Chronological Age Research Articles

Forensic age estimation in adults based on multidetector computed tomography analysis of bone density in the medial meta-epiphyseal region of clavicle.

The aim of this study was to investigate the potential of using multidetector computed tomography (MDCT) to measure the bone mineral density (BMD) in the medial meta-epiphyseal region of clavicle (MERC) for adult age estimation. A total of 1064 chest MDCT scans from individuals aged 21 to 102years were utilized to determine the MERC BMD. The Mimics software was used for the BMD measurements, and the average BMD of both MERC was also calculated. Regression analysis was conducted with chronological age as a dependent variable and MERC BMD as an independent variable to establish a mathematical model for age estimation. The mean absolute error (MAE) was calculated to evaluate the accuracy of the regression model using an independent validation sample. Among all the models, the cubic regression model showed the highest correlation between MERC BMD and chronological age and also provided the most accurate age prediction for both males and females (MAE = 9.41 for males, MAE = 10.38 for females). Our study suggests that BMD measured by MERC can be utilized for age estimation in adults when more reliable indicators are not available.

Time Since Injury Is Key to Modeling Trends in Aging and Overall Functioning of Persons With Spinal Cord Injury: A Longitudinal Analysis of a Swiss Community Survey.

The aim of the study is to model the health indicator of functioning for persons with spinal cord injury or disorder living in Switzerland in the long-term context, considering chronological age, time since injury, and age at injury. This is a longitudinal analysis of three waves of a community survey. An interval-scaled functioning metric was constructed using modern test theory. Random effects within-between mixed models were used to investigate trends in functioning over time. Persons with complete injuries have lower levels of functioning than persons with incomplete injuries, and persons with complete tetraplegia have the lowest levels of functioning. The person's age when injured, the level and completeness of the injury, the presence of secondary health conditions, and various combinations of these factors significantly influenced how functioning changed over time. Our study shows that chronological age is not necessarily the best time variable for understanding the aging process of people with spinal cord injury in terms of changes in functioning over time and that modeling functioning over time since injury provides important insights into how functioning of people with complete or incomplete injuries declines with increasing time living with the injury.

Old by obsolescence: The paradox of aging in the digital era.

Geroscience and philosophy of aging have tended to focus their analyses on the biological and chronological dimensions of aging. Namely, one ages with the passage of time and by experiencing the cellular-molecular deterioration that accompanies this process. However, our concept of aging depends decisively on the social valuations held about it. In this article, we will argue that, if we study social aging in the contemporary world, a novel phenomenon can be identified: the paradox of aging in the digital era. If the social understanding of aging today is linked to unproductivity and obsolescence; then there is a possibility that, given the pace of change of digital technologies, we become obsolete at an early chronological and biological age, andtherefore, feel old at a younger age. First, we will present the social dimension of aging based on Rowe and Kahn's model of successful aging. We will also show that their notion of social aging hardly considers structural aspects and weakens their approach. Second, departing from social aging in its structural sense, we will develop the paradox of aging in the digital era. On the one hand, we will explain how the institutionalization of aging has occurred in modern societies and how it is anchored in the concepts of obsolescence and productivity. On the other hand, we will state the kind of obsolescence that digitalization produces and argue that it can make cohorts of biologically and chronologically young individuals obsolete, and thus they would be personally and socially perceived as old.

Chronological age estimation based on dental mineralization for Syrian population.

Dental age assessment based on evaluating dental mineralization status is one of the most common methods used in forensic practice. The aim of this study is to enhance the accuracy of age diagnostics and provide reference data from the Syrian population for forensic application. After several selection steps, a total of 280 orthopantomograms (OPGs) from 140 males and 140 females from the Syrian population divided into 14 age groups between 12 and 25 years were analysed. Based on Demirjian's classification system, the mineralization stages of third molars (18, 28, 38 and 48) as well as lower second molars (37 and 47) were evaluated. Statistical investigations and evaluations were carried out to estimate the marginal probabilities of the subjects having attained ages 14 and 18 by generalized estimating equation models. Our results show that no significant differences can be revealed in the mineralization status with respect to jaw side and sex. In the Syrian population, third molars showing mineralization stage G provide evidence of reaching the age of 14 years with the highest standard of proof ("beyond reasonable doubt"). A completed mineralization in lower second molars (stage H) provides very high marginal probabilities (more than 90%) of the subjects having attained age 14 years. Nevertheless, this cannot exclude an age under 14 years. For the age threshold of 18 years, third molars showing incomplete root development (G dental stage or lower) are associated with a low probability (less than 40%) of the subject having reached 18 years of age. A person's probability of having attained 18 years of age is very high (82- 95%) when the roots of third molars are fully developed (stage H). Nevertheless, third molars at stage H do not conclusively exclude an age under 18 years.

