Aging and age-related diseases can be explained by a combination of several theories and multifactorial models.
Biological aging is defined as the reduction of regenerative potential in organs and tissues [1]. Aging inevitably occurs in all organisms and is a primary risk factor for chronic diseases. Senescence is the most prominent occurrence of aging at the tissue level that occurs due to several genetic and environmental factors.
The proportion of older individuals is reported to rise globally and is estimated to approximately double from 12% in 2015 to 22% by 2050. Previous studies indicated that the accumulation of particular factors such as telomere shortening, somatic mutations, mitochondria dysfunction, or protein damage leads to aging. However, studies during the late 1990s indicated that individual mechanisms were not adequate for understanding the aging process [2].
Aging is difficult to understand for two reasons. Firstly, aging is a multifactorial process [3], and secondly, the contributing factors that cause aging are context dependent and variable. Therefore, understanding how the individual processes combine to cause aging and age-related diseases is a challenge for biogerontology.
Longevity.Technology: Several multifactorial models have emphasised antagonistic pleiotropy (AP) and programmatic mechanisms to determine the pathophysiological basis of the process of senescence. These include Misha Blagosklonny’s hyperfunction theory, Tom Kirkwood’s disposable soma theory, Vladimir Dilman’s ontogenetic theory and João Pedro de Magalhães’ developmental theory.
Dilman’s, Magalhães’ and Blagosklonny’s theories have been found to share similar features and are together referred to as the programmatic theory. Together, these theories are known as the ultimate proximate theory and help to understand the accurate mechanism of aging thereby improving age-related pathologies and the overall quality of life for individuals.
Two interpretations of how gene variants can adversely impact late-life health can be that these harmful mutations appear at later stages of life or they can be gene variants that provide fitness benefits during early life but lead to detrimental effects during late life. These alleles, therefore, display pleiotropy concerning the impact on fitness. This phenomenon is known as antagonistic pleiotropy (AP). AP genes cannot be termed as defective since they provide fitness benefits and therefore are considered normal genes. These normal genes that cause diseases of aging are termed gerontogenes.
The ultimate-proximate theory comprises the disposable soma theory and the programmatic theory. Previous theories have indicated that the accumulation of random molecular damage is the most prominent cause of aging. Therefore, preventing the accumulation of damage by the maintenance of the level of cellular maintenance is important in determining the aging rate. Moreover, trade-offs have been found to exist between reproductive effort and cellular maintenance [4].
Due to the limited availability of resources, optimisation of trade-offs would lower the levels of cellular maintenance below those which are required to prevent aging, thereby leading to rapid aging. The disposable soma theory provides an understanding of how trade-offs could arise between fitness traits at different times during life history.
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Previous research has indicated that the insulin/IGF-1 signalling (IIS), mammalian (or mechanistic) target of rapamycin (mTOR), and growth hormone (GH) belong to a nutrient-sensitive signalling network that causes growth, development and aging. However, there have been three challenges to these findings. Firstly, these findings suggest the existence of a program for aging, but evolutionary theory suggests that aging is not programmed. Secondly, growth and damage are not linked to the accumulation of molecular damage and cellular maintenance. Thirdly, it is difficult to understand how growth processes could limit lifespan.
The programmatic theory was capable of providing answers to each of the challenges. The work of George Williams on AP genes, the quasi-programmed approach of Blagosklonny, and Magalhães’ explanation of how developmental programs promote aging help to overcome the previous limitations. Moreover, Blagosklonny indicated that the mechanism of aging does not involve loss of function – it rather involves too much function or hyperfunction [4].
Furthermore, the programmatic theory also highlighted that developmental changes during adulthood that continue into late-life developmental programs can disrupt tissue and organ function. One prominent example is the prostate gland in men which gradually increases in size with age. It can then lead to benign prostatic hyperplasia and increase the risk of prostate cancer.
Dilman reported that aging along with age-related diseases are etiologically multifactorial and can be attributed to four disease models, genetic, ecological, ontogenic and accumulation. The accumulation model involves the accumulation of molecular damage and other pathological accumulation while the ontogenic model (development of an individual organism or a part of an organism from inception to maturity) involves the developmental processes that can lead to late-life senescence. The genetic model involves inherited genetic diseases and the ecological model involve all extrinsic factors that can result in diseases. The ontogenic model is reported to be an important predecessor of the programmatic theory. Other additional ideas by de Magalhães and Blagosklonny further strengthen the programmatic theory.
Taken together, these theories can help to better understand aging as well as the pathophysiology of late-life diseases. This could provide the field of biogerontology with an accurate explanation of the mechanism of aging. These theories would also help in the development of therapies to prevent aging and age-related diseases as well as improve the longevity of individuals.
[1] https://pubmed.ncbi.nlm.nih.gov/28544158/
[2] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5322748/
[3] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3295054/
[4] https://www.sciencedirect.com/science/article/pii/S1568163721003044
