Chronological Age Research Articles

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.

When to Trust Epigenetic Clocks: Avoiding False Positives in Aging Interventions.

Recent human studies have suggested that aging interventions can reduce aging biomarkers related to morbidity and mortality risk. Such biomarkers may potentially serve as early, rapid indicators of effects on healthspan. An increasing number of studies are measuring intervention effects on epigenetic clocks, commonly used aging biomarkers based on DNA methylation profiles. However, with dozens of clocks to choose from, different clocks may not agree on the effect of an intervention. Furthermore, changes in some clocks may simply be the result of technical noise causing a false positive result. To address these issues, we measured the variability between 6 popular epigenetic clocks across a range of longitudinal datasets containing either an aging intervention or an age-accelerating event. We further compared them to the same clocks re-trained to have high test-retest reliability. We find the newer generation of clocks, trained on mortality or rate-of-aging, capture aging events more reliably than those clocks trained on chronological age, as these show consistent effects (or lack thereof) across multiple clocks including high-reliability versions, and including after multiple testing correction. In contrast, clocks trained on chronological age frequently show sporadic changes that are not replicable when using high-reliability versions of those same clocks, or when using newer generations of clocks and these results do not survive multiple-testing correction. These are likely false positive results, and we note that some of these clock changes were previously published, suggesting the literature should be re-examined. This work lays the foundation for future clinical trials that aim to measure aging interventions with epigenetic clocks, by establishing when to attribute a given change in biological age to a bona fide change in the aging process.

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.

Gerotranscendence and satisfaction with life: contributions of the centrality of events and religiosity

Tornstam identified a potential maturation process during aging that leads to a new vision and understanding of life, which he called gerotranscendence and which proved to be linked to the maintenance of well-being levels. Although his contributions have aroused considerable interest, they have also prompted much criticism. In order to empirically explore the proposal, an incidental sample of 370 Argentine adults was administered a battery of instruments that evaluated: age, spiritual transcendence, life satisfaction, centrality of events, and religiosity. The results indicate that age partially explains spiritual transcendence, just as age partially explains life satisfaction. However, the relationship between age and spiritual transcendence is not exclusive to old age. On the other hand, in the exploration of latent variables at chronological age, the centrality of events makes it possible to partially explain both the variance of spiritual transcendence and life satisfaction. While religiosity, in its socio-community contribution, is excluded from the explanatory model of gerotranscendence. Future studies should delve into the possible latent variables at chronological age and the socio-community contributions that could add to the understanding of the link between aging, spiritual transcendence and well-being.

Epigenetic aging and fecundability: the Norwegian Mother, Father and Child Cohort Study.

Is there an association between male or female epigenetic age acceleration (EAA) or deceleration (EAD) and fecundability? We do not find compelling evidence of an association between EAA or EAD and fecundability. Prior research has shown that female accelerated epigenetic aging is associated with unfavorable clinical fecundity outcomes and use of in vitro fertilization, and that epigenetic aging in sperm cells is associated with unfavorable sperm parameters. Studies of epigenetic aging and fecundability among individuals who conceive naturally are lacking. This study is based on the Norwegian Mother, Father and Child Cohort Study (MoBa), a population-based pregnancy cohort which recruited pregnant couples between 1999 and 2008. We used data from 1657 couples (women and men) with planned naturally conceived pregnancies and available blood samples. Methylation levels were measured in DNA from blood samples taken recruitment (at ∼18 gestational weeks) from pregnant women and their partners using the Illumina Methylation EPIC Array. To obtain a measure of EAA/EAD, we performed a linear regression of each of seven different established epigenetic biomarkers (DNAmAge by Horvath, DNAmAge by Hannum et al., PhenoAge by Levine et al., DunedinPoAm by Belsky et al., DunedinPACE by Belsky et al., DNAmTL by Lu et al., and GrimAge by Lu et al.) against chronological age. We fitted proportional probability regression models to obtain fecundability ratios (FRs) for each standard deviation increase in epigenetic aging, and obtained crude and adjusted (for body mass index, smoking, and education level) estimates. Results were evaluated at a false discovery rate (FDR) of 5%. We evaluated all models for non-linear associations using categories of epigenetic age where appropriate. Although the DunedinPACE clock in males demonstrated slightly increasing fecundability with increasing EAA (adjusted FR 1.05 per one standard deviation increase in EAA, 95% CI 1.00-1.10), this was not robust when evaluated at an FDR of 5%. We found evidence of non-linearity between biological aging and fecundability in two models in females and three models in males, but non-linear associations were weak and conflicting. As MoBa is a pregnancy cohort, our findings may not be generalizable to all couples attempting conception. Fecundability is a couple-level measure, and any impacts of epigenetic aging in each partner may be obscured by effects of the other partner. Our findings contrast with those of prior studies, which have indicated an association between EAA and unfavorable clinical fertility outcomes in populations using fertility treatments, possibly due to less important effects of epigenetic aging among couples who conceive naturally. More research is needed on the association between blood-based EAA and clinical fertility parameters in both sexes. The study was supported by the Research Council of Norway through its Medical Student Research Program funding scheme (project number 271555/F20), its Centres of Excellence funding scheme (project number 262700), and a grant from the Women's Health Program (320656). Co-funding was also received from the Strategic Research Council (SRC), FLUX consortium, decision numbers 345130 and 345131; the National Institute on Aging (R01AG075208); grants to the Max Planck-University of Helsinki Center from the Max Planck Society (decision number 5714240218), Jane and Aatos Erkko Foundation, Faculty of Social Sciences at the University of Helsinki, and Cities of Helsinki, Vantaa, and Espoo; and the European Research Council; and the European Research Council (ERC Synergy, BIOSFER, grant number 101071773, and the Horizon 2020 research and innovation program, grant number 947684). The authors declare no conflicts of interest. N/A.

