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What are some experimental methods we can use to study the aging process?

Image credit: https://pubchem.ncbi.nlm.nih.gov/compound/D-galactose#section=Biologic-Description

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Brett M.
Brett M. Dec 03, 2020
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Necessity

Is the problem still unsolved?

Conciseness

Is it concisely described?

Aging is a dynamic process, and there seems to be a significant interest in discovering compounds and methods that will slow or reverse aging, even on this platform alone . It makes sense then to not only discover compounds/methods found to mitigate this process but also some ways we can assess changes associated with aging by using preclinical experimental manipulations.

For example, recent evidence has described the use of D-galactose as an accelerated aging tool to model the aging process in rodents. By administering D-galactose, researchers have discovered a way to accelerate aging processes within the brain such as increasing mitochondrial dysfunction, oxidative stress, and metabolic abnormalities . This has utility in expediting the scientific process of examining how aging progresses in the body and the brain.

How does D-galactose work?

In vitro studies have shown that D-galactose may accelerate the aging process in astrocytes by promoting cellular senescence and increasing inflammatory mediators such as IL-6 and IL-8 through the canonical "pro-inflammatory pathway" involving NFkB activation. Moreover, the authors suggest that D-galactose may play a primary role in the progression of tumor cells within the brain as well .

In vivo studies have shown that that oral administration of D-galactose led to abnormal mitochondrial respiration in the rat brain, most prominently within the hippocampus following chronic treatment, and was suggested to cause changes similarly observed in conditions of neurodegeneration within the brain . As well, it was found that D-galactose administration for 4 or 8 weeks increased advanced glycation end products, microglial activation, and was found to impair processes associated with synaptic plasticity that resulted in cognitive decline. Interestingly, these findings were similar to those found in rats fed a high-fat diet (HFD), but the combination of HFD + D-galactose did not exacerbate these parameters, suggesting that HFD and D-galactose equally promote aging processes in the brain .

Prevention of D-galactose-induced abnormalities

At the same time, studies have found utility in D-galactose as an accelerated aging tool by investigating ways to suppress its activity. For example, studies have found that compounds such as astaxanthin and tetrahydropalmatine can protect against memory impairments as well as mitochondrial and metabolic dysfunction induced by D-galactose administration. Finally, an interesting study discovered that exposure to swimming substantially reduced aging-associated deficits in cognition and physiology induced by D-galactose, and this mechanism was found to occur through downregulation of miR-34a in the rat brain. A follow-up in vitro study of miR-34a inhibition in human cells confirmed the role of this micro RNA in the regulation of dysfunctional autophagic processes and age-related increases in brain physiology .

Limitations

One limitation that has been observed with this model is that D-galactose-induced age-related deficits did not occur in young male rats . Thus, D-galactose may only be effective in the examination of adulthood aging processes and still needs to be investigated using female subjects.

What other approaches like this one exist, and how do they aim to solve the question we all want to answer?

Nevertheless, these studies provide evidence for an interesting approach to studying the aging process that provides insight towards mechanisms and biomarkers that are associated with aging as well as interventions that may slow this process. This is only one approach, however--I am interested to know if or what other methods exist that experimentally induce the aging process... post them in the contributions below!

[1]https://brainstorming.com/search?search=anti-aging

[2]Liu H , Zhang X , Xiao J , Song M , Cao Y , Xiao H , Liu X . Astaxanthin attenuates d-galactose-induced brain aging in rats by ameliorating oxidative stress, mitochondrial dysfunction, and regulating metabolic markers. Food Funct. 2020 May 1;11(5):4103-4113. doi: 10.1039/d0fo00633e. Epub 2020 Apr 28. PMID: 32343758.

[3]Hou J, Yun Y, Xue J, Sun M, Kim S. D‑galactose induces astrocytic aging and contributes to astrocytoma progression and chemoresistance via cellular senescence. Mol Med Rep. 2019 Nov;20(5):4111-4118. doi: 10.3892/mmr.2019.10677. Epub 2019 Sep 12. PMID: 31545444; PMCID: PMC6797969.

