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Journal of Translational Medicine Jul 2023Diet may influence biological aging and the discrepancy (∆age) between a subject's biological age (BA) and chronological age (CA). We aimed to investigate the...
BACKGROUND
Diet may influence biological aging and the discrepancy (∆age) between a subject's biological age (BA) and chronological age (CA). We aimed to investigate the correlation of dietary flavonoids with the ∆age of organs (heart, kidney, liver) and the whole body.
METHOD
A total of 3193 United States adults were extracted from the National Health and Nutrition Examination Survey (NHANES) in 2007-2008 and 2017-2018. Dietary flavonoids intake was assessed using 24-h dietary recall method. Multiple linear regression analysis was performed to evaluate the association of dietary flavonoids intake with the ∆age of organs (heart, kidney, liver) and the whole body. BA was computed based on circulating biomarkers, and the resulting ∆age was tested as an outcome in linear regression analysis.
RESULTS
The ∆age of the whole body, heart, and liver was inversely associated with higher flavonoids intake (the whole body ∆age β = - 0.58, cardiovascular ∆age β = - 0.96, liver ∆age β = - 3.19) after adjustment for variables. However, higher flavonoids intake positively related to renal ∆age (β = 0.40) in participants with chronic kidney disease (CKD). Associations were influenced by population characteristics, such as age, health behavior, or chronic diseases. Anthocyanidins, isoflavones and flavones had the strongest inverse associations between the whole body ∆age and cardiovascular ∆age among all the flavonoids subclasses.
CONCLUSION
Flavonoids intake positively contributes to delaying the biological aging process, especially in the heart, and liver organ, which may be beneficial for reducing the long-term risk of cardiovascular or liver disease.
Topics: Adult; Humans; Flavonoids; Nutrition Surveys; Heart; Liver; Aging
PubMed: 37480074
DOI: 10.1186/s12967-023-04321-1 -
BMC Public Health Aug 2023Even though cadmium (Cd) exposure and cellular senescence (telomere length) have been linked in previous studies, composite molecular aging biomarkers are more...
INTRODUCTION
Even though cadmium (Cd) exposure and cellular senescence (telomere length) have been linked in previous studies, composite molecular aging biomarkers are more significant and reliable factors to consider when examining the connection between metal exposure and health outcomes. The purpose of this research was to assess the association between urinary cadmium (U-Cd) and whole-body aging (phenotypic age).
METHODS
Phenotypic age was calculated from chronological age and 9 molecular biomarkers. Multivariate linear regression models, subgroup analysis, and smoothing curve fitting were used to explore the linear and nonlinear relationship between U-Cd and phenotypic age. Mediation analysis was performed to explore the mediating effect of U-Cd on the association between smoking and phenotypic age.
RESULTS
This study included 10,083 participants with a mean chronological age and a mean phenotypic age of 42.24 years and 42.34 years, respectively. In the fully adjusted model, there was a positive relationship between U-Cd and phenotypic age [2.13 years per 1 ng/g U-Cd, (1.67, 2.58)]. This association differed by sex, age, and smoking subgroups (P for interaction < 0.05). U-Cd mediated a positive association between serum cotinine and phenotypic age, mediating a proportion of 23.2%.
CONCLUSIONS
Our results suggest that high levels of Cd exposure are associated with whole-body aging.
Topics: Adult; Humans; Aging; Cadmium; Cotinine; Mediation Analysis; Nutrition Surveys; Male; Female
PubMed: 37653508
DOI: 10.1186/s12889-023-16643-2 -
Zeitschrift Fur Psychosomatische... Feb 2024Does the therapeutic style differ in age-homogeneous and age-heterogeneous therapeutic dyads?
UNLABELLED
Does the therapeutic style differ in age-homogeneous and age-heterogeneous therapeutic dyads?
BACKGROUND AND AIMS
Differences between age-homogeneous and age-heterogeneous therapeutic dyads have rarely been the subject of research.The present study aimed to investigate differences in therapeutic style (Healing and Stressful Involvement).
METHOD
A sample of 527 questionnaires completed by therapists of different ages was available. Therapy style was compared between two patient groups (under 40 and over 65 years old) and three therapist groups (25-39, 40-59, ≥ 60).
RESULTS
The results show in particular more stress experienced by younger therapists in the treatment of older patients, while older therapists report less stress.There were no or fewer differences in the treatment of younger patients.The regression-analytical results show that the experience of stress in the therapy of older people is associated with a greater fear of old age.
CONCLUSION
Finally, some conclusions are discussed with regard to training and supervision of therapists in the treatment of older people.
Topics: Humans; Aged; Psychotherapy; Surveys and Questionnaires; Fear; Professional-Patient Relations
PubMed: 38598707
DOI: 10.13109/zptm.2024.70.1.77 -
The Lancet. Healthy Longevity Dec 2023Biological age is a measure of health that offers insights into ageing. The existing age clocks, although valuable, often trade off accuracy and interpretability. We...
