Animal Models for the Social Determinant of Health and Aging-Related Diseases

This project has been launched to comprehend how social determinants influence health and age-related ailments. This project investigates how social factors impact aging processes by utilizing laboratory mice as a model system due to practical and ethical limitations. In humans, studying these relationships directly. Adjusting specific variables within a mouse’s surroundings is how the investigators aim to produce evaluation methods for Social Determinants of Health & Aging (SDOHA) ^{10, 7}. They intend to reveal fundamental biological mechanisms pertaining to it while also predicting functional impairment along with life span so they can design interventions to reverse ill effects resulting from unfavorable communal interactions³. Humans could potentially experience improved health outcomes and increased lifespan through these interventions.

The noteworthy broader effects of this research are substantial. The project can identify chances for intervention to boost health span and alleviate lifespan reduction by understanding how social factors affect health and aging. The impact on the field of physiology can be significant due to innovative assessment growth². Additionally, this study could aid in enhancing comprehension of how genetic, epigenetic factors and socio-behavioral variables interact. Shaping individual aging trajectories can be facilitated by understanding accelerated aging.

The research could be practically applied to develop interventions or policies to improve social factors and decrease the burden of aging-related diseases⁸. Addressing social determinants of health and promoting healthy aging can be informed by these findings for public health strategies. Besides that, the research will augment our overall understanding of how social context impacts human health and culture.

This investigation aims to establish models that evaluate the social determinants connected with health and ageing⁶. Additionally, it strives to produce an extensive aging index algorithm while discovering how these two topics are affected by investigating DNA methylation¹. Moreover, the research aims to evaluate how social determinants influence health span and aging-related consequences in mouse models. Using Illumina’s protocol, the researchers intend to systematically conduct DNA methylation testing on the mice. Sodium bisulfite conversion will be performed after isolating genomic DNA^{5, 2}. Metabolic processing examination will occur alongside a repeated assessment of the Social Frailty index². In order to understand how social factors affect health and aging, the researchers will work with other labs and leverage current algorithms.

The study proposal seems viable since it builds upon current knowledge while collaborating with other researchers. Mouse models enable researchers to conduct controlled experiments and manipulate social variables⁹. The researchers must have backup plans if they encounter unexpected challenges or obstacles while conducting their research. Technical difficulties or unforeseen ethical considerations may be among the challenges.

This research outcome contributes to the field of study by better comprehending how social factors impact health and aging. Assessment tools and the comprehensive aging index algorithm are essential resources for conducting valuable research on aging¹⁰. This research’s novel insights shall be communicated through scholarly publications, conference talks, and cooperation with colleagues who work on related topics.

Aside from disseminating the research results through scholarly means, this project’s achievements may also be practically useful with potential implications on public policies. Improving social factors through targeted interventions and policies can be informed by the understanding gained from this research on social determinants of health and aging⁴. Reducing health disparities in aging populations can also be achieved through this. This research can improve individuals’ quality of life and healthcare systems by addressing the social gradient in health and aging^{1, 3}. The whole society can be impacted by it too. Healthy aging promotion and improved population health outcomes can be accomplished by tackling the role played by the social context in age-related illnesses.

References

1. Aziz, V. M. Deficit in Sensorimotor Gating in Alzheimer’s Disease (AD): Measuring Prepulse Inhibition (PPI) as a Measure of Liability to AD. Annals of Psychiatry and Clinical Neuroscience2, (Remedy et al., 2010).
2. Elliott, M. L. et al. Disparities in the pace of biological aging among midlife adults of the same chronological age have implications for future frailty risk and policy. Aging1, 295–308 (2021).
3. Lupien, S. J., McEwen, B. S., Gunnar, M. R. & Heim, C. Effects of stress throughout the lifespan on the brain, behavior, and cognition. Nature Reviews Neuroscience10, 434–445 (2009).
4. Mitchell, S. J., Scheibye-Knudsen, M., Longo, D. L. & De Cabo, R. Animal models of aging research: Implications for human aging and age-related diseases. Rev. Anim. Biosci.3, 283–303 (2015).
5. Moran, J. M., Jolly, E. & Mitchell, J. P. Social-cognitive deficits in normal aging. Neurosci.32, 5553–5561 (2012).
6. Schultz, M. B. et al. Age and life expectancy clocks based on machine learning analysis of mouse frailty. Commun.11, 1–12 (2020).
7. Vorland, C. J. et al. Errors in implementing, analyzing, and reporting randomization within obesity and nutrition research: a guide to their avoidance. International Journal of Obesity (2021). doi:10.1038/s41366-021-00909-z
8. Yang, Y. C., Boen, C. & Mullan Harris, K. Social relationships and hypertension in late life: Evidence from a nationally representative longitudinal study of older adults. Aging Health27, 403–431 (2015).
9. Whitehead, J. C. et al. A clinical frailty index in aging mice: Comparisons with frailty index data in humans. Journals Gerontol. – Ser. A Biol. Sci. Med. Sci.69, 621–632 (2014).
10. Young, J. W., Wallace, C. K., Geyer, M. A. & Risbrough, V. B. Age-Associated Improvements in Cross-Modal Prepulse Inhibition in Mice. Neurosci.124, 133–140 (2010).