Artificial Intelligence and Technology Collaboratories: Innovating aging research and Alzheimer's care.

This perspective outlines the Artificial Intelligence and Technology Collaboratories (AITC) at Johns Hopkins University, University of Pennsylvania, and University of Massachusetts, highlighting their roles in developing AI-based technologies for older adult care, particularly targeting Alzheimer's disease (AD). These National Institute on Aging (NIA) centers foster collaboration among clinicians, gerontologists, ethicists, business professionals, and engineers to create AI solutions. Key activities include identifying technology needs, stakeholder engagement, training, mentoring, data integration, and navigating ethical challenges. The objective is to apply these innovations effectively in real-world scenarios, including in rural settings. In addition, the AITC focuses on developing best practices for AI application in the care of older adults, facilitating pilot studies, and addressing ethical concerns related to technology development for older adults with cognitive impairment, with the ultimate aim of improving the lives of older adults and their caregivers. HIGHLIGHTS: Addressing the complex needs of older adults with Alzheimer's disease (AD) requires a comprehensive approach, integrating medical and social support. Current gaps in training, techniques, tools, and expertise hinder uniform access across communities and health care settings. Artificial intelligence (AI) and digital technologies hold promise in transforming care for this demographic. Yet, transitioning these innovations from concept to marketable products presents significant challenges, often stalling promising advancements in the developmental phase. The Artificial Intelligence and Technology Collaboratories (AITC) program, funded by the National Institute on Aging (NIA), presents a viable model. These Collaboratories foster the development and implementation of AI methods and technologies through projects aimed at improving care for older Americans, particularly those with AD, and promote the sharing of best practices in AI and technology integration. Why Does This Matter? The National Institute on Aging (NIA) Artificial Intelligence and Technology Collaboratories (AITC) program's mission is to accelerate the adoption of artificial intelligence (AI) and new technologies for the betterment of older adults, especially those with dementia. By bridging scientific and technological expertise, fostering clinical and industry partnerships, and enhancing the sharing of best practices, this program can significantly improve the health and quality of life for older adults with Alzheimer's disease (AD).

Enhancing Care for Older Adults and Dementia Patients With Large Language Models: Proceedings of the National Institute on Aging-Artificial Intelligence & Technology Collaboratory for Aging Research Symposium.

Large Language Models (LLMs) stand on the brink of reshaping the field of aging and dementia care, challenging the one-size-fits-all paradigm with their capacity for precision medicine and individualized treatment strategies. The "Large Pre-Trained Models with a Focus on AD/ADRD and Healthy Aging" symposium, organized by the National Institute on Aging and the Johns Hopkins Artificial Intelligence & Technology Collaboratory for Aging Research, served as a platform for exploring this potential. The symposium brought together diverse experts to discuss the integration of LLMs in aging and dementia care. They highlighted the roles LLMs can play in clinical decision support and predictive analytics, while also addressing critical ethical concerns including bias, privacy, and the responsible use of artificial intelligence (AI). The discussions focused on the need to balance technological advancement with ethical considerations in AI deployment. In conclusion, the symposium projected a future where LLMs not only revolutionize healthcare practices but also pose significant challenges that require careful navigation.

Metabolic dysfunction and the development of physical frailty: an aging war of attrition.

The World Health Organization recently declared 2021-2030 the decade of healthy aging. Such emphasis on healthy aging requires an understanding of the biologic challenges aging populations face. Physical frailty is a syndrome of vulnerability that puts a subset of older adults at high risk for adverse health outcomes including functional and cognitive decline, falls, hospitalization, and mortality. The physiology driving physical frailty is complex with age-related biological changes, dysregulated stress response systems, chronic inflammatory pathway activation, and altered energy metabolism all likely contributing. Indeed, a series of recent studies suggests circulating metabolomic distinctions can be made between frail and non-frail older adults. For example, marked restrictions on glycolytic and mitochondrial energy production have been independently observed in frail older adults and collectively appear to yield a reliance on the highly fatigable ATP-phosphocreatine (PCr) energy system. Further, there is evidence that age-associated impairments in the primary ATP generating systems (glycolysis, TCA cycle, electron transport) yield cumulative deficits and fail to adequately support the ATP-PCr system. This in turn may acutely contribute to several major components of the physical frailty phenotype including muscular fatigue, weakness, slow walking speed and, over time, result in low physical activity and accelerate reductions in lean body mass. This review describes specific age-associated metabolic declines and how they can collectively lead to metabolic inflexibility, ATP-PCr reliance, and the development of physical frailty. Further investigation remains necessary to understand the etiology of age-associated metabolic deficits and develop targeted preventive strategies that maintain robust metabolic health in older adults.

