Соматотропный гормон может "разбудить" тимус и обратить вспять один из механизмов старения

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 sexual activity close to the time of PSA testing. No change in testosterone levels was observed.

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 mean 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).

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).

2.3 Immune cell subset and cytokine changes

Analysis of CyTOF‐defined immune cell populations revealed the most robust changes to be decreases in total and CD38‐positive monocytes (Figure 4a, b) and resulting increases in the lymphocyte‐to‐monocyte ratio (LMR) (Figure 4c). Linear mixed‐model correlation analysis also showed a significant correlation after 9 and 12 months of treatment between TFFF and the reduction in CD38⁺ monocyte percentage ( r ² = .59,  p  = .01) as well as a correlation between TFFF and the ratio of lymphocytes to CD38⁺ monocytes ( r ² = .55,  p  = .018). Similar trends were seen for total monocytes vs. TFFF and for the overall LMR vs. TFFF (respectively,  r ² = .40,  p  = .039, and  r ² = .45,  p  = .019) when correlated across all treatment times (0, 9, and 12 months). The changes in mean monocyte populations persisted 6 months after discontinuation of treatment ( p  = .012 for normalized CD38⁺ monocytes and  p  = .022 for normalized total monocytes: Table S1), and the increase in LMR remained highly significant at 18 months as well.

PD‐1‐positive CD8 T cells declined significantly with treatment (Figure 4d). By gating on naïve T cells, we were able to detect significant increases in both naïve CD4 and naïve CD8 T cells (Figure 4e-f) and a significant ( p  = .017) increase in the percentage of CD31⁺CD45RA⁺CD4⁺ cells (CD4 recent thymic emigrants, or RTEs) over the course of treatment (linear correlation, Figure S1). We did not detect consistent changes in senescent T cells measured either as CD57⁺ cells or as CD28^‐ cells. However, we did detect a significant increase in serum FGF‐21 levels (Figure S2).

2.4 Epigenetic age regression

Although, on average, trial volunteer epigenetic ages (EAs) were lower than their chronological ages (As) at baseline [(EA‐A)₀ < 0, Table 1], epigenetic age was nevertheless significantly decreased by treatment based on the results of all four epigenetic clocks (Figure 5a-d), with a mean change in EA‐A after 12 months of about 2.5 years (Figure 5e). The effect of treatment on epigenetic age regression at 12 months was not dependent on age at onset of treatment or on (EA‐A)₀ (Table 1). Linear mixed‐model analysis (LMMA) showed significance levels for individual clocks ranging from  p  = .0009-.012 over months 0-12 to  p  = .0016-.028 for months 0-18 (Figure 5 legend). Horvath clock LMMA results were unchanged ( p  = .018) after adjusting for changes in blood cell composition (lymphocyte count, percent of senescent CD8 T cells among CD8 T cells, and the LMR). Although there was a general tendency for EA‐A to trend back toward (EA‐A)₀ 6 months after discontinuation of treatment, this trend was incomplete, leaving on average an improvement of more than 1.5 years at trial month 18 (Figure 5e,  p  < .001). In addition, the GrimAge clock, which is specifically able to predict human life expectancy (Lu et al., 2019), showed no regression of (EA‐A)‐(EA‐A)₀ after treatment, with a gain of approximately 2.1 years at 12 months still remaining at trial month 18 (Figure 5d; Table 1). Furthermore, comparing the rates of aging regression between 0-9 and 9-12 months showed that, for every age estimator, the rate of aging regression appeared to accelerate substantially with increasing treatment time (Figure 5a-d and Table 1), with a mean slope over all four clocks of −1.56 ± 0.46 years/year in the first 9 months to −6.48 ± 0.34 years/year in the last 3 months of treatment ( p  < .005, Figure 5f).