[4]Budni J, Garcez ML, Mina F, Bellettini-Santos T, da Silva S, Luz APD, Schiavo GL, Batista-Silva H, Scaini G, Streck EL, Quevedo J. The oral administration of D-galactose induces abnormalities within the mitochondrial respiratory chain in the brain of rats. Metab Brain Dis. 2017 Jun;32(3):811-817. doi: 10.1007/s11011-017-9972-9. Epub 2017 Feb 24. PMID: 28236040.

[5]Shwe T, Bo-Htay C, Leech T, Ongnok B, Jaiwongkum T, Kerdphoo S, Palee S, Pratchayasakul W, Chattipakorn N, Chattipakorn SC. D-galactose-induced aging does not cause further deterioration in brain pathologies and cognitive decline in the obese condition. Exp Gerontol. 2020 Sep;138:111001. doi: 10.1016/j.exger.2020.111001. Epub 2020 Jun 6. PMID: 32522583.

[6]Qu Z, Zhang J, Yang H, Huo L, Gao J, Chen H, Gao W. Protective effect of tetrahydropalmatine against d-galactose induced memory impairment in rat. Physiol Behav. 2016 Feb 1;154:114-25. doi: 10.1016/j.physbeh.2015.11.016. Epub 2015 Nov 22. PMID: 26592138.

[7]Liu H , Zhang X , Xiao J , Song M , Cao Y , Xiao H , Liu X . Astaxanthin attenuates d-galactose-induced brain aging in rats by ameliorating oxidative stress, mitochondrial dysfunction, and regulating metabolic markers. Food Funct. 2020 May 1;11(5):4103-4113. doi: 10.1039/d0fo00633e. Epub 2020 Apr 28. PMID: 32343758.

[8]Kou X, Li J, Liu X, Chang J, Zhao Q, Jia S, Fan J, Chen N. Swimming attenuates d-galactose-induced brain aging via suppressing miR-34a-mediated autophagy impairment and abnormal mitochondrial dynamics. J Appl Physiol (1985). 2017 Jun 1;122(6):1462-1469. doi: 10.1152/japplphysiol.00018.2017. Epub 2017 Mar 16. PMID: 28302704.

[9]Cardoso A, Magano S, Marrana F, Andrade JP. D-Galactose High-Dose Administration Failed to Induce Accelerated Aging Changes in Neurogenesis, Anxiety, and Spatial Memory on Young Male Wistar Rats. Rejuvenation Res. 2015 Dec;18(6):497-507. doi: 10.1089/rej.2015.1684. Epub 2015 Aug 20. PMID: 25936362; PMCID: PMC4685507.

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Computational models to study ageing

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Subash Chapagain
Subash Chapagain Dec 04, 2020
Apart from wet-lab experimental (including in vivo animal) models to explicate the ageing phenotype and the search for potential interventions to prolong the lifespan, computational methods are very useful as complementary tools for designing models associated with ageing. With the recent rise in AI applications for biological systems and the AlphaFold’s breakthrough achievement in solving the age-old protein folding, this approach seems more likely now than ever.

Whats and hows of biological systems modelling:

Like in any other fields of engineering and material science, computer models can be used to see the relationship and dynamism of the biological processes. Computer modelling is the process of defining a system of interest in the most approximate manner by using mathematical tools . Eventually, the mathematics is then fed to a computer to simulate the dynamic behaviour of the biological system in question. If properly formulated, such a model for ageing can have advantages like hypothesis exploration, an alternative view of biological mechanisms and the ability to generate novel insights. Also, the model can be used to predict the temporal development of the system and it also offers flexibility update and cohesive input whereby different biological pathways can be taken into account.

How to go about building a model for ageing?

First and foremost, we define a hypothesis as in what precise mechanism of the ageing phenomenon we are trying to simulate with our model. Once it is defined , the biological entities (these could be proteins, enzymes or any other molecules) that are accounted for the model are listed. More than often, it is possible to use mathematics in order to summarize numerous interactions into one interaction that can tentatively capture the behaviour of the overall pathway in a collective manner. For example, Insulin-like IGF signalling, mTOR and ROS signalling pathways all converge down to a common point involving the FOXO transcription factors, which is more or less conserved from C. elegans to higher mammals alike. Such a process of finding a common summarizing point is called abstraction, and on the modeller’s part, this process can get more refined with rigorous literature and experimental review.

