In a world where the population aged 60 or over doubled in the last 30 years, and is expected to double again by 2050, how’s that for a sensational headline? The story, written in the journal Nature, is referring to a study published by Aging Cell. Amazingly, only 137 of you sent it to me within the first 24 hours of its release.
Nine healthy men, given a cocktail of human growth hormone (hGH), metformin, DHEA, vitamin D3, and zinc for 1-year, shed about 2.5 years off their biological ages, according to an analysis of their epigenome.
As a result of this study, I’ve had more people than usual ask the following questions:
Should I be taking hGH? Should I be taking metformin? Should I be taking DHEA?
To address these questions (and others) will be a bit of an undertaking, so I’m breaking this topic up into at least two emails. In today’s email, I want to explain the study’s purpose, how it was done, what it found, as well as some of the nuts and bolts behind it, and—most importantly—propose a framework for evaluating studies in general. I’ve covered a lot of the groundwork in the Studying Studies series so I may sound a little like a broken record in places. That said, if you are tired of being held hostage by the media’s interpretation of science, you will need to buck up and learn this stuff. The Studying Studies series is the starting point. I realize it may seem like Groundhog Day for you to see more prose from me about how to think about studies rather than the tactical bits we think we can immediately extract and employ from them. Just remember, it’s better to learn how to fish than to be given … you get it.
On to the study.
The stated purpose was to investigate the possibility that using hGH in a population of men in their 50s and early 60s can prevent or reverse signs of the gradual deterioration of the immune system that has been attributed to natural age development (i.e., immunosenescence). The trial, dubbed Thymus Regeneration, Immunorestoration, and Insulin Mitigation, or TRIIM, reveals its aims. (Note that nothing in the initial aim of the study dealt with assessing the impact of the hormone/drug cocktail on the epigenome, for which all the attention has been generated.)
The thymus, a gland located in the middle of the upper chest, converts white blood cells from bone marrow into T-cells, which play a central role in the immune response. The “T” in T-cells is named after the thymus. As it turns out, the thymus reaches its maximum size by the end of the first year of life. After that, the thymus decreases in size and activity, particularly after puberty, in a process referred to as thymic involution. Along with the decrease in size and activity of the immune system with age comes an associated functional decline. The lead investigator of the study, Greg Fahy, wanted to see if he could regenerate the thymus and restore immune system function using hGH.
All things equal, a more youthful immune system would suggest greater longevity. But there was a catch with using hGH. The investigators worried that using hGH to regenerate the thymus might induce hyperinsulinemia (high insulin) and noted a “diabetogenic” effect of growth hormone. Hyperinsulinemia and diabetes are obviously not desired side effects, regardless of how much thymic regeneration takes place. So Fahy and his colleagues added metformin and DHEA to try and counter these potential effects. Vitamin D3 and zinc were also added as a hedge against cancer and inactive thymulin, according to Fahy (personal communication, email).
It’s not a surprise that the investigators chose metformin as a drug that can aid in “Insulin Mitigation” (the “IM” in TRIIM; for a nice overview of why, revisit the interview with Nir Barzilai), but DHEA? This was news to me. After doing a little, I mean a lot of digging, I would say there is not much in the way of evidence supporting the use of DHEA as an insulin lowering agent. According to a related article, it appears Fahy was working off his own hypothesis. Young people have higher growth hormone without an increase in insulin, and Fahy believed this to be due to them having higher levels of DHEA. Fahy tested this on himself by taking hGH alone for a week and found his insulin levels elevated by 50%. He then added DHEA and the increase was apparently reversed.
In the TRIIM study 9 men, ages 51-65, first took hGH alone (0.015 mg/kg, or ~3 IU for a person weighing 70 kg) 3-4 times per week for one week and then added DHEA (50 mg) the next week, similar to Fahy’s n=1. The week after that, the same doses of hGH and DHEA were combined with metformin (500 mg). At the start of the fourth week, the doses were individualized based on each participant’s particular responses. (To put the hGH dosing into context, while it’s individualized, athletes using it for performance enhancement may take 10-25 IU 3-4 times a week and “longevity” clinics may prescribe somewhere in the ballpark of 1-2 IU/day.) The goal of this titration approach was to maximize IGF-1 and minimize insulin by varying each of the hormones and drugs. The study didn’t reveal what the effect DHEA hay have had after week 2, so we contacted Fahy to check. He wrote that the results with DHEA were qualitatively the same but quantitatively different, with each person having their own specific response (personal communication, email).
