Lo copypasteo:
Reversal of epigenetic aging and immunosenescent trends in humans
Gregory M. Fahy
Robert T. Brooke
James P. Watson
Zinaida Good
Shreyas S. Vasanawala
Holden Maecker
Michael D. Leipold
David T. S. Lin
Michael S. Kobor
Steve Horvath
First published: 08 September 2019
Error - Cookies Turned Off
Abstract
Epigenetic “clocks” can now surpass chronological age in accuracy for estimating biological age. Here, we use four such age estimators to show that epigenetic aging can be reversed in humans. Using a protocol intended to regenerate the thymus, we observed protective immunological changes, improved risk indices for many age‐related diseases, and a miccionan epigenetic age approximately 1.5 years less than baseline after 1 year of treatment (−2.5‐year change compared to no treatment at the end of the study). The rate of epigenetic aging reversal relative to chronological age accelerated from −1.6 year/year from 0–9 month to −6.5 year/year from 9–12 month. The GrimAge predictor of human morbidity and mortality showed a 2‐year decrease in epigenetic vs. chronological age that persisted six months after discontinuing treatment. This is to our knowledge the first report of an increase, based on an epigenetic age estimator, in predicted human lifespan by means of a currently accessible aging intervention.
1 INTRODUCTION
Population aging is an increasingly important problem in developed countries, bringing with it a host of medical, social, economic, political, and psychological problems (Rae et al.,
2010). Over the last several years, many biomedical approaches to ameliorating aging have been investigated in animal models, and some of these seem able to reverse general aspects of aging in adult mammals based on a variety of physiological measurements (Das et al.,
2018; Ocampo et al.,
2016; Zhang et al.,
2017). However, to date, evidence that systemic aging can be reversed has not been substantiated by determinations of epigenetic age, which can now provide a simple but compelling indication of biological as opposed to chronological age (Horvath & Raj,
2018; Jylhava, Pedersen, & Hagg,
2017). In addition, there is a need to specifically address immunosenescence stemming from thymic involution (Bodey, Bodey, Siegel, & Kaiser,
1997). Thymic involution leads to the depletion of critical immune cell populations (Arnold, Wolf, Brunner, Herndler‐Brandstetter, & Grubeck‐Loebenstein,
2011), resulting in a collapse of the T‐cell receptor (TCR) repertoire in humans after the age of ~63 (Naylor et al.,
2005), and is linked to age‐related increases in cancer incidence (Falci et al.,
2013), infectious disease (Ventevogel & Sempowski,
2013), autoimmune conditions (Goronzy & Weyand,
2003), generalized inflammation (Goronzy & Weyand,
2003), atherosclerosis (Dai, Zhang, Wang, Wu, & Liang,
2018), and all‐cause mortality (Fernando‐Martinez et al.,
2013; Roberts‐Thomson, Whittingham, Youngschaiyud, & Mackay,
1974; Strindhall et al.,
2007). In contrast, maintained immune function is seen in centenarians (Strindhall et al.,
2007). Although thymic function in aging also depends on the supply of T‐cell progenitors from the bone marrow, which declines in relation to the output of myeloid HSCs with age (Akunuru & Geiger,
2016), the net number of lymphoid precursors does not change with age (Montecino‐Rodriguez et al.,
2019), and migration of T‐cell precursors from the bone marrow also appears to depend on thymic function (Haar, Taubenberger, Doane, & Kenyon,
1989).
For these reasons, we conducted what may be the first human clinical trial designed to reverse aspects of human aging, the TRIIM (Thymus Regeneration, Immunorestoration, and Insulin Mitigation) trial, in 2015–2017. The purpose of the TRIIM trial was to investigate the possibility of using recombinant human growth hormone (rhGH) to prevent or reverse signs of immunosenescence in a population of 51‐ to 65‐year‐old frutatively healthy men, which represents the age range that just precedes the collapse of the TCR repertoire. rhGH was used based on prior evidence that growth hormone (GH) has thymotrophic and immune reconstituting effects in animals (Kelley et al.,
1986) and human HIV patients (Napolitano et al.,
2008; Plana et al.,
2011). Because GH‐induced hyperinsulinemia (Marcus et al.,
1990) is undesirable and might affect thymic regeneration and immunological reconstitution, we combined rhGH with both dehydroepiandrosterone (DHEA) and metformin in an attempt to limit the “diabetogenic” effect of GH (Fahy,
2003,
2010; Weiss, Villareal, Fontana, Han, & Holloszy,
2011). DHEA has many effects, in both men and women, that oppose deleterious effects of normal aging (Cappola et al.,
2009; Forti et al.,
2012; Shufelt et al.,
2010; Weiss et al.,
2011). Metformin is a powerful calorie restriction mimetic in aging mice (Dhahbi, Mote, Fahy, & Spindler,
2005) and has been proposed as a candidate for slowing aging in humans (Barzilai, Crandall, Kritchevsky, & Espeland,
2016). Neither DHEA (Riley, Fitzmaurice, & Regelson,
1990) nor metformin are known to have any thymotrophic effects of their own.
2 RESULTS
2.1 Treatment safety and side effects
A primary concern in this study was whether increased levels of a mitogen (IGF‐1) might exacerbate cancerous or precancerous foci in the prostate. Both of these changes should be detectable by measuring PSA or percent free PSA levels. However, PSA, percent free PSA, and the ratio of PSA to percent free PSA, an overall index of prostate cancer risk, improved significantly by day 15 of treatment and remained favorably altered to the end of 12 months (Figure
1a–c). A brief spike in PSA at 6 months in two volunteers was rapidly reversed and, after volunteer consultation, was interpreted as reflecting sensual activity close to the time of PSA testing. No change in testosterone levels was observed.