On the necessity of accounting for age structure in human malaria transmission modeling

Malaria is one of the most common mosquito-borne diseases widespread in tropical and subtropical regions, causing thousands of deaths every year in the world. In a previous paper, we formulated an age-structured model containing three structural variables: (i) the chronological age of human and mosquito populations, (ii) the time since they are infected, and (iii) humans waning immunity (i.e. the progressive loss of protective antibodies after recovery). In the present paper, we expand the analysis of this age-structured model and focus on the derivation of entomological and epidemiological results commonly used in the literature, following the works of Smith and McKenzie. We generalize their results to the age-structured case. In order to quantify the impact of neglecting structuring variables such as chronological age, we assigned values from the literature to our model parameters. While some parameters values are readily accessible from the literature, at least those about the human population, the parameters concerning mosquitoes are less commonly documented and the values of a number of them (e.g. mosquito survival in the presence or in absence of infection) can be discussed extensively. Our analysis, informed by parameter values from the literature, demonstrates that overlooking those structural variables of human and mosquito populations may result in inaccurate epidemiological predictions and suboptimal control strategies. We highlight the epidemiological implications of these findings and emphasize the necessity of considering age structure in future malaria control programs.

Longitudinal accelerated brain age in mild cognitive impairment and Alzheimer's disease.

Brain age is a machine learning-derived estimate that captures lower brain volume. Previous studies have found that brain age is significantly higher in mild cognitive impairment and Alzheimer's disease (AD) compared to healthy controls. Few studies have investigated changes in brain age longitudinally in MCI and AD. We hypothesized that individuals with MCI and AD would show heightened brain age over time and across the lifespan. We also hypothesized that both MCI and AD would show faster rates of brain aging (higher slopes) over time compared to healthy controls. We utilized data from an archival dataset, mainly Alzheimer's disease Neuroimaging Initiative (ADNI) 1 with 3Tesla (3 T) data which totaled 677 scans from 183 participants. This constitutes a secondary data analysis on existing data. We included control participants (healthy controls or HC), individuals with MCI, and individuals with AD. We predicted brain age using a pre-trained model and tested for accuracy. We investigated cross-sectional differences in brain age by group [healthy controls or HC, mild cognitive impairment (MCI), and AD]. We conducted longitudinal modeling of age and brain age by group using time from baseline in one model and chronological age in another model. We predicted brain age with a mean absolute error (MAE) < 5 years. Brain age was associated with age across the study and individuals with MCI and AD had greater brain age on average. We found that the MCI group had significantly higher rates of change in brain age over time compared to the HC group regardless of individual chronologic age, while the AD group did not differ in rate of brain age change. We replicated past studies that showed that MCI and AD had greater brain age than HC. We additionally found that this was true over time, both groups showed higher brain age longitudinally. Contrary to our hypothesis, we found that the MCI, but not the AD group, showed faster rates of brain aging. We essentially found that while the MCI group was actively experiencing faster rates of brain aging, the AD group may have already experienced this acceleration (as they show higher brain age). Individuals with MCI may experience higher rates of brain aging than AD and controls. AD may represent a homeostatic endpoint after significant neurodegeneration. Future work may focus on individuals with MCI as one potential therapeutic option is to alter rates of brain aging, which ultimately may slow cognitive decline in the long-term.