Vaccinations in older adults: Optimization, strategies, and latest guidelines.

This article is a summary of the first AGS Symposium entitled "Update on Vaccination Strategies for Older Adults: Matching the Approach to the Individual and the Care Setting." Given declines in host defenses and immune function with aging, vaccinations play a pivotal role in fortifying older adults against preventable infections, resulting diseases, disability, and death. Current guidelines generally list recommendations applicable for an average older adult of a given chronological age. However, growing evidence indicates that heterogeneity in terms of factors as varied as biological sex, frailty, functional status, and multimorbidity may impact vaccine responses and clinical outcomes. As a result, clinicians will increasingly need to take these additional factors into consideration as they seek to improve outcomes through improved targeting of such aging-related heterogeneity. Moreover, efforts at protecting older citizens through vaccination must also include strategies to overcome barriers to the adoption of vaccine recommendations in varied settings including long-term care. This 2023 AGS Plenary Symposium sought to commence a broader dialogue across AGS and beyond on optimizing vaccinations for older adults, ensuring not only extended lifespans but also healthier and more active lives. This report is not a systematic review, and thus should not be considered comprehensive.

The association between cardiovascular health and physiologic age as determined by artificial intelligence-enabled electrocardiography

Abstract Background The concept of ideal cardiovascular health (CVH) was developed by the American Heart Association (AHA) as a composite of seven health factors and behaviors labeled as the Life’s Simple 7 (LS7). Ideal CVH has been associated with complex measures of cardiovascular aging. We hypothesized that physiologic aging, as determined by a simple, routinely used tool such as the electrocardiogram (ECG) enabled by an artificial intelligence algorithm to predict an individual’s physiologic age (ECG age), will be associated with poor CVH as defined by an adapted version of the AHA LS7. Methods We conducted an observational retrospective study of consecutive patients aged ≥18 years who sought care between June 2019 and March 2022, self-reported behavioral history through a validated intake questionnaire in clinical outpatient visits, had a clinically indicated ECG within one year, and provided research authorization. We applied a previously developed AI-ECG convolutional neural network to derive the ECG-Age. We excluded patients with missing data, those with body mass index (BMI) <18.5 kg/m2, previous history of left-bundle branch block, atherosclerotic cardiovascular disease events, atrial fibrillation, heart failure, and non-skin cancer. An adapted version of the LS7 was used to define CVH, which includes anthropometrics (e.g., BMI), behavioral history (e.g., smoking, physical activity, and diet), vital signs (e.g., blood pressure), and laboratory values (e.g., total cholesterol, and fasting glucose). CVH was stratified as ideal, intermediate, and poor if the LS7 score had values of 9-14, 7-8, and 0-6, respectively, constructed by adding 2,1 or 0 points to each metric that met each category criteria, respectively. Accelerated aging was defined as having a Delta-Age ≥1SD above the mean, whereas Delta-age was defined as ECG minus chronological age. Logistic regression models tested the association between CVH categories and positive Delta-Age while adjusting for chronological age and sex. Results We included 114,214 individuals (53.3% females, 86.9% Caucasian). Mean chronological age was 57.7±14 years and mean AI-ECG- Age was 58 ± 12.44 years (R2 0.80, p-value <0.001). Mean Delta-Age was 0.28 ±8.8 years. LS7 average score was 8.3 ± 2.23; 46.8% had ideal, 31.5% intermediate, and 21.7% had poor CVH (Figure A). Compared to those with ideal CVH, those with poor CVH were more than 50% more likely to have Delta-Age ≥1SD (OR 1.61, CI 95% 1.54-1.69, p-value: <0.001), independent of chronological age and sex. (Figure B). Conclusion Poor CVH, defined by the AHA LS7, is linked with biological aging as determined by AI-enabled ECG, highlighting the importance of promoting CVH in adults as a strategy to decelerate biological aging and reduce its associated mortality.

