Association of healthy aging with parental longevity

Genetic and phenotypic associations of frailty with cardiovascular indicators and behavioral characteristics

INTRODUCTION

Frailty Index (FI) is a common measure of frailty, which has been advocated as a routine clinical test by many guidelines. The genetic and phenotypic relationships of FI with cardiovascular indicators (CIs) and behavioral characteristics (BCs) are unclear, which has hampered ability to monitor FI using easily collected data.

OBJECTIVES

This study is designed to investigate the genetic and phenotypic associations of frailty with CIs and BCs, and further to construct a model to predict FI.

METHOD

Genetic relationships of FI with 288 CIs and 90 BCs were assessed by the cross-trait LD score regression (LDSC) and Mendelian randomization (MR). The phenotypic data of these CIs and BCs were integrated with a machine-learning model to predict FI of individuals in UK-biobank. The relationships of the predicted FI with risks of type 2 diabetes (T2D) and neurodegenerative diseases were tested by the Kaplan-Meier estimator and Cox proportional hazards model.

RESULTS

MR revealed putative causal effects of seven CIs and eight BCs on FI. These CIs and BCs were integrated to establish a model for predicting FI. The predicted FI is significantly correlated with the observed FI (Pearson correlation coefficient = 0.660, P-value = 4.96 × 10-62). The prediction model indicated "usual walking pace" contributes the most to prediction. Patients who were predicted with high FI are in significantly higher risk of T2D (HR = 2.635, P < 2 × 10-16) and neurodegenerative diseases (HR = 2.307, P = 1.62 × 10-3) than other patients.

CONCLUSION

This study supports associations of FI with CIs and BCs from genetic and phenotypic perspectives. The model that is developed by integrating easily collected CIs and BCs data in predicting FI has the potential to monitor disease risk.

Unraveling the relationship between high-sensitivity C-reactive protein and frailty: evidence from longitudinal cohort study and genetic analysis

BACKGROUND

This study aimed to investigate the association of high-sensitivity C-reactive protein (hs-CRP) with incident frailty as well as its effects on pre-frailty progression and regression among middle-aged and older adults.

METHODS

Based on the frailty index (FI) calculated with 41 items, 6890 eligible participants without frailty at baseline from China Health and Retirement Longitudinal Study (CHARLS) were categorized into health, pre-frailty, and frailty groups. Logistic regression models were used to estimate the longitudinal association between baseline hs-CRP and incident frailty. Furthermore, a series of genetic approaches were conducted to confirm the causal relationship between CRP and frailty, including Linkage disequilibrium score regression (LDSC), pleiotropic analysis, and Mendelian randomization (MR). Finally, we evaluated the association of hs-CRP with pre-frailty progression and regression.

RESULTS

The risk of developing frailty was 1.18 times (95% CI: 1.03-1.34) higher in participants with high levels of hs-CRP at baseline than low levels of hs-CRP participants during the 3-year follow-up. MR analysis suggested that genetically determined hs-CRP was potentially positively associated with the risk of frailty (OR: 1.06, 95% CI: 1.03-1.08). Among 5241 participants with pre-frailty at baseline, we found pre-frailty participants with high levels of hs-CRP exhibit increased odds of progression to frailty (OR: 1.39, 95% CI: 1.09-1.79) and decreased odds of regression to health (OR: 0.84, 95% CI: 0.72-0.98) when compared with participants with low levels of hs-CRP.

CONCLUSIONS

Our results suggest that reducing systemic inflammation is significant for developing strategies for frailty prevention and pre-frailty reversion in the middle-aged and elderly population.

Utilizing multimodal approach to identify candidate pathways and biomarkers and predicting frailty syndrome in individuals from UK Biobank

Frailty, a prevalent clinical syndrome in aging adults, is characterized by poor health outcomes, represented via a standardized frailty-phenotype (FP), and Frailty Index (FI). While the relevance of the syndrome is gaining awareness, much remains unclear about its underlying biology. Further elucidation of the genetic determinants and possible underlying mechanisms may help improve patients' outcomes allowing healthy aging.Genotype, clinical and demographic data of subjects (aged 60-73 years) from UK Biobank were utilized. FP was defined on Fried's criteria. FI was calculated using electronic-health-records. Genome-wide-association-studies (GWAS) were conducted and polygenic-risk-scores (PRS) were calculated for both FP and FI. Functional analysis provided interpretations of underlying biology. Finally, machine-learning (ML) models were trained using clinical, demographic and PRS towards identifying frail from non-frail individuals.Thirty-one loci were significantly associated with FI accounting for 12% heritability. Seventeen of those were known associations for body-mass-index, coronary diseases, cholesterol-levels, and longevity, while the rest were novel. Significant genes CDKN2B and APOE, previously implicated in aging, were reported to be enriched in lipoprotein-particle-remodeling. Linkage-disequilibrium-regression identified specific regulation in limbic-system, associated with long-term memory and cognitive-function. XGboost was established as the best performing ML model with area-under-curve as 85%, sensitivity and specificity as 0.75 and 0.8, respectively.This study provides novel insights into increased vulnerability and risk stratification of frailty syndrome via a multi-modal approach. The findings suggest frailty as a highly polygenic-trait, enriched in cholesterol-remodeling and metabolism and to be genetically associated with cognitive abilities. ML models utilizing FP and FI + PRS were established that identified frailty-syndrome patients with high accuracy.

