Articles

<b>Introduction:</b> Stroke remains one of the leading causes of death and disability worldwide, with ischemic stroke accounting for most cases. Structural vascular factors such as carotid artery tortuosity have gained attention as potential markers of vascular aging and cerebrovascular risk. The carotid tortuosity index (CTI), defined as the ratio of actual vessel length to the straight-line distance between two fixed points, provides a quantitative measure of arterial curvature. A CTI value of ≥1.2 indicates pathological tortuosity. Although noninvasive modalities such as CTA and MRA are frequently used, digital subtraction angiography (DSA) remains the gold standard for evaluating vessel geometry due to its higher spatial precision. This study aimed to determine the association of age, sex, and hypertension with CTI measured by DSA.<b> </b><b>Methods:</b> A cross-sectional study was conducted from November to December 2025 at the Neurointervention Clinic, RS Pelni Jakarta, Indonesia, involving 61 adult patients who underwent carotid DSA. CTI was measured bilaterally using digital imaging software and classified as <1.2 (non-tortuous) or ≥1.2 (tortuous). Clinical data, including age, sex, and hypertension status, were collected from medical records and analyzed using bivariate tests. <b>Results:</b> Older age (≥65 years), female sex, and hypertension were significantly associated with higher CTI values on both carotid sides. Tortuosity was more common among hypertensive patients and elderly females, indicating the influence of vascular remodeling and chronic hemodynamic stress. <b>Conclusion:</b> Carotid tortuosity increases with age, hypertension, and female sex. DSA-based CTI measurement provides a reliable and precise approach for evaluating vascular changes associated with cerebrovascular risk.

<b>Background</b><b>: </b>Epigenetic aging, measured through various DNA methylation-based clocks, may have implications for predicting disease risk. However, the sensitivity of different epigenetic clocks that have emerged as biomarkers for biological aging and in predicting physical and mental health outcomes remains uncertain. This study examines the age and sex-adjusted associations between multiple epigenetic age acceleration measures and three key health indicators, including self-rated health, depressive symptoms, and body mass index (BMI), in a nationally representative sample of U.S. middle-aged and older adults. <b>Methods</b><b>: </b>We analyzed data from 4,018 adults in the 2016 wave of the Health and Retirement Study (HRS), which included several epigenetic age acceleration measures: HORVATH, HANNUM, LEVINE, HORVATHSKIN, LIN, WEIDNER, VIDALBRALO, YANG, ZHANG, BOCKLANDT, GARAGNANI, and GRIMAGE. Linear regression models were used to assess the associations between epigenetic age acceleration and self-rated health (poor health), depressive symptoms, and BMI, adjusting for age and sex. <b>Results</b><b>: </b>We found significant positive associations between epigenetic age acceleration and worse self-rated health, higher depressive symptoms, and increased BMI. However, these associations varied across different epigenetic clocks, with some measures potentially having more consistent utility for specific health outcomes than others. <b>Conclusion</b><b>: </b>Epigenetic age acceleration is linked to poorer self-rated health, greater depressive symptoms, and higher BMI, but choosing which epigenetic clock(s) to use is also important. These findings underscore the need to consider multiple epigenetic aging markers when assessing health risks and highlight the potential for particular clocks to serve as more sensitive indicators of physical and mental health outcomes.

Digital therapeutics (DTx) are emerging as a pivotal tool in promoting longevity by addressing non-communicable diseases (NCDs) such as diabetes, cardiovascular diseases, and mental health disorders. These software-driven interventions offer personalized, evidence-based treatments that can be accessed via digital devices, making healthcare more accessible and scalable. One of the key advancements in DTx is the integration of artificial intelligence (AI) and machine learning (ML) to tailor interventions based on individual health data. This personalization enhances the effectiveness of treatments and supports preventive care by identifying risk factors early. The need for digital therapeutics is underscored by the rising prevalence of NCDs, which are responsible for a significant portion of global mortality and healthcare costs. Traditional healthcare systems often struggle to provide timely and personalized care, especially in low-resource settings. DTx can bridge this gap by offering cost-effective solutions that are easily scalable. Moreover, digital therapeutics can address health inequities by providing low-cost interventions to underserved populations, thereby reducing the burden of NCDs and improving overall health outcomes. As technology continues to evolve, the potential for DTx to enhance longevity and quality of life becomes increasingly promising. Recent advancements in longevity medicine and technology have focused on extending both lifespan and healthspan, ensuring that people not only live longer but also maintain good health throughout their extended years. This review article highlights these advancements that are contributing to this compelling subject of Longevity.

Background:</b> Early life socioeconomic conditions and race/ethnicity are critical determinants of long-term health and behavioral outcomes. Epigenetic changes, particularly those measured by the GrimAge biomarker, may mediate the impact of these early adversities on later life outcomes. This study investigates the relationships between race/ethnicity, poverty at birth, epigenetic aging at age 15, and subsequent self-rated health, school discipline, depression, and school dropout at age 22. We explored sex differences in these paths. <b>Methods:</b> Data were drawn from the Fragile Families and Child Wellbeing Study (FFCWS), which included 733 youth with comprehensive follow-up data up to age 22. Structural Equation Modeling (SEM) was employed to assess the pathways from race/ethnicity and poverty at birth to epigenetic aging (GrimAge) at age 15, and subsequently to self-rated health and school discipline at age 22. The model controlled for potential confounders including sex, family structure, and parental education. <b>Results:</b> Race/ethnicity and poverty at birth were significantly associated with higher GrimAge scores at age 15 (p < 0.05). Higher GrimAge scores were predictive of poorer self-rated health (β = -0.08, p < 0.05) and increased instances of school discipline (β = 0.13, p < 0.01) at age 22. The indirect effects of race/ethnicity and poverty at birth on self-rated health and school discipline through GrimAge were also significant (p < 0.05), suggesting that epigenetic aging partially mediates these relationships. Sex differences were also observed. Poverty at birth predicted faster epigenetic aging at age 15 for males not females. We also observed that faster epigenetic aging at age 15 was predictive of school discipline of male not female participants at age 22. In contrast, faster epigenetic aging at age 15 was predictive of self-rated health (SRH) of female not male participants at age 22. <b>Conclusions:</b> This study provides evidence that with some sex differences, race/ethnicity and poverty at birth contribute to accelerated epigenetic aging (GrimAge) by age 15, which in turn predicts poorer self-rated health and increased school discipline issues by age 22. These findings emphasize the importance of early interventions targeting social determinants to mitigate long-term health and behavioral disparities. Addressing these early life conditions is crucial for improving health equity and outcomes in young adulthood.