Prognostic assessment based on the trajectory of artificial intelligence-enabled electrocardiograms for age estimation

Abstract Background Artificial Intelligence (AI) estimation of age using data from the 12-lead electrocardiogram (ECG) has demonstrated its utility as a biomarker in predicting the future risk of cardiac diseases and mortality. However, expected long-term outcomes based on temporal changes in the AI-ECG age gap remain unclear. Purpose This study aims to utilize the difference between ages predicted by AI-ECG and actual ages longitudinally for follow-up and to assess whether there are significant clinical differences in mortality among specific groups. Methods Among patients at a hospital who had not been used for the AI-ECG age prediction model (121,702 individuals, 522,261 ECGs), 6,685 individuals with 31,858 ECGs who had an index ECG and the subsequent ECG in the fifth year, with at least one intervening ECG were included for analysis. The absolute age gap, defined as the absolute difference between ECG-predicted and chronological age, was limited to ±10 for analysis. Latent class trajectory modeling was then utilized to classify groups based on the AI-ECG age gap. Results Data-driven analysis revealed four distinct trajectory groups based on age gap trajectories: Consistently Young ECG (27.2%), Reversed ECG Aging (35.3%), Accelerated ECG Aging (8.0%), and Consistently Old ECG (29.5%). Notably, the Accelerated ECG Aging group exhibited a significantly higher cumulative incidence of all-cause death (adjusted hazard ratio (HR) 1.66 (95% confidence interval (CI) 1.30-2.12, p-value <0.001) and non-cardiac death (adjusted HR 1.83 (95% CI 1.38-2.43, p-value <0.001) compared to the Consistently Young ECG group. Conclusion Among 4 distinct trajectories based on the changes in the AI-ECG age gap, Accelerated ECG Aging was associated with a heightened risk of all-cause mortality. Additional in-depth analysis of factors associated with Accelerated ECG Aging is warranted.

Comparison of the predicting value for incident cardiovascular events by vascular age based on brachial-ankle pulse wave velocity or carotid-femoral pulse wave velocity

Abstract Background There is a good correlation between brachial-ankle pulse wave velocity（baPWV）and carotid-femoral pulse wave velocity (cfPWV). Whether baPWV has the same predictability for cardiovascular risk as cfPWV in calculating vascular age remains to be clarified. Purpose This study examines which vascular age calculated by baPWV or cfPWV has a stronger association with the risk of cardiovascular events. Methods This prospective cohort study included 5725 participants from a community atherosclerosis cohort in Beijing, China. Vascular age was the predicted age in a multivariable regression model, including classical cardiovascular risk factors (sex, systolic and diastolic blood pressure, glycemia, triglyceride, low density lipoprotein cholesterol, waist, body mass index, heart rate, smoking) and treatment (anti-hypertensive agents, hypoglycemic agents, lipid-lowering agents) and pulse wave velocity. Residuals between chronological age and vascular age were defined as ∆-age, and the 10th and 90th percentiles of ∆-age were used as cutoffs to define supernormal vascular aging, normal vascular aging, and early vascular aging, respectively. The major adverse cardiovascular event was a composite of myocardial infarction, stroke, and cardiovascular death. The study used Cox proportional hazards regression to examine the association between vascular age and major adverse cardiovascular events. Results During the median 3-year follow-up period, 172 endpoint events were observed. After adjusting for age and sex, ∆-age as a continuous variable calculated by baPWV was significantly and increasingly associated with cardiovascular events (every 1-year increase led to a rise of 1.04% [95% confidence interval [CI]: 1.00%-1.08%; p=0.03] in cardiovascular events risk). After adjusting for age, sex, smoking, body mass index, hypertension, diabetes, dyslipidemia, anti-hypertensive agents, lipid-lowering agents, hypoglycemic agents, family history of atherosclerotic cardiovascular disease, and estimated glomerular filtration rate, early vascular aging group calculated by baPWV had an increased risk of cardiovascular events (hazard ratio: 1.76; 95% CI: 1.19-2.62 p=0.005), compared with the normal vascular aging group. In contrast, no significant results were observed in vascular age calculated by cfPWV. Conclusion(s) Vascular age calculated by baPWV has a stronger predictive ability for cardiovascular events than that calculated by cfPWV in the Chinese population. Considering baPWV is a simple and convenient method, it is recommended for vascular age calculation.