BACKGROUND
Biological age is a measure of health that offers insights into ageing. The existing age clocks, although valuable, often trade off accuracy and interpretability. We introduce ExplaiNAble BioLogical Age (ENABL Age), a computational framework that combines machine-learning models with explainable artificial intelligence (XAI) methods to accurately estimate biological age with individualised explanations.
METHODS
To construct the ENABL Age clock, we first predicted an age-related outcome (eg, all-cause or cause-specific mortality), and then rescaled these predictions to estimate biological age, using UK Biobank and National Health and Nutrition Examination Survey (NHANES) datasets. We adapted existing XAI methods to decompose individual ENABL Ages into contributing risk factors. For broad accessibility, we developed two versions: ENABL Age-L, based on blood tests, and ENABL Age-Q, based on questionnaire characteristics. Finally, we validated diverse ageing mechanisms captured by each ENABL Age clock through genome-wide association studies (GWAS) association analyses.
FINDINGS
Our ENABL Age clock was significantly correlated with chronological age (r=0·7867, p<0·0001 for UK Biobank; r=0·7126, p<0·0001 for NHANES). These clocks distinguish individuals who are healthy (ie, their ENABL Age is lower than their chronological age) from those who are unhealthy (ie, their ENABL Age is higher than their chronological age), predicting mortality more effectively than existing clocks. Groups of individuals who were unhealthy showed approximately three to 12 times higher log hazard ratio than healthy groups, as per ENABL Age. The clocks achieved high mortality prediction power with an area under the receiver operating characteristic curve of 0·8179 for 5-year mortality and 0·8115 for 10-year mortality on the UK Biobank dataset, and 0·8935 for 5-year mortality and 0·9107 for 10-year mortality on the NHANES dataset. The individualised explanations that revealed the contribution of specific characteristics to ENABL Age provided insights into the important characteristics for ageing. An association analysis with risk factors and ageing-related morbidities and GWAS results on ENABL Age clocks trained on different mortality causes showed that each clock captures distinct ageing mechanisms.
INTERPRETATION
ENABL Age brings an important leap forward in the application of XAI for interpreting biological age clocks. ENABL Age also carries substantial potential in practical settings, assisting medical professionals in untangling the complexity of ageing mechanisms, and potentially becoming a valuable tool in informed clinical decision-making processes.
FUNDING
National Science Foundation and National Institutes of Health.
Topics: United States; Humans; Artificial Intelligence; Genome-Wide Association Study; Nutrition Surveys; Machine Learning; Aging
PubMed: 37944549
DOI: 10.1016/S2666-7568(23)00189-7 -
Aging and Disease Aug 2023With aging, the incidence of age-related diseases increases. Hence, age-related diseases are inevitable. However, the mechanisms by which aging leads to the onset and... (Review)
Review
With aging, the incidence of age-related diseases increases. Hence, age-related diseases are inevitable. However, the mechanisms by which aging leads to the onset and progression of age-related diseases remain unclear. It has been reported that inflammation is closely associated with age-related diseases and that the cGAS-STING signaling pathway, which can sense the aberrant presence of cytosolic DNA during aging and induce an inflammatory response, is an important mediator of inflammation in age-related diseases. With a better understanding of the structure and molecular biology of the cGAS-STING signaling axis, numerous selective inhibitors and agonists targeting the cGAS-STING pathway in human age-related diseases have been developed to modulate inflammatory responses. Here, we provide a narrative review of the activity of the cGAS-STING pathway in age-related diseases and discuss its general mechanisms in the onset and progression of age-related diseases. In addition, we outline treatments targeting the cGAS-STING pathway, which may constitute a potential therapeutic alternative for age-related diseases.
PubMed: 37163421
DOI: 10.14336/AD.2023.0117 -
Neurobiology of Aging Dec 2023Biological age and brain age estimated using biological and neuroimaging measures have recently emerged as surrogate aging biomarkers shown to be predictive of diverse...
Biological age and brain age estimated using biological and neuroimaging measures have recently emerged as surrogate aging biomarkers shown to be predictive of diverse health outcomes. As aging underlies the development of many chronic conditions, surrogate aging biomarkers capture health at the whole person level, having the potential to improve our understanding of multimorbidity. Our study investigates whether elevated biological age and brain age are associated with an increased risk of multimorbidity using a large dataset from the Midlife in the United States Refresher study. Ensemble learning is utilized to combine multiple machine learning models to estimate biological age using a comprehensive set of biological markers. Brain age is obtained using Gaussian processes regression and neuroimaging data. Our study is the first to examine the relationship between accelerated brain age and multimorbidity. Furthermore, it is the first attempt to explore how biological age and brain age are related to multimorbidity in mental health. Our findings hold the potential to advance the understanding of disease accumulation and their relationship with aging.
