostic toolbox to measure residual risk in elderly individuals. A major challenge in the quest to discover interventions that delay age-related conditions in humans is to identify molecular switches that can delay the onset of ageing changes. To overcome this roadblock, future clinical trials need to provide evidence that gerotherapeutics directly affect one or several hallmarks of ageing in such a manner as to delay, prevent, alleviate or treat age-associated dysfunction and diseases.

The mouse Social Frailty Index (mSFI): a novel behavioral assessment for impaired social functioning in aging mice

Various approaches exist to quantify the aging process and estimate biological age on an individual level. Frailty indices based on an age-related accumulation of physical deficits have been developed for human use and translated into mouse models. However, declines observed in aging are not limited to physical functioning but also involve social capabilities. The concept of "social frailty" has been recently introduced into human literature, but no index of social frailty exists for laboratory mice yet. To fill this gap, we developed a mouse Social Frailty Index (mSFI) consisting of seven distinct assays designed to quantify social functioning which is relatively simple to execute and is minimally invasive. Application of the mSFI in group-housed male C57BL/6 mice demonstrated a progressively elevated levels of social frailty through the lifespan. Conversely, group-housed females C57BL/6 mice manifested social frailty only at a very old age. Female mice also showed significantly lower mSFI score from 10 months of age onward when compared to males. We also applied the mSFI in male C57BL/6 mice under chronic subordination stress and in chronic isolation, both of which induced larger increases in social frailty compared to age-matched group-housed males. Lastly, we show that the mSFI is enhanced in mouse models that show accelerated biological aging such as progeroid Ercc1-/Δ and Xpg-/- mice of both sexes compared to age matched littermate wild types. In summary, the mSFI represents a novel index to quantify trajectories of biological aging in mice and may help elucidate links between impaired social behavior and the aging process.

Unraveling the influence of childhood emotional support on adult aging: Insights from the UK Biobank

BACKGROUND

Exploring the association between Childhood Emotional Support (CES) and the mechanisms of aging is pivotal for understanding its potential to lessen the incidence of age-related pathologies and promote a milieu for healthy aging.

METHODS

Utilizing data from the UK Biobank comprising nearly 160,000 individuals, comprehensive analyses were conducted to explore associations between CES levels and age-related diseases, biological age and aging hallmarks. Cox proportional hazards regression models were used to investigate the relationship between CES and the risk of hospitalization for age-related diseases. Linear regression models were employed to explore the associations between CES and the frailty index (FI), Klemera-Doubal method (KDM) biological age acceleration, homeostatic dysregulation (HD), C-reactive protein (CRP), white blood cell (WBC) count, and telomere length.

RESULTS

The analyses revealed a significant association between higher CES levels and a decreased risk of hospitalization for age-related diseases in later life. After adjustments for covariates, the hazard ratio for age-related diseases was 0.87 (95 % confidence interval, 0.83-0.91, p < 0.001) in those with the highest CES level compared to those with the lowest CES level. Participants with the highest CES level exhibited lower FI scores (coefficient = -0.033, p < 0.001), reduced CRP level (coefficient = -0.097, p < 0.05) and lower WBC counts (coefficient = -0.034, p < 0.05). Stratified analyses based on genetic susceptibility further elucidated the protective role of CES against age-related diseases.

CONCLUSION

These findings underscore the potential of early interventions targeting CES to promote healthy aging and alleviating the burden of age-related diseases.

Biologically informed machine learning modeling of immune cells to reveal physiological and pathological aging process

The immune system undergoes progressive functional remodeling from neonatal stages to old age. Therefore, understanding how aging shapes immune cell function is vital for precise treatment of patients at different life stages. Here, we constructed the first transcriptomic atlas of immune cells encompassing human lifespan, ranging from newborns to supercentenarians, and comprehensively examined gene expression signatures involving cell signaling, metabolism, differentiation, and functions in all cell types to investigate immune aging changes. By comparing immune cell composition among different age groups, HLA highly expressing NK cells and CD83 positive B cells were identified with high percentages exclusively in the teenager (Tg) group, whereas unknown_T cells were exclusively enriched in the supercentenarian (Sc) group. Notably, we found that the biological age (BA) of pediatric COVID-19 patients with multisystem inflammatory syndrome accelerated aging according to their chronological age (CA). Besides, we proved that inflammatory shift- myeloid abundance and signature correlate with the progression of complications in Kawasaki disease (KD). The shift- myeloid signature was also found to be associated with KD treatment resistance, and effective therapies improve treatment outcomes by reducing this signaling. Finally, based on those age-related immune cell compositions, we developed a novel BA prediction model PHARE ( https://xiazlab.org/phare/ ), which can apply to both scRNA-seq and bulk RNA-seq data. Using this model, we found patients with coronary artery disease (CAD) also exhibit accelerated aging compared to healthy individuals. Overall, our study revealed changes in immune cell proportions and function associated with aging, both in health and disease, and provided a novel tool for successfully capturing features that accelerate or delay aging.

Machine vision based frailty assessment for genetically diverse mice

Frailty indexes (FIs) capture health status in humans and model organisms. To accelerate our understanding of biological aging and carry out scalable interventional studies, high-throughput approaches are necessary. We previously introduced a machine vision-based visual frailty index (vFI) that uses mouse behavior in the open field to assess frailty using C57BL/6J (B6J) data. Aging trajectories are highly genetic and are frequently modeled in genetically diverse animals. In order to extend the vFI to genetically diverse mouse populations, we collect frailty and behavior data on a large cohort of aged Diversity Outbred (DO) mice. Combined with previous data, this represents one of the largest video-based aging behavior datasets to date. Using these data, we build accurate predictive models of frailty, chronological age, and even the proportion of life lived. The extension of automated and objective frailty assessment tools to genetically diverse mice will enable better modeling of aging mechanisms and enable high-throughput interventional aging studies.

Using non-invasive behavioral and physiological data to measure biological age in wild baboons

Biological aging is near-ubiquitous in the animal kingdom, but its timing and pace vary between individuals and over lifespans. Prospective, individual-based studies of wild animals-especially non-human primates-help identify the social and environmental drivers of this variation by indicating the conditions and exposure windows that affect aging processes. However, measuring individual biological age in wild primates is challenging because several of the most promising methods require invasive sampling. Here, we leverage observational data on behavior and physiology, collected non-invasively from 319 wild female baboons across 2402 female-years of study, to develop a composite predictor of age: the non-invasive physiology and behavior (NPB) clock. We found that age predictions from the NPB clock explained 51% of the variation in females' known ages. Further, deviations from the clock's age predictions predicted female survival: females predicted to be older than their known ages had higher adult mortality. Finally, females who experienced harsh early-life conditions were predicted to be about 6 months older than those who grew up in more benign conditions. While the relationship between early adversity and NPB age is noisy, this estimate translates to a predicted 2-3 year reduction in mean adult lifespan in our model. A constraint of our clock is that it is tailored to data collection approaches implemented in our study population. However, many of the clock's components have analogs in other populations, suggesting that non-invasive data can provide broadly applicable insight into heterogeneity in biological age in natural populations.