Effect of Familial Longevity on Frailty and Sarcopenia: A Case-Control Study

Familial longevity confers advantages in terms of health, functionality, and longevity. We sought to assess potential differences in frailty and sarcopenia in older adults according to a parental history of extraordinary longevity. A total of 176 community-dwelling subjects aged 65-80 years were recruited in this observational case-control study, pair-matched 1:1 for gender, age, and place of birth and residence: 88 centenarians' offspring (case group) and 88 non-centenarians' offspring (control group). The main variables were frailty and sarcopenia based on Fried's phenotype and the European Working Group on Sarcopenia in Older People (EWGSOP) definitions, respectively. Sociodemographics, comorbidities, clinical and functional variables, the presence of geriatric syndromes, and laboratory parameters were also collected. Related sample tests were applied, and conditional logistic regression was performed. Cases had a higher percentage of robust patients (31.8% vs. 15.9%), lower percentages of frailty (9.1% vs. 21.6%) and pre-frailty (59.1% vs. 62.5%) (p = 0.001), and lower levels of IL-6 (p = 0.044) than controls. The robust adjusted OR for cases was 3.00 (95% CI = 1.06-8.47, p = 0.038). No significant differences in muscle mass were found. Familial longevity was also associated with less obesity, insomnia, pain, and polypharmacy and a higher education level and total and low-density lipoprotein cholesterol. The results suggest an inherited genetic component in the frailty phenotype, while the sarcopenia association with familial longevity remains challenging.

Comparison between frail and non-frail older adults' gut microbiota: A systematic review and meta-analysis

BACKGROUND

Emerging evidence suggests that the intestinal microbiota (IM) undergoes remodelling as we age, and this impacts the ageing trajectory and mortality in older adults. The aim was to investigate IM diversity differences between frail and non-frail older adults by meta-analysing previous studies.

METHODS

The protocol of this systematic review with meta-analysis was registered on PROSPERO (CRD42021276733). We searched for studies comparing IM diversity of frail and non-frail older adults indexed on PubMed, Embase, Cochrane, and Web of Science in November 2021.

RESULTS

We included 11 studies with 1239 participants, of which 340 were meta-analysed. Frailty was defined by a variety of criteria (i.e. Fried Scale, European Consensus on Sarcopenia). There were no differences in the meta-analyses between the frail and non-frail groups for species richness index (SMD = -0.147; 95% CI = -0.394, 0.100; p = 0.243) and species diversity index (SMD = -0.033; 95% CI = -0.315, 0.250; p = 0.820). However, we identified almost 50 differences between frail and non-frail within the relative abundance of bacteria phyla, families, genera, and species in the primary studies.

CONCLUSIONS

The evidence to prove that there are differences between frail and non-frail IM diversity by meta-analysis is still lacking. The present results suggest that further investigation into the role of specific bacteria, their function, and their influence on the physiopathology of frailty is needed.

Familial aggregation of the aging process: biological age measured in young adult offspring as a predictor of parental mortality

Measures of biological age (BA) integrate information across organ systems to quantify "biological aging," i.e., inter-individual differences in aging-related health decline. While longevity and lifespan aggregate in families, reflecting transmission of genes and environments across generations, little is known about intergenerational continuity of biological aging or the extent to which this continuity may be modified by environmental factors. Using data from the Jerusalem Perinatal Study (JPS), we tested if differences in offspring BA were related to mortality in their parents. We measured BA using biomarker data collected from 1473 offspring during clinical exams in 2007-2009, at age 32 ± 1.1. Parental mortality was obtained from population registry data for the years 2004-2016. We fitted parametric survival models to investigate the associations between offspring BA and parental all-cause and cause-specific mortality. We explored potential differences in these relationships by socioeconomic position (SEP) and offspring sex. Participants' BAs widely varied (SD = 6.95). Among those measured to be biologically older, parents had increased all-cause mortality (HR = 1.10, 95% CI: 1.08, 1.13), diabetes mortality (HR = 1.19, 95% CI: 1.08, 1.30), and cancer mortality (HR = 1.07, 95% CI: 1.02, 1.13). The association with all-cause mortality was stronger for families with low compared with high SEP (P_interaction = 0.04) and for daughters as compared to sons (P_interaction < 0.001). Using a clinical-biomarker-based BA estimate, observable by young adulthood prior to the onset of aging-related diseases, we demonstrate intergenerational continuity of the aging process. Furthermore, variation in this familial aggregation according to household socioeconomic position (SEP) at offspring birth and between families of sons and daughters proposes that the environment alters individuals' aging trajectory set by their parents.

A frailty index based on routine laboratory data predicts increased risk of mortality in Chinese community-dwelling adults aged over 55 years: a five-year prospective study

Background

Frailty can be operationalized based on the accumulation of deficits using a frailty index (FI) and is associated with an increased risk of adverse health outcomes. Here, we aim to compare validity of a FI from laboratory data with that of the common clinical FI for prediction of mortality in adults aged 55 + years, also examine whether combined FI could improve identification of adults aged 55 + years at increased risk of death.