It’s important to highlight that not only was this study multifaceted in the number of independent variables introduced (i.e., hGH, metformin, DHEA, vitamin D3, zinc), it was also personalized, since the subjects did not all receive the same dose of each agent. It’s possible (actually, likely) that all nine subjects were consuming a different cocktail in terms of the dosing of hGH, DHEA, and metformin. Also, it was a very small sample size and lacked a control group, consisting entirely of 9 healthy (see Supplement 2 for exclusion criteria) 51-65-year-old men.
So why, you might (rightly) ask, all the media hype for a very small, not especially well-controlled preliminary/exploratory study?
The investigators reported a mean “epigenetic age” approximately 1.5 years less than baseline after the 1-year intervention. In other words, their epigenetic age got 1.5 years younger while their chronological age obviously went up another year. For example, let’s say “John” entered the trial with a chronological and epigenetic age of 60. After the trial his chronological age is 61 and his epigenetic age is 58.5. Presumably, he increased his life expectancy (LE) by ~2.5 years, or got ~2.5 years younger biologically, depending on how you look at it. And it’s exactly for this reason that this study is being talked about at all.
Which brings us to the framework I would suggest you apply to every study you read or attempt to evaluate. In a study like this, lacking a control group and utilizing a surrogate outcome (i.e., something other than actual morbidity or mortality), such an analysis is essential. Let’s walk through the possible outcomes with respect to the intervention (the independent variables) and biological aging using the epigenetic clocks (the dependent variable). So now consider a 2×2 matrix of the following scenarios:
(i) the dependent variable (the clock) is a correct (i.e., representative) output measurement versus it is not.
(ii) the independent variables (the cocktail of inputs) did versus did not lead to the outcome we saw.
Again, the former question is necessary whenever evaluating a study with surrogate (i.e., not “hard”) outcomes and the latter question is essential in the absence of a control group.
The exercise, then, is to evaluate each of the 4 quadrants in this matrix and ultimately decide, for yourself, which one has the highest probability of being correct. This is the scientific method. It is not absolute. There are no “proofs.” It’s all about probabilities. Let’s start with the assumption that there was no foul play by anyone involved in the study. In this case, either:
1. The intervention accounted for the improvement, or
2. Something other than the intervention accounted for the improvement.
In the first case, there are also many scenarios, and in the latter, there are also many scenarios. In the first case, it may be that the metformin alone accounted for the improvement, or the hGH alone, or there was a synergistic effect between the hGH, DHEA, and metformin, or perhaps one compound in the cocktail was detrimental, but the other compounds more than made up for it. And, remember, not only was there no control group, there was no consistency in the intervention. Everyone got their own signature cocktail. In the second case, it could be the Hawthorne effect at play. This is a type of bias where individuals change aspects of their behavior in response to knowing that they’re being observed. Maybe the participants changed their eating, sleeping, or exercising, for example, which confounded the experiment.
So this tells us how to consider the inputs to the study, but what about the output? Next, we consider if there was some sort of epigenetic clock malfunction? Here, we’ll consider the next two scenarios:
3. The clock estimate accurately represents biological age, or
4. The clock estimate is inaccurate.
Either we’re not being fooled and the clock is accurately picking up a change in mortality risk in this study or we’re being fooled and the clock is malfunctioning for some reason. We’ll pick this up next week (or the week after) to assess the likelihood of each matrix quadrant.
Oh, and I almost forgot, what may have gotten lost in the shuffle is whether the treatment showed promise for TRIIM, the intended aim of the study. After 1-year of treatment, there was “highly significant” evidence of a restoration of thymic functional mass along with improvements in age-related immunological parameters, based on MRI imaging and favorable changes in monocytes and T-cell changes. Insulin levels were reportedly controlled, so as far as preliminary studies go, it’s an intriguing finding, with certainly a lot more to learn.