DNA methylation-estimated phenotypes, telomere length and risk of ischemic stroke: epigenetic age acceleration of screening and a Mendelian randomization study

BACKGROUND

Aging is a complex biological process that may be accelerated in certain pathological conditions. DNA methylation age (DNAmAge) has emerged as a biomarker for biological age, which can differ from chronological age. This research peels back the layers of the relationship between fast-forward aging and ischemic stroke, poking and prodding the potential two-way causal influences between stroke and biological aging indicators.

METHODS

We analyzed a cohort of ischemic stroke patients, comparing DNAmAge with chronological age to measure age acceleration. We assessed variations in age acceleration among stroke subtypes and between sexes. Using Mendelian randomization, we examined the causal links between stroke, aging biomarkers like telomere length, and age acceleration's effect on stroke risk.

RESULTS

Our investigation reveals a pronounced association between ischemic stroke and age acceleration, most notably in patients with cardioembolic strokes, who exhibited a striking median difference of 9 years between DNAmAge and chronological age. Furthermore, age acceleration differed significantly across stroke subtypes and was higher in women than in men. In terms of causality, MR analysis indicated a modest negative effect of stroke on telomere length, but no causal effect of age phenotypes on stroke or its subtypes. However, some indication of a potential causal effect of ischemic stroke on PhenoAge acceleration was observed.

CONCLUSION

The study provides insight into the relationship between DNAmAge and ischemic stroke, particularly cardioembolic stroke, and suggests possible gender differences. These insights carry profound clinical significance and set stage for future investigations into the entwined pathways of stroke and accelerated aging.

Association of phenotypic frailty and frailty index with type 2 diabetes and dyslipidemia in middle-aged and elderly Chinese: A longitudinal cohort study

PURPOSE

Frailty, type 2 diabetes (T2D) and dyslipidemia are highly prevalent in middle-aged and elderly populations. However, evidence on the longitudinal association of frailty with T2D and dyslipidemia is limited. The aim of our study was to explore the cross-sectional and longitudinal effects of frailty levels on T2D and dyslipidemia in combination with phenotypic frailty and frailty index (FI).

MATERIALS AND METHODS

Multivariate logistic regression model was used to explore the association of frailty status with T2D and dyslipidemia. Area under curve (AUC) of the receiver operating characteristic curve (ROC) to estimate the predictive values of phenotypic frailty and frailty index for T2D and dyslipidemia. In addition, depressive symptom was used as a mediating variable to examine whether it mediates the association between frailty and T2D or dyslipidemia.

RESULTS

10,203 and 9587 participants were chosen for the longitudinal association analysis of frailty with T2D and dyslipidemia. Frailty was associated with T2D (phenotypic frailty: OR=1.50, 95 %CI=1.03, 2.17; FI: OR=1.17, 95 %CI=1.08, 1.26) and dyslipidemia (phenotypic frailty: OR=1.56, 95 %CI=1.16, 2.10; FI: OR=1.17, 95 %CI=1.10, 1.25). Phenotypic frailty and frailty index significantly improved the risk discrimination of T2D and dyslipidemia (p<0.05). Depressive symptoms played a mediating role in the association between frailty and long-term T2D or dyslipidemia (p<0.05).

CONCLUSION

Frailty had adverse effects on type 2 diabetes and dyslipidemia, with depressive symptoms acting as the mediator.

Interplay of body mass index and metabolic syndrome: association with physiological age from midlife to late-life

Obesity and metabolic syndrome (MetS) share common pathophysiological characteristics with aging. To better understand their interplay, we examined how body mass index (BMI) and MetS jointly associate with physiological age, and if the associations changed from midlife to late-life. We used longitudinal data from 1,825 Swedish twins. Physiological age was measured as frailty index (FI) and functional aging index (FAI) and modeled independently in linear mixed-effects models adjusted for chronological age, sex, education, and smoking. We assessed curvilinear associations of BMI and chronological age with physiological age, and interactions between BMI, MetS, and chronological age. We found a significant three-way interaction between BMI, MetS, and chronological age on FI (p-interaction = 0·006), not FAI. Consequently, we stratified FI analyses by age: < 65, 65-85, and ≥ 85 years, and modeled FAI across ages. Except for FI at ages ≥ 85, BMI had U-shaped associations with FI and FAI, where BMI around 26-28 kg/m2 was associated with the lowest physiological age. MetS was associated with higher FI and FAI, except for FI at ages < 65, and modified the BMI-FI association at ages 65-85 (p-interaction = 0·02), whereby the association between higher BMI levels and FI was stronger in individuals with MetS. Age modified the MetS-FI association in ages ≥ 85, such that it was stronger at higher ages (p-interaction = 0·01). Low BMI, high BMI, and metabolic syndrome were associated with higher physiological age, contributing to overall health status among older individuals and potentially accelerating aging.

Unveiling the multifaceted nexus of subjective aging, biological aging, and chronological age: Findings from a nationally representative cohort study

OBJECTIVES

This study aims to investigate how subjective aging influences the psychological and behavioral responses of older individuals, specifically focusing on the associations between subjective aging and longitudinal changes in biological age.

METHODS

This is a retrospective cohort study retrieving data from the Taiwan Longitudinal Study on Aging (TLSA), over a 4-year follow-up period. Subjective aging is assessed by asking participants if they perceive themselves as old, while frailty is measured using a frailty index comprising 34 deficits from various domains. Participants are categorized into three groups based on their chronological age. The association between subjective aging and transition of biological age (as indicated by an increased frailty index) from 2011 to 2015 is examined using logistic regression models.

RESULTS

The study consisted of 2412 participants, who were categorized into middle-age (n = 1,082), young-old (n = 779), and old-old (n = 551) groups. Among them, individuals exhibiting subjective aging at baseline were more likely to be older in chronological age, female, illiterate, and unemployed, compared to those without subjective aging. The adjusted odds ratios (aORs) for the association between subjective aging and an increased biological age were 1.72 [95% CI: 0.88-3.34], 1.61 [0.77-3.37], and 1.08 [0.65-1.80], in the middle-age, young-old, and old-old groups, respectively.

DISCUSSIONS

No significant associations were found between changes in biological age and subjective aging across various chronological age groups. Notably, within the younger age group, a discernible trend towards an association was observed, indicating the potential age-related nuances in the complex interrelation between subjective age, biological aging, and chronological aging.