Methods

Data for this analysis were obtained from the Beijing Longitudinal Study of Aging that involved 1,257 community-dwelling Chinese people, aged 55 + years at baseline. The main outcome measure was 5-year mortality. An FI-self-report based on 30 self-reported health-related data was constructed. An FI-lab was developed using laboratory data, in addition to pulse, systolic and diastolic blood pressure, pulse pressure, body mass index (BMI) and waist. A combined FI comprised all items from each FI. Kaplan–Meier survival curve and Cox proportional hazards models were performed to evaluate the risk of each FI on death. The area under receiver operating characteristic(ROC) curves were used to compare the discriminative performance of each FI.

Results

Of 1257 participants, 155 died and 156 lost at the end of the 5-year follow-up. The mean FI-self-report score was 0.11 ± 0.10, the FI-lab score was 0.33 ± 0.14 and FI-combined score was 0.19 ± 0.09. Higher frailty level defined by each FI was associated with higher risk of death. After adjustment for age and sex, Cox proportional hazards models showed that the higher scores of frailty were associated with a higher risk of mortality for each FI, the hazard ratios for the FI-self-report and FI-lab and FI-combined were 1.04 (1.03 to 1.05) and 1.02 (1.01 to 1.03) and 1.05 (1.04 to 1.07), respectively. The areas under the ROC curve were 0.79 (0.77–0.82) for the FI-self-report, 0.77(0.75–0.80) for the FI-lab and 0.81(0.78–0.82) for FI-combined.

Conclusions

A FI from laboratory data can stratify older adults at increased risk of death alone and in combination with FI based on self-report data. Assessment in clinical settings of creating an FI using routine collected laboratory data needs to be further developed.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12877-022-03374-z.

A genome-wide association study of the frailty index highlights brain pathways in ageing

Frailty is a common geriatric syndrome and strongly associated with disability, mortality and hospitalization. Frailty is commonly measured using the frailty index (FI), based on the accumulation of a number of health deficits during the life course. The mechanisms underlying FI are multifactorial and not well understood, but a genetic basis has been suggested with heritability estimates between 30 and 45%. Understanding the genetic determinants and biological mechanisms underpinning FI may help to delay or even prevent frailty. We performed a genome-wide association study (GWAS) meta-analysis of a frailty index in European descent UK Biobank participants (n = 164,610, 60-70 years) and Swedish TwinGene participants (n = 10,616, 41-87 years). FI calculation was based on 49 or 44 self-reported items on symptoms, disabilities and diagnosed diseases for UK Biobank and TwinGene, respectively. 14 loci were associated with the FI (p < 5*10-8 ). Many FI-associated loci have established associations with traits such as body mass index, cardiovascular disease, smoking, HLA proteins, depression and neuroticism; however, one appears to be novel. The estimated single nucleotide polymorphism (SNP) heritability of the FI was 11% (0.11, SE 0.005). In enrichment analysis, genes expressed in the frontal cortex and hippocampus were significantly downregulated (adjusted p < 0.05). We also used Mendelian randomization to identify modifiable traits and exposures that may affect frailty risk, with a higher educational attainment genetic risk score being associated with a lower degree of frailty. Risk of frailty is influenced by many genetic factors, including well-known disease risk factors and mental health, with particular emphasis on pathways in the brain.

A cross-sectional analysis of racial differences in accelerated aging and cognitive function among patients with atrial fibrillation: The SAGE-AF study: Forrester, Accelerated aging and cognitive function

BACKGROUND

Non-Whites are more likely to suffer from cognitive impairment and complications of atrial fibrillation (AF) than Whites, though Whites are more likely to be diagnosed with AF. We examined whether non-Whites with AF are biologically older than Whites with AF and whether accelerated biological aging is associated with cognitive functioning.

METHODS

We used baseline data from the ongoing Systematic Assessment of Geriatric Elements in Atrial Fibrillation prospective cohort study, collected 2016-2020 across ambulatory care practices in Massachusetts and Georgia. Of 1244 enrolled, 974 participants with full biological data were included in the present analysis. Accelerated aging (AccA) was calculated based on a combination of biomarkers associated with age and physiological "wear and tear."

FINDINGS

The main outcome was score on Montreal Cognitive Assessment (MoCA). Non-Whites had 2.9 years more AccA than Whites and higher AccA was associated with a lower MoCA score among both Whites (-0.06, 95% CI: -0.10, -0.03) and non-Whites (-0.14, 95% CI: -0.27, 0.02). This association was significantly greater among non-whites (-0.11, 95% CI: -0.20, -0.01).

INTERPRETATION

Non-White AF patients are functionally "older" than their White counterparts and experience a stronger deleterious association between AccA and cognition. These findings underscore the importance of taking functional age into account when treating patients with AF, particularly non-White patients, to enhance treatment and improve AF outcomes.

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.

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.

Genetics of frailty: A longevity perspective

Frailty is a complex late life phenotype characterized by cumulative declines in multiple physiological systems that increases the risk for disability and mortality. The biological changes associated with aging are risk factors for frailty as well as for complex diseases; whereas longevity is assumed to be an outcome of protective biological mechanisms. Understanding the interplay between biological alterations associated with aging and protective mechanisms associated with longevity in the context of frailty may help guide development of interventions to increase healthspan and promote successful aging. The complexity of these phenotypes and relatively low heritability in studies are the main roadblocks in deciphering genetic mechanisms of these age associated conditions. We review genetic research related to frailty, and discuss the possible intertwined biology of frailty and longevity.