IUP Technological Signatures or Mousterian Variability? The Case of Riparo l'Oscurusciuto (Southern Italy)

In the Italian peninsula, the Late Middle Paleolithic exhibits significant technological diversity, featuring blades, points, and bladelets. Assemblages displaying these distinctive characteristics have, in some cases, been labeled as Musteriano evoluto or Evolved Mousterian, and they are interpreted as contributing to the technological and typological variability within the Middle Paleolithic. In this study, we report the results of a detailed technological analysis of the lithics recovered from the latest layers preserved at Riparo l’Oscurusciuto (SU1 to SU3) in southern Italy. These layers were previously attributed to the Late Mousterian based on their chronological age and a preliminary techno/typological analysis of a small number of artifacts. Our comprehensive analysis of entire assemblages reveals the presence of original technological features, including blades, bladelets, and specific production of micro-points on flake cores. Some of these technological traits are comparable to those recently described at Grotte Mandrin in south-eastern France, which have been attributed to an early phase of the Initial Upper Paleolithic and associated with one modern human tooth. The study confirms the variability of the Late Middle Paleolithic in southern Italy and emphasizes the necessity to reassess it considering recent theories on the earlier arrival of Homo sapiens in Europe and their potential interaction with local populations.

A review of artificial intelligence-based brain age estimation and its applications for related diseases.

The study of brain age has emerged over the past decade, aiming to estimate a person's age based on brain imaging scans. Ideally, predicted brain age should match chronological age in healthy individuals. However, brain structure and function change in the presence of brain-related diseases. Consequently, brain age also changes in affected individuals, making the brain age gap (BAG)-the difference between brain age and chronological age-a potential biomarker for brain health, early screening, and identifying age-related cognitive decline and disorders. With the recent successes of artificial intelligence in healthcare, it is essential to track the latest advancements and highlight promising directions. This review paper presents recent machine learning techniques used in brain age estimation (BAE) studies. Typically, BAE models involve developing a machine learning regression model to capture age-related variations in brain structure from imaging scans of healthy individuals and automatically predict brain age for new subjects. The process also involves estimating BAG as a measure of brain health. While we discuss recent clinical applications of BAE methods, we also review studies of biological age that can be integrated into BAE research. Finally, we point out the current limitations of BAE's studies.

A Systematic Review Toward a Comprehensive Framework for Measuring Non-chronological Age

As populations age globally, there is a need to refine the understanding of aging beyond simply chronological age. Therefore, this study used the PRISMA framework to synthesize literature on alternative aging metrics beyond chronological age. We analyzed articles from 6 databases (SCOPUS, PsychINFO, Web of Science, CINAHL, Engineering Village, and PubMed) and selected 18 articles that explored factors that lead to older adults’ individual differences. Findings indicated the necessity of a consistent terminology that better describes one’s cognitive and physical functioning, such as “non-chronological age.” Also, the review found that daily cognitive, physical, and social activities may result in different non-chronological ages. A comprehensive tool to measure non-chronological age and thereby provide a more accurate representation of aging that incorporates these critical factors is needed. The findings may contribute to the development of theories in non-chronological age.

Integrating aging biomarkers and immune function to predict kidney health: insights from the future of families and child wellbeing study.

Biomarkers of biological aging predict health outcomes more accurately than chronological age. This study examines the relationship between aging biomarkers, immune function, and kidney health using the Future of Families and Child Wellbeing Study Biomarker Dataset. Using Wave 5 (year 9) and Wave 6 (year 15), we examined biomarker data from a total of 4898 individuals. The panel of aging biomarkers, comprised of epigenetic clocks (GrimAge, Horvath), immune function markers (CD8 + T cells, plasmablasts), and metabolic indicators (GDF-15, leptin), was evaluated in depth. We used principal component analysis (PCA) and K-means clustering for subtype identification. A random forest regressor was employed to predict kidney function using Cystatin C levels, and the importance of features was assessed. Three clusters with unique biological age and immune function profiles were identified. Cluster 1 had younger biological age markers. In Cluster 2, both GrimAge and GDF-15 levels were significantly increased, indicating an elevated risk for age-related diseases. According to predictive modeling, GrimAge, Pack Years, and immune function markers had the strongest influence on Cystatin C levels (R2 = 0.856). The incorporation of immune aging markers enhanced the predictive power, emphasizing the importance of immunosenescence in renal health. Aging biomarkers and immune function significantly impact kidney health prediction. The study results call for the utilization of extensive biomarker tests for individualized elderly care and early recognition of kidney deterioration. Clinical practice can be improved by incorporating biological age assessments for the prevention and management of age-related diseases.