The epigenetics of frailty

The conceptual change of frailty, from a physical to a biopsychosocial phenotype, expanded the field of frailty, including social and behavioral domains with critical interaction between different frailty models. Environmental exposures - including physical exercise, psychosocial factors and diet - may play a role in the frailty pathophysiology. Complex underlying mechanisms involve the progressive interactions of genetics with epigenetics and of multimorbidity with environmental factors. Here we review the literature on possible mechanisms explaining the association between epigenetic hallmarks (i.e., global DNA methylation, DNA methylation age acceleration and microRNAs) and frailty, considered as biomarkers of aging. Frailty could be considered the result of environmental epigenetic factors on biological aging, caused by conflicting DNA methylation age and chronological age.

Inclusion of functional measures and frailty in the development and evaluation of medicines for older adults

The International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) E7, the guidance for the conduct of clinical trials in people older than age 65 years, dates from 1994. Since then, the inclusion of older people in clinical trials has hardly improved, particularly for the oldest old age group (individuals older than age 75 years), which is the fastest growing demographic bracket in the EU. Even though most medications are taken by this group, relevant endpoints and safety outcomes for this cohort are rarely included and reported, both in clinical trials and regulatory approval documents. To improve the critical appraisal and the regulatory review of medicines taken by frail older adults, eight recommendations are presented and discussed in this Health Policy. These recommendations are brought together from different perspectives and experience of the treatment of older patients. On one side, the perspective of medical practitioners from various clinical disciplines, with their direct experience of clinical decision making; on the other, the perspective of regulators assessing the data submitted in medicine registration dossiers, their relevance to the risk-benefit balance for older patients, and the communication of the findings in the product information. Efforts to improve the participation of older people in clinical trials have been in place for more than a decade, with little success. The recommendations presented here are relevant for stakeholders, authorities, pharmaceutical companies, and researchers alike, as the implementation of these measures is not under the capacity of a single entity. Improving the inclusion of frail older adults requires awareness, focus, and action on the part of those who can effect a much needed change.

Epigenetic and Metabolomic Biomarkers for Biological Age: A Comparative Analysis of Mortality and Frailty Risk

Biological age captures a person's age-related risk of unfavorable outcomes using biophysiological information. Multivariate biological age measures include frailty scores and molecular biomarkers. These measures are often studied in isolation, but here we present a large-scale study comparing them. In 2 prospective cohorts (n = 3 222), we compared epigenetic (DNAm Horvath, DNAm Hannum, DNAm Lin, DNAm epiTOC, DNAm PhenoAge, DNAm DunedinPoAm, DNAm GrimAge, and DNAm Zhang) and metabolomic-based (MetaboAge and MetaboHealth) biomarkers in reflection of biological age, as represented by 5 frailty measures and overall mortality. Biomarkers trained on outcomes with biophysiological and/or mortality information outperformed age-trained biomarkers in frailty reflection and mortality prediction. DNAm GrimAge and MetaboHealth, trained on mortality, showed the strongest association with these outcomes. The associations of DNAm GrimAge and MetaboHealth with frailty and mortality were independent of each other and of the frailty score mimicking clinical geriatric assessment. Epigenetic, metabolomic, and clinical biological age markers seem to capture different aspects of aging. These findings suggest that mortality-trained molecular markers may provide novel phenotype reflecting biological age and strengthen current clinical geriatric health and well-being assessment.

Biological age is superior to chronological age in predicting hospital mortality of the critically ill

Biological age is increasingly recognized as being more accurate than chronological age in determining chronic health outcomes. This study assessed whether biological age, assessed on intensive care unit (ICU) admission, can predict hospital mortality. This retrospective cohort study, conducted in a tertiary multidisciplinary ICU in Western Australia, used the Levine PhenoAge model to estimate each patient's biological age (also called PhenoAge). Each patient's PhenoAge was calibrated to generate a regression residual which was equivalent to biological age unexplained by chronological age in the local context. PhenoAgeAccel was a dichotomized measure of the residuals, and its presence suggested that one was biologically older than the corresponding chronological age. Of the 2950 critically ill adult patients analyzed, 291 died (9.9%) before hospital discharge. Both PhenoAge and its residuals (after regressing on chronological age) had a significantly better ability to differentiate between hospital survivors and non-survivors than chronological age (area under the receiver-operating-characteristic curve 0.648 and 0.654 vs. 0.547 respectively). Being phenotypically older than one's chronological age was associated with an increased risk of mortality (PhenoAgeAccel hazard ratio [HR] 1.997, 95% confidence interval [CI] 1.568-2.542; p = 0.001) in a dose-related fashion and did not reach a plateau until at least a 20-year gap. This adverse association remained significant (adjusted HR 1.386, 95% CI 1.077-1.784; p = 0.011) after adjusted for severity of acute illness and comorbidities. PhenoAgeAccel was more prevalent among those with pre-existing chronic cardiovascular disease, end-stage renal failure, cirrhosis, immune disease, diabetes mellitus, or those treated with immunosuppressive therapy. Being phenotypically older than one's chronological age was more common among those with comorbidities, and this was associated with an increased risk of mortality in a dose-related fashion in the critically ill that was not fully explained by comorbidities and severity of acute illness.

An overview of the resilience world: Proceedings of the American Geriatrics Society and National Institute on Aging State of Resilience Science Conference

Resilience, which relates to one's ability to respond to stressors, typically declines with age and the development of comorbid conditions in older organisms. Although progress has been made to improve our understanding of resilience in older adults, disciplines have employed different frameworks and definitions to study various aspects of older adults' response to acute or chronic stressors. "Overview of the Resilience World: State of the Science," a bench-to-bedside conference on October 12-13, 2022, was sponsored by the American Geriatrics Society and National Institute on Aging. This conference, summarized in this report, explored commonalities and differences among the frameworks of resilience most commonly used in aging research in the three domains of resilience: physical, cognitive, and psychosocial. These three main domains are intertwined, and stressors in one domain can lead to effects in other domains. The themes of the conference sessions included underlying contributors to resilience, the dynamic nature of resilience throughout the life span, and the role of resilience in health equity. Although participants did not agree on a single definition of "resilience(s)," they identified common core elements of a definition that can be applied to all domains and noted unique features that are domain specific. The presentations and discussions led to recommendations for new longitudinal studies of the impact of exposures to stressors on resilience in older adults, the use of new and existing cohort study data, natural experiments (including the COVID-19 pandemic), and preclinical models for resilience research, as well as translational research to bring findings on resilience to patient care.