The Frailty Index in centenarians and their offspring

Frailty has been indicated as a way for capturing biological aging of the individual and Frailty Index (FI) may serve for this purpose. This study designed the FI in a cohort of centenarians, their offspring and control subjects sex- and age-matched with offspring. The FI mean value was 0.47 (SD 0.13) in centenarians, 0.15 (SD 0.12) in their offspring, and 0.22 (SD 0.14) in controls (p < 0.001). The difference between offspring and controls was statistically significant (p = 0.003). The correlation between FI and age was significant in offspring (r = 0.46, p < 0.001), close to significance in controls (r = 0.25, p = 0.08) and not significant in centenarians. Our study confirms that FI is a marker of biological age useful to discriminate different degrees of frailty even at extremely advanced age.

Bed rest and accelerated aging in relation to the musculoskeletal and cardiovascular systems and frailty biomarkers: A review

Prolonged bed rest and lifelong physical inactivity cause deleterious effects to multiple physiological systems that appear to hasten aging processes. Many such changes are similar to those seen with microgravity in space, but at a much faster rate. Head down tilt bed rest models are used to study whole-body changes that occur with spaceflight. We propose that bed rest can be used to quantify accelerated human aging in relation to frailty. In particular, frailty as a measure of the accumulation of deficits estimates the variability in aging across systems, and moves away from the traditional single-system approach. Here, we provide an overview of the impact of bed rest on the musculoskeletal and cardiovascular systems as well as frailty-related biological markers and inflammatory cytokines. We also propose future inquiries to study the accumulation of deficits with head down bed rest and bed rest in the clinical setting, specifically to understand how unrepaired and unremoved subclinical and subcellular damage give rise to clinically observable health problems.

Examination of the Dimensions of Biological Age

The concept of biological age has been used more and more frequently in aging research in attempts to measure the progress of the biological aging process as opposed to the simple passage of time. Several approaches to quantify biological age have been utilized, including the use of biomarkers in the form of serum analytes, epigenetic markers, and deficit or frailty indices. Among these methods, the deficit index possesses a theoretical basis grounded in systems biology by incorporating networks, with their emergent properties, to describe the complex aging system. Application of the deficit index in human aging studies points to the increased energetic demands posed by an aging system that is losing integration. Different aspects of mitochondrial function appear to be responsible in males and females. The gut microbiome loses complexity in tandem with the host, as biological age increases, with likely impact on host metabolism and immunity. Specific DNA methylation changes are associated with biological age. They suggest declining connectivity within the aging network, at the cellular level. The deficit/frailty index may account for at least part of the departure at older ages of the observed mortality in the population from the exponential increase modeled by the Gompertz equation.

Associations between a laboratory frailty index and adverse health outcomes across age and sex

OBJECTIVE

Early frailty may be captured by a frailty index (FI) based entirely on vital signs and laboratory tests. Our aim was to examine associations between a laboratory-based FI (FI-Lab) and adverse health outcomes, and investigate how this changed with age.

METHODS

Up to 8988 individuals aged 20+ years from the 2003-2004 and 2005-2006 National Health and Nutrition Examination Survey cohorts were included. Characteristics of the FI-Lab were compared to those of a self-reported clinical FI. Associations between each FI and health care use, self-reported health, and disability were examined in the full sample and across age groups.

RESULTS

Laboratory-based FI scores increased with age but did not demonstrate expected sex differences. Women aged 20-39 years had higher FI scores than men; this pattern reversed after age 60 years. FI-Lab scores were associated with poor self-reported health (odds ratio[95% confidence interval]: 1.46[1.39-1.54]), high health care use (1.35[1.29-1.42]), and high disability (1.41[1.32-1.50]), even among those aged 20-39 years.

CONCLUSION

Higher FI-Lab scores were associated with poor health outcomes at all ages. Associations in the youngest group support the notion that deficit accumulation occurs across the lifespan. FI-Lab scores could be utilized as an early screening tool to identify deficit accumulation at the cellular and molecular level before they become clinically visible.

CLOCK gene polymorphisms and quality of aging in a cohort of nonagenarians - The MUGELLO Study

A total of 356 elderly subjects [257F; 88–106 years] were genotyped for three polymorphisms of the CLOCK gene by TaqMan real-time PCR approach, in order to find associations with quality of aging. Subjects homozygous for the minor allele of rs1801260 were less frequently overweight (p = 0.046), had higher fasting glucose levels (p = 0.037), better scores at the Clock Drawing Test (CDT) (p = 0.047) and worse scores at the Geriatric Depression Scale (p = 0.032). Subjects homozygous for the minor allele of rs11932595 showed higher fasting glucose levels (p = 0.044) and better scores at CDT (p = 0.030). Conversely, subjects homozygous for the minor allele of rs4580704 showed higher triglyceride (p = 0.012), and LDL-cholesterol levels (p = 0.44), and a greater adherence to the Mediterranean diet (MD) (p = 0.044). In addition, AAC, AAG, GGC and AGC (rs1801260–rs11932595–rs4580704) haplotypes were analyzed: AAG was associated with higher risk of overweight (p = 0.008), hypertriglyceridemia (p = 0.040) and hypercholesterolemia (p = 0.036); GGC with lower risk of hyperglycemia (p = 0.022), better sleep pattern (p = 0.001) and with better score at mini-mental state examination (p = 0.010); AGC with lower risk of depression (p = 0.026) and AAC with lower adherence to the MD (p = 0.028). Therefore, CLOCK gene polymorphisms let us hypothesize an involvement in the quality of aging in a cohort of nonagenarians.