Figure 1
Open in figure viewerPowerPoint
Treatment safety indices. In this and other figures, error bars depict SEMs; baseline SEMs for normalized data were obtained as described in Experimental Procedures. Asterisks denote
p < .05 (*),
p < .01 (**), and
p ≤ .001 (***). (a) Prostate‐specific antigen (PSA). (b) Fold change (FC) in percent free PSA. (c) Fold change in the ratio of PSA to percent free PSA (“risk factor”), which rises as prostate cancer risk rises. (d, e) Pro‐inflammatory indices (c‐reactive protein (CRP)) and IL‐6). (f) Serum alkaline phosphatase (AP), aspartate aminotransferase (AST), and alanine aminotransferase (ALT). The increase in AP, while statistically significant, was quantitatively negligible and remained well within the normal range. (g) Maintenance of insulin levels within the upper and lower limits of the normal range (indicated by the horizontal lines). (h) Lack of change in serum glucose, which remained within the normal range. (i) Improvement in estimated GFR at 9 and 12 months of treatment, with a trend toward continued improvement 6 months after discontinuation of treatment
Another significant concern was whether augmenting immune activity might exacerbate age‐related inflammation. However, CRP declined with treatment, the decline reaching statistical significance by 9–12 months (Figure
1d). The pro‐inflammatory cytokine, IL‐6, did not change (Figure
1e).
No remarkable changes were noted in serum albumin, lipids, hemoglobin, hematocrit, platelet count, electrolytes, and hepatic enzymes (Figure
1f). Insulin levels were in general adequately controlled by co‐administration of DHEA and metformin (Figure
1g) (although one outlier increased miccionan insulin at 12 months), and glucose levels did not change (Figure
1h). Finally, estimated glomerular filtration rates (eGFR), which are relevant to the potential for lactic acidosis with metformin as well as to treatment efficacy, showed a statistically significant improvement after 9–12 months (with a trend toward improvement at 18 months as well) (Figure
1i). Side effects were mild, typical of rhGH administration, and did not require dosing modification except in two cases. Side effects included arthralgias (2 cases), anxiety (1 case), carpal tunnel syndrome (1 case), fluid retention (1 case), mild gynecomastia (1 case), and muscle soreness (1 case). One trial volunteer was removed from the study after approximately one month due to self‐reported bradycardia, which preceded the trial, and belated admission of a strong familial history of cancer.
2.2 Thymic and bone marrow regenerative responses
Obvious qualitative improvements in thymic MRI density were observed and are illustrated in Figure
2. Quantitatively, the overall increase in the thymic fat‐free fraction (TFFF) was significant at the
p = 8.57 × 10−17 level based on linear mixed‐model analysis, implying a restoration of thymic functional mass. The improvements were significant in 7 of 9 volunteers (Figure
3a–c). Two volunteers had abnormally low levels of thymic fat (high TFFF) at baseline, and their TFFFs did not significantly improve with treatment (peak relative changes of +9.6% (
p > .3) and +12.4% (
p > .2); Figure
3b). Their lack of response was not age‐dependent. Instead, improvement in TFFF was dependent upon baseline TFFF, regardless of baseline age (Figure
3c).
Figure 2
Open in figure viewerPowerPoint
Example of treatment‐induced change in thymic MRI appearance. Darkening corresponds to replacement of fat with nonadipose tissue. White lines denote the thymic boundary. Volunteer 2 at 0 (a) and 9 (b) months
Figure 3
Open in figure viewerPowerPoint
Quantitative MRI‐based regeneration outcomes. Like symbols denote the same individuals in each panel. (a) Individual absolute changes in TFFF. Statistically significant improvements are evident for each individual with the exception of two volunteers who showed high TFFF at baseline (stars); overall significance by linear mixed‐model analysis:
p < 9 × 10–17 (see text). (b) Relative changes in TFFF for each individual. The age of each individual at trial entry (noted adjacent to each line) does not correlate with the magnitude of the depicted changes. The highest p values (
p > .01) for significant individual responses are denoted with asterisks; for clarity, higher significance levels are not designated. (c) Sigmoidal dependence of TFFF change at 12 months on baseline TFFF, showing greater improvements for thymi with lower basal TFFFs (
p < .007). (d) Individual changes in sternal bone marrow fat‐free fraction (BMFFF) (volunteer age noted adjacent to each line). (e) miccionan overall changes in BMFFF. (f) Linear dependence of 12‐month BMFFF on basal BMFFF (
p = .012), showing the largest relative changes in individuals with the lowest baseline BMFFFs. BMFFF data for one volunteer could not be evaluated
By comparison, sternal BMFFF increased to a much lesser degree, but with such consistency (Figure
3d) as to reach high statistical significance (Figure
3e;
p < .001 for single‐point comparison, or
p = 9.5 × 10−12 for formal linear mixed‐model analysis). Bone marrow, similar to thymus, showed a pattern of increased BMFFF with increased baseline fat content, but the details of the pattern were different (Figure
3f). This difference plus the more robust replacement of thymic vs. bone marrow fat is consistent both with a specific reversal of thymic involution rather than generalized regression of body fat owing to GH administration and with possible stimulation of bone marrow T‐cell progenitor production by GH (French et al.,
2002; Hanley, Napolitano, & McCune,
2005).