<b>Background:</b> Telomere length is a critical biomarker of cellular aging and overall health. While childhood socioeconomic status (SES) indicators such as education and poverty can have long-lasting effects on biological aging, research has shown contradictory results regarding the impact of adulthood SES on future telomere length, particularly in racially and ethnically diverse individuals. This study investigates the effects of baseline adulthood SES indicators such as education and poverty on telomere length nine years later in women, using data from the Future of Families and Child Wellbeing Study (FFCWS). <b>Methods:</b> We analyzed data from the FFCWS, a longitudinal cohort study. The sample included baseline adulthood SES and follow-up telomere length measure of women (n = 2,421) with varying socioeconomic conditions. Telomere length was measured from saliva samples nine years after the baseline measure of adulthood SES. Education, poverty, and marital status at baseline were assessed. Multivariate linear regression models were used to examine the association between adulthood SES indicators at baseline and future telomere length, controlling for potential confounders. <b>Results:</b> From the total 2,421 women, 675 were Latino White, 1,158 were non-Latino Black, and 588 were non-Latino White. Our findings indicate that for women in our study, no adulthood SES indicators such as poverty status, education, or marital status at baseline were predictive of telomere lengths nine years later. <b>Conclusion: </b>Our observations challenge that expected longitudinal association between adulthood SES indicators and subsequent telomere length almost a decade later in racially and ethnically diverse group of women. These findings underscore the need for additional research on the validity of TL as a mediator of the effects of adulthood SES on future rate of biological aging.

<b>Introduction</b><b>:</b><b> </b>College education is typically associated with the ability to work in less physically demanding occupations, allowing for a later retirement age. However, research indicates that highly educated Black individuals often work in more demanding occupations, which affects their retirement age.<b> </b><b>Aim</b><b>: </b>Building on the Minorities’ Diminished Returns (MDRs) literature, we tested whether the benefit of college education on delaying the time of retirement is weaker for Black compared to White middle-aged and older adults.<b> </b><b>Methods</b><b>: </b>We utilized data from the Health and Retirement Study (HRS), which includes a 30-year longitudinal follow-up of a nationally representative sample of middle-aged and older adults in the United States. Education levels at baseline were categorized as less than college graduate (some high school, GED, high school diploma, or some college) and college graduate. The outcome was the time to retirement, measured from wave 2 to wave 15 (baseline to 30 years later). We graphed survival curves and used independent samples t-tests to assess associations between college graduation and time of retirement, overall and by race. <b>Results</b><b>:</b><b> </b>Our analysis included 6,803 White and Black participants who were employed at baseline and followed for up to 30 years. Overall, there was a positive association between college graduation and retirement timing, with individuals with higher education retiring later. However, we found significant racial differences in the retirement age of college graduates, indicating notable racial disparities in the effects of college graduation on retirement timing, disadvantaging Black college-educated individuals. Specifically, among Whites, but not Blacks, college education was associated with later retirement.<b> </b><b>Conclusion</b><b>:</b><b> </b>Consistent with Minorities’ Diminished Returns theory, the positive effect of college education on retirement timing are weaker for Black than for White middle-aged and older Americans. To address racial disparities, it is insufficient to focus solely on economic disparities. While closing the educational gap is important, we must also work to equalize labor market experiences for Black and White individuals with similar educational credentials. Structural factors contributing to the diminished returns of college education for Black populations must be addressed to effectively close racial disparities.

Neurodegeneration and aging are pressing issues with significant personal, economic, ethical, and social consequences. However, the underlying biological mechanisms of these conditions remain largely unknown, making the development of effective treatments challenging. The difficulty in early detection and diagnosis of neurodegenerative diseases further compounds the issue. Recent advancements in genetics, genomics, and brain imaging technology hold great promise for improving our understanding of neurodegeneration and aging, as well as the development of personalized medicine and new drugs and therapies. Addressing these challenges will require a multi-disciplinary and collaborative approach from researchers in various fields. This Special Issue offers valuable insights and perspectives on this critical area of research, which can help advance our understanding and improve the health and well-being of our aging population.

There is a growing body of evidence that the number of individuals suffering from chronic and acute pain is under-reported and the burden of the veteran, aging, athletic, and working populations is rising. Current pain management is limited by our capacity to collaborate with individuals continuing normal daily functions and self-administration of pain treatments outside of traditional healthcare appointments and hospital settings. In this review, the current gap in clinical care for real-time feedback and guidance with pain management decision-making for chronic and post-operative pain treatment is defined. We examine the recent and future applications for predictive analytics of opioid use after surgery and implementing real-time neural networks for personalized pain management goal setting for particular individuals on the path to discharge to normal function. Integration of personalized neural networks with longitudinal data may enable the development of future treatment personalizations paired with electrical simulations.