Does PhenoAge acceleration impact adverse outcomes following major surgery (rehospitalization or death)? A genome-wide association study and mendelian randomisation

Abstract Background Biological aging reflects the state of an individual's cellular and molecular function, and is distinct from chronological age; which reflects the passage of time[1]. Validated measures of biological aging, such as Phenotypic aging (PhenoAge) estimated from nine routine clinical biomarkers, have been associated with increased risk of age-related diseases and mortality in cohort studies[2]. However, the potential causal relationship between them remains unclear. In this study, we aim to identify genetic variants associated with PhenoAge Acceleration (PhenoAgeAccl) and adverse outcome after surgery (rehospitalisation or death within 365-days] and assess the causal relationship between these two phenotypes using Mendelian Randomization framework. Objectives -Perform a genome-wide association study(GWAS) on adverse outcomes following major surgery. -Assess the potential causal relationship between PhenoAgeAccl and adverse outcomes following major surgery using Mendelian randomization analysis. Methods We conducted a GWAS via plink using participants from the UKBiobank (Application 77596) to generate summary statistics for 6509 participants who had undergone major surgery within 365 days of their baseline assessment date(3). For quality control, SNPs were excluded if they didn't meet the following criteria: (1) imputation information score <0.3 (2) minor allele frequency <1%, (3) Hardy–Weinberg equilibrium test p-value significant at the Bonferroni-corrected level. Summary statistics for PhenoAgeAccl was obtained from a previous GWAS of European descents[4]. Gene enrichment analysis was performed using Multi-marker Analysis of GenoMic Annotation (MAGMA) in Functional Mapping and Annotation (FUMA). Then, genetic instruments for PhenoAgeAccl and adverse outcomes following surgery were identified to conduct a summary-based Mendelian Randomisation [Assumptions are listed in Figure 1]. Results GWAS for adverse outcomes following major surgery identified 3 lead SNPs (rs1360618, rs11848297, rs7978) [Figure 2]. Gene-set analysis showed enrichment in the immune system, cell migration, fear response, and iron metabolism. For PhenoAge Acceleration, gene-set analysis showed enrichment in immune system, and cell signalling pathways. 17 SNPs were selected which were used for the Two-Sample MR. We found no causal relationship, as the beta coefficient for Inverse variance weighted was -0.308 [standard error - 0.075, p=0.68]. For every 5-year increase in PhenoAge compared to chronological age, the risk of emergency re-hospitalisation or death increased by 21% (HR 1.16 – 1.3, p < 0.001). Conclusion Gene-set analysis for adverse outcomes following major surgery showed enrichment in immune system, iron metabolism, and fear response which have all recently been associated with mortality post-surgery in cohort studies(4,5). No causal relationship between PhenoAgeAccel and mortality post-surgery was established.Mendelian Randomisation assumptionsManhattan plot adverse-outcomes post-sur

Mortality risk in patients with carotid artery disease undergoing carotid endarterectomy compared to the general population