Association of Cardiovascular Fibrosis, Remodeling, and Dysfunction With Frailty, Prefrailty, and Functional Performance: The Multi-Ethnic Study of Atherosclerosis.

BACKGROUND: Cardiovascular disease is associated with higher incidence of frailty. However, the nature of the mechanisms underlying this association remains unclear. The purpose of this study is to identify cardiovascular phenotypes most associated with physical frailty and functional performance in the Multi-Ethnic Study of Atherosclerosis (MESA). METHODS: As part of the MESA study, 3 045 participants underwent cardiovascular magnetic resonance and computed tomography between 2010 and 2012. Of these, 1 743 completed a Six-Minute Walk test (6MWT) and questionnaires (follow-up exam: 2016-2018) which were used to generate a binary combined frail/prefrail versus robust score according to a modified FRAIL Scale (self-report questionnaire). Multivariable logistic (binary frail outcome) or linear (6MWT) regression assessed the association between frailty and cardiovascular structure and function, aortic stiffness, coronary artery calcium, and myocardial fibrosis (ECV, extracellular volume fraction). RESULTS: Participants were 66 ±â€…8 years, 52% female at the time of imaging, and 29.4% were classified as frail or prefrail. Older age and female gender were associated with greater odds of being in the frail/prefrail group. Concentric left ventricular remodeling (odds ratio [OR] 1.89, p = .008; Coef. -52.9, p < .001), increased ECV (OR 1.10, p = .002; Coef. -4.0, p = .001), and worsening left atrial strain rate at early diastole (OR 1.56, p ≤ .001; Coef. -22.75, p = .027) were found to be associated with a greater likelihood of being in a frail state and lower 6MWT distance (m). All associations with 6MWT performance were attenuated with adjustments for risk factors whereas ECV and LA strain rate remained independently associated with frailty. CONCLUSIONS: These findings suggest a significant overlap in pathways associated with subclinical cardiac dysfunction, cardiovascular fibrosis, and physical frailty.

Mid-life plasma proteins associated with late-life prefrailty and frailty: a proteomic analysis.

Physical frailty is a syndrome that typically manifests in later life, although the pathogenic process causing physical frailty likely begins decades earlier. To date, few studies have examined the biological signatures in mid-life associated with physical frailty later in life. Among 4,189 middle-aged participants (57.8 ± 5.0 years, 55.8% women) from the Atherosclerosis Risk in Community (ARIC) study, we evaluated the associations of 4,955 plasma proteins (log 2-transformed and standardized) measured using the SomaScan platform with their frailty status approximately 20 years later. Using multinomial logistic regression models adjusting for demographics, health behaviors, kidney function, total cholesterol, and comorbidities, 12 and 221 proteins were associated with prefrailty and frailty in later life, respectively (FDR p < 0.05). Top frailty-associated proteins included neurocan core protein (NCAN, OR = 0.66), fatty acid-binding protein heart (FABP3, OR = 1.62) and adipocyte (FABP4, OR = 1.65), as well proteins involved in the contactin-1 (CNTN1), toll-like receptor 5 (TLR5), and neurogenic locus notch homolog protein 1 (NOTCH1) signaling pathway relevant to skeletal muscle regeneration, myelination, and inflammation. Pathway analyses suggest midlife dysregulation of inflammation, metabolism, extracellular matrix, angiogenesis, and lysosomal autophagy among those at risk for late-life frailty. After further adjusting for midlife body mass index (BMI) - an established frailty risk factor - only CNTN1 (OR = 0.75) remained significantly associated with frailty. Post-hoc analyses demonstrated that the top 41 midlife frailty-associated proteins mediate 32% of the association between mid-life BMI and late-life frailty. Our findings provide new insights into frailty etiology earlier in the life course, enhancing the potential for prevention.

Prevalence and risk factors for dysphagia in older adults after thyroid and parathyroid surgery.

