Conceptual Overview of Biological Age Estimation

Multivariate analysis of immunosenescence data in healthy humans and diverse diseases

Introduction

Immunosenescence is a dynamic process, where both genetic and environmental factors account for the substantial inter-individual variability. This paper integrates all the data on immunosenescence markers generated in our laboratory and describes the differences and/or similarities between individuals based on their biological conditions (immunosenescence markers) and their associations with chronological age and health status.

Materials and Methods

The dataset consisted of immunological data from healthy donors, centenarians, patients diagnosed with chronic kidney disease, COVID-19 and non-small cell lung cancer (NSCLC), treatment-naïve or treated with platinum-based chemotherapy. To determine whether there are groups of immunologically different individuals despite their age or clinical condition, cluster analysis was performed. Canonical discriminant analysis was performed to determine which variables characterize each cluster.

Results

There are differences in the expression of immunosenescence markers between healthy subjects and patients diagnosed with different pathological conditions, regardless of their age. Meanwhile, the distribution of the clusters indicates the presence of two separate groups of healthy participants, one of them characterized by a high frequency of naïve lymphocytes, and the other with high expression of terminally differentiated lymphocyte subsets. Advanced NSCLC treatment-naïve patients were in the same cluster as a group of healthy subjects. Additionally, centenarians belong to a different cluster than healthy subjects, suggesting they might have a unique immune signature.

Conclusion

The distribution of clusters appears to be more appropriate than univariate associations of single markers for health and disease research. The present work reveals which immune markers are relevant in different physiological and pathological contexts and indicates the need for deeper studies on the biological age of the immune system.

Epigenetic Age in Prader-Willi Syndrome and Essential Obesity: A Comparison with Chronological and Vascular Ages

Background: Prader-Willi syndrome (PWS) is a rare genetic disorder mapping to the imprinted 15q11-13 locus, specifically at the paternally expressed snord116 region, which has been implicated in controlling epigenetic mechanisms. Some aspects of the PWS-related clinical phenotype, such as the high mortality rate in adulthood, might be attributed to accelerated epigenetic ageing. Objectives: The aim of the present case-control study was to evaluate epigenetic age, age acceleration, vascular age (VA), and vascular ageing in adults with PWS (n = 24; F/M = 11/13; age = 36.8 [26.6; 45.3] years; body mass index, BMI = 36.8 [33.9; 44.8] kg/m2), compared with a sex- and age-matched group of subjects with essential obesity (EOB) (n = 36; F/M = 19/17; age = 43.4 [30.6; 49.5] years; BMI = 44.8 [41.2; 51.7] kg/m2). Results: In subjects with PWS, there was a younger epigenetic age and a lower age acceleration than in subjects with EOB. No differences were found between VA and vascular ageing in the two groups. Epigenetic age was associated with chronological age and VA within each group. For each group, no relevant associations of epigenetic age or age acceleration with demographic, biochemical, and clinical parameters were found. When considering individuals with PWS, there were no associations of epigenetic age with growth hormone (GH) deficiency, duration of hormone replacement therapy, and plasma levels of insulin-like growth factor 1 (IGF-1). Conclusions: The hypothesis of accelerated epigenetic ageing in PWS should be rejected. Additionally, considering the existence of a SNORD116-dependent epigenetic dysregulation in PWS, the results of the present study might be misleading, since an epigenetics-based approach was used to measure ageing.

Optimal channel and feature selection for automatic prediction of functional brain age of preterm infant based on EEG

Introduction

Approximately 15 million premature infants are born each year, many of whom face risks of neurological impairments. Accurate assessment of brain maturity is crucial for timely intervention and treatment planning. Electroencephalography (EEG) is a noninvasive method commonly used for this purpose. However, using all channels and features for brain maturity assessment can lead to high computational burden and overfitting, which can decrease the performance of the prediction system.

Methods

In this study, we propose an automatic prediction framework based on EEG to predict functional brain age (FBA) for assessing brain maturity in preterm infants. To optimize channel selection, we combine Binary Particle Swarm Optimization (BPSO) with Forward Addition (FA) and Backward Elimination (BE) methods. For feature selection, we combine the Pearson Correlation Coefficient (PCC), Recursive Feature Elimination (RFE), and Support Vector Regression (SVR) model.

