A Review: Multi-Omics Approach to Studying the Association between Ionizing Radiation Effects on Biological Aging

A Review: Multi-Omics Approach to Studying the Association between Ionizing Radiation Effects on Biological Aging

A cornerstone in the exploration of the intricate relationship between ionizing radiation and biological aging is the free radical theory of aging proposed by Denham Harman in 1955. Harman’s theory posits that the aging process is characterized by the cumulative oxidative damage inflicted upon cells by free radicals [80]. However, the landscape of aging theories is far from static, and contemporary perspectives have expanded beyond the notion that oxidative damage alone drives aging. Instead, it is increasingly recognized that aging is a multifaceted phenomenon shaped by a multitude of factors. While oxidative damage remains a fundamental contributor, it represents just one facet of the intricate web of processes culminating in cellular dysfunction and, ultimately, aging-related outcomes [81]. In this section, we delve into the evolving landscape of biological aging theory, encompassing not only oxidative stress but also factors such as inflammation, disease development, and multi-omic alterations that collectively contribute to the nuanced understanding of the aging process.

3.1. Hallmarks and Indicators

In 2013, López-Otín and colleagues introduced a foundational framework known as the “nine hallmarks of aging” that provided a comprehensive overview of the key processes and pathways contributing to the aging phenomenon [82]. These hallmarks serve as a crucial starting point for understanding the molecular intricacies of aging, and they encompass a broad spectrum of biological phenomena that impact the aging process. In this exploration, we not only delve into these hallmarks but also delve deeper into the genetic, epigenetic, and proteomic biomarkers that provide insights into the aging process, offering a glimpse into recent scientific advancements in this ever-evolving field.

Genomic Instability: At the core of aging lies genomic instability, characterized by accumulating DNA damage and mutations over time. Recent advances in genomics have unveiled a plethora of genes intricately involved in DNA repair and maintenance. Notable genes such as P16, P21, FOXO1, P53, SIRT1, SIRT6, TNFa, IL6, TFAM, GATA6, ALOX15B, MAOA, TSC1, mTOR, RIPK1, RIPK3, and MLKL have emerged as central players in safeguarding genomic integrity. Moreover, modern technologies like next-generation sequencing have empowered researchers to explore the genomic landscape of aging with unprecedented precision, uncovering novel genetic factors and intricate pathways contributing to the aging process.

Telomere Attrition: Telomeres, the protective caps at the ends of chromosomes, erode with each cell division, serving as a cellular countdown clock. Recent breakthroughs in telomere biology have unveiled the roles of telomerase and shelterin complex proteins in preserving telomere length and stability. The identification of genetic and epigenetic factors influencing telomere maintenance has illuminated potential avenues for interventions to counteract telomere attrition and extend cellular lifespan.

Epigenetic Alterations: Epigenetics, the study of heritable changes in gene expression that do not involve alterations to the underlying DNA sequence, plays a pivotal role in aging. Recent research has spotlighted DNA methylation as a prominent epigenetic marker of aging. Advances in epigenome-wide association studies (EWASs) have identified specific methylation patterns associated with aging, paving the way for the development of epigenetic clocks that accurately estimate an individual’s biological age. MicroRNA research, while promising, remains an area ripe for further exploration to harness its full potential in unraveling the epigenetic intricacies of aging.

Loss of Proteostasis: Maintenance of proteostasis, i.e., the balance between protein synthesis, folding, and degradation, is essential for cellular health. Recent studies have highlighted the roles of chaperones, autophagy, and the ubiquitin–proteasome system in maintaining proteostasis. Advancements in proteomic techniques have enabled the identification of specific protein aggregates and misfolded proteins associated with age-related diseases, shedding light on potential therapeutic targets to mitigate proteostatic decline.

Deregulated Nutrient Sensing: The deregulation of nutrient-sensing pathways, including insulin and mTOR signaling, contributes to aging-related metabolic dysfunction. Recent research has elucidated the roles of genes like FOXO, AMPK, and SIRT1 in nutrient sensing and longevity. Additionally, proteomic studies have unveiled key proteins involved in nutrient-sensing pathways, providing insights into their intricate regulation and potential interventions to modulate nutrient sensing in aging.

Mitochondrial Dysfunction: Mitochondria, the cellular powerhouses, play a pivotal role in aging through their influence on energy production and the generation of reactive oxygen species (ROS). Recent advancements in mitochondrial research have unveiled the intricate mechanisms underlying mitochondrial dysfunction, including mitochondrial DNA mutations and impaired electron transport chain activity. Novel mitochondrially targeted therapies and antioxidants hold promise in mitigating the age-associated decline in mitochondrial function.