Abstract Background Patients with carotid artery disease undergoing carotid endarterectomy (CEA) appear to be at high risk of all-cause mortality during their follow-up. However, the possible predictors of this adverse outcome are not elucidated yet. Purpose We aimed to test cardiovascular (CV) risk factors as possible predictors of all-cause mortality in patients undergoing CEA and to determine whether there is a difference between the mortality rate of our study cohort and the general population. We hypothesize that CEA-treated patients have a higher mortality rate during follow-up after surgery than the general population, which is not related to the presence of CV risk factors. Methods We prospectively enrolled and followed up patients undergoing CEA. Univariate and multivariable Cox regression were performed to test whether common CV risk factors (demographics, laboratory values, comorbidities, and events before CEA) were predictive of all-cause mortality. We then compared the standardized mortality ratio (SMR) of our population and the age-sex-matched Minnesota (MN) general population at 5,10, and 21 years of follow-up after CEA. Results A total of 710 patients were followed for a median of 7.1 [2.9-11.9] years after CEA. Mortality occurred in 198 (27.0%) patients during the follow-up. The median time to event was 11.0 [10.0-12.0] years. Age was the only predictor of all-cause mortality in CEA-treated patients in multivariable models adjusted for known CV risk factors (Model 1: HR 1.03 [95%CI 1.01-1.06]; p=0.004; Model 2: HR 1.02 [95%CI 1.00-1.04]; p=0.023) (Table 1). However, the CEA-treated group had a higher mortality rate than would have been expected in the MN population (log-rank p<0.001) (Fig.1A). Compared to the MN population, at 5 years of follow-up only patients <60 years old had a higher SMR (4.53 [1.95-8.92]; p=0.001), whereas, at 10 years and 21 years of follow-up, our study population had a higher mortality rate at any age (Fig.1B). Conclusions The elevated mortality risk post-CEA in our cohort cannot be solely attributed to chronological aging. The underlying process of atherosclerosis likely contributes to all-cause mortality, and plaque characteristics may offer additional insight into the risk stratification of this patient group.All cause mortality predictors after CEAStudy population vs. MN population SMR

Multi-modality deep learning prediction of heart age: insights from UK-Biobank

Abstract Background Identification of apparently healthy individuals at high cardiovascular risk who may benefit from targeted preventive strategies remains challenging. Predicted heart age, the chronological age of individuals with similar absolute cardiovascular disease (CVD) risk but with no risk factors, has been proposed to appropriately describe the absolute CVD risk over conventional prediction tools. Purpose To develop multi-modality based deep learning predictors for heart age in UK-Biobank individuals. Methods We used deep learning to develop a heart age predictor based on videos from heart magnetic resonance imaging (MRI) (anatomical dimension), electrocardiograms (ECG) (electrical dimension) or both from 45000 individuals of the UK Biobank cohort (age range 45-81 years) (Figure). We estimated the heritability of heart aging and identified single-nucleotide polymorphisms associated with accelerated heart aging. We performed X-wide association study to identify non-genetic factors potentially associated with accelerated heart aging. Results We predicted age with a mean absolute error (MAE) of 2.5±0.03 years (R2=85.6±0.6%) and found that accelerated heart aging is heritable at more than 35%. MRI-based anatomical features predicted age better than ECG-based electro-physiological features (MAE=2.29±0.03 years versus 4.90±.0.08 years), and heart anatomical and electrical aging were weakly correlated (Pearson correlation=0.249±0.002). Our attention maps highlighted the aorta, the mitral valve, and the interventricular septum as key anatomical features driving heart age prediction in videos that capture the entire heartbeat cycle. We found accelerated anatomical and electrical heart aging to be genetically correlated (Pearson correlation=0.508±0.089). The most significant locus was in TTN (Titin) and it was associated with heart anatomical aging. Cardiovascular diseases, general health status, and mental health disorders were highly associated with accelerated heart aging. Conclusions A multi-modality based deep learning approach was highly predictive of heart age. Accelerated heart age has a complex biological basis with genetic and environmental correlates. Leveraging of the described pipeline to predict survival and the onset of age-related cardiovascular diseases may shed light on heart aging role in health outcomes and effectively guide personalized preventive strategies.

Validity of specific CPT indices in differentiating school-aged children previously diagnosed with attention deficit/hyperactivity disorder from school-aged children with non-attention deficit/hyperactivity disorder in general education classrooms: a case control study