BACKGROUND: We aimed to determine the prevalence and risk factors for dysphagia in adults 65 years and older before and after thyroidectomy or parathyroidectomy. METHODS: We performed a longitudinal prospective cohort study of older adults undergoing initial thyroidectomy or parathyroidectomy. We administered the Dysphagia Handicap Index questionnaire preoperatively and 1, 3, and 6 months postoperatively. We compared preoperative and postoperative total and domain-specific scores using paired t tests and identified risk factors for worse postoperative scores using multivariable logistic regression. RESULTS: Of the 175 patients evaluated, the mean age was 71.1 years (range = 65-94), 73.7% were female, 40.6% underwent thyroidectomy, 57% underwent bilateral procedures, and 21.1% had malignant diagnoses. Preoperative swallowing dysfunction was reported by 77.7%, with the prevalence 22.4% greater in frail than robust patients (P = .013). Compared to preoperative scores, 43.4% and 49.1% had worse scores at 3 and 6 months postoperatively. Mean functional domain scores increased by 62.3% at 3 months postoperatively (P = .007). Preoperative swallowing dysfunction was associated with a 3.07-fold increased likelihood of worse functional scores at 3 months. Whereas frailty was associated with preoperative dysphagia, there was no association between worse postoperative score and age, sex, race, frailty, body mass index, smoking status, gastroesophageal reflux disease, comorbidity index, malignancy, surgical extent, or type of surgery. CONCLUSION: Adults 65 years and older commonly report swallowing impairment preoperatively, which is associated with a 3.07-fold increased likelihood of worsened dysphagia after thyroid and parathyroid surgery that may persist up to 6 months postoperatively.

Single-cell morphology encodes functional subtypes of senescence in aging human dermal fibroblasts.

Cellular senescence is an established driver of aging, exhibiting context-dependent phenotypes across multiple biological length-scales. Despite its mechanistic importance, profiling senescence within cell populations is challenging. This is in part due to the limitations of current biomarkers to robustly identify senescent cells across biological settings, and the heterogeneous, non-binary phenotypes exhibited by senescent cells. Using a panel of primary dermal fibroblasts, we combined live single-cell imaging, machine learning, multiple senescence induction conditions, and multiple protein-based senescence biomarkers to show the emergence of functional subtypes of senescence. Leveraging single-cell morphologies, we defined eleven distinct morphology clusters, with the abundance of cells in each cluster being dependent on the mode of senescence induction, the time post-induction, and the age of the donor. Of these eleven clusters, we identified three bona-fide senescence subtypes (C7, C10, C11), with C10 showing the strongest age-dependence across a cohort of fifty aging individuals. To determine the functional significance of these senescence subtypes, we profiled their responses to senotherapies, specifically focusing on Dasatinib + Quercetin (D+Q). Results indicated subtype-dependent responses, with senescent cells in C7 being most responsive to D+Q. Altogether, we provide a robust single-cell framework to identify and classify functional senescence subtypes with applications for next-generation senotherapy screens, and the potential to explain heterogeneous senescence phenotypes across biological settings based on the presence and abundance of distinct senescence subtypes.

Recipient clonal hematopoiesis in allogeneic bone marrow transplantation for lymphoid malignancies.

ABSTRACT: Allogeneic blood and marrow transplantation (alloBMT) is increasingly being used in older patients with blood cancer. Aging is associated with an increasing incidence of clonal hematopoiesis (CH). Although the effects of donor CH on alloBMT has been reported, the impact of recipient CH on alloBMT outcomes is unknown. In this retrospective study, alloBMT recipients age 60 and older with lymphoid malignancies were included. Among 97 consecutive patients who received alloBMT between 2017 and 2022, CH was detected in 60 (62%; 95% confidence interval [CI], 51-72). CH was found in 45% (95% CI, 28-64) of patients aged 60 to 64, 64% (95% CI, 44-81) of patients aged 65% to 69%, and 73% (95% CI, 59-87) in those above 70. Pretransplant CH was associated with worse survival after alloBMT: 3-year overall survival (OS) was 78% (95% CI, 65-94) for patients without CH vs 47% (95% CI, 35-63) for those with CH, (unadjusted HR, 3.1; [95% CI, 1.4-6.8; P < .001]). Nonrelapse mortality (NRM) was higher in patients with CH; cumulative incidence of NRM at 1-year was 11% (95% CI, 1-22) vs 35% (95% CI, 23-48), (HR, 3.4; [95% CI, 1.4-8.5], P = .009]). Among CH patients, worse OS and NRM was associated with CH burden and number of mutations. Recipient CH had no effect on relapse. In conclusion, older patients with CH experience worse outcomes after alloBMT, almost exclusively attributable to increased NRM. CH is a strong, independent predictor of outcomes. Novel strategies to ameliorate the adverse impacts of patient CH on transplant outcomes are being evaluated.