Cellular Senescence: Cellular senescence, characterized by irreversible cell cycle arrest, contributes to aging-related tissue dysfunction. Recent discoveries have illuminated the roles of the senescence-associated secretory phenotype (SASP) and specific senescence-inducing genes in driving senescence-related inflammation and tissue deterioration. Proteomic investigations have identified key SASP components, offering potential targets to modulate the detrimental effects of cellular senescence.

Stem Cell Exhaustion: Stem cell exhaustion, a hallmark of aging, limits tissue regeneration and repair. Recent advances in stem cell biology have unraveled the genetic and epigenetic mechanisms governing stem cell function. Techniques such as single-cell RNA sequencing have provided insights into the heterogeneity of stem cell populations and their responses to aging-related cues. Strategies to rejuvenate or replace exhausted stem cells hold promise for rejuvenation therapies.

Altered Intercellular Communication: Age-related changes in intercellular communication through factors like cytokines and growth factors contribute to aging-related pathologies. Recent studies have unveiled the roles of specific genes and pathways in mediating intercellular communication in aging. Proteomic approaches have identified key signaling molecules and their dysregulation in aging-related diseases, offering potential avenues for targeted interventions.

In addition to these hallmarks, metabolomics has emerged as a burgeoning field poised to provide comprehensive insights into the metabolic changes associated with aging and age-related diseases. Metabolomic profiling, combined with genetic, epigenetic, and proteomic data, promises a holistic understanding of the aging process, enabling the identification of biomarkers and therapeutic targets to enhance healthy aging and extend lifespan.

3.2. Overlapping Health Concerns

The intricate interplay between aging, radiation exposure, and associated health concerns transcends age groups, encompassing individuals across the entire lifespan. Recent advancements in medical research have unveiled trends and disparities in the impact of aging and radiation on health, shedding light on varying susceptibilities and implications in both young and older populations. Here, we delve into the multifaceted relationship between aging, radiation exposure, and age-related diseases, emphasizing trends observed in different age groups and incorporating relevant citations.

Inflammation and Aging Across Age Groups: Chronic inflammation, a hallmark of aging, exhibits varying trends across different age groups. In younger individuals, acute inflammation is often a protective response to infections and injuries. However, in older adults, chronic low-grade inflammation, known as “inflammaging”, becomes increasingly prevalent. Recent research, as cited by Podolskiy et al. and Manoharan et al., highlights the role of inflammation in age-related diseases across age groups. For instance, neurodegenerative conditions like Alzheimer’s, Huntington’s, and Parkinson’s diseases exhibit distinct trends in younger versus older populations, with genetic and epigenetic factors contributing to these disparities [83,84,85].
Cardiovascular Health Across Age Groups: Cardiovascular diseases, a common consequence of aging, can manifest differently in various age groups. Recent advancements in cardiology, as mentioned by Curtis et al. and De et al., reveal age-specific trends in cardiovascular health. While older individuals are more susceptible to age-related cardiovascular changes, radiation exposure can exacerbate these trends, affecting both young and older populations. Understanding these nuances is crucial for effective cardiovascular disease management across age groups [86,87,88,89].
Autoimmune Diseases Across Age Groups: Autoimmune diseases, characterized by dysregulated immune responses, can affect individuals of all ages. Recent research cited by Goronzy et al. and Weck elucidates age-specific trends in autoimmune diseases. In pediatric populations, autoimmune conditions may have distinct genetic and environmental triggers, while older adults may experience age-related changes in immune function that contribute to autoimmune pathogenesis. Understanding these age-related disparities is crucial for tailored diagnosis and management across age groups [43,90,91].
Cancer Across Age Groups: Cancer, often considered a hallmark of aging, exhibits variations in incidence and pathogenesis across different age groups. Recent genomic studies, as noted by Podolskiy et al., have unveiled age-specific genetic alterations underlying various cancer types. While older individuals are more prone to age-related genomic changes, radiation exposure can influence cancer development in both young and older populations. Research into these age-related trends is essential for personalized cancer care across age groups [83,92].
Other Health Concerns Across Age Groups: Age-related health concerns extend beyond the aforementioned categories to encompass a wide range of conditions. Recent advancements in medical research have provided insights into age-specific trends in conditions such as cerebrovascular diseases, fibrosis, osteoporosis, chronic inflammation, hypertension, and cataracts. These trends can vary significantly between young and older populations, emphasizing the need for age-appropriate prevention and management strategies [87,89,93,94,95,96].

3.3. Estimating Biological Age

Associating oxidative stress with age presents a complex challenge, primarily because defining age as a baseline reference point is not straightforward. Chronological age, representing the number of years an individual has lived, is an uncontrollable factor that cannot be influenced to observe the effects of stressors. In contrast, biological age, which reflects an individual’s overall health and physiological state, cannot be directly measured or independently estimated. To navigate this complexity, researchers have developed gold standards for estimating biological age, aiming to bridge the gap between chronological and biological age.