The next step would be to graphically map the entities and interactions to formalize the model. There already are a number of existing systems biology standard graphical assembly tools . Once the network diagram is created by mapping the pathways, the rest is mostly done using software tools used to simulate the dynamics of the models. Different mathematical frameworks (for example stochastic vs deterministic) are used for the simulation. The parameters (which can be the features of the biomolecules, for example, half-life, concentrations, physicochemical properties, enzyme kinetics) for initial variables are set (databases like BRENDA are available online for this), then the model is set for simulation. If the model gives satisfactory output reflecting the basic biological system, then we can test the hypothesis and use the model for further experimentation. If not, we could go back and troubleshoot in the critical control points.

CellDesigner and COPASI are some intuitive tools already available for assembling models.



In the case of ageing, we can build up a bottom-up model that starts from intracellular pathways and subsequently moves up through intercellular, organismal to the population level.

Intracellular dynamics:

While considering the environment limited within a cell only, the phenomenon of ageing is most canonically influenced by the telomere dynamics. For instance, the models accounting for telomere dynamics have been developed by Wattis and Byrne and by Hirt et al .

Keeping in view the role of ROS on telomere attrition, another model has been devised.

These are just some representative examples of how intracellular mechanisms are already being mapped and modelled using computational tools. The existing tools can be extended to cover as many possible mechanisms as possible as long as they are pertinent to the phenomenon of ageing.

Intercellular to organismal (whole-body) dynamics:

As a metabolic hub used to detect oxidative stress in, mTOR pathways have been integrated into other aspects of ageing for example mitochondrial biosynthesis . The mTOR pathway has also been modelled in conjunction with insulin signalling as well . These models are more reflective for real biological implications because they take into account the long-distance cross-talk between the pathways involved in non-adjacent cells and tissues of the body. Such models pave a way for whole-body simulation.

Regarding whole-body dynamics, cholesterol levels have been implied to play a major role in ageing and longevity. A group of researchers have developed a model that simulate the whole body cholesterol dynamics. Using the already determined parameters from the decade long data on cholesterol ingestion, excretion, synthesis, LDL receptor turnover and steps in cholesterol transport; the model was used to predict the interplay between cholesterol metabolism and ageing. The model showed a 30% increase in cholesterol metabolism between the ages of 20–60years caused a 34mg/dL increase in plasma LDL-C. However, the key finding of the model centred on how the age-associated changes to the number of hepatic LDLr impact plasma LDL-C. It was discovered a decline in hepatic LDLr by 50% by age 65years, resulted in an increase in LDL-C in LDL-C by as much as 34mg/dL which is an important finding on the face of the fact that LDLr decrease with age. This offers a promising frontier to alleviate the downsides of ageing as long as lipid metabolism in the liver is considered.

Hence, such models could be used, first individually, then holistically to determine and map the mechanisms associated with ageing. Such computational tools are beneficial because
  • They can be used for preclinical research purposes and offer a cost-effective method.
  • There is no resource-exhaustion while using these models (apart from the time and expertise spent)
  • These are easy for troubleshooting and updating with the latest information



[1]Kitano, H., 2002. Computational systems biology. Nature 420 (6912), 206–210

[2]Mark Mc Auley, Kathleen Mooney, Chapter 7 - Using Computational Models to Study Aging, Editor(s): Jeffrey L. Ram, P. Michael Conn, Conn's Handbook of Models for Human Aging (Second Edition), Academic Press, 2018

[3]Le Novère N, Hucka M, Mi H, Moodie S, Schreiber F, Sorokin A, Demir E, Wegner K, Aladjem MI, Wimalaratne SM, Bergman FT, Gauges R, Ghazal P, Kawaji H, Li L, Matsuoka Y, Villéger A, Boyd SE, Calzone L, Courtot M, Dogrusoz U, Freeman TC, Funahashi A, Ghosh S, Jouraku A, Kim S, Kolpakov F, Luna A, Sahle S, Schmidt E, Watterson S, Wu G, Goryanin I, Kell DB, Sander C, Sauro H, Snoep JL, Kohn K, Kitano H. The Systems Biology Graphical Notation. Nat Biotechnol. 2009 Aug;27(8):735-41. doi: 10.1038/nbt.1558. Epub 2009 Aug 7. Erratum in: Nat Biotechnol. 2009 Sep;27(9):864. PMID: 19668183.

