Longevity and lifespan are a real hot topic right now. The amount of interest in the field has been increasing significantly and several labs are cranking out exciting new research. However, there is a lot of data out there and it can be tricky to interpret promising data from the noise. In response to this, a few researchers came together and put out a preprint on how to best gauge the quality of longevity interventions.
https://www.sciencedirect.com/science/article/abs/pii/S1568163724003301
They argue in favor of choosing long lived controls, in this case lab mice, as a better marker for the robustness of any longevity intervention and state…
“In the absence of independent replication, a putative mouse longevity intervention should only be considered with high confidence when control lifespans are close to 900 days or if the final lifespan of the treated group is considerably above 900 days.”
This is the 900-day rule. Now, why is this important? Many studies in longevity research around mouse models use a variety of lab strains, and their median lifespans can differ dramatically. Let’s say we have two studies, one has an intervention that is able to increase lifespan by 12% (intervention A) and the other shows a whopping 30% increase (intervention B) in lifespan. Which of these two sounds more interesting, more exciting, more desirable? In most cases that 30% increase is going to generate a lot of discussion and excitement, more so than a measly 12% lifespan bump. Details are key, and in this case the details are the longevity curves.
I just said details are key, but I’m going to gloss over A LOT of information and just focus on longevity curves and median lifespans from a few different papers just to help illustrate this point.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9171804/
This study looks at gene therapy (and was mentioned in another post here) and boasts that “…mouse lifespan was increased up to 41%…”
I’ve included their longevity curve and have added my own lines to help illustrate this, in black I’ve plotted the median lifespan of the control group, and red is median lifespan of the longest lived intervention group. We can easily see that the lifespan increase is significant, but does it pass the 900-day rule? The controls averaged just over 26 months, let’s say approximately 790 days. Definitely under 900 days, barely hitting 800. So far it doesn’t quite make the grade.
Let’s look at another paper. https://elifesciences.org/articles/16351
This one tested lifespan changes with rapamycin. There were multiple interventions, for now I’m just looking at one figure, longevity of the male cohort with ramapycin injections.
Once again I’ve plotted the median lifespan on the controls and intervention. We can already see the stark difference compared to the first paper. This lifespan bump looks pretty small by comparison, but let’s look at the 900-day rule. Controls lasted on average a little over 200 days past 24 months, approximately 920 days. This is a long lived control.
So, why is this important? If we can set a standard for longevity trials to only use long lived controls it significantly helps quantify the data as a whole. If you were quickly pursuing these papers, the takeaway might be that rapamycin is an inferior treatment compared to the gene therapy Jaiyan et al. tested. If nothing else the 900-day rule is the ultimate hype tester. I want to see exciting data showing lifespan increases as much as these researchers do, but be wary of the hype.
Delish! Thanks for the Qs
“Aren’t the shorter-lived strains functioning under genetic pressures in order to be short-lived?” They weren’t intentionally bred to be short-lived. It’s more of an unintended consequence. The goal was to create a docile, general-purpose lab mouse, and in the process of enriching for these traits, genetic diversity decreased. This reduction in diversity inadvertently shortened the lifespan in certain strains.
“From a research perspective, this makes sense as conducting studies through end-of-life would be more exhaustive if longer-lived strains were used.” I see your point but the actual difference in lifespan is only about 0.5 to 1 year—so not as big a difference as it might seem when considering the added effort for end-of-life studies or even just dealing with the mice that have several more months of health/life. To take your numbers, it would only be 110 days which is less than half a year.
“Outside of longevity, it would be better to use short-lived models.” Not necessarily. For example, heart disease is heart disease, and you don’t need to artificially impose unrelated lifespan limits to study it effectively. Long-lived models can still provide meaningful data on a variety of conditions without the confounding factor of an “unnaturally” short lifespan.
“Any intervention would undoubtedly help a short-lived strain…” That depends. For instance, if a strain is highly susceptible to cancer, interventions targeting cancer might extend its lifespan. However, if the strain tends to die of kidney disease, cancer therapeutics won’t affect longevity. The effectiveness of an intervention varies depending on the underlying/predominant cause of death in these strains.
“It would essentially be undoing years of genetic constraints that caused them to be short-lived in the first place.” Exactly—this is what I was getting at. In the study we’re discussing, the intervention not only had to counteract these added genetic or environmental stresses but also extend lifespan beyond the norm for long-lived strains. That’s what makes the result more meaningful in a way.
“There seems to be an invisible, yet squishy ceiling on lifespan up to a certain age with interventions…” The point I was trying to make was that the gene therapy in this case surpassed both the softer and harder limits you are referring to, suggesting that the therapy had a significant impact not just on addressing the deficits these animals had but also pushed these shorter lived animals past the hard ceiling for longevity set by the (theoretical) long-lived controls.