A gerophysiology perspective on healthy ageing

The oral organ: A new vision of the mouth as a whole for a gerophysiological approach to healthy aging

This article brings a new perspective on oral physiology by presenting the oral organ as an integrated entity within the entire organism and its surrounding environment. Rather than considering the mouth solely as a collection of discrete functions, this novel approach emphasizes its role as a dynamic interphase, supporting interactions between the body and external factors. As a resilient ecosystem, the equilibrium of mouth ecological niches is the result of a large number of interconnected factors including the heterogeneity of different oral structures, diversity of resources, external and internal pressures and biological actors. The manuscript seeks to deepen the understanding of age-related changes within the oral cavity and throughout the organism, aligning with the evolving field of gerophysiology. The strategic position and fundamental function of the mouth make it an invaluable target for early prevention, diagnosis, treatment, and even reversal of aging effects throughout the entire organism. Recognizing the oral cavity capacity for sensory perception, element capture and information processing underscores its vital role in continuous health monitoring. Overall, this integrated understanding of the oral physiology aims at advancing comprehensive approaches to the oral healthcare and promoting broader awareness of its implications on the overall well-being.

The Global Research Trends on Intrinsic Capacity of Older Adults: A Bibliometric and Visual Analysis of Papers Published During 2015-2023

Objective

The concept of intrinsic capacity (IC) revolves around healthy aging and active aging. Since the Introduction of the concept by the World Health Organization in 2015, a series of studies have been conducted by scholars from multiple fields. However, no bibliometric analysis has systematically investigated this issue. We aim to identify the current landscape and frontier trends of scientific achievements on IC in older adults through bibliometric approaches.

Methods

Quantitative analysis of publications relating to IC in older adults from 2015 to 2023 was interpreted and graphed through the Web of Science Core Collection database on December 5, 2023. A variety of quantitative variables was analyzed, including publication and citation counts, H-index, and journal citation reports. Co-authorship, citation, co-citation, and co-occurrence analyses were performed for countries/regions, institutions, authors, and keywords using the VOSviewer and CiteSpace.

Results

A total of 952 original and review articles in English were identified. The European countries possessed an absolute advantage in this field. The most contributive institution was the University of São Paulo. The most productive author is Cesari Matteo from France, followed by Qaisar Rizwan from the United Arab Emirates. However, a relatively low level of research cooperation existed between institutions and authors. Important topics mainly include the connotations, theoretical framework models, evaluation, screening tools, and application scenarios of IC. Among the promising hotspots, "biological aging", "ICOPE", "Covid-19", "prevention", "inflammation", "caf22", "prevalence", and "randomized controlled trial" displayed relatively latest average appearing year.

Conclusion

Global trends indicate a growing scientific output on IC in older adults, and developed countries are leading the way. There is still room for improvement in research team collaboration. The focus gradually shifts from theoretical research to empirical research. It is recommended to pay attention to the latest hot spots, such as "biological aging", "ICOPE implementation", "post-COVID-19 syndrome", and "biomarkers".

Factors associated with intrinsic capacity impairment in hospitalized older adults: a latent class analysis

BACKGROUND

Intrinsic capacity (IC) is proposed by the World Health Organization (WHO) to promote healthy aging. Although some studies have examined the factors influencing IC, few studies have comprehensively confirmed lifestyle factors on IC, especially IC impairment patterns. The present study aimed to identify the patterns of IC impairment and explore the lifestyle and other factors associated with different patterns of IC impairment.

METHODS

This cross-sectional study was conducted in a Chinese geriatric hospital. IC was evaluated in five domains according to the recommendations of WHO: cognition, locomotion, vitality, sensory and psychological domains. The sociodemographic and health-related characteristics of participants were assessed.The health promoting lifestyle was evaluated using the Health-Promoting Lifestyle Profile-II scale, including nutrition, health responsibility, interpersonal relationships, physical activity, spiritual growth and stress management. We applied latent class analysis to identify IC impairment patterns and compared basic activities of daily living, instrumental activities of daily living, frailty, quality of life and falls among different IC impairment patterns. Multinomial logistic regression analysis was conducted to identify factors influencing the IC impairment patterns.