[4]Qi, Q., Wattis, J.A., Byrne, H.M., 2014. Stochastic simulations of normal aging and Werner’s syndrome. Bull Math Biol 76 (6), 1241–1269.

[5]Hirt, B.V., Wattis, J.A., Preston, S.P., 2014. Modelling the regulation of telomere length: the effects of telomerase and G-quadruplex stabilising drugs. J Math Biol 68 (6), 1521–1552.

[6]Trusina, A., 2014. Stress induced telomere shortening: longer life with less mutations? BMC Syst Biol 8, 27.

[7]Kriete, A., Bosl, W.J., Booker, G., 2010. Rule-based cell systems model of aging using feedback loop motifs mediated by stress responses. PLoS Comput Biol 6 (6), e1000820.

[8]Brännmark, C., Nyman, E., Fagerholm, S., Bergenholm, L., Ekstrand, E.M., Cedersund, G., et al., 2013. Insulin signaling in type 2 diabetes: experimental and modeling analyses reveal mechanisms of insulin resistance in human adipocytes. J Biol Chem 288 (14), 9867–9880.

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Brett M.
Brett M.a year ago
Thanks for your contribution! I have found computational modeling to be such an interesting tool, though I have never been able to take the time to quite understand it, so thank you for providing a brief overview--very informative.

As I am most interested in the concept of predictability, "Also, the model can be used to predict the temporal development of the system and it also offers flexibility update and cohesive input whereby different biological pathways can be taken into account," sounds like a very interesting utility of computational modeling.

Have these models only been used as predictive measures, or have they been correlated with actual outcomes? This is super interesting and I would love to know if there is evidence describing the accuracy in the model predictions (also, it may be too early as I am not sure how old this technology is).

As I'm thinking about the utility of this technology, I think this would be super useful to identify lifestyle patterns that individuals could take to promote longevity. Almost like a "cookbook" of longevity lifestyles in a sense, that you can pick from (or move between) to optimize your aging process. Very interesting contribution--thank you!
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Subash Chapagain
Subash Chapagaina year ago
Thanks for the comment. Some of these models (for instance the one that looked into Cholesterol dynamics and the one IGF-like insulin signaling) have been reported to reflect the real outcomes. However, I think there is still a need for rigorous validation. Talking about the utility, like you said, in future if we could develop models not just for the biochemical reactions but also for the holistic effect of lifestyle choices, it would indeed be a leap for anti-ageing research. As more models become validated, we can expect that to happen: how much of physical activity of what sort, in synergy with what particular kind of diet and lifestyle choices could one follow so as to age the most slowly? This could be interesting if we could be advised by an AI assisted computer model.
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Monitor the lifestyle of centenarians and compare it with that of others

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Shubhankar Kulkarni
Shubhankar Kulkarni Jan 08, 2021
Centenarian studies have, no doubt, contributed to our knowledge of the aging process . A lot of work has already been done in this area but it is fragmented (each parameter has been studied separately). Centenarian diet has been studied the most (see for example). Such studies have focussed on the blue zones of the planet (where the average lifespan is much longer than the world average). I propose a holistic approach where every aspect of the centenarian lifestyle is monitored.

Monitoring the lifestyle of randomly selected centenarians from around the globe will help pinpoint the common factors that these people indulge in. Along with diet, other factors of the centenarians can be recorded such as their daily chores - how many hours they work, what type of work they do (percent physical, percent mental), their sources of income, etc.; their family composition - how much time they spend alone, how often do they visit their relatives, how much time do they spend with their friends; some philosophical questions like their belief system, their take on life (these are important because people in blue zones mention that having a "purpose" in life is what gets them going).

Each of these points can then be compared with those of the non-centenarians. Some studies have compared centenarian parameters to those of the children (now adults) of the non-centenarians and some others have compared those of the siblings of the centenarians to the general population.

A proportion of the triggers to the aging process can be identified using such a study. Once we identify the cause(s), we can think of solutions to counter it.