Comprehensive longitudinal non-invasive quantification of healthspan and frailty in a large cohort (n = 546) of geriatric C57BL/6 J mice

Frailty is an age-related condition characterized by a multisystem functional decline, increased vulnerability to stressors, and adverse health outcomes. Quantifying the degree of frailty in humans and animals is a health measure useful for translational geroscience research. Two frailty measurements, namely the frailty phenotype (FP) and the clinical frailty index (CFI), have been validated in mice and are frequently applied in preclinical research. However, these two tools are based on different concepts and do not necessarily identify the same mice as frail. In particular, the FP is based on a dichotomous classification that suffers from high sample size requirements and misclassification problems. Based on the monthly longitudinal non-invasive assessment of frailty in a large cohort of mice, here we develop an alternative scoring method, which we called physical function score (PFS), proposed as a continuous variable that resumes into a unique function, the five criteria included in the FP. This score would not only reduce misclassification of frailty but it also makes the two tools, PFS and CFI, integrable to provide an overall measurement of health, named vitality score (VS) in aging mice. VS displays a higher association with mortality than PFS or CFI and correlates with biomarkers related to the accumulation of senescent cells and the epigenetic clock. This longitudinal non-invasive assessment strategy and the VS may help to overcome the different sensitivity in frailty identification, reduce the sample size in longitudinal experiments, and establish the effectiveness of therapeutic/preventive interventions for frailty or other age-related diseases in geriatric animals.

Biomarkers of aging

Aging biomarkers are a combination of biological parameters to (i) assess age-related changes, (ii) track the physiological aging process, and (iii) predict the transition into a pathological status. Although a broad spectrum of aging biomarkers has been developed, their potential uses and limitations remain poorly characterized. An immediate goal of biomarkers is to help us answer the following three fundamental questions in aging research: How old are we? Why do we get old? And how can we age slower? This review aims to address this need. Here, we summarize our current knowledge of biomarkers developed for cellular, organ, and organismal levels of aging, comprising six pillars: physiological characteristics, medical imaging, histological features, cellular alterations, molecular changes, and secretory factors. To fulfill all these requisites, we propose that aging biomarkers should qualify for being specific, systemic, and clinically relevant.

Association between B-vitamins intake and frailty among patients with chronic obstructive pulmonary disease

PURPOSE

Gain insight into the impact of B vitamins, including vitamin B1, vitamin B2, niacin, vitamin B6, total folate, and vitamin B12 on the risk of frailty in patients with chronic obstructive pulmonary disease (COPD).

METHODS

This study was an American population-based cross-sectional study using data from the National Health and Nutrition Examination Survey (NHANES). A total of 1201 COPD patients were included in the analysis. Of these, the intake of B vitamins was determined by the two 24-h recall interviews. We followed the method constructed by Hakeem et al. to calculate the frailty index (FI), which is used as a reliable tool to assess the debilitating status of patients with COPD. Missing data were imputed by the MissForest method based on random forests. Multivariate logistic regression model and inverse probability weighted based on propensity scores were used to correct for confoundings.

RESULTS

Logistic regression models showed that vitamin B6 intake was negatively correlated with frailty risk in COPD patients, while other B vitamins including B1, B2, niacin (vitamin B3), total folic acid and vitamin B12 were not. After adjusting for covariates, the association between vitamin B6 and frailty risk (adjusted OR = 0.80, 95%CI = 0.66-0.95, P = 0.013) remained significant. At the same time, sensitivity analysis proves the robustness of the results.

CONCLUSION

COPD patients with lower vitamin B6 intake have a higher risk of frailty. However, intake of vitamin B1, B2, niacin, total folic acid, and vitamin B12 was not associated with frailty risk in COPD patients.

[Plastic-surgical reconstruction of the lower extremity in senior patients]

The proportion of patients in the population beyond the 7th decade of life is increasing worldwide, especially in highly developed countries. Consequently, there is also an increasing need for complex lower extremity reconstructions after trauma, tumors, or infections in this age group. The reconstruction of soft tissue defects of the lower extremity should be performed according to the principle of the plastic-reconstructive ladder or elevator. The goal of reconstruction is to restore anatomy and function of the lower extremity to enable pain-free and stable standing and walking; however, for older patients in particular, a careful preoperative multidisciplinary planning, detailed preoperative assessment and optimization of comorbidities, such as diabetes, malnutrition or pathological vascular alterations, as well an age-adapted perioperative management are necessary. By implementing these principles, older and very old patients can maintain their mobility and autonomy, which are crucial for a high quality of life.

Association between the Inflammatory Potential of the Diet and Biological Aging: A Cross-Sectional Analysis of 4510 Adults from the Moli-Sani Study Cohort

Chronological age (CA) may not accurately reflect the health status of an individual. Rather, biological age (BA) or hypothetical underlying "functional" age has been proposed as a relevant indicator of healthy aging. Observational studies have found that decelerated biological aging or Δage (BA-CA) is associated with a lower risk of disease and mortality. In general, CA is associated with low-grade inflammation, a condition linked to the risk of the incidence of disease and overall cause-specific mortality, and is modulated by diet. To address the hypothesis that diet-related inflammation is associated with Δage, a cross-sectional analysis of data from a sub-cohort from the Moli-sani Study (2005-2010, Italy) was performed. The inflammatory potential of the diet was measured using the Energy-adjusted Dietary Inflammatory Index (E-DII^TM) and a novel literature-based dietary inflammation score (DIS). A deep neural network approach based on circulating biomarkers was used to compute BA, and the resulting Δage was fit as the dependent variable. In 4510 participants (men 52.0%), the mean of CA (SD) was 55.6 y (±11.6), BA 54.8 y (±8.6), and Δage -0.77 (±7.7). In a multivariable-adjusted analysis, an increase in E-DII^TM and DIS scores led to an increase in Δage (β = 0.22; 95%CI 0.05, 0.38; β = 0.27; 95%CI 0.10, 0.44, respectively). We found interaction for DIS by sex and for E-DII^TM by BMI. In conclusion, a pro-inflammatory diet is associated with accelerated biological aging, which likely leads to an increased long-term risk of inflammation-related diseases and mortality.