Memory Self-Efficacy and Beliefs about Memory and Aging in Oldest-Old Adults in the Louisiana Healthy Aging Study (LHAS)

Background/Study Context. Adaptation to normative age-related declines in memory is an important but understudied aspect of successful aging. The purpose of the present study was to shed new light on memory self-efficacy and beliefs about memory and aging as two integral aspects of adult cognition with relevance to successful aging.

METHODS

Young (19 to 27 years) and community-dwelling older adults (60 to 94 years) from the Louisiana Healthy Aging Study (LHAS) completed an adapted Memory Functioning Questionnaire (MFQ) which includes a memory self-efficacy subscale, the Memory Controllability Inventory (MCI), and the Aging Concerns Scale (ACS).

RESULTS

Nonagenarians' self-reported memory and beliefs about memory and aging were of central interest. We compared their responses to three younger reference groups to examine hypothesized differences in self-reported memory and beliefs about memory and aging in very late life. Results yielded age effects for most of the MFQ and MCI subscales demonstrating more positive subjective views about memory functioning and control over memory for the young adults. Correlation and regression analyses were conducted to isolate factors that may be associated with memory self-efficacy. Age, symptoms of depression, and memory control beliefs accounted for approximately half of the variance in memory self-efficacy ratings.

CONCLUSION

These data indicate that although memory self-efficacy may be age sensitive, we detected no differences in subjective views across the three older groups. Implications for cognitive adaptability and successful aging are considered.

DNA methylation associated with healthy aging of elderly twins

Variation in healthy aging and lifespan is ascribed more to various non-genetic factors than to inherited genetic determinants, and a major goal in aging research is to reveal the epigenetic basis of aging. One approach to this goal is to find genomic sites or regions where DNA methylation correlates with biological age. Using health data from 134 elderly twins, we calculated a frailty index as a quantitative indicator of biological age, and by applying the Infinium HumanMethylation450K BeadChip technology to their leukocyte DNA samples, we obtained quantitative DNA methylation data on genome-wide CpG sites. We analyzed the health and epigenome data by taking two independent associative approaches: the parametric regression-based approach and a non-parametric machine learning approach followed by GO ontology analysis. Our results indicate that DNA methylation at CpG sites in the promoter region of PCDHGA3 is associated with biological age. PCDHGA3 belongs to clustered protocadherin genes, which are all located in a single locus on chromosome 5 in human. Previous studies of the clustered protocadherin genes showed that (1) DNA methylation is associated with age or age-related phenotypes; (2) DNA methylation can modulate gene expression; (3) dysregulated gene expression is associated with various pathologies; and (4) DNA methylation patterns at this locus are associated with adverse lifetime experiences. All these observations suggest that DNA methylation at the clustered protocadherin genes, including PCDHGA3, is a key mediator of healthy aging.

Methylomic survival predictors, frailty, and mortality

Survival predictors are of potential use for informing on biological age and targeting prevention of aging-related morbidity. We assessed associations of 2 novel methylomic survival indicators, a methylation-based mortality risk score (MRscore) and the epigenetic clock-derived age acceleration (AA), with a well-known survival predictor, frailty index (FI), and compared the 3 indicators in mortality prediction. In a large population-based cohort with 14-year follow-up, we found both MRscore and AA to be independently associated with FI, but the association was much stronger for MRscore than for AA. Although all 3 indicators were individually associated with all-cause mortality, robust associations only persisted for MRscore and FI when simultaneously including the 3 indicators in regression models, with hazard ratios (95% CI) of 1.91 (1.63-2.22), 1.37 (1.25-1.51), and 1.05 (0.90-1.22), respectively, per standard deviation increase of MRscore, FI, and AA. Prediction error curves, Harrell's C-statistics, and time-dependent AUCs all showed higher predictive accuracy for MRscore than for FI and AA. These findings were validated in independent samples. Our study demonstrates the ability of the MRscore to strongly enhance survival prediction beyond established markers of biological age, such as FI and AA, and it thus bears potential of a surrogate endpoint for clinical research and intervention.

Association between Sleep Patterns and Health in Families with Exceptional Longevity

BACKGROUND

Sleep patterns such as longer sleep duration or napping are associated with poor health outcomes. Although centenarians and their offspring demonstrate a delayed onset of age-related diseases, it is not known whether they have healthier sleep patterns or are protected against the negative effects of sleep disturbances.

METHODS

Data on sleep patterns and health history were collected from Ashkenazi Jewish subjects of the Longevity Genes Project using standardized questionnaires. Participants included individuals with exceptional longevity (centenarians) with preserved cognition (n = 348, median age 97 years), their offspring (n = 513, median age 69 years), and controls (n = 199) age-matched to the offspring. Centenarians reported on their sleep patterns at age 70, while the offspring and controls on their current sleep patterns. Biochemical parameters were measured at baseline. Models were adjusted for age, sex, BMI, and use of sleep medication.