4.1 Volunteer recruitment and screening

Ten nominally healthy adult men from 51-65 years of age were recruited for the study by word of mouth following public announcements of the objectives of the trial. There were two cohorts, the first consisting of seven men and the second comprising three men. Cohort 1 was treated from October of 2015 to October of 2016, and cohort 2 was treated from April 2016 to April 2017. Volunteers first completed an online screening questionnaire, and qualifying candidates then provided a blood sample for objective confirmation of health eligibility. Candidates passing the second screen attended a meeting to enable physical evaluation, verification of ability to self‐inject rhGH, baseline magnetic resonance (MR) imaging of the thymus, two additional blood collections (see Appendix S2), informed consent, and instruction on how to fill out provided medication diaries intended to remind volunteers of dosing schedules and to report any adverse effects or any irregularities in dosing.

4.2 Trial conduct

The TRIIM trial was carried out after approval of the study protocol by the Aspire Institutional Review Board (Santee, California) and under the auspices of IND 125,851 from the Food and Drug Administration. Volunteers received MRI examinations at the Lucas Center for Imaging, blood collections at the Stanford Blood Center, and CyTOF analysis by the Human Immune Monitoring Center (HIMC) under the approval of the Stanford University Research Compliance Office. The trial was conducted consistently with the Declaration of Helsinki, Protection of Human Volunteers (21 CFR 50), Institutional Review Boards (21 CFR 56), and Obligations of Clinical Investigators (21 CFR 312). It was, in accordance with FDA guidelines for pre‐Phase I exploratory studies (https://prsinfo.clinicaltrials.gov/ACT_Checklist.pdf), not preregistered on clinicaltrials.gov.

During the first week of the trial, rhGH alone (0.015 mg/kg) was administered to obtain an initial insulin response, and during the second week, rhGH was combined with 50 mg DHEA to evaluate insulin suppression by DHEA alone. During the third week, the same doses of rhGH and DHEA were combined with 500 mg metformin. Beginning at the fourth week, all doses were individualized based on each volunteer's particular responses. Thereafter, blood was collected one week prior to trial months 2, 3, 4, 6, and 9 to enable further dose adjustments at those time points (to maximize IGF‐1 and minimize insulin), and additional blood samples were obtained at 12 months to conclude the treatment monitoring period. Dosing compliance was verified by the response of IGF‐1, DHEAS, and insulin to administration of rhGH, DHEA, and metformin, respectively; by frequent communication with trial volunteers; and by retrospective review of returned medication diaries. Additional follow‐up blood testing was done at 18 months for cohort 1; cohort 2 was not available. In selected cases, as deemed useful, supplemental blood sampling was carried out at other times.

Serum and peripheral blood mononuclear cells (PBMCs) were cryopreserved at the SBC, in the latter case using 10% dimethyl sulfoxide and fetal bovine serum (FBS), and stored there and then at the HIMC until the end of the trial to enable simultaneous CyTOF analysis and further distribution of some samples to other centers. Complete blood counts were performed by Quest Diagnostics to enable correction for cell losses caused by freezing and thawing or other factors, but in general, the need for correction was avoided by reporting cell subsets as percentages of their reference populations (e.g., naïve CD4 cells as a percent of total CD4 cells) and by gating to intact viable singlets. Other blood assays are described in Appendix S2.

rhGH (Omnitrope, Sandoz) was provided to trial volunteers and was self‐administered 3-4 times per week, depending on side effects, at bedtime along with other study medications. All volunteers were also provided with and asked to take supplements of 3,000 IU vitamin D3 and 50 mg of elemental zinc daily.

4.3 MR imaging and analysis

All imaging scans were performed on the same 3T GE Premier MRI scanner at the Lucas Center for Imaging at Stanford University. Standardized methods for quantitatively determining thymic fat content have not been previously described, so we employed a computational methodology that has previously been applied to the analysis of bone marrow fat content (Hu, Nayak, & Goran, 2011). These methods for quantifying fat have been shown to be more accurate than standard histopathological assessment by biopsy (Fischer et al., 2012) and provide the thymic fat fraction (TFF) as a number from 0% to 100%. TFF was determined in three replicate central thymic regions at each tested time point and was used to compute the thymic fat‐free fraction (TFFF) as 100% ‐ TFF. The same methods were also applied to determine the sternal bone marrow fat‐free fraction (BMFFF). For more details, see Appendix S2.