Profiling Dynamic Patterns of Single-Cell Motility.

Cell motility plays an essential role in many biological processes as cells move and interact within their local microenvironments. Current methods for quantifying cell motility typically involve tracking individual cells over time, but the results are often presented as averaged values across cell populations. While informative, these ensemble approaches have limitations in assessing cellular heterogeneity and identifying generalizable patterns of single-cell behaviors, at baseline and in response to perturbations. In this study, CaMI is introduced, a computational framework designed to leverage the single-cell nature of motility data. CaMI identifies and classifies distinct spatio-temporal behaviors of individual cells, enabling robust classification of single-cell motility patterns in a large dataset (n = 74 253 cells). This framework allows quantification of spatial and temporal heterogeneities, determination of single-cell motility behaviors across various biological conditions and provides a visualization scheme for direct interpretation of dynamic cell behaviors. Importantly, CaMI reveals insights that conventional cell motility analyses may overlook, showcasing its utility in uncovering robust biological insights. Together, a multivariate framework is presented to classify emergent patterns of single-cell motility, emphasizing the critical role of cellular heterogeneity in shaping cell behaviors across populations.

Dual treatment with kynurenine pathway inhibitors and NAD+ precursors synergistically extends life span in Drosophila.

Tryptophan catabolism is highly conserved and generates important bioactive metabolites, including kynurenines, and in some animals, NAD+. Aging and inflammation are associated with increased levels of kynurenine pathway (KP) metabolites and depleted NAD+, factors which are implicated as contributors to frailty and morbidity. Contrastingly, KP suppression and NAD+ supplementation are associated with increased life span in some animals. Here, we used DGRP_229 Drosophila to elucidate the effects of KP elevation, KP suppression, and NAD+ supplementation on physical performance and survivorship. Flies were chronically fed kynurenines, KP inhibitors, NAD+ precursors, or a combination of KP inhibitors with NAD+ precursors. Flies with elevated kynurenines had reduced climbing speed, endurance, and life span. Treatment with a combination of KP inhibitors and NAD+ precursors preserved physical function and synergistically increased maximum life span. We conclude that KP flux can regulate health span and life span in Drosophila and that targeting KP and NAD+ metabolism can synergistically increase life span.

qMAP enabled microanatomical mapping of human skin aging.

Aging is a major driver of diseases in humans. Identifying features associated with aging is essential for designing robust intervention strategies and discovering novel biomarkers of aging. Extensive studies at both the molecular and organ/whole-body physiological scales have helped determined features associated with aging. However, the lack of meso-scale studies, particularly at the tissue level, limits the ability to translate findings made at molecular scale to impaired tissue functions associated with aging. In this work, we established a tissue image analysis workflow - quantitative micro-anatomical phenotyping (qMAP) - that leverages deep learning and machine vision to fully label tissue and cellular compartments in tissue sections. The fully mapped tissue images address the challenges of finding an interpretable feature set to quantitatively profile age-related microanatomic changes. We optimized qMAP for skin tissues and applied it to a cohort of 99 donors aged 14 to 92. We extracted 914 microanatomic features and found that a broad spectrum of these features, represented by 10 cores processes, are strongly associated with aging. Our analysis shows that microanatomical features of the skin can predict aging with a mean absolute error (MAE) of 7.7 years, comparable to state-of-the-art epigenetic clocks. Our study demonstrates that tissue-level architectural changes are strongly associated with aging and represent a novel category of aging biomarkers that complement molecular markers. Our results highlight the complex and underexplored multi-scale relationship between molecular and tissue microanatomic scales.

Frailty, what are we talking about? Implications for the daily clinical practice / Fragilidad: ¿de qué estamos hablando? Implicaciones para la práctica clínica diaria

No disponible