Associations between Frailty and Ambient Temperature in Winter: Findings from a Population-Based Study

Frailty is an accumulation of deficits characterized by reduced resistance to stressors and increased vulnerability to adverse outcomes. However, there is little known about the effect of ambient temperature in winter on frailty among older adults, a population segment with the highest frailty prevalence. Thus, the objective of this study is to investigate the associations between frailty and ambient temperature in winter among older adults. This study was based on the Chinese Longitudinal Healthy Longevity Survey (CLHLS) of older adults aged ≥65 years from the 2005, 2008, 2011, and 2014 waves. The 39-item accumulation of frailty index (FI) was used to assess the frailty status of the participants. The FI was categorized into three groups as follows: robust (FI ≤ 0.10), prefrail (FI > 0.10 to <0.25), and frail (FI ≥ 0.25). Generalized linear mixed models (GLMMs) were conducted to explore the associations between frailty and ambient temperature in winter. A generalized estimating equation (GEE) modification was applied in the sensitivity analysis. A total of 9421 participants were included with a mean age of 82.81 (SD: 11.32) years. Compared with respondents living in the highest quartile (≥7.5 °C) of average temperature in January, those in the lowest quartile (<−1.9 °C) had higher odds of prefrailty (OR = 1.35, 95% CI 1.17−1.57) and frailty (OR = 1.61, 95%CI 1.32−1.95). The associations were stronger among the low-education groups, agricultural workers before retirement, and non-current exercisers. Additionally, results from the GEE model reported consistent findings. Lower levels of ambient temperature in winter were associated with higher likelihoods of prefrailty and frailty. The findings on vulnerability characteristics could help improve public health practices to tailor cold temperature health education and warning information.

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.

Frailty in individuals with depression, bipolar disorder and anxiety disorders: longitudinal analyses of all-cause mortality

BACKGROUND

Frailty is a medical syndrome that is strongly associated with mortality risk and an emerging global health burden. Mental disorders are associated with reduced life expectancy and elevated levels of frailty. In this study, we examined the mortality risk associated with frailty in individuals with a lifetime history of mental disorders compared to individuals without a history of mental disorders.

METHODS

The UK Biobank study recruited > 500,000 adults, aged 37-73, between 2006 and 2010. We derived the two most common albeit distinctive measures of frailty, the frailty phenotype and the frailty index. Individuals with lifetime depression, bipolar disorder or anxiety disorders were identified from multiple data sources. The primary outcome was all-cause mortality. We have also examined differences in frailty, separately by sex and age.

RESULTS

Analyses included up to 297,380 middle-aged and older adults with a median follow-up of 12.19 (interquartile range = 1.31) years, yielding 3,516,706 person-years of follow-up. We observed higher levels of frailty in individuals with mental disorders for both frailty measures. Standardised mean differences in the frailty index ranged from 0.66 (95% confidence interval [CI] 0.65-0.67) in individuals with anxiety disorders to 0.94 (95% CI 0.90-0.97) in individuals with bipolar disorder, compared to people without mental disorders. For key comparisons, individuals with a mental disorder had greater all-cause mortality hazards than the comparison group without mental disorders. The highest hazard ratio (3.65, 95% CI 2.40-5.54) was observed among individuals with bipolar disorder and frailty, relative to non-frail individuals without mental disorders.

CONCLUSIONS

Our findings highlight elevated levels of frailty across three common mental disorders. Frailty and mental disorders represent potentially modifiable targets for prevention and treatment to improve population health and life expectancy, especially where both conditions coexist.

A machine-vision-based frailty index for mice

Heterogeneity in biological aging manifests itself in health status and mortality. Frailty indices (FIs) capture health status in humans and model organisms. To accelerate our understanding of biological aging and carry out scalable interventional studies, high-throughput approaches are necessary. Here we introduce a machine-learning-based visual FI for mice that operates on video data from an open-field assay. We use machine vision to extract morphometric, gait and other behavioral features that correlate with FI score and age. We use these features to train a regression model that accurately predicts the normalized FI score within 0.04 ± 0.002 (mean absolute error). Unnormalized, this error is 1.08 ± 0.05, which is comparable to one FI item being mis-scored by 1 point or two FI items mis-scored by 0.5 points. This visual FI provides increased reproducibility and scalability that will enable large-scale mechanistic and interventional studies of aging in mice.

Frailty Index and Cardiovascular Disease among Middle-Aged and Older Chinese Adults: A Nationally Representative Cross-Sectional and Follow-Up Study

Evidence for the association between the frailty index and cardiovascular disease (CVD) is inconclusive, and this association has not been evaluated in Chinese adults. We aim to examine the association between the frailty index and CVD among middle-aged and older Chinese adults. We conducted cross-sectional and cohort analyses using nationally representative data from the China Health and Retirement Longitudinal Study (CHARLS). From 2011 to 2018, 17,708 participants aged 45 years and older were included in the CHARLS. The primary outcome was CVD events (composite of heart disease and stroke). Multivariable adjusted logistic regression and Cox proportional hazards models were used to estimate the association between the frailty index and CVD in cross-sectional and follow-up studies, respectively. A restricted cubic spline model was used to characterize dose−response relationships. A total of 16,293 and 13,580 participants aged 45 years and older were included in the cross-sectional and cohort analyses, respectively. In the cross-sectional study, the prevalence of CVD in robust, pre-frailty and frailty was 7.83%, 18.70% and 32.39%, respectively. After multivariable adjustment, pre-frailty and frailty were associated with CVD; ORs were 2.54 (95% confidence interval [CI], 2.28−2.84) and 4.76 (95% CI, 4.10−5.52), respectively. During the 7 years of follow-up, 2122 participants without previous CVD developed incident CVD; pre-frailty and frailty were associated with increased risk of CVD events; HRs were 1.53 (95% CI, 1.39−1.68) and 2.17 (95% CI, 1.88−2.50), respectively. Furthermore, a stronger association of the frailty index with CVD was observed in participants aged <55, men, rural community-dwellers, BMI ≥ 25, without hypertension, diabetes or dyslipidemia. A clear nonlinear dose−response pattern between the frailty index and CVD was widely observed (p < 0.001 for nonlinearity), the frailty index was above 0.08, and the hazard ratio per standard deviation was 1.18 (95% CI 1.13−1.25). We observed the association between the frailty index and CVD among middle-aged and elderly adults in China, independent of chronological age and other CVD risk factors. Our findings are important for prevention strategies aimed at reducing the growing burden of CVD in older adults.

Operationalization of the Clinical Frailty Scale in Korean Community-Dwelling Older People

Background

The Clinical Frailty Scale (CFS) is a simple measure of global fitness validated in various populations in real-world settings. In this study, we aimed to assess the characteristics and validities of the CFS in community-dwelling older people in Korea, with the original classification tree (oCFS) and a culturally modified tree (mCFS).

Methods

The comprehensive geriatric assessment records of 1,064 individuals of the Aging Study of the Pyeongchang Rural Area were used for this study. For mCFS, we considered the dependency of the food preparations and household chores not to be deficits in the male population. The frailty index was used as a reference for construct validity. We used a composite outcome of death and institutionalization for outcome validity.