RESULTS

The offspring and controls reported similar sleep patterns, with 33% sleeping ≥8 h and 17% napping in each group. At age 70, centenarians were more likely to have slept ≥8 h (55%) and to have napped (28%) compared with offspring and controls, p < 0.01. Among centenarians, no association was noted between sleep patterns and health outcomes. Sleeping for ≥8 h was associated with lower high-density lipoprotein cholesterol levels in the offspring and controls, and with insulin resistance in the offspring, but not with diabetes. Napping was associated with insulin resistance among the controls (p < 0.01), but not the offspring. Controls, but not offspring, who napped were 2.79 times more likely to have one or more of the following diseases: hypertension, myocardial infarction, stroke, or diabetes (OR 2.79, 95% CI 1.08-7.21, p = 0.04).

CONCLUSION

Despite being more likely to exhibit risky sleep patterns at age 70 compared with the offspring and controls, the centenarians were protected from age-related morbidities. The offspring of centenarians did exhibit metabolic disturbances in association with less healthy sleep patterns; however, unlike the controls, they were much less likely to manifest age-related diseases. This suggests that offspring may have inherited resilience genotypes from their centenarian parents that protect them against the harmful effects of sleep disturbances.

Biological Aging and the Human Gut Microbiota

The human gastrointestinal microbiota plays a key homeostatic role in normal functioning of physiologic processes commonly undermined by aging. We used a previously validated 34-item frailty index (FI34) to identify changes in gut microbiota community structure associated with biological age of community-dwelling adults. Stool 16S rRNA cDNA libraries from 85 subjects ranging in age (43-79) and FI34 score (0-0.365) were deep sequenced, denoised, and clustered using DADA2. Subject biological age but not chronological age correlated with a decrease in stool microbial diversity. Specific microbial genera were differentially abundant in the lower, middle, and upper 33rd percentiles of biological age. Using Sparse Inverse Covariance Estimation for Ecological Association and Statistical Inference (SPIEC-EASI) and Weighted Gene Co-Expression Network Analysis (WGCNA), we identified modules of coabundant microbial genera that distinguished biological from chronological aging. A biological age-associated module composed of Eggerthella, Ruminococcus, and Coprobacillus genera was robust to correction for subject age, sex, body mass index, antibiotic usage, and other confounders. Subject FI34 score positively correlated with the abundance of this module, which exhibited a distinct inferred metagenome as predicted by Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt). We conclude that increasing biological age in community-dwelling adults is associated with gastrointestinal dysbiosis.

Phenome and genome based studies into human ageing and longevity: An overview

Human ageing is an extremely personal process leading across the life course of individuals to large population heterogeneity in the decline of functional capacity, health and lifespan. The extremes of this process are witnessed by the healthy vital 100-year-olds on one end and the 60-year-olds suffering from multiple morbid conditions on the other end of the spectrum. Molecular studies into the basis of this heterogeneity have focused on a range of endpoints and methodological approaches. The phenotype definitions most prominently investigated in these studies are either lifespan-related or biomarker based indices of the biological ageing rate of individuals and their tissues. Unlike for many complex, age-related diseases, consensus on the ultimate set of multi-biomarker ageing or lifespan-related phenotypes for genetic and genomic studies has not been reached yet. Comparable to animal models, hallmarks of age-related disease risk, healthy ageing and longevity include immune and metabolic pathways. Potentially novel genomic regions and pathways have been identified among many (epi)genomic studies into chronological age and studies into human lifespan regulation, with APOE and FOXO3A representing yet the most robust loci. Functional analysis of a handful of genes in cell-based and animal models is ongoing. The way forward in human ageing and longevity studies seems through improvements in the interpretation of the biology of the genome, in application of computational and systems biology, integration with animal models and by harmonization of repeated phenotypic and omics measures in longitudinal and intervention studies. This article is part of a Special Issue entitled: Model Systems of Aging - edited by "Houtkooper Riekelt".

A frailty index from common clinical and laboratory tests predicts increased risk of death across the life course

A frailty index (FI) based entirely on common clinical and laboratory tests might offer scientific advantages in understanding ageing and pragmatic advantages in screening. Our main objective was to compare an FI based on common laboratory tests with an FI based on self-reported data; we additionally investigated if the combination of subclinical deficits with clinical ones increased the ability of the FI to predict mortality. In this secondary analysis of the 2003–2004 and 2005–2006 National Health and Nutrition Examination Survey data, 8888 individuals aged 20+ were evaluated. Three FIs were constructed: a 36-item FI using self-reported questionnaire data (FI-Self-report); a 32-item FI using data from laboratory test values plus pulse and blood pressure measures (FI-Lab); and a 68-item FI that combined all items from each index (FI-Combined). The mean FI-Lab score was 0.15 ± 0.09, the FI-Self-report was 0.11 ± 0.11 and FI-Combined was 0.13 ± 0.08. Each index showed some typical FI characteristics (skewed distribution with long right tail, non-linear increase with age). Even so, there were fewer people with low frailty levels and a slower increase with age for the FI-Lab compared to the FI-Self-report. Higher frailty level was associated with higher risk of death, although it was strongest at older ages. Both FI-Lab and FI-Self-report remained significant in a combined model predicting death. The FI-Lab was feasible and valid, demonstrating that even subclinical deficit accumulation increased mortality risk. This suggests that deficit accumulation, from the subcellular to the clinically visible is a useful construct that may advance our understanding of the ageing process.

