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Brain Research Jan 2024Brain age in neuroimaging has emerged over the last decade and reflects the estimated age based on the brain MRI scan from a person. As a person ages, their brain... (Review)
Review
Brain age in neuroimaging has emerged over the last decade and reflects the estimated age based on the brain MRI scan from a person. As a person ages, their brain structure will change, and these changes will be exclusive to males and females and will differ for each. White matter and grey matter density have a deeper relationship with brain aging. Hence, if the white matter and grey matter concentrations vary, the rate at which the brain ages will also vary. Neurodegenerative illnesses can be detected using the biomarker known as brain age. The development of deep learning has made it possible to analyze structural neuroimaging data in new ways, notably by predicting brain ages. We introduce the techniques and possible therapeutic uses of brain age prediction in this cutting-edge review. Creating a machine learning regression model to analyze age-related changes in brain structure among healthy individuals is a typical procedure in studies focused on brain aging. Subsequently, this model is employed to forecast the aging of brains in new individuals. The concept of the "brain-age gap" refers to the difference between an individual's predicted brain age and their actual chronological age. This score may serve as a gauge of the general state of the brain's health while also reflecting neuroanatomical disorders. It may help differential diagnosis, prognosis, and therapy decisions as well as early identification of brain-based illnesses. The following is a summary of the many forecasting techniques utilized over the past 11 years to estimate brain age. The study's conundrums and potential outcomes of the brain age predicted by current models will both be covered.
Topics: Male; Female; Humans; Brain; Magnetic Resonance Imaging; Aging; Neuroimaging; White Matter
PubMed: 37951563
DOI: 10.1016/j.brainres.2023.148668 -
Mathematical Biosciences and... Oct 2023The chronological age used in demography describes the linear evolution of the life of a living being. The chronological age cannot give precise information about the...
The chronological age used in demography describes the linear evolution of the life of a living being. The chronological age cannot give precise information about the exact developmental stage or aging processes an organism has reached. On the contrary, the biological age (or epigenetic age) represents the true evolution of the tissues and organs of the living being. Biological age is not always linear and sometimes proceeds by discontinuous jumps. These jumps can be negative (we then speak of rejuvenation) or positive (in the event of premature aging), and they can be dependent on endogenous events such as pregnancy (negative jump) or stroke (positive jump) or exogenous ones such as surgical treatment (negative jump) or infectious disease (positive jump). The article proposes a mathematical model of the biological age by defining a valid model for the two types of jumps (positive and negative). The existence and uniqueness of the solution are solved, and its temporal dynamic is analyzed using a moments equation. We also provide some individual-based stochastic simulations.
Topics: Stochastic Processes; Models, Biological; Population Dynamics
PubMed: 38052618
DOI: 10.3934/mbe.2023870 -
Journal of Forensic Sciences Nov 2023Conventional dental age estimation relies on destructive methods such as sectioning and staining, which are unpreferable when the tooth is required for evidential or... (Review)
Review
Conventional dental age estimation relies on destructive methods such as sectioning and staining, which are unpreferable when the tooth is required for evidential or archeological preservation. MicroCT is a non-destructive, high-resolution imaging technique that allows for accurate morphometrical measurement. Although microCT technology has been applied in a variety of dental studies, studies focusing on dental age-related change and dental age estimation based on microCT imaging remain lacking. Based on the question: "How has microCT technology been applied in studying human age-related tooth morphological change and dental age estimation studies?", the authors conducted a scoping review in accordance with the Arksey and O'Malley (2005) and the PRISMA-ScR guidelines. A literature search using five major scientific databases identified 452 articles, with 11 full-text articles being eligible to be included in the scoping review. Furthermore, 6 out of the 11 studies performed dental age estimation modeling. An overview of the parameters used in the selected articles revealed a variety of tooth characteristics, such as pulp cavity to whole tooth volume ratio, secondary dentin, as well as the diameter of root canal orifice. The findings of this scoping review highlight the extent microCT is used in studying dental age-related changes, as well as the effectiveness of microCT in dental age estimation studies. This review serves as a guide for future forensic odontology age estimation studies.
Topics: Humans; Research; Tooth; X-Ray Microtomography
PubMed: 37529884
DOI: 10.1111/1556-4029.15352 -
European Journal of Preventive... Apr 2024Age is a crucial risk factor for cardiovascular (CV) and non-CV diseases. As people age at different rates, the concept of biological age has been introduced as a...