Results

The correlation coefficients with frailty index were higher in mCFS (.535) than in oCFS (.468). The mean frailty index was lower in individuals reclassified by mCFS (5 to 4) than people who stayed in mCFS 5. The classification coefficient of mCFS was significantly higher than that of oCFS (p <0.001) in determining people with frailty (frailty index.25 or higher). Trends of a higher incidence of the composite outcome were observed in both higher oCFS and mCFS, in which oCFS and mCFS did not differ significantly in predicting the risk of the outcome.

Conclusion

The classification tree of CFS could be culturally adopted in a community-dwelling population of Korea and considered valid in detecting the vulnerable population.

Physiological Systems in Promoting Frailty

Frailty is a complex syndrome affecting a growing sector of the global population as medical developments have advanced human mortality rates across the world. Our current understanding of frailty is derived from studies conducted in the laboratory as well as the clinic, which have generated largely phenotypic information. Far fewer studies have uncovered biological underpinnings driving the onset and progression of frailty, but the stage is set to advance the field with preclinical and clinical assessment tools, multiomics approaches together with physiological and biochemical methodologies. In this article, we provide comprehensive coverage of topics regarding frailty assessment, preclinical models, interventions, and challenges as well as clinical frameworks and prevalence. We also identify central biological mechanisms that may be at play including mitochondrial dysfunction, epigenetic alterations, and oxidative stress that in turn, affect metabolism, stress responses, and endocrine and neuromuscular systems. We review the role of metabolic syndrome, insulin resistance and visceral obesity, focusing on glucose homeostasis, adenosine monophosphate-activated protein kinase (AMPK), mammalian target of rapamycin (mTOR), and nicotinamide adenine dinucleotide (NAD+ ) as critical players influencing the age-related loss of health. We further focus on how immunometabolic dysfunction associates with oxidative stress in promoting sarcopenia, a key contributor to slowness, weakness, and fatigue. We explore the biological mechanisms involved in stem cell exhaustion that affect regeneration and may contribute to the frailty-associated decline in resilience and adaptation to stress. Together, an overview of the interplay of aging biology with genetic, lifestyle, and environmental factors that contribute to frailty, as well as potential therapeutic targets to lower risk and slow the progression of ongoing disease is covered. © 2022 American Physiological Society. Compr Physiol 12:1-46, 2022.

Epigenetic Age Acceleration and Change in Frailty in MOBILIZE Boston

Age-associated changes in DNA methylation have been implicated as 1 mechanism to explain the development of frailty; however, previous cross-sectional studies of epigenetic age acceleration (eAA) and frailty have had inconsistent findings. Few longitudinal studies have considered the association of eAA with change in frailty. We sought to determine the association between eAA and change in frailty in the MOBILIZE Boston cohort. Participants were assessed at 2 visits 12-18 months apart. Intrinsic, extrinsic, GrimAge, and PhenoAge eAA were assessed from whole-blood DNA methylation at baseline using the Infinium 450k array. Frailty was assessed by a continuous frailty score based on the frailty phenotype and by frailty index (FI). Analysis was by correlation and linear regression with adjustment for age, sex, smoking status, and body mass index. Three hundred and ninety-five participants with a frailty score and 431 with an FI had epigenetic and follow-up frailty measures. Mean (standard deviation) ages were 77.8 (5.49) and 77.9 (5.47) for the frailty score and the FI cohorts respectively, and 232 (58.7%) and 257 (59.6%) were female. All participants with epigenetic data identified as White. Baseline frailty score was not correlated with intrinsic or extrinsic eAA, but was correlated with PhenoAge and, even after adjustment for covariates, GrimAge. Baseline FI was correlated with extrinsic, GrimAge, and PhenoAge eAA with and without adjustment. No eAA measure was associated with change in frailty, with or without adjustment. Our results suggest that no eAA measure was associated with change in frailty. Further studies should consider longer periods of follow-up and repeated eAA measurement.

Relationship between five Epigenetic Clocks, Telomere Length and Functional Capacity assessed in Older Adults: Cross-sectional and Longitudinal Analyses

DNA methylation age acceleration (DNAmAA, derived from an epigenetic clock) and relative leukocyte telomere length (rLTL) are widely accepted biomarkers of aging. Nevertheless, it is still unclear which aspects of aging they represent best. Here we evaluated longitudinal associations between baseline rLTL and DNAmAA (estimated with 7-CpG clock) and functional assessments covering different domains of aging. Additionally, we made use of cross-sectional data on these assessments and examined their association with DNAmAA estimated by five different DNAm age measures. Two-wave longitudinal data was available for 1,083 participants of the Berlin Aging Study II (BASE-II) who were re-examined on average 7.4 years after baseline as part of the GendAge study. Functional outcomes were assessed with Fried's frailty score, Tinetti mobility test, falls in the past 12 months (yes/no), Finger-floor distance, Mini Mental State Examination (MMSE), Center for Epidemiologic Studies Depression Scale (CES-D), Activities of Daily Living (ADL), Instrumented ADL (IADL) and Mini Nutritional Assessment (MNA). Overall, we found no evidence for an association between the molecular biomarkers measured at baseline, rLTL and DNAmAA (7-CpG clock), and functional assessments assessed at follow-up. Similarly, a cross-sectional analyses of follow-up data did also not show evidence for associations of the various DNAmAA measures (7-CpG clock, Horvath's clock, Hannum's clock PhenoAge, and GrimAge) with functional assessments. In conclusion, neither rLTL nor 7-CpG DNAmAA were able to predict impairment in the analyzed assessments over a ~7-year time-course. Similarly, DNAmAA estimated from five epigenetic clocks was not a good cross-sectional marker of health deterioration either.

Frailty in Caregivers and Its Relationship with Psychological Stress and Resilience: A Cross-SectionalStudy Based on the Deficit Accumulation Model

BACKGROUND

Studies increasingly suggest that chronic exposure to psychological stress can lead to health deterioration and accelerated ageing, thus possibly contributing to the development of frailty. Recent approaches based on the deficit accumulation model measure frailty on a continuous grading through the "Frailty Index" (FI), i.e. a macroscopic indicator of biological senescence and functional status.

OBJECTIVES

The study aimed at testing the relationship of FI with caregiving, psychological stress, and psychological resilience.

DESIGN

Cross-sectional study, with case-control and correlational analyses.

PARTICIPANTS

Caregivers of patients with dementia (n=64), i.e. individuals a priori considered to be exposed to prolonged psychosocial stressors, and matched controls (n=64) were enrolled.

MEASUREMENTS

The two groups were compared using a 38-item FI condensing biological, clinical, and functional assessments. Within caregivers, the association of FI with Perceived Stress Scale (PSS) and Brief Resilience Scale (BRS) was tested.

RESULTS

Caregivers had higher FI than controls (F=8.308, p=0.005). FI was associated directly with PSS (r=0.660, p<0.001) and inversely with BRS (r=-0.637, p<0.001). Findings remained significant after adjusting for certain confounding variables, after excluding from the FI the conditions directly related to psychological stress, and when the analyses were performed separately among participants older and younger than 65 years.