RESULTS

Among 237 participants included, the latent class analysis identified three patterns of IC impairment: 44.7% high IC (Class 1), 31.2% intermediate IC mainly locomotor impairment (Class 2) and 24.1% low IC mainly cognitive impairment (Class 3). Older adults in class 1 had the best function ability and quality of life, while class 3 had the highest levels of disability and frailty, the poorest quality of life and a higher prevalence of falls. Compared with class 1, older adults with advanced age (OR = 22.046, 95%CI:1.735-280.149), osteoporosis (OR = 3.377, 95%CI:1.161-9.825), and lower scores in physical activity (OR = 0.842, 95%CI:0.749-0.945), stress management (OR = 0.762, 95%CI:0.585-0.993) and social support (OR = 0.897, 95%CI:0.833-0.965) were more likely to belong to the class 2. Simultaneously, compared with class 1, older adults with advanced age (OR = 104.435, 95%CI:6.038-1806.410), stroke (OR = 3.877, 95%CI:1.172-12.823) and lower scores in physical activity (OR = 0.784, 95%CI:0.667-0.922) and social support (OR = 0.909, 95%CI:0.828-0.998) were more likely to be class 3. In addition, compared with class 2, older adults with a lower score in nutrition (OR = 0.764, 95%CI:0.615-0.950) were more likely to belong to the class 3.

CONCLUSIONS

This study provides evidence that there are heterogeneous IC impairment patterns in older adults and identifies various associated factors in each pattern, including age, stroke, osteoporosis, social support and lifestyle behaviors such as nutrition, physical activity and stress management. It informs stakeholders on which modifiable factors should be targeted through public health policy or early intervention to promote IC and healthy aging in older adults.

Risk of Social Isolation as a Contributing Factor to Diet Quality in Community-Dwelling Older Persons Living in the Australian Capital Territory-A Pilot Study

OBJECTIVES

Social isolation is recognised as a risk factor in the inflammatory process. This study explored the association between social isolation and the Dietary Inflammatory Index (DII) in community-dwelling older persons.

METHODS

This cross-sectional pilot study recruited 107 community-dwelling people aged over 55 years living in the Australian Capital Territory. Participants completed an extensive food frequency questionnaire and provided anthropometric and sociodemographic data. Social isolation was evaluated using the Lubben Social Network Scale (LSNS). Diet quality was assessed using DII.

RESULTS

Average age was 70.1 (±8.61) years and 62.8% were female. The average DII score was -1.10 (±1.21), indicating an anti-inflammatory diet. Higher LSNS was associated with lower DII (b (95% CI) = -0.041 (-0.066, -0.17); p < 0.01) and was positively influenced by the number of people in household (b (95% CI) = 5.731 (2.336, 9.127); p = 0.001).

CONCLUSION

Increased risk of social isolation was associated with an increased tendency towards a more inflammatory diet. Reducing social isolation may decrease the inflammatory component of dietary intake for older persons living independently in the community.

Age-related positive emotional reactivity decline associated with the anterior insula based resting-state functional connectivity

Recent studies have suggested that emotional reactivity changes with age, but the neural basis is still unclear. The insula may be critical for the emotional reactivity. The current study examined how ageing affects emotional reactivity using the emotional reactivity task data from a human sample (Cambridge Center for Age and Neuroscience, N = 243, age 18-88 years). The resting-state magnetic resonance measurements from the same sample were used to investigate the potential mechanisms of the insula. In the initial analysis, we conducted partial correlation assessments to examine the associations between emotional reactivity and age, as well as between the gray matter volume (GMV) of the insula and age. Our results revealed that emotional reactivity, especially positive emotional reactivity, decreased with age and that the GMV of the insula was negatively correlated with age. Subsequently, the bilateral insula was divided into six subregions to calculate the whole brain resting-state functional connectivity (rsFC). The mediating effect of the rsFC on age and emotional reactivity was then calculated. The results showed that the rsFC of the left anterior insula (AI) with the right hippocampus, and the rsFCs of the right AI with the striatum and the thalamus were mediated the relationship between positive emotional reactivity and age. Our findings suggest that attenuating emotional reactivity with age may be a strategic adaptation fostering emotional stability and diminishing emotional vulnerability. Meanwhile, the findings implicate a key role for the AI in the changes in positive emotional reactivity with age.