AIMS
Age is a crucial risk factor for cardiovascular (CV) and non-CV diseases. As people age at different rates, the concept of biological age has been introduced as a personalized measure of functional deterioration. Associations of age with echocardiographic quantitative traits were analysed to assess different heart ageing rates and their ability to predict outcomes and reflect biological age.
METHODS AND RESULTS
Associations of age with left ventricular mass, geometry, diastolic function, left atrial volume, and aortic root size were measured in 2614 healthy subjects. Based on the 95% two-sided tolerance intervals of each correlation, three discrete ageing trajectories were identified and categorized as 'slow', 'normal', and 'accelerated' heart ageing patterns. The primary endpoint included fatal and non-fatal CV events, and the secondary endpoint was a composite of CV and non-CV events and all-cause death. The phenotypic age of the heart (HeartPhAge) was estimated as a proxy of biological age. The slow ageing pattern was found in 8.7% of healthy participants, the normal pattern in 76.9%, and the accelerated pattern in 14.4%. Kaplan-Meier curves of the heart ageing patterns diverged significantly (P = 0.0001) for both primary and secondary endpoints, with the event rate being lowest in the slow, intermediate in the normal, and highest in the accelerated pattern. In the Cox proportional hazards model, heart ageing patterns predicted both primary (P = 0.01) and secondary (P = 0.03 to <0.0001) endpoints, independent of chronological age and risk factors. Compared with chronological age, HeartPhAge was 9 years younger in slow, 4 years older in accelerated (both P < 0.0001), and overlapping in normal ageing patterns.
CONCLUSION
Standard Doppler echocardiography detects slow, normal, and accelerated heart ageing patterns. They predict CV and non-CV events, reflect biological age, and provide a new tool to calibrate prevention timing and intensity.
Topics: Humans; Child; Ventricular Function, Left; Echocardiography; Echocardiography, Doppler; Risk Factors; Aging
PubMed: 37527539
DOI: 10.1093/eurjpc/zwad254 -
Cerebral Cortex (New York, N.Y. : 1991) Jan 2024The hippocampus, essential for cognitive and affective processes, develops exponentially with differential trajectories seen in girls and boys, yet less is known about...
The hippocampus, essential for cognitive and affective processes, develops exponentially with differential trajectories seen in girls and boys, yet less is known about its development during early fetal life until early childhood. In a cross-sectional and longitudinal study, we examined the sex-, age-, and laterality-related developmental trajectories of hippocampal volumes in fetuses, infants, and toddlers associated with age. Third trimester fetuses (27-38 weeks' gestational age), newborns (0-4 weeks' postnatal age), infants (5-50 weeks' postnatal age), and toddlers (2-3 years postnatal age) were scanned with magnetic resonance imaging. A total of 133 datasets (62 female, postmenstrual age [weeks] M = 69.38, SD = 51.39, range = 27.6-195.3) were processed using semiautomatic segmentation methods. Hippocampal volumes increased exponentially during the third trimester and the first year of life, beginning to slow at approximately 2 years. Overall, boys had larger hippocampal volumes than girls. Lateralization differences were evident, with left hippocampal growth beginning to plateau sooner than the right. This period of rapid growth from the third trimester, continuing through the first year of life, may support the development of cognitive and affective function during this period.
Topics: Male; Pregnancy; Humans; Child, Preschool; Infant, Newborn; Female; Longitudinal Studies; Cross-Sectional Studies; Pregnancy Trimester, Third; Gestational Age; Hippocampus; Magnetic Resonance Imaging; Fetus
PubMed: 37950876
DOI: 10.1093/cercor/bhad421 -
MedRxiv : the Preprint Server For... Oct 2023Epigenetic age, a biological aging marker measured by DNA methylation, is a potential mechanism by which social factors drive disparities in age-related health....
