Aging is a complex biological process characterized by hallmark features accumulating over the life course, shaping the individual's aging trajectory and subsequent disease risks. There is ...substantial individual variability in the aging process between men and women. In general, women live longer than men, consistent with lower biological ages as assessed by molecular biomarkers, but there is a paradox. Women are frailer and have worse health at the end of life, while men still perform better in physical function examinations. Moreover, many age-related diseases show sex-specific patterns. In this review, we aim to summarize the current knowledge on sexual dimorphism in human studies, with support from animal research, on biological aging and illnesses. We also attempt to place it in the context of the theories of aging, as well as discuss the explanations for the sex differences, for example, the sex-chromosome linked mechanisms and hormonally driven differences.
Biological age measurements (BAs) assess aging-related physiological change and predict health risks among individuals of the same chronological age (CA). Multiple BAs have been proposed and are well ...studied individually but not jointly. We included 845 individuals and 3973 repeated measurements from a Swedish population-based cohort and examined longitudinal trajectories, correlations, and mortality associations of nine BAs across 20 years follow-up. We found the longitudinal growth of functional BAs accelerated around age 70; average levels of BA curves differed by sex across the age span (50-90 years). All BAs were correlated to varying degrees; correlations were mostly explained by CA. Individually, all BAs except for telomere length were associated with mortality risk independently of CA. The largest effects were seen for methylation age estimators (GrimAge) and the frailty index (FI). In joint models, two methylation age estimators (Horvath and GrimAge) and FI remained predictive, suggesting they are complementary in predicting mortality.
While chronological age is the single biggest risk factor for cancer, it is less clear whether frailty, an age-related state of physiological decline, may also predict cancer incidence. We assessed ...the associations of frailty index (FI) and frailty phenotype (FP) scores with the incidence of any cancer and five common cancers (breast, prostate, lung, colorectal, melanoma) in 453,144 UK Biobank (UKB) and 36,888 Screening Across the Lifespan Twin study (SALT) participants, who aged 38–73 years and had no cancer diagnosis at baseline. During a median follow-up of 10.9 and 10.7 years, 53,049 (11.7%) and 4,362 (11.8%) incident cancers were documented in UKB and SALT, respectively. Using multivariable-adjusted Cox models, we found a higher risk of any cancer in frail vs. non-frail UKB participants, when defined by both FI (hazard ratio HR = 1.22; 95% confidence interval CI = 1.17–1.28) and FP (HR = 1.16; 95% CI = 1.11–1.21). The FI in SALT similarly predicted risk of any cancer (HR = 1.31; 95% CI = 1.15–1.49). Moreover, frailty was predictive of lung cancer in UKB, although this association was not observed in SALT. Adding frailty scores to models including age, sex, and traditional cancer risk factors resulted in little improvement in C-statistics for most cancers. In a within-twin-pair analysis in SALT, the association between FI and any cancer was attenuated within monozygotic but not dizygotic twins, indicating that it may partly be explained by genetic factors. Our findings suggest that frailty scores are associated with the incidence of any cancer and lung cancer, although their clinical utility for predicting cancers may be limited.
Biological Age Predictors Jylhävä, Juulia; Pedersen, Nancy L.; Hägg, Sara
EBioMedicine,
07/2017, Letnik:
21, Številka:
C
Journal Article
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The search for reliable indicators of biological age, rather than chronological age, has been ongoing for over three decades, and until recently, largely without success. Advances in the fields of ...molecular biology have increased the variety of potential candidate biomarkers that may be considered as biological age predictors. In this review, we summarize current state-of-the-art findings considering six potential types of biological age predictors: epigenetic clocks, telomere length, transcriptomic predictors, proteomic predictors, metabolomics-based predictors, and composite biomarker predictors. Promising developments consider multiple combinations of these various types of predictors, which may shed light on the aging process and provide further understanding of what contributes to healthy aging. Thus far, the most promising, new biological age predictor is the epigenetic clock; however its true value as a biomarker of aging requires longitudinal confirmation.
•Telomere length is the most well studied biological age predictor, but many new predictors are emerging.•The epigenetic clock is currently the best biological age predictor, as it correlates well with age and predicts mortality.•The various biological age predictors tend to reflect different aspects of the aging process.
Mitochondrial (MT) dysfunction is a hallmark of aging and has been associated with most aging-related diseases as well as immunological processes. However, little is known about aging, lifestyle and ...genetic factors influencing mitochondrial DNA (mtDNA) abundance. In this study, mtDNA abundance was estimated from the weighted intensities of probes mapping to the MT genome in 295,150 participants from the UK Biobank. We found that the abundance of mtDNA was significantly elevated in women compared to men, was negatively correlated with advanced age, higher smoking exposure, greater body-mass index, higher frailty index as well as elevated red and white blood cell count and lower mortality. In addition, several biochemistry markers in blood-related to cholesterol metabolism, ion homeostasis and kidney function were found to be significantly associated with mtDNA abundance. By performing a genome-wide association study, we identified 50 independent regions genome-wide significantly associated with mtDNA abundance which harbour multiple genes involved in the immune system, cancer as well as mitochondrial function. Using mixed effects models, we estimated the SNP-heritability of mtDNA abundance to be around 8%. To investigate the consequence of altered mtDNA abundance, we performed a phenome-wide association study and found that mtDNA abundance is involved in risk for leukaemia, hematologic diseases as well as hypertension. Thus, estimating mtDNA abundance from genotyping arrays has the potential to provide novel insights into age- and disease-relevant processes, particularly those related to immunity and established mitochondrial functions.