New Possibilities for Evaluating the Development of Age-Related Pathologies Using the Dynamical Network Biomarkers Theory

Aging is the slowest process in a living organism. During this process, mortality rate increases exponentially due to the accumulation of damage at the cellular level. Cellular senescence is a well-established hallmark of aging, as well as a promising target for preventing aging and age-related diseases. However, mapping the senescent cells in tissues is extremely challenging, as their low abundance, lack of specific markers, and variability arise from heterogeneity. Hence, methodologies for identifying or predicting the development of senescent cells are necessary for achieving healthy aging. A new wave of bioinformatic methodologies based on mathematics/physics theories have been proposed to be applied to aging biology, which is altering the way we approach our understand of aging. Here, we discuss the dynamical network biomarkers (DNB) theory, which allows for the prediction of state transition in complex systems such as living organisms, as well as usage of Raman spectroscopy that offers a non-invasive and label-free imaging, and provide a perspective on potential applications for the study of aging.

Neurosurgical perioperative management of frail elderly patients

With the rapid increase in global aging, the prevalence of frailty is increasing and frailty has emerged as an emerging public health burden. Frail elderly patients suffer from reduced homeostatic reserve capacity, which is associated with a disproportionate decline in physical status after exposure to stress and an increased risk of adverse events. Frailty is closely associated with changes in the volume of the white and gray matter of the brain. Sarcopenia has been suggested to be an important component of frailty, and reductions in muscle strength and muscle mass lead to reductions in physical function and independence, which are critical factors contributing to poor prognosis. Approximately 10-32% of patients undergoing neurological surgery are frail, and the risk of frailty increases with age, which is significantly associated with the occurrence of adverse postoperative events (major complications, total duration of hospitalization, and need for discharge to a nursing facility). The postoperative mortality rate in severely frail patients is 9-11 times higher than that in non-frail individuals. Therefore, due attention must be paid to neurosurgical frailty and muscle assessment in elderly patients. Specialized interventions in the perioperative period of neurosurgery in frail elderly patients may improve their postoperative prognosis.

Explainable machine learning framework to predict personalized physiological aging

Attaining personalized healthy aging requires accurate monitoring of physiological changes and identifying subclinical markers that predict accelerated or delayed aging. Classic biostatistical methods most rely on supervised variables to estimate physiological aging and do not capture the full complexity of inter-parameter interactions. Machine learning (ML) is promising, but its black box nature eludes direct understanding, substantially limiting physician confidence and clinical usage. Using a broad population dataset from the National Health and Nutrition Examination Survey (NHANES) study including routine biological variables and after selection of XGBoost as the most appropriate algorithm, we created an innovative explainable ML framework to determine a Personalized physiological age (PPA). PPA predicted both chronic disease and mortality independently of chronological age. Twenty-six variables were sufficient to predict PPA. Using SHapley Additive exPlanations (SHAP), we implemented a precise quantitative associated metric for each variable explaining physiological (i.e., accelerated or delayed) deviations from age-specific normative data. Among the variables, glycated hemoglobin (HbA1c) displays a major relative weight in the estimation of PPA. Finally, clustering profiles of identical contextualized explanations reveal different aging trajectories opening opportunities to specific clinical follow-up. These data show that PPA is a robust, quantitative and explainable ML-based metric that monitors personalized health status. Our approach also provides a complete framework applicable to different datasets or variables, allowing precision physiological age estimation.