Results

The proposed framework achieved a prediction accuracy of 76.71% within ±1 week and 94.52% within ±2 weeks. Effective channel and feature selection significantly improved model performance while reducing computational costs.

Discussion

These results demonstrate that optimizing channel and feature selection can enhance the performance of FBA prediction in preterm infants, offering a more efficient and accurate tool for brain maturity assessment.

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.

Reduction in neurons immunoreactive for calcium-binding proteins in the anteroventral thalamic nuclei of individuals with Down syndrome

The anterior thalamic nuclei are important for cognition, and memory in particular. However, little is known about how the anterior thalamic nuclei are affected in many neurological disorders partly due to difficulties in selective segmentation in in vivo scans, due to their size and location. Post-mortem studies, therefore, remain a valuable source of information about the status of the anterior thalamic nuclei. We used post-mortem tissue to assess the status of the anteroventral thalamic nucleus in Down syndrome using samples from males and females ranging from 22-65 years in age and comparing to tissue from age matched controls. As expected, there was increased beta-amyloid plaque expression in the Down syndrome group. While there was a significant increase in neuronal density in the Down syndrome group, the values showed more variation consistent with a heterogeneous population. The surface area of the anteroventral thalamic nucleus was smaller in the Down syndrome group suggesting the increased neuronal density was due to greater neuronal packing but likely fewer overall neurons. There was a marked reduction in the proportion of neurons immunoreactive for the calcium-binding proteins calbindin, calretinin, and parvalbumin in individuals with Down syndrome. These findings highlight the vulnerability of calcium-binding proteins in the anteroventral nucleus in Down syndrome, which could both be driven by, and exacerbate, Alzheimer-related pathology in this region.

A study on the development of a fitness age prediction model: the national fitness award cohort study 2017-2021

BACKGROUND

Physical fitness is considered an important indicator of the health of the general public. In particular, the physical fitness of the older adults is an important requirement for determining the possibility of independent living. Therefore, the purpose of this study was to examine the association between chronological age and physical fitness variables in the National Fitness Award Cohort study data and to develop multiple linear regression analyses to predict fitness age using dependent variables.

METHODS

Data from 501,774 (359,303 adults, 142,471 older adults) individuals who participated in the Korea National Fitness Award Cohort Study from 2017 to 2021 were used. The physical fitness tests consisted of 5 candidate markers for adults and 6 candidate markers for the older adults to measure muscle strength, muscle endurance, cardiopulmonary endurance, flexibility, balance, and agility. Pearson's correlation and stepwise regression analyses were used to analyze the data.

RESULTS

We obtained a predicted individual fitness age values from physical fitness indicators for adults and older adults individuals, and the mean explanatory power of the fitness age for adults was [100.882 - (0.029 × VO2max) - (1.171 × Relative Grip Strength) - (0.032 × Sit-up) + (0.032 × Sit and reach) + (0.769 × Sex male = 1; female = 2)] was 93.6% (adjusted R2); additionally, the fitness age for older adults individuals was [79.807 - (0.017 × 2-min step test) - (0.203 × Grip Strength) - (0.031 × 30-s chair stand) - (0.052 × Sit and reach) + (0.985 × TUG) - (3.468 × Sex male = 1; female = 2) was 24.3% (adjusted R2).

CONCLUSIONS

We suggest the use of fitness age as a valid indicator of fitness in adults and older adults as well as a useful motivational tool for undertaking exercise prescription programs along with exercise recommendations at the national level.

The Gut and Skin Microbiome and Its Association with Aging Clocks

Aging clocks are predictive models of biological age derived from age-related changes, such as epigenetic changes, blood biomarkers, and, more recently, the microbiome. Gut and skin microbiota regulate more than barrier and immune function. Recent studies have shown that human microbiomes may predict aging. In this narrative review, we aim to discuss how the gut and skin microbiomes influence aging clocks as well as clarify the distinction between chronological and biological age. A literature search was performed on PubMed/MEDLINE databases with the following keywords: "skin microbiome" OR "gut microbiome" AND "aging clock" OR "epigenetic". Gut and skin microbiomes may be utilized to create aging clocks based on taxonomy, biodiversity, and functionality. The top contributing microbiota or metabolic pathways in these aging clocks may influence aging clock predictions and biological age. Furthermore, gut and skin microbiota may directly and indirectly influence aging clocks through the regulation of clock genes and the production of metabolites that serve as substrates or enzymatic regulators. Microbiome-based aging clock models may have therapeutic potential. However, more research is needed to advance our understanding of the role of microbiota in aging clocks.