Background/Objectives
Frailty has been linked to increased risk of COVID‐19 mortality, but evidence is mainly limited to hospitalized older individuals. This study aimed to assess and compare ...predictive abilities of different frailty and comorbidity measures for COVID‐19 mortality in a community sample and COVID‐19 inpatients.
Design
Population‐based cohort study.
Setting
Community.
Participants
We analyzed (i) the full sample of 410,199 U.K. Biobank participants in England, aged 49–86 years, and (ii) a subsample of 2812 COVID‐19 inpatients with COVID‐19 data from March 1 to November 30, 2020.
Measurements
Frailty was defined using the physical frailty phenotype (PFP), frailty index (FI), and Hospital Frailty Risk Score (HFRS), and comorbidity using the Charlson Comorbidity Index (CCI). PFP and FI were available at baseline, whereas HFRS and CCI were assessed both at baseline and concurrently with the start of the pandemic. Inpatient COVID‐19 cases were confirmed by PCR and/or hospital records. COVID‐19 mortality was ascertained from death registers.
Results
Overall, 514 individuals died of COVID‐19. In the full sample, all frailty and comorbidity measures were associated with higher COVID‐19 mortality risk after adjusting for age and sex. However, the associations were stronger for the concurrent versus baseline HFRS and CCI, with odds ratios of 20.40 (95% confidence interval = 16.24–25.63) comparing high (>15) to low (<5) concurrent HFRS risk category and 1.53 (1.48–1.59) per point increase in concurrent CCI. Moreover, only the concurrent HFRS or CCI significantly improved predictive ability of a model including age and sex, yielding areas under the receiver operating characteristic curve (AUC) >0.8. When restricting analyses to COVID‐19 inpatients, similar improvement in AUC was not observed.
Conclusion
HFRS and CCI constructed from medical records concurrent with the start of the pandemic can be used in COVID‐19 mortality risk stratification at the population level, but they show limited added value in COVID‐19 inpatients.
Circulating cell-free DNA (cf-DNA) has emerged as a promising biomarker of ageing, tissue damage and cellular stress. However, less is known about health behaviours, ageing phenotypes and metabolic ...processes that lead to elevated cf-DNA levels. We sought to analyse the relationship of circulating cf-DNA level to age, sex, smoking, physical activity, vegetable consumption, ageing phenotypes (physical functioning, the number of diseases, frailty) and an extensive panel of biomarkers including blood and urine metabolites and inflammatory markers in three human cohorts (N = 5385; 17–82 years). The relationships were assessed using correlation statistics, and linear and penalised regressions (the Lasso), also stratified by sex.
cf-DNA levels were significantly higher in men than in women, and especially in middle-aged men and women who smoke, and in older more frail individuals. Correlation statistics of biomarker data showed that cf-DNA level was higher with elevated inflammation (C-reactive protein, interleukin-6), and higher levels of homocysteine, and proportion of red blood cells and lower levels of ascorbic acid. Inflammation (C-reactive protein, glycoprotein acetylation), amino acids (isoleucine, leucine, tyrosine), and ketogenesis (3-hydroxybutyrate) were included in the cf-DNA level-related biomarker profiles in at least two of the cohorts.
In conclusion, circulating cf-DNA level is different by sex, and related to health behaviour, health decline and metabolic processes common in health and disease. These results can inform future studies where epidemiological and biological pathways of cf-DNA are to be analysed in details, and for studies evaluating cf-DNA as a potential clinical marker.
A Frailty Index for UK Biobank Participants Williams, Dylan M; Jylhävä, Juulia; Pedersen, Nancy L ...
The journals of gerontology. Series A, Biological sciences and medical sciences,
03/2019, Letnik:
74, Številka:
4
Journal Article
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Abstract
Background
Frailty indices (FIs) measure variation in health between aging individuals. Researching FIs in resources with large-scale genetic and phenotypic data will provide insights into ...the causes and consequences of frailty. Thus, we aimed to develop an FI using UK Biobank data, a cohort study of 500,000 middle-aged and older adults.
Methods
An FI was calculated using 49 self-reported questionnaire items on traits covering health, presence of diseases and disabilities, and mental well-being, according to standard protocol. We used multiple imputation to derive FI values for the entire eligible sample in the presence of missing item data (N = 500,336). To validate the measure, we assessed associations of the FI with age, sex, and risk of all-cause mortality (follow-up ≤ 9.7 years) using linear and Cox proportional hazards regression models.
Results
Mean FI in the cohort was 0.125 (SD = 0.075), and there was a curvilinear trend toward higher values in older participants. FI values were also marginally higher on average in women than in men. In survival models, 10% higher baseline frailty (ie, a 0.1 FI increment) was associated with higher risk of death (hazard ratio = 1.65; 95% confidence interval: 1.62–1.68). Associations were stronger in younger participants than in older participants, and in men than in women (hazard ratios: 1.72 vs. 1.56, respectively).
Conclusions
The FI is a valid measure of frailty in UK Biobank. The cohort’s data are open access for researchers to use, and we provide script for deriving this tool to facilitate future studies on frailty.
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.
This genome‐wide association study meta‐analysis of the frailty index (FI) in UK Biobank and TwinGene, identified 14 loci associated with the FI. Many FI‐associated loci have established associations with well‐known disease risk factors such as BMI, cardiovascular disease, smoking, HLA proteins, depression and neuroticism. However 1 was novel. 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.
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