Characterization and Safety Profile of a New Combined Advanced Therapeutic Medical Product Platelet Lysate-Based Fibrin Hydrogel for Mesenchymal Stromal Cell Local Delivery in Regenerative Medicine

Adipose-derived mesenchymal stromal cells (ASC) transplant to recover the optimal tissue structure/function relationship is a promising strategy to regenerate tissue lesions. Because filling local tissue defects by injection alone is often challenging, designing adequate cell carriers with suitable characteristics is critical for in situ ASC delivery. The aim of this study was to optimize the generation phase of a platelet-lysate-based fibrin hydrogel (PLFH) as a proper carrier for in situ ASC implantation and (1) to investigate in vitro PLFH biomechanical properties, cell viability, proliferation and migration sustainability, and (2) to comprehensively assess the local in vivo PLFH/ASC safety profile (local tolerance, ASC fate, biodistribution and toxicity). We first defined the experimental conditions to enhance physicochemical properties and microscopic features of PLFH as an adequate ASC vehicle. When ASC were mixed with PLFH, in vitro assays exhibited hydrogel supporting cell migration, viability and proliferation. In vivo local subcutaneous and subgingival PLFH/ASC administration in nude mice allowed us to generate biosafety data, including biodegradability, tolerance, ASC fate and engraftment, and the absence of biodistribution and toxicity to non-target tissues. Our data strongly suggest that this novel combined ATMP for in situ administration is safe with an efficient local ASC engraftment, supporting the further development for human clinical cell therapy.

Anti-Aging Effect of Agrocybe aegerita Polysaccharide through Regulation of Oxidative Stress and Gut Microbiota

Polysaccharides extracted from Agrocybe aegerita (AAPS) have various physiological effects. In this study, we used the naturally aging Drosophila melanogaster and D-galactose-induced aging mice as animal models to study the anti-aging effects of AAPS via the alleviation of oxidative stress and regulation of gut microbiota. Results showed that AAPS could significantly prolong lifespan and alleviate oxidative stress induced by H₂O₂ of Drosophila melanogaster. In addition, AAPS significantly increased the activities of antioxidant enzymes in Drosophila melanogaster and mice, and reduced the content of MDA. Furthermore, AAPS reshaped the disordered intestinal flora, increased the abundance ratio of Firmicutes to Bacteroidetes, and increased the abundance of beneficial bacteria Lactobacillus. Our results demonstrated that AAPS had good antioxidant and potential anti-aging effects in vivo.

Physical resilience in older adults: Potential use in promoting healthy aging

Physical resilience is a dynamic concept referring to the physiological response when the body is exposed to stressors. The level of physical resilience is the sum of underlying physiological reserves. Moreover, it may not only be determined by age, genetics, or exposure to a variety of diseases, but is also closely related to the psychological, social, and environmental factors of an individual. This paper summarizes our present understanding of the relationship between physical resilience and other concepts closely related to it. Furthermore, we illustrate the current research progress on physical resilience models and clinical resilience assessment. Besides, this paper intends to present a better understanding of physical resilience and its use in treatment decision-making, personalized diagnosis and disease management, and prevention and rehabilitation strategies.

Systemic Periodontal Risk Score Using an Innovative Machine Learning Strategy: An Observational Study

Early diagnosis is crucial for individuals who are susceptible to tooth-supporting tissue diseases (e.g., periodontitis) that may lead to tooth loss, so as to prevent systemic implications and maintain quality of life. The aim of this study was to propose a personalized explainable machine learning algorithm, solely based on non-invasive predictors that can easily be collected in a clinic, to identify subjects at risk of developing periodontal diseases. To this end, the individual data and periodontal health of 532 subjects was assessed. A machine learning pipeline combining a feature selection step, multilayer perceptron, and SHapley Additive exPlanations (SHAP) explainability, was used to build the algorithm. The prediction scores for healthy periodontium and periodontitis gave final F1-scores of 0.74 and 0.68, respectively, while gingival inflammation was harder to predict (F1-score of 0.32). Age, body mass index, smoking habits, systemic pathologies, diet, alcohol, educational level, and hormonal status were found to be the most contributive variables for periodontal health prediction. The algorithm clearly shows different risk profiles before and after 35 years of age and suggests transition ages in the predisposition to developing gingival inflammation or periodontitis. This innovative approach to systemic periodontal disease risk profiles, combining both ML and up-to-date explainability algorithms, paves the way for new periodontal health prediction strategies.