Resting state electroencephalographic brain activity in neonates can predict age and is indicative of neurodevelopmental outcome

OBJECTIVE

Electroencephalography (EEG) can be used to estimate neonates' biological brain age. Discrepancies between postmenstrual age and brain age, termed the brain age gap, can potentially quantify maturational deviation. Existing brain age EEG models are not well suited to clinical cot-side use for estimating neonates' brain age gap due to their dependency on relatively large data and pre-processing requirements.

METHODS

We trained a deep learning model on resting state EEG data from preterm neonates with normal neurodevelopmental Bayley Scale of Infant and Toddler Development (BSID) outcomes, using substantially reduced data requirements. We subsequently tested this model in two independent datasets from two clinical sites.

RESULTS

In both test datasets, using only 20 min of resting-state EEG activity from a single channel, the model generated accurate age predictions: mean absolute error = 1.03 weeks (p-value = 0.0001) and 0.98 weeks (p-value = 0.0001). In one test dataset, where 9-month follow-up BSID outcomes were available, the average neonatal brain age gap in the severe abnormal outcome group was significantly larger than that of the normal outcome group: difference in mean brain age gap = 0.50 weeks (p-value = 0.04).

CONCLUSIONS

These findings demonstrate that the deep learning model generalises to independent datasets from two clinical sites, and that the model's brain age gap magnitudes differ between neonates with normal and severe abnormal follow-up neurodevelopmental outcomes.

SIGNIFICANCE

The magnitude of neonates' brain age gap, estimated using only 20 min of resting state EEG data from a single channel, can encode information of clinical neurodevelopmental value.

Noninvasive Techniques for Tracking Biological Aging of the Cardiovascular System: JACC Family Series

Population aging is one of the most important demographic transformations of our time. Increasing the "health span"-the proportion of life spent in good health-is a global priority. Biological aging comprises molecular and cellular modifications over many years, which culminate in gradual physiological decline across multiple organ systems and predispose to age-related illnesses. Cardiovascular disease is a major cause of ill health and premature death in older people. The rate at which biological aging occurs varies across individuals of the same age and is influenced by a wide range of genetic and environmental exposures. The authors review the hallmarks of biological cardiovascular aging and their capture using imaging and other noninvasive techniques and examine how this information may be used to understand aging trajectories, with the aim of guiding individual- and population-level interventions to promote healthy aging.

Sensory event-related potential morphology predicts age in premature infants

OBJECTIVE

We investigated whether sensory-evoked cortical potentials could be used to estimate the age of an infant. Such a model could be used to identify infants who deviate from normal neurodevelopment.

METHODS

Infants aged between 28- and 40-weeks post-menstrual age (PMA) (166 recording sessions in 96 infants) received trains of visual and tactile stimuli. Neurodynamic response functions for each stimulus were derived using principal component analysis and a machine learning model trained and validated to predict infant age.

RESULTS

PMA could be predicted accurately from the magnitude of the evoked responses (training set mean absolute error and 95% confidence intervals: 1.41 [1.14; 1.74] weeks,p = 0.0001; test set mean absolute error: 1.55 [1.21; 1.95] weeks,p = 0.0002). Moreover, we show that their predicted age (their brain age) is correlated with a measure known to relate to maturity of the nervous system and is linked to long-term neurodevelopment.

CONCLUSIONS

Sensory-evoked potentials are predictive of age in premature infants and brain age deviations are related to biologically and clinically meaningful individual differences in nervous system maturation.

SIGNIFICANCE

This model could be used to detect abnormal development of infants' response to sensory stimuli in their environment and may be predictive of neurodevelopmental outcome.