Epigenetic age, a biological aging marker measured by DNA methylation, is a potential mechanism by which social factors drive disparities in age-related health. Epigenetic age gap is the residual between epigenetic age measures and chronological age. Previous studies showed associations between epigenetic age gap and age-related outcomes including cognitive capacity and performance on some functional measures, but whether epigenetic age gap contributes to disparities in these outcomes is unknown. We use data from the Health and Retirement Study to examine the role of epigenetic age gap in racial disparities in cognitive and functional outcomes and consider the role of socioeconomic status (SES). Epigenetic age measures are GrimAge or Dunedin Pace of Aging methylation (DPoAm). Cognitive outcomes are cross-sectional score and two-year change in Telephone Interview for Cognitive Status (TICS). Functional outcomes are prevalence and incidence of limitations performing Instrumental Activities of Daily Living (IADLs). We find, relative to White participants, Black participants have lower scores and greater decline in TICS, higher prevalence and incidence rates of IADL limitations, and higher epigenetic age gap. Age- and gender-adjusted analyses reveal that higher GrimAge and DPoAm gap are both associated with worse cognitive and functional outcomes and mediate 6-11% of racial disparities in cognitive outcomes and 19-39% of disparities in functional outcomes. Adjusting for SES attenuates most DPoAm associations and most mediation effects. These results support that epigenetic age gap contributes to racial disparities in cognition and functioning and may be an important mechanism linking social factors to disparities in health outcomes.
PubMed: 37873230
DOI: 10.1101/2023.09.29.23296351 -
BMC Plant Biology Feb 2024Early selection in tree breeding could be achieved by addressing the longevity of tree improvement activities. Genetic parameter changes and age-age correlations are...
BACKGROUND
Early selection in tree breeding could be achieved by addressing the longevity of tree improvement activities. Genetic parameter changes and age-age correlations are essential for determining the optimal timing of early selection. Practical tracking of genetic parameters of Pinus koraiensis, a major timber species with economic and ecological value, has become feasible as its progeny testing has entered the mid-term age in Korea. However, research on the age-age correlation of P. koraiensis as progeny trials approach rotation age is limited. This study aimed to investigate genetic parameter trends and age-age correlations in P. koraiensis progeny. P. koraiensis progeny were assessed at two sites using a linear mixed-effects model with two-dimensional spatial autoregressive structure. Height, diameter, and volume growth were measured in 11 assessments over 40 years.
RESULTS
Genetic parameters, such as height and diameter, showed different patterns of change. The heritability ranged for the three growth traits in 0.083-0.710, 0.288-0.781, and 0.299-0.755 across the sites and age. Height heritability and its coefficient of variance decreased, whereas the diameter and volume estimates remained relatively constant. Correlations with Age 40 for phenotypic, genetic, and rank of breeding values ranged between 0.16 and 0.92, 0.594 and 0.988, and 0.412 and 0.965, respectively. These correlations generally increased as the age approached Age 40, with particularly high levels observed at Age 26 and Age 30.
CONCLUSION
The observed genetic trends in P. koraiensis progeny testing offer valuable insights for early and precise selection. Notably, selecting superior genotypes at Ages 26-30 is supported by discernible genetic gains and robust correlations. Future research should integrate unbalanced data for selecting mother trees or families and conduct a comprehensive economic analysis of early selection to validate its practical benefits.
Topics: Humans; Adult; Forests; Pinus; Plant Breeding; Trees; Phenotype
PubMed: 38310225
DOI: 10.1186/s12870-024-04752-y -
American Journal of Physical Medicine &... Jul 2024The aim of the present systematic review is to synthesize existing evidence (qualitative and quantitative) regarding age- and sex-specific differences with glenohumeral... (Meta-Analysis)
Meta-Analysis
OBJECTIVE
The aim of the present systematic review is to synthesize existing evidence (qualitative and quantitative) regarding age- and sex-specific differences with glenohumeral osteoarthritis.
DESIGN
The electronic databases PubMed, MEDLINE, and Web of Science were searched up to March 15, 2023. Articles reporting on the association of risk factors (age and sex) with glenohumeral osteoarthritis were considered. We used Newcastle-Ottawa Scale to assess study quality. Meta-analysis was conducted to quantitatively summarize the association of age and sex with glenohumeral osteoarthritis.
RESULTS
A total of 24 articles were retrieved for full-text review. Of 24 articles, 8 reporting age-specific and 5 articles reporting sex-specific associations with glenohumeral osteoarthritis were included. The odds ratio for the age (odds ratio = 3.18; 95% confidence interval = 1.10-15.92) and female sex (odds ratio = 1.78; 95% confidence interval = 0.95-3.42) were increased and observed statistically significant.