Adaptation to metabolic dysfunction during aging: Making the best of a bad situation

Mitochondria play a central role in energy metabolism in the process of oxidative phosphorylation. As importantly, they are key in several anabolic processes, including amino acid biosynthesis, nucleotide biosynthesis, heme biosynthesis, and the formation of iron‑sulfur clusters. Mitochondria are also engaged in waste removal in the urea cycle. Their activity can lead to the formation of reactive oxygen species which have damaging effects in the cell. These organelles are dynamic, undergoing cycles of fission and fusion which can be coupled to their removal by mitophagy. In addition to these widely recognized processes, mitochondria communicate with other subcellular compartments. Various components of mitochondrial complexes are encoded by either the nuclear or the mitochondrial genome necessitating coordination between these two organelles. This article reviews another form of communication between the mitochondria and the nucleus, in which the dysfunction of the former triggers changes in the expression of nuclear genes to compensate for it. The most extensively studied of these signaling pathways is the retrograde response whose effectors and downstream targets have been characterized. This response extends yeast replicative lifespan by adapting the organism to the mitochondrial dysfunction. Similar responses have been found in several other organisms, including mammals. Declining health and function during human aging incurs energetic costs. This compensation plays out differently in males and females, and variation in nuclear genes whose products affect mitochondrial function influences the outcome. Thus, the theme of mitochondria-nucleus communication as an adaptive response during aging appears very widespread.

Metabolic and Genetic Markers of Biological Age

Biological age is a concept that takes into account the heterogeneity of the aging process in different individuals that results in differences in survival and variations in relative health. Any measure of biological age must be better than chronological age at predicting mortality. Several quantitative measures of biological age have been developed. Among them are frailty indices, one of which called FI₃₄ is discussed here in greater detail. FI₃₄ increases exponentially with age reflecting decline in health and function ability. It readily depicts different patterns and trajectories of aging, and it is moderately heritable. Thus, it has been used to identify a genomic region on chromosome 12 associated with healthy aging. FI₃₄ has also been useful in describing the metabolic characteristics of this phenotype, revealing both sex and genetic differences. These differences give rise to specific, testable models regarding healthy aging, which involve cell and tissue damage and mitochondrial metabolism. FI₃₄ has been directly compared to various metrics based on DNA methylation as a predictor of mortality, demonstrating that it outperforms them uniformly. This and other frailty indices take a top-down, systems based view of aging that is cognizant of the integrated function of the complex aging system.

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

The measurement of biological age as opposed to chronological age is important to allow the study of factors that are responsible for the heterogeneity in the decline in health and function ability among individuals during aging. Various measures of biological aging have been proposed. Frailty indices based on health deficits in diverse body systems have been well studied, and we have documented the use of a frailty index (FI₃₄) composed of 34 health items, for measuring biological age. A different approach is based on leukocyte DNA methylation. It has been termed DNA methylation age, and derivatives of this metric called age acceleration difference and age acceleration residual have also been employed. Any useful measure of biological age must predict survival better than chronological age does. Meta-analyses indicate that age acceleration difference and age acceleration residual are significant predictors of mortality, qualifying them as indicators of biological age. In this article, we compared the measures based on DNA methylation with FI₃₄. Using a well-studied cohort, we assessed the efficiency of these measures side by side in predicting mortality. In the presence of chronological age as a covariate, FI₃₄ was a significant predictor of mortality, whereas none of the DNA methylation age-based metrics were. The outperformance of FI₃₄ over DNA methylation age measures was apparent when FI₃₄ and each of the DNA methylation age measures were used together as explanatory variables, along with chronological age: FI₃₄ remained significant but the DNA methylation measures did not. These results indicate that FI₃₄ is a robust predictor of biological age, while these DNA methylation measures are largely a statistical reflection of the passage of chronological time.

Single nucleotide polymorphisms linked to mitochondrial uncoupling protein genes UCP2 and UCP3 affect mitochondrial metabolism and healthy aging in female nonagenarians

Energy expenditure decreases with age, but in the oldest-old, energy demand for maintenance of body functions increases with declining health. Uncoupling proteins have profound impact on mitochondrial metabolic processes; therefore, we focused attention on mitochondrial uncoupling protein genes. Alongside resting metabolic rate (RMR), two SNPs in the promoter region of UCP2 were associated with healthy aging. These SNPs mark potential binding sites for several transcription factors; thus, they may affect expression of the gene. A third SNP in the 3'-UTR of UCP3 interacted with RMR. This UCP3 SNP is known to impact UCP3 expression in tissue culture cells, and it has been associated with body weight and mitochondrial energy metabolism. The significant main effects of the UCP2 SNPs and the interaction effect of the UCP3 SNP were also observed after controlling for fat-free mass (FFM) and physical-activity related energy consumption. The association of UCP2/3 with healthy aging was not found in males. Thus, our study provides evidence that the genetic risk factors for healthy aging differ in males and females, as expected from the differences in the phenotypes associated with healthy aging between the two sexes. It also has implications for how mitochondrial function changes during aging.