CONCLUSIONS

The results provide insight on the relationship of frailty with caregiving, psychological stress, and resilience, with potential implications for the clinical management of individuals exposed to chronic emotional strain.

A Simplified Frailty Index Predicts Mortality in Older Adults in Beijing

Objective

The comprehensive geriatric assessment (CGA) is an integral tool used to identify vulnerable older adults in need of individualized plans to delay the course of diseases and monitor treatment outcomes. We previously developed and validated a 68-item frailty index (FI) based on the CGA in a large, older, Chinese population. However, substantial time is needed to evaluate the 68 items. Therefore, we aimed to develop and validate a simplified FI for use in Chinese older population.

Design

Longitudinal study.

Setting and Participants

Data were drawn from the Beijing Longitudinal Study of Aging. The study was conducted in 2004 with 1808 participants evaluated using the CGA and was followed-up for 13 years. Mortality was recorded at 3, 5, 8, 10, and 13 years intervals.

Measures

27-Item, 50-item, and 68-item frailty indices were investigated. A Cox proportional hazards model and area under the curve of the receiver operating characteristic (AUC-ROC) were calculated to compare mortality predictions.

Results

The FI was positively correlated with age in males (r = 0.174, P <0.001) and females (r = 0.270, P <0.001). The mean baseline FI was 0.225 ± 0.085 (range: 0.04-0.56) as evaluated by the 27-item FI, 0.181 ± 0.117 (range: 0.02-0.62) by the 50-item FI, and 0.167 ± 0.101 (range: 0.02-0.59) by the 68-item FI. Cox regression models showed that mortality was significantly higher in frail people than in non-frail people for all 3 indices (p<0.001). The AUCs of the 68-item FI, 50-item FI, and 27-item FI for predicting mortality were 0.720, 0.717, and 0.677, respectively (p<0.001).

Conclusion

The 27-item FI is reasonable to expect that the AUC of the indices with the higher items number is inferior to the performance of the indices with higher number of items (FI50 and FI68). But 27-item maybe used as a tool to identify frail older adults and predict mortality in clinical and primary care practices in China.

Epidemiological and genetic overlap among biological aging clocks: New challenges in biogerontology

Estimators of biological age (BA) - defined as the hypothetical underlying age of an organism - have attracted more and more attention in the last years, especially after the advent of new algorithms based on machine learning and genetic markers. While different aging clocks reportedly predict mortality in the general population, very little is known on their overlap. Here we review the evidence reported so far to support the existence of a partial overlap among different BA acceleration estimators, both from an epidemiological and a genetic perspective. On the epidemiological side, we review evidence supporting shared and independent influence on mortality risk of different aging clocks - including telomere length, brain, blood and epigenetic aging - and provide an overview of how an important exposure like diet may affect the different aging systems. On the genetic side, we apply linkage disequilibrium score regression analyses to support the existence of partly shared genomic overlap among these aging clocks. Through multivariate analysis of published genetic associations with these clocks, we also identified the most associated variants, genes, and pathways, which may affect common mechanisms underlying biological aging of different systems within the body. Based on our analyses, the most implicated pathways were involved in inflammation, lipid and carbohydrate metabolism, suggesting them as potential molecular targets for future anti-aging interventions. Overall, this review is meant as a contribution to the knowledge on the overlap of aging clocks, trying to clarify their shared biological basis and epidemiological implications.

Walking Speed and Mortality: An Updated Systematic Review

OBJECTIVE

The aim of our systematic review was to update the current evidence on the association between slow walking speed (WS) and mortality, expanding the current knowledge available in the literature.

METHODS

A systematic review of the published data on the association of WS and mortality was carried out by searching on PubMed and ISI Web of Knowledge databases.

RESULTS

From a title and abstract analysis, 61 articles were included that met the prespecified criteria. After a full-text analysis, 6 articles were excluded and the remaining articles accounted for 120,838 patients and > 25,148 deaths were registered. The duration of follow-ups ranged between 2 and 21 years. In general, studies have shown a consistent association between WS and mortality from all causes.

CONCLUSIONS

WS showed continuous and consistent evidence to be a good predictor of mortality. As such, our study supports the use of this tool in clinical practice as a way to improve health care.

Frailty Status Among the Elderly of Different Genders and the Death Risk: A Follow-Up Study

Background: Frailty in the elderly population is currently a frontier and focus in the field of health and aging. The goal of this study was to explore the frailty status among the elderly of different genders and its influence on the risk of death during 11 years.

Methods: Frailty index (FI) was used to evaluate the frailty status in the elderly based on the baseline data conducted in 2009; and death as outcome variables collected in 2020 were analyzed. The difference of the frailty level and mortality of different genders was compared. Cox regression and Kaplan–Meier curves were applied to evaluate the influence on the risk of death and the 11-year survival of the elderly at different level of frailty, respectively.

Results: Totally, 1,246 elderly people were recruited. The mortality in men (43.7%, 227/519) was statistically higher than that in women (34.3%, 249/727) (x² = 11.546, P = 0.001). Deficits accumulated exponentially with age, and at all ages, women accumulated more deficits than do men on average (B = 0.030 vs. 0.028, t = 4.137, P = 0.023). For any given level of frailty, the mortality rate is higher in men than in women, and the difference in mortality between genders reached the peak when FI value was 0.26. Cox regression analysis showed that FI value had a greater impact on the risk of death in older men (HR = 1.171, 95%CI: 1.139~1.249)than that in older women (HR = 1.119, 95%CI: 1.039~1.137). Survival analysis showed that the median 11-year survival time in women was longer than that in men (95.26 vs. 89.52 months, Log rank = 9.249, P = 0.002). Kaplan–Meier curves showed that the survival rate decreased with the increase of frailty, and at the same level of frailty, survival time in older women was longer than that in older men, except for severe frailty (FI ≥ 0.5).

Conclusion: The frailty status and its influence on mortality are different among the older people of different genders; therefore, specific interventions for frailty should be conducted in the elderly population of different genders, as well as of different degrees of frailty.