The quest for accurate biological age estimation began in 1985 with Nakamura et al.’s pioneering work [97]. They conducted principal component analysis (PCA) of uncorrelated biomarkers and employed Hochschild’s method in 1989 to address the paradox of chronological age. Hochschild’s approach assumes that biological age depends on chronological age for regression analysis, laying the groundwork for subsequent developments in biological age estimation. Building upon Nakamura’s foundation, modern standards emerged, with the Klemera and Doubal (KD) method taking center stage in 2006 [1]. The KD method is widely regarded as the go-to approach for estimating biological age. It incorporates chronological age as one of the primary indicators and integrates it into the modeling process. Researchers such as Levine [2] and Cho [3] extended the KD method’s utility by employing it to predict mortality and supplementing it with factors like the Work Ability Index (WAI).
However, recent studies in the field of biological age estimation have expanded our understanding and refined our ability to separate chronological and biological age. One notable advancement is the development of epigenetic clocks, which utilize DNA methylation patterns to estimate an individual’s biological age [5]. These clocks, pioneered by Horvath in 2013, have demonstrated remarkable accuracy in predicting age, outperforming traditional methods in some cases. The epigenetic clock concept has since evolved, with various clock models tailored to different tissues and populations, enhancing our ability to estimate biological age with precision [6,98,99]. Additionally, recent research has explored the integration of multi-omic data, including genomics, epigenomics, transcriptomics, and proteomics, to develop comprehensive biological age estimators [100]. These integrative approaches consider a broader spectrum of molecular markers, providing a more holistic view of an individual’s biological age. Despite these advances, the pursuit of refined biological age estimation continues, fueled by the recognition that a more comprehensive understanding of aging and its associated oxidative stressors can yield valuable insights into health and longevity.
Indicators of biological age can be categorized into three major groups: functional, physiological, and psychological well-being (PWB). Each category provides valuable insights into the aging process and can help estimate an individual’s biological age more accurately. Table 1 presents a comprehensive list of potential biomarkers within these categories, highlighting their significance in assessing the extent of cellular damage and repair, thereby contributing to a more precise estimation of biological age. This table is a unique contribution to aging research, since it is the first holistic overview of markers including the psychological aspect. Here, we highlight the required measures to tackle complete and accurate estimation of biological age, allowing individualized studies to dive deeper into how to measure each entry via targeted biomarkers. An argument is that no study to date has completed its objective in setting a reference for BA estimation, since these categories have not been sufficiently explored.
  • Functional biomarkers encompass both cognitive and physical aspects of an individual’s health. These biomarkers offer valuable insights into how well an individual’s mind and body are functioning. Within the cognitive realm, indicators such as memory, decision reaction time, and verbal fluency provide critical information about cognitive decline or preservation. On the physical front, biomarkers like grip strength, walking speed, and visual perception and measures like height, weight/BMI, and lung capacity offer insights into an individual’s physical vitality and resilience [101,102,103,104,105,106].
  • Physiological biomarkers delve into the state of an individual’s organs, tissues, and cellular health. These biomarkers provide a deeper understanding of the body’s internal processes and can shed light on the effects of aging. Metrics such as brain size, blood composition, blood pressure, muscle mass, and bone density, among others, offer valuable data for assessing an individual’s physiological age [107,108].
  • Psychological well-being biomarkers are a unique category that delves into an individual’s emotional and mental state. This category is further divided into hedonic and eudaimonic dimensions. Hedonic aspects focus on happiness, subjective well-being, and positive emotions, while eudaimonic dimensions include self-acceptance, environmental mastery, positive relationships, personal growth, purpose in life, and autonomy. These biomarkers offer insights into an individual’s psychological resilience and overall well-being, which can influence their biological age [112,113].
While these biomarkers provide a comprehensive foundation for estimating biological age, it is essential to recognize that the field of biological age estimation has evolved significantly in recent years. Mentioned earlier was the groundbreaking development of epigenetic clocks that have revolutionized the way we approach biological age estimation. These clocks utilize DNA methylation patterns to provide highly accurate estimations of an individual’s biological age. Pioneered by researchers like Horvath and Hannum, epigenetic clocks have showcased their remarkable precision and have become valuable tools in aging research [114,115]. The next significant developments coming in the near future involve the integration of multi-omic data, including genomics, epigenomics, transcriptomics, and proteomics, opening up new avenues for the development of comprehensive biological age estimators. These integrative approaches consider a wide range of molecular markers, offering a holistic view of an individual’s biological age and health status.
As the field continues to advance, the standard for biological age estimation continues to evolve, with new methodologies and interpretations constantly adding to our understanding of the aging process [116]. This progress is crucial for gaining insights into age-related diseases and their potential associations with radiation exposure. Developing optimized models and techniques for clear and meaningful results will expedite theoretical progress and enhance our ability to mitigate the effects of oxidative stressors on biological age.

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