BackgroundContinuous performance tests (CPTs) are a popular tool for evaluating the symptoms of attention-deficit/hyperactivity disorder (ADHD). Performance measurements are typically linked to the biological features and cognitive functions of individuals. To determine the validity of specific CPT indices in differentiating between school-aged children with ADHD from with non-ADHD, each student’s sex, chronological age, and cognitive abilities should be considered.MethodsIn this prospective case–control study, a total of 30 non-ADHD students and 26 with ADHD who were aged 6 to 12 years were from general education classrooms. All students completed the Continuous Performance Tests (CPTs) and the Peabody Picture Vocabulary Test-revised (Mandarin-Chinese version). Demographic data were collected from the students’ parents.ResultsDetectability, Omissions, Commissions, and Hit Reaction Time Standard Deviation (HRT SD) yielded higher T-scores in children with ADHD than those without. Compared with non-ADHD students, those with ADHD had higher classification scores for Detectability, Omissions, Perseverations, and HRT SD. For each CPT index, after individual factors were controlled for, logistic regression revealed that only students with positive scores for Detectability, Omission, and HRT SD (adjusted odds ratios = 4.627, 9.977, and 3.908, Ps < 0.05) were likely to receive a diagnosis of ADHD. Furthermore, after individual characteristics were controlled for, Logistic regression also revealed that the cumulative positive scores of the Detectability, Omission, or and HRT SD remained associated with an increased risk of ADHD (adjusted odds ratio = 3.116, P < 0.01).ConclusionsCompared with school-aged children with non-ADHD in general education classrooms, those with ADHD exhibited significantly lower performance in inattention-related CPT indices. To reach an accurate diagnosis through CPTs, clinicians should pay attention to Detectability, Omission, and HRT SD. Compared with other CPT indices, Detectability, Omission, and HRT SD may function as more suitable indicators for distinguishing between school-aged children with and non-ADHD in general education classrooms. These indicators are robust and unobscured by individual characteristics.

Profile of Medico-legal Age Estimation Cases in a Tertiary Hospital in Kathmandu

Introduction: Medico-legal age estimation is the estimation of chronological age of an individual which is based upon analysis of physical development of the body, development of secondary sexual traits, tooth eruption, appearance of ossification centers of bones, and the degree of epiphyseal union. Chronological age of the subject whether a minor (less than 18 years) or an adult determines the fate of the case in the court of law. Objective: The present study revealed varied profiles of the perpetrator and the victim who came for medico-legal age estimation. Methods: This is a retrospective, descriptive cross-sectional study conducted in the Department of Forensic Medicine, Maharajgunj Medical Campus, Institute of Medicine (IoM). The data of medico-legal age estimation of 5 years period was analyzed using the statistical software SPSS 20. Results: Total of 313 individuals were studied. Majority of the individuals were within the age group of 16-17 years. Males (70.3 %) outnumbered females (29.7 %). Theft (31.9 %) was common among subjects, followed by sexual assault (18.2 %) and majority of subjects (59.7%) came from Province 3. Sixty percent of the subject’s alleged age, matched the estimated ages. Conclusion: This study shows the status of children in crime which demands attention of concerned authority. Males were significantly involved in theft and females were victims of sexual assault. Forty percent of the alleged age given by the subject differed from the estimated age, to change the fate of the case. These results could greatly benefit the judiciary system of Nepal.

An explainable machine learning estimated biological age based on morphological parameters of the spine.

Accurately estimating biological age is beneficial for measuring aging and predicting risk. It is widely accepted that the prevalence of spine compression increases significantly with age. However, biological age based on vertebral morphological data is rarely reported. In this study, a total of 2,364 participants from the National Health and Nutrition Examination Survey were enrolled, and morphological parameters of the spine were collected from lateral radiographs scanned by dual energy X-ray absorptiometry. The biological age of the spine, called SpineAge, was calculated with the parameters by machine learning models. The SHapley Additive exPlanation was used for better interpreting each parameter's contribution. Besides, an Accelerated Aging Index (AAI) was defined as SpineAge minus chronological age and was used to quantify the accelerating aging degree of the spine. The results indicated that the SpineAge performed better than chronological age did in predicting 2-year and 5-year all-cause mortality. After adjusting all covariates, there was a significant association between AAI and all-cause mortality risk. Specifically, each 1-year increase in AAI was associated with a 25.9% increase in all-cause mortality risk (Hazards ratio, 1.259; 95% CI, 1.087-1.457; P < 0.001). Considering the first quartile of AAI as a reference, the mortality risks for the second, third, and fourth quartiles were 2.389 (95% CI, 1.064-5.364; P = 0.035), 5.911 (95% CI, 2.241-15.590; P < 0.001) and 22.925 (95% CI, 4.744-110.769; P < 0.001) times higher, respectively. Our study developed a novel and highly applicable biological-age predictor for predicting individualized long-term prognosis and facilitating personalized care.

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

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

Age-associated imbalance in immune cell regeneration varies across individuals and arises from a distinct subset of stem cells.