Topics: Humans; United States; Multimorbidity; Mental Health; Aging; Brain; Biomarkers; Chronic Disease
PubMed: 37804610
DOI: 10.1016/j.neurobiolaging.2023.09.003 -
The Journals of Gerontology. Series A,... Jan 2024DNA methylation-derived epigenetic clocks offer the opportunity to examine aspects of age acceleration (ie, the difference between an individual's biological age and...
DNA methylation-derived epigenetic clocks offer the opportunity to examine aspects of age acceleration (ie, the difference between an individual's biological age and chronological age), which vary among individuals and may better account for age-related changes in cognitive function than chronological age. Leveraging existing ambulatory cognitive assessments in daily life from a genetically diverse sample of 142 adults in midlife, we examined associations between 5 measures of epigenetic age acceleration and performance on tasks of processing speed and working memory. Covarying for chronological age, we used multilevel models to examine associations of epigenetic age acceleration (Horvath 1, Horvath 2, Hannum, PhenoAge, and GrimAge clocks) with both average level and variability of cognitive performance. Positive age acceleration (ie, epigenetic age greater than chronological age) was associated with poorer mean processing speed (Horvath 1 and 2) and working memory (GrimAge). Higher chronological age was also associated with poorer mean processing speed and working memory performance. Further, positive age acceleration was generally associated with greater intraindividual variability in working memory and processing speed tasks, whereas being chronologically older was associated with less intraindividual variability. Although further work is needed, our results indicate age acceleration effects have comparable or greater size as those for chronological age differences, suggesting that epigenetic age acceleration may account for additional risk and interindividual variation in cognitive performance above chronological age.
Topics: Humans; Aging; Epigenesis, Genetic; DNA Methylation; Cognition; Acceleration
PubMed: 37899644
DOI: 10.1093/gerona/glad242 -
CNS Neuroscience & Therapeutics Jul 2023Age and sex are important individual factors modifying the clinical symptoms of patients with Parkinson's disease (PD). Our goal is to evaluate the effects of age and...
AIMS
Age and sex are important individual factors modifying the clinical symptoms of patients with Parkinson's disease (PD). Our goal is to evaluate the effects of age and sex on brain networks and clinical manifestations of PD patients.
METHODS
Parkinson's disease participants (n = 198) receiving functional magnetic resonance imaging from Parkinson's Progression Markers Initiative database were investigated. Participants were classified into lower quartile group (age rank: 0%~25%), interquartile group (age rank: 26%~75%), and upper quartile group (age rank: 76%~100%) according to their age quartiles to examine how age shapes brain network topology. The differences of brain network topological properties between male and female participants were also investigated.
RESULTS
Parkinson's disease patients in the upper quartile age group exhibited disrupted network topology of white matter networks and impaired integrity of white matter fibers compared to lower quartile age group. In contrast, sex preferentially shaped the small-world topology of gray matter covariance network. Differential network metrics mediated the effects of age and sex on cognitive function of PD patients.
CONCLUSION
Age and sex have diverse effects on brain structural networks and cognitive function of PD patients, highlighting their roles in the clinical management of PD.
Topics: Humans; Male; Female; Parkinson Disease; Brain; Gray Matter; Magnetic Resonance Imaging; White Matter
PubMed: 36890620
DOI: 10.1111/cns.14149 -
BioRxiv : the Preprint Server For... Oct 2023The differentiation of human pluripotent stem cells (hPSCs) provides access to most cell types and tissues. However, hPSC-derived lineages capture a fetal-stage of...
The differentiation of human pluripotent stem cells (hPSCs) provides access to most cell types and tissues. However, hPSC-derived lineages capture a fetal-stage of development and methods to accelerate progression to an aged identity are limited. Understanding the factors driving cellular age and rejuvenation is also essential for efforts aimed at extending human life and health span. A prerequisite for such studies is the development of methods to score cellular age and simple readouts to assess the relative impact of various age modifying strategies. Here we established a transcriptional score (RNAge) in young versus old primary fibroblasts, frontal cortex and substantia nigra tissue. We validated the score in independent RNA-seq datasets and demonstrated a strong cell and tissue specificity. In fibroblasts we observed a reset of RNAge during iPSC reprogramming while direct reprogramming of aged fibroblasts to induced neurons (iN) resulted in the maintenance of both a neuronal and a fibroblast aging signature. Increased RNAge in hPSC-derived neurons was confirmed for several age-inducing strategies such as SATB1 loss, progerin expression or chemical induction of senescence (SLO). Using RNAge as a probe set, we next performed an in-silico screen using the LINCS L1000 dataset. We identified and validated several novel age-inducing and rejuvenating compounds, and we observed that RNAage captures age-related changes associated with distinct cellular hallmarks of age. Our study presents a simple tool to score age manipulations and identifies compounds that greatly expand the toolset of age-modifying strategies in hPSC derived lineages.
PubMed: 37461485
DOI: 10.1101/2023.07.03.547539