CONCLUSIONS
The present systematic review and meta-analysis suggests the role of increasing age as one of the significant contributors to glenohumeral osteoarthritis. However, association of female sex with glenohumeral osteoarthritis is least convincing. Future studies are required to understand the molecular mechanisms behind the contributory role of increasing age and female sex in the establishment of glenohumeral osteoarthritis.
Topics: Humans; Osteoarthritis; Sex Factors; Female; Male; Shoulder Joint; Age of Onset; Risk Factors; Age Factors
PubMed: 38207175
DOI: 10.1097/PHM.0000000000002419 -
Frontiers in Pharmacology 2023Triple-combination cystic fibrosis transmembrane conductance regulator (CFTR) modulator therapy with elexacaftor/tezacaftor/ivacaftor (ETI) was introduced in August...
Triple-combination cystic fibrosis transmembrane conductance regulator (CFTR) modulator therapy with elexacaftor/tezacaftor/ivacaftor (ETI) was introduced in August 2020 in Germany for people with CF (pwCF) ≥12 years (yrs.) of age and in June 2021 for pwCF ≥6 yrs of age. In this single-center study, we analyzed longitudinal data on the percent-predicted forced expiratory volume (ppFEV1) and body-mass-index (BMI) for 12 months (mo.) after initiation of ETI by linear mixed models and regression analyses to identify age- and severity-dependent determinants of response to ETI. We obtained data on 42 children ≥6-11 yrs, 41 adolescents ≥12-17 yrs, and 143 adults by spirometry and anthropometry prior to ETI, and 3 and 12 mo. after ETI initiation. Data were stratified by the age group and further sub-divided into age-specific ppFEV1 impairment. To achieve this, the age strata were divided into three groups, each according to their baseline ppFEV1: lowest 25%, middle 50%, and top 25% of ppFEV1. Adolescents and children with more severe lung disease prior to ETI (within the lowest 25% of age-specific ppFEV1) showed higher improvements in lung function than adults in this severity group (+18.5 vs. +7.5; = 0.002 after 3 mo. and +13.8 vs. +7.2; = 0.012 after 12 mo. of ETI therapy for ≥12-17 years and +19.8 vs. +7.5; = 0.007 after 3 mo. for children ≥6-11 yrs). In all age groups, participants with more severe lung disease showed higher BMI gains than those with medium or good lung function (within the middle 50% or top 25% of age-specific ppFEV1). Regression analyses identified age as a predictive factor for FEV1 increase at 3 mo. after ETI initiation, and age and ppFEV1 at ETI initiation as predictive factors for FEV1 increase 12 mo. after ETI initiation. We report initial data, which suggest that clinical response toward ETI depends on age and lung disease severity prior to ETI initiation, which argue for early initiation of ETI.
PubMed: 37469865
DOI: 10.3389/fphar.2023.1171544 -
GeroScience Feb 2024We can study how fast our biological aging clocks tick by calculating the difference (i.e., age gaps) between machine learning estimations of biological age and...
We can study how fast our biological aging clocks tick by calculating the difference (i.e., age gaps) between machine learning estimations of biological age and chronological age. While this approach has been increasingly used to study various aspects of aging, few had applied this approach to study cognitive and physical age gaps; not much is known about the behavioral and neurocognitive factors associated with these age gaps. In the present study, we examined these age gaps in relation to behavioral phenotypes and mild cognitive impairment (MCI) among community-dwelling older adults. Participants (N = 822, Age = 67.6) were partitioned into equally-sized training and testing samples. Cognitive and physical age-prediction models were fitted using nine cognitive and eight physical fitness test scores, respectively, within the training samples, and subsequently used to estimate cognitive and physical age gaps for each subject in the testing sample. These age gaps were then compared among those with and without MCI and correlated with 17 behavioral phenotypes in the domains of lifestyle, well-being, and attitudes. Across 5000 random train-test split iterations, we showed that older cognitive age gaps were significantly associated with MCI (versus cognitively normal) and worse outcomes across several well-being and attitude-related measures. Both age gaps were also significantly correlated with each other. These results suggest accelerated cognitive and physical aging were linked to worse well-being and more negative attitudes about the self and others and reinforce the link between cognitive and physical aging. Importantly, we have also validated the use of cognitive age gaps in the diagnosis of MCI.
Topics: Humans; Aged; Cognitive Dysfunction; Aging; Independent Living; Cognition; Phenotype
PubMed: 37428365
DOI: 10.1007/s11357-023-00864-9