[1]Willcox DC, Willcox BJ, Poon LW. Centenarian studies: important contributors to our understanding of the aging process and longevity. Curr Gerontol Geriatr Res. 2010;2010:484529. doi:10.1155/2010/484529

[2]Appel LJ. Dietary patterns and longevity: expanding the blue zones. Circulation. 2008 Jul 15;118(3):214-5. doi: 10.1161/CIRCULATIONAHA.108.788497. PMID: 18625902.

[3]Buettner D, Skemp S. Blue Zones: Lessons From the World's Longest Lived. Am J Lifestyle Med. 2016;10(5):318-321. Published 2016 Jul 7. doi:10.1177/1559827616637066

[4]Functionally significant insulin-like growth factor I receptor mutations in centenarians Yousin Suh, Gil Atzmon, Mi-Ook Cho, David Hwang, Bingrong Liu, Daniel J. Leahy, Nir Barzilai, Pinchas Cohen Proceedings of the National Academy of Sciences Mar 2008, 105 (9) 3438-3442; DOI: 10.1073/pnas.0705467105

[5]Bradley J. Willcox, D. Craig Willcox, Qimei He, J. David Curb, Makoto Suzuki, Siblings of Okinawan Centenarians Share Lifelong Mortality Advantages, The Journals of Gerontology: Series A, Volume 61, Issue 4, April 2006, Pages 345–354, https://doi.org/10.1093/gerona/61.4.345

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Brett M.
Brett M.a year ago
Thanks for your contribution! You've highlighted a good point in measuring the aging process - while the diet is inherently important, it is not the sole mediator. Psychological and other physical factors (perhaps a combination) that you have highlighted are important to consider as well. In fact, group-based exercises in the aged Japenese population greatly benefit health status (https://pubmed.ncbi.nlm.nih.gov/28830443/) - a process that may be facilitated by having senior leaders in political and public health roles to promote exercise in the elderly (https://pubmed.ncbi.nlm.nih.gov/33008403/). This type of exercise clearly has social, psychological, and physical effects, and may all be working together to improve health status in old age.

Interestingly, serum albumin levels were found to protect against the decline in daily activities in the aged Chinese population (https://pubmed.ncbi.nlm.nih.gov/32605543/). As well, heart-rate variability appears to be linked to longevity (https://pubmed.ncbi.nlm.nih.gov/33041862/). These represent additional measures that could be assessed to understand the link between physiology and longevity.

Of course, adding this measurement with the other measures you have mentioned would be incredibly important, as this adds to the holistic approach you've described, which would definitely paint a better picture than assessing each component in isolation. Thanks again!
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Shubhankar Kulkarni
Shubhankar Kulkarnia year ago
Brett M. Thank you for directing me towards the other studies. I did not know that albumin contributed significantly to the daily activity levels and, as an effect, to the energy levels of an individual and, ultimately, their lifespan.

I always knew that heart-rate variability decreases with age but I was hoping to find a difference between that of centenarians and octogenarians. But I think there are a few factors from the study that may have contributed to it. Firstly, the small sample size (20 is the maximum group size in the study). Secondly, the age difference between the young and the aged is too high, which shows an obvious difference in the heart-rate variability. Comparatively, the age difference between octogenarians and centenarians is less. The non-significant difference may be due to this reason. Thirdly, all the participants come from the same area and lifespan may not be a significant variable there. Who is to say that most of the octogenarian participants did not survive into their hundreds? Although not perfect, a way around this is to compare the siblings (or progeny) of the non-centenarians to the centenarians. Here is another study that compared the heart-rate variability of the aged population (above 75 years of age) and the centenarians and found significant differences (https://pubmed.ncbi.nlm.nih.gov/10545308/#:~:text=Heart%20rate%20variability%20(HRV)%20has,aged%20subjects%20and%20healthy%20centenarians.)

What we both are hinting at here is the exposome of an individual, which is "the measure of all the exposures of an individual in a lifetime and how those exposures relate to health. An individual's exposure begins before birth and includes insults from environmental and occupational sources." (https://www.cdc.gov/niosh/topics/exposome/default.html#:~:text=The%20exposome%20can%20be%20defined,from%20environmental%20and%20occupational%20sources.) I hadn't come across this word earlier so thanks for sharing that, too!
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