The age-associated decline in immunity manifests as imbalanced adaptive and innate immune cells, which originate from the aging of the stem cells that sustain their regeneration. Aging variation across individuals is well recognized, but its mechanism remains unclear. Here, we used high-throughput single-cell technologies to compare mice of the same chronological age that exhibited early or delayed immune aging phenotypes. We found that some hematopoietic stem cells (HSCs) in earlyaging mice upregulated genes related to aging, myeloid differentiation, and stem cell proliferation. Delayed aging was instead associated with genes involved in stem cell regulation and the response to external signals. These molecular changes align with shifts in HSC function. We found that the lineage biases of 30% to 40% of the HSC clones shifted with age. Moreover, their lineage biases shifted in opposite directions in mice exhibiting an early or delayed aging phenotype. In earlyaging mice, the HSC lineage bias shifted toward the myeloid lineage, driving the aging phenotype. In delayedaging mice, HSC lineage bias shifted toward the lymphoid lineage, effectively counteracting aging progression. Furthermore, the anti-aging HSC clones did not increase lymphoid production but instead decreased myeloid production. Additionally, we systematically quantified the frequency of various changes in HSC differentiation and their roles in driving the immune aging phenotype. Taken together, our findings suggest that temporal variation in the aging of immune cell regeneration among individuals primarily arises from differences in the myelopoiesis of a distinct subset of HSCs. Therefore, interventions to delay aging may be possible by targeting a subset of stem cells.

Examining the Influence of Season Phase, Age, and Anthropometrics on Body Composition Trends in National Basketball Association Athletes.

Russell, JL, Baker, BS, Mercer, RA, and McLean, BD. Examining the influence of season phase, age, and anthropometrics on body composition trends in NBA athletes. J Strength Cond Res XX(X): 000-000, 2024-This study aimed to describe seasonal body composition changes in NBA athletes using Dual-energy X-ray Absorptiometry (DXA) and to explore the relationship between these changes and factors such as age and anthropometrics. A retrospective analysis was conducted using 402 DXA scans from 62 professional male basketball players, obtained from one NBA team between 2012 and 2023. Seasonal phases were defined as preseason, pre all-star, post all-star, and offseason. A custom region of interest (ROI) method was used to ensure consistent scan areas for taller athletes who exceeded the DXA bed length. Linear mixed models analyzed the influence of seasonal phases, age, and height on lean mass (LM), fat mass (FM), and bone mineral content (BMC). In-season phases (pre all-star and post all-star) showed small increases in LM and decreases in FM compared with the preseason, with no significant BMC changes. Chronological age was associated with increases in LM but not FM. Taller athletes exhibited increases in LM, FM, and BMC. Significant random variance across players and seasons indicated that additional unmeasured factors might influence body composition. NBA athletes generally gain LM and lose FM during the season, with older players showing increased LM gain. The study's novel ROI method provide reliable data for athletes exceeding standard DXA limits, highlighting the importance of tailored body composition assessments in professional basketball. These findings can inform strength and conditioning, nutrition, and player development strategies across different seasonal phases and ages.

Lack of Association of Chronological Age and Antithrombotic Agents With Acute Intracranial Hemorrhage in the Group of Older Adults With Traumatic Brain Injury.

Some reports suggest that older patients with traumatic brain injury (TBI) are more likely to experience acute intracranial hemorrhage, resulting in poor outcomes. However, the association between precise chronological age and use of antithrombotic agents with acute intracranial hemorrhage in these patients remains unknown. The aim of this study was to determine factors associated with acute intracranial hemorrhage and poor outcomes in patients with TBI, including chronological age and use of antithrombotic agents. Patients hospitalized for TBI between January 2006 and December 2021 were included. Patients were categorized by age groups of <65 years, 65 to 74 years, 75 to 84 years, and ≥85 years. Associations between each age group and acute intracranial hemorrhage, a poor outcome at discharge, and in-hospital mortality were evaluated. The cohort included 1086 patients, with 713 (65.7%) in the ≥65 age group. Although chronological age was associated with acute intracranial hemorrhage in patients aged <65 years (odds ratio [OR] 1.02; 95% CI 1.01-1.03), it was not associated with patients aged ≥65 years. None of the antithrombotic agents investigated were associated with acute intracranial hemorrhage in the group aged ≥65 years. Although chronological age was associated with a poor outcome in patients aged <65 years (OR 1.03; 95% CI 1.01-1.07), it was not associated in those aged ≥65 years. The ≥85 year age group (OR 2.30; 95% CI 1.18-4.51) compared with <65 years were significantly associated with a poor outcome. None of the antithrombotic agents investigated were associated with a poor outcome in the group aged ≥65 years. Our findings confirmed the lack of an association of chronological age and antithrombotic agents with acute intracranial hemorrhage in the group of older adults with TBI. Our findings suggest that antithrombotic agents may be safely used, even in older adults.