4.4 Immunophenotyping

Cryopreserved PBMCs were thawed in warm media, washed twice, and resuspended in CyFACS buffer (PBS supplemented with 2% BSA, 2 mM EDTA, and 0.1% sodium azide). Viability was determined by trypan blue dye exclusion (Vi‐CELL XR assay, Beckman Coulter Life Sciences). Cells were added to a V‐bottom microtiter plate (1.5 × 10⁶ viable cells/well) and washed once by pelleting and resuspension in fresh CyFACS buffer. The cells were stained for 60 min on ice with 50 μl of a cocktail of heavy metal isotope‐labeled antibodies directed against 35 cell surface markers (for details, and for details on cell washing, staining, permeabilization, and gating, see Appendix S2).

4.5 Determination of epigenetic age

The state of genomic DNA methylation was determined in previously cryopreserved PBMCs at the Centre for Molecular Medicine and Therapeutics at the University of British Columbia. Genomic DNA was extracted with the DNeasy Blood & Tissue Kit (Qiagen, Hilden, Germany), and bisulfite conversion of extracted DNA was performed using a Zymo EZ DNA Methylation Kit (Zymo Research, Irvine, CA). DNA methylation (DNAm) analysis was performed using the Illumina Infinium MethylationEPIC BeadChip (Illumina, San Diego, CA), which measures single‐CpG resolution DNAm levels at 866,836 CpG sites in the human genome. Methylation at specific sites was calculated as β = Max(M,0)/[Max(M,0) + Max(U,0)], where Max(M,0) is the fluorescence intensity of methylated (M) alleles (signal A) and Max(U,0) is the fluorescence of un‐methylated (U) alleles (signal B). Thus, β values range from 0 (completely un‐methylated) to 1 (completely methylated) (Dunning, Barbosa‐Morais, Lynch, Tavare, & Ritchie, 2008).

The specific epigenetic "clocks" chosen for use in this study were those derived by Horvath (2013; DNAm age), Hannum et al. (2013; DNAm age H), Levine et al. (2018; DNAm PhenoAge), and Lu et al. (2019; DNAm age G or "GrimAge"). The specific rationales for these choices are based on Horvath and Levine (2015), Marioni et al. (2015), and Triche, Weisenberger, van den Berg, Laird, and Siegmund, (2013) and are described in Appendix S2.

We calculated the effects of intervention on epigenetic age by first fitting all time point data to a linear mixed‐effects model for longitudinal data (multiple blood draws from the same person, adjusted for the initial DNAm age) to follow global epigenetic aging trends. Second, to isolate more detailed individual‐ and time‐specific changes, we calculated the change in epigenetic versus chronological age for each volunteer as (EA‐A) - (EA‐A)₀ as described in the text, where EA is the epigenetic age at chronological age A and (EA‐A)₀ is the baseline difference between EA and A.

4.6 Statistical analysis

We used linear mixed‐effects regression to properly account for the longitudinal nature of the information (multiple draws from the same person). When correction for time was not necessary, we used ordinary linear or nonlinear regressions. For comparisons between pretreatment and specific post‐treatment endpoints, we employed the  t  test, the  t  test for paired comparisons, or alternatives selected by SigmaPlot when the assumptions underlying  t  tests were not justified. To enable normalized comparisons to month zero baselines, replicate results for each volunteer at time zero were averaged and each replicate result was divided by the mean to produce a population of departures from 100% that could then be used for comparison against later time points. Reported  P  values do not correct for multiple comparisons because independent hypothesis testing was not carried out in most cases, and in the case of both multiple tests for epigenetic age and significant differences in CyTOF results, all significant test results were highly correlated, making Bonferroni correction overly conservative.