Neuronal biomarkers of Parkinson's disease are present in healthy aging

The prevalence of Parkinson's disease (PD) increases with aging and both processes share similar cellular mechanisms and alterations in the dopaminergic system. Yet it remains to be investigated whether aging can also demonstrate electrophysiological neuronal signatures typically associated with PD. Previous work has shown that phase-amplitude coupling (PAC) between the phase of beta oscillations and the amplitude of gamma oscillations as well as beta bursts features can serve as electrophysiological biomarkers for PD. Here we hypothesize that these metrics are also present in apparently healthy elderly subjects. Using resting state multichannel EEG measurements, we show that PAC between beta oscillation and broadband gamma activity (50-150 Hz) is elevated in a group of elderly (59-77 years) compared to young volunteers (20-35 years) without PD. Importantly, the increase of PAC is statistically significant even after ruling out confounds relating to changes in spectral power and non-sinusoidal shape of beta oscillation. Moreover, a trend for a higher percentage of longer beta bursts (> 0.2 s) along with the increase in their incidence rate is also observed for elderly subjects. Using inverse modeling, we further show that elevated PAC and longer beta bursts are most pronounced in the sensorimotor areas. Moreover, we show that PAC and longer beta bursts might reflect distinct mechanisms, since their spatial patterns only partially overlap and the correlation between them is weak. Taken together, our findings provide novel evidence that electrophysiological biomarkers of PD may already occur in apparently healthy elderly subjects. We hypothesize that PAC and beta bursts characteristics in aging might reflect a pre-clinical state of PD and suggest their predictive value to be tested in prospective longitudinal studies.

Epigenetic age is associated with baseline and 3-year change in frailty in the Canadian Longitudinal Study on Aging

BACKGROUND

The trajectory of frailty in older adults is important to public health; therefore, markers that may help predict this and other important outcomes could be beneficial. Epigenetic clocks have been developed and are associated with various health-related outcomes and sociodemographic factors, but associations with frailty are poorly described. Further, it is uncertain whether newer generations of epigenetic clocks, trained on variables other than chronological age, would be more strongly associated with frailty than earlier developed clocks. Using data from the Canadian Longitudinal Study on Aging (CLSA), we tested the hypothesis that clocks trained on phenotypic markers of health or mortality (i.e., Dunedin PoAm, GrimAge, PhenoAge and Zhang in Nat Commun 8:14617, 2017) would best predict changes in a 76-item frailty index (FI) over a 3-year interval, as compared to clocks trained on chronological age (i.e., Hannum in Mol Cell 49:359-367, 2013, Horvath in Genome Biol 14:R115, 2013, Lin in Aging 8:394-401, 2016, and Yang Genome Biol 17:205, 2016).

RESULTS

We show that in 1446 participants, phenotype/mortality-trained clocks outperformed age-trained clocks with regard to the association with baseline frailty (mean = 0.141, SD = 0.075), the greatest of which is GrimAge, where a 1-SD increase in ΔGrimAge (i.e., the difference from chronological age) was associated with a 0.020 increase in frailty (95% CI 0.016, 0.024), or ~ 27% relative to the SD in frailty. Only GrimAge and Hannum (Mol Cell 49:359-367, 2013) were significantly associated with change in frailty over time, where a 1-SD increase in ΔGrimAge and ΔHannum 2013 was associated with a 0.0030 (95% CI 0.0007, 0.0050) and 0.0028 (95% CI 0.0007, 0.0050) increase over 3 years, respectively, or ~ 7% relative to the SD in frailty change.

CONCLUSION

Both prevalence and change in frailty are associated with increased epigenetic age. However, not all clocks are equally sensitive to these outcomes and depend on their underlying relationship with chronological age, healthspan and lifespan. Certain clocks were significantly associated with relatively short-term changes in frailty, thereby supporting their utility in initiatives and interventions to promote healthy 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.

Contributing factors to advanced brain aging in depression and anxiety disorders

Depression and anxiety are common and often comorbid mental health disorders that represent risk factors for aging-related conditions. Brain aging has shown to be more advanced in patients with major depressive disorder (MDD). Here, we extend prior work by investigating multivariate brain aging in patients with MDD, anxiety disorders, or both, and examine which factors contribute to older-appearing brains. Adults aged 18-57 years from the Netherlands Study of Depression and Anxiety underwent structural MRI. A pretrained brain-age prediction model based on >2000 samples from the ENIGMA consortium was applied to obtain brain-predicted age differences (brain PAD, predicted brain age minus chronological age) in 65 controls and 220 patients with current MDD and/or anxiety. Brain-PAD estimates were associated with clinical, somatic, lifestyle, and biological factors. After correcting for antidepressant use, brain PAD was significantly higher in MDD (+2.78 years, Cohen's d = 0.25, 95% CI -0.10-0.60) and anxiety patients (+2.91 years, Cohen's d = 0.27, 95% CI -0.08-0.61), compared with controls. There were no significant associations with lifestyle or biological stress systems. A multivariable model indicated unique contributions of higher severity of somatic depression symptoms (b = 4.21 years per unit increase on average sum score) and antidepressant use (-2.53 years) to brain PAD. Advanced brain aging in patients with MDD and anxiety was most strongly associated with somatic depressive symptomatology. We also present clinically relevant evidence for a potential neuroprotective antidepressant effect on the brain-PAD metric that requires follow-up in future research.

Determination of Biological Age: Geriatric Assessment vs Biological Biomarkers

Purpose of Review

Biological age is the concept of using biophysiological measures to more accurately determine an individual’s age-related risk of adverse outcomes. Grading of the degree of frailty and measuring biomarkers are distinct methods of measuring biological age. This review compares these strategies for estimating biological age for clinical purposes.

Recent Findings

The degree of frailty predicts susceptibility to adverse outcomes independently of chronological age. The utility of this approach has been demonstrated across a range of clinical contexts. Biomarkers from various levels of the biological aging process are improving in accuracy, with the potential to identify aberrant aging trajectories before the onset of clinically manifest frailty.

Summary

Grading of frailty is a demonstrably, clinically, and research-relevant proxy estimate of biological age. Emerging biomarkers can supplement this approach by identifying accelerated aging before it is clinically apparent. Some biomarkers may even offer a means by which interventions to reduce the rate of aging can be developed.

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

Biological age captures some of the variance in life expectancy for which chronological age is not accountable, and it quantifies the heterogeneity in the presentation of the aging phenotype in various individuals. Among the many quantitative measures of biological age, the mathematically uncomplicated frailty/deficit index is simply the proportion of the total health deficits in various health items surveyed in different individuals. We used 3 different statistical methods that are popular in machine learning to select 17-28 health items that together are highly predictive of survival/mortality, from independent study cohorts. From the selected sets, we calculated frailty indexes and Klemera-Doubal's biological age estimates, and then compared their mortality prediction performance using Cox proportional hazards regression models. Our results indicate that the frailty index outperforms age and Klemera-Doubal's biological age estimates, especially among the oldest old who are most prone to biological aging-caused mortality. We also showed that a DNA methylation index, which was generated by applying the frailty/deficit index calculation method to 38 CpG sites that were selected using the same machine learning algorithms, can predict mortality even better than the best performing frailty index constructed from health, function, and blood chemistry.