56 Iron deficiency is common in extreme preterm infants despite prophylactic iron supplementation

Abstract Background Iron deficiency (ID) in childhood is linked to adverse neurodevelopmental outcomes in the long-term. Extremely premature infants (EPI; born &amp;lt; 28 weeks gestation) are more susceptible to ID due to a multitude of factors including lower iron reserves at birth, rapid growth, immature erythropoiesis, and multiple phlebotomy tests during hospital stay. Therefore, professional societies advise prophylactic iron therapy for these infants from 2-6 weeks age until 6-12 months. However, there is paucity of data on the iron status in EPI receiving this prophylactic iron supplementation. Objectives To investigate the prevalence and risk factors associated with ID in EPI. Design/Methods A retrospective cohort study analyzed a Provincial database of EPIs born between 2005-2018. Infants with congenital malformations, chromosomal anomalies and blood disorders were excluded. All included infants received prophylactic iron supplementation from 2-4 weeks of chronological age that was recommended to continue at discharge until 9-12 months corrected age (CA). At 4-6 months CA, these infants underwent blood testing. ID was defined as serum ferritin &amp;lt; 20mcg/L at 4 months CA or SF &amp;lt; 12mcg/L at 6 months CA. Through a univariate analysis using single-variable logistic regression, factors linked to ID were identified. Those with a p-value &amp;lt; 0.20 progressed to a multivariable model, retaining variables with a p-value &amp;lt; 0.05. Results Of 146 infants, 45.9% had ID. ID group had lower mean ferritin (16.4 µg/L vs 50.0 µg/L, p &amp;lt; .001) and reticulocyte hemoglobin equivalent (28.4pg vs 31.6pg, p &amp;lt; .001) compared to the non-ID group. ID prevalence reduced from 59.7% in 2005-2011 to 40.3% in 2012-2018 cohort. Exclusive or partial breastfeeding at 4-6 months CA was protective against ID (Odds: 0.2, p = 0.003). Iron therapy at 4-6 months CA was not protective in the final model. Notably, 27.4% stopped iron therapy before 4-6 months CA, and exclusive formula-fed infants had notably lower iron intake than breastfed infants (66.1% vs 99.1%, p = 0.006). Conclusion Nearly half of the EPI exhibited ID at 4-6 months CA despite prophylactic iron supplementation. About a quarter discontinued therapy before reaching this age. Breastfeeding at this age was protective against ID. This high prevalence of ID in EPI underscores the need of future studies to discern strategies to mitigate ID in this vulnerable population.

The environmental impact on aging: Insights from buccal mucosa and molecular biomarkers

Buccal epithelial cells serve as a primary barrier against the inhalation and ingestion of harmful substances, working alongside immune system cells such as natural killer cells to protect the body from health-damaging factors. These epithelial cells can also be used as an alternative tissue source for monitoring the genotoxic effects of external factors such as chemical exposure. This assessment can be performed using molecular biomarkers of aging, which reflect biological age and indicate cellular aging acceleration due to internal and external damage factors, such as environmental hazards. In contrast to chronological age, which merely reflects the passage of time, biological age accounts for individual variation in aging processes. Molecular biomarkers are crucial for distinguishing between normal and pathological processes in the body and for identifying the effects of external factors such as chemical exposures. The identification of specific biomarkers enhances the ability to detect and monitor adverse biological responses and accelerated aging. This review aims to highlight the routes through which environmental hazards enter the body, the application of buccal epithelial cells in assessing genetic modifications, and the introduction of potential molecular biomarkers. However, further research is necessary to elucidate the roles of these biomarkers in determining aging rates and individual variability. Understanding their implications may also help identify new therapeutic targets for preventing premature aging, treating age-related diseases, and developing potential treatments.