Programmed Cell Death Genes Are Linked to Elevated Creatine Kinase Levels in Unhealthy Male Nonagenarians

Declining health in the oldest-old takes an energy toll for the simple maintenance of body functions. The underlying mechanisms, however, differ in males and females. In females, the declines are explained by loss of muscle mass; but this is not the case in males, in whom they are associated with increased levels of circulating creatine kinase. This relationship raises the possibility that muscle damage rather than muscle loss is the cause of the increased energy demands of unhealthy aging in males. We have now examined factors that contribute to the increase in creatine kinase. Much of it (60%) can be explained by a history of cardiac problems and lower kidney function, while being mitigated by moderate physical activity, reinforcing the notion that tissue damage is a likely source. In a search for genetic risk factors associated with elevated creatine kinase, the Ku70 gene XRCC6 and the ceramide synthase gene LASS1 were investigated because of their roles in telomere length and longevity and healthy aging, respectively. Single nucleotide polymorphisms in these two genes were independently associated with creatine kinase levels. The XRCC6 variant was epistatic to one of the LASS1 variants but not to the other. These gene variants have potential regulatory activity. Ku70 is an inhibitor of the proapoptotic Bax, while the product of Lass1, ceramide, operates in both caspase-dependent and -independent pathways of programmed cell death, providing a potential cellular mechanism for the effects of these genes on tissue damage and circulating creatine kinase.

Non-coding genomic regions possessing enhancer and silencer potential are associated with healthy aging and exceptional survival

We have completed a genome-wide linkage scan for healthy aging using data collected from a family study, followed by fine-mapping by association in a separate population, the first such attempt reported. The family cohort consisted of parents of age 90 or above and their children ranging in age from 50 to 80. As a quantitative measure of healthy aging, we used a frailty index, called FI34, based on 34 health and function variables. The linkage scan found a single significant linkage peak on chromosome 12. Using an independent cohort of unrelated nonagenarians, we carried out a fine-scale association mapping of the region suggestive of linkage and identified three sites associated with healthy aging. These healthy-aging sites (HASs) are located in intergenic regions at 12q13-14. HAS-1 has been previously associated with multiple diseases, and an enhancer was recently mapped and experimentally validated within the site. HAS-2 is a previously uncharacterized site possessing genomic features suggestive of enhancer activity. HAS-3 contains features associated with Polycomb repression. The HASs also contain variants associated with exceptional longevity, based on a separate analysis. Our results provide insight into functional genomic networks involving non-coding regulatory elements that are involved in healthy aging and longevity.

Quantitative measures of healthy aging and biological age

Numerous genetic and non-genetic factors contribute to aging. To facilitate the study of these factors, various descriptors of biological aging, including 'successful aging' and 'frailty', have been put forth as integrative functional measures of aging. A separate but related quantitative approach is the 'frailty index', which has been operationalized and frequently used. Various frailty indices have been constructed. Although based on different numbers and types of health variables, frailty indices possess several common properties that make them useful across different studies. We have been using a frailty index termed FI34 based on 34 health variables. Like other frailty indices, FI34 increases non-linearly with advancing age and is a better indicator of biological aging than chronological age. FI34 has a substantial genetic basis. Using FI34, we found elevated levels of resting metabolic rate linked to declining health in nonagenarians. Using FI34 as a quantitative phenotype, we have also found a genomic region on chromosome 12 that is associated with healthy aging and longevity.

An elevation of resting metabolic rate with declining health in nonagenarians may be associated with decreased muscle mass and function in women and men, respectively

Previously, we showed that FI34, a frailty index based on 34 health and function ability variables, is heritable and a reliable phenotypic indicator of healthy aging. We have now examined the relationship between major components of energy expenditure and the FI34 in participants of the Louisiana Healthy Aging Study. Resting metabolic rate was associated with FI34, even after adjustment for fat-free mass, fat mass, age, sex, thyroid hormones, and insulin-like growth factor 1 levels, in multiple regression analyses. In contrast, there was no association between total daily energy expenditure and FI34. Circulating creatine phosphokinase, a clinical marker of muscle damage, was also significantly associated with FI34. However, these associations of resting metabolic rate with FI34 were restricted to the oldest old (≥90 years) and absent in younger age groups. In oldest old men, the association of FI34 with creatine phosphokinase persisted, whereas in the oldest old women, only the association with resting metabolic rate pertained with the appearance of an effect of body size and composition. These results point toward an increasing metabolic burden for the maintenance of homeodynamics as health declines in nonagenarians, and this has implications for contraction of metabolic reserve that may potentially accelerate the path to disability.

Frailty Index in Europeans: association with determinants of health

AIM

The Frailty Index (FI) summarizes differences in health status within individuals, and the determinants of health drive that variability. The aim of the present study was to investigate the influence of education, income, smoking, alcohol intake and parental longevity on the FI variability in subjects of the same chronological age group.

METHODS

Analyses were based on a 40-item FI based on the first wave of the Survey of Health, Aging and Retirement in Europe (SHARE, http://www.share-project.org/), including 29 905 participants aged ≥50 years from 12 countries. For each sex, the sample was divided into age categories (50s, 60s, 70s, 80s and ≥90), and FI quartiles within age categories were calculated. Multivariate ordinal regressions were computed to assess the relative contribution of the health determinants on the FI quartiles in each age group.

RESULTS

In women, the most significant multivariate predictors were years of education (odds ratios [OR] around 0.9), and difficulties making ends meet (OR between 1.8 and 2.1). In men, the most significant multivariate predictors were years of education (OR around 0.9), difficulties making ends meet (OR between 1.6 and 2.1), mother's age at death (OR under 1), and father's age at death (OR under 1).

CONCLUSIONS

Consistently with the literature, education and income explained, in both sexes, cross-sectional variability in FI in subjects of the same chronological age group. The influence of parental longevity seemed to be greater in men, which mirrors previous studies showing that genetic factors might have a higher impact on longevity in men.