Human Data.

Fernandes et al. report a systematic analysis of the gerontome, revealing links between aging and age-related diseases. The article supports our observation that the set of 308 most validated genes associated with human aging (https://genomics.senescence.info/genes/allgenes.php) strongly overlaps with the genes implicated in the chronic disease. This parallel becomes apparent after Metascape analysis of the gene list. These results are presented in Figure 11.

Figure 11: Metascape ontological analysis of the most validated human genes associated with aging. The Metascape tool can be found at https://metascape.org. The gene list was compiled from different types of
evidence, including human GWAS observations, model studies, murine knockout and knock-in experiments, and cellular-level experiments. The common denominator is the consensus that all these genes are relevant to human aging. In Metascape, the explored category is aligned with other gene lists available across diverse databases, such as Disgenet or KEGG Pathways. The explored category and the comparison category form an overlap, which is compared to an overlap estimate based on random model. The fold ratio of the observed gene list overlap vs. random expectation and the size of the overlapping lists define the p-value of the hypothesis that the overlap arises randomly. The brown bars shown in the figure are the log 10 (P). 

The aging-related list aligns with diabetes (first in the rank), multiple forms of cancer, memory loss, premature aging, and fatty liver disease. Even greater association with chronic disease can be noticed for the aging-related subset collected by analyzing the genome-wide association of human longevity and its genetic mediators (GWAS). This list can be downloaded at https://genomics.senescence.info/longevity/. Figure 12 presents these results.

Figure 12: Metascape ontological analysis of human genes associated with longevity. The gene list was compiled by one specific method: the association of human longevity to the polymorphisms detected in the reported sequences in diverse populations (GWAS). The analysis is the same as in Figure 11.

The gene list 2 produces a more diverse but statistically weaker association of aging with chronic disease while still prioritizing diabetes. The more methodologically diversified list 1 includes a massive subset of DNA-repair components, such as WRN (Werner syndrome helicase) or lamin (progerin precursor). These genes are important for accelerated and premature aging scenarios and play a role in the aging of the feebler edge of the normal human population. The gene list 2 is more important for reaching record lifespans, and it is related to both median and maximal lifespan. List 2 is recruited by combining 354 independent population studies in one meta-analysis. List 2 is unbiased; the genes show point mutation signatures linked to reaching elevated lifespans; they are not included based on a pre-existing concept. The ontological analysis of gene lists 1 and 2 suggests that the genes involved in the diseases of aging control the rate of the latter. If this hypothesis is true, individuals reaching an exceptional lifespan must experience chronic disease later in life, and perhaps disproportionally later in life.

According to Gellert et al., one quarter prior to death, individuals who died as centenarians had, on average, 3.3 comorbidities. Octogenarians had 4.6 comorbidities one quarter prior to death. Further, there was a significant time-to-death by age-at-death interaction (b = −.03, p <.001), where centenarians showed a less steep increase in the number of comorbidities than the comparison groups in their last 6 years prior to death.

Andersen et al. analyzed the relationship between age of survival, morbidity, and disability among centenarians (age 100–104 years), semisupercentenarians (age 105–109 years), and supercentenarians (age 110–119 years). One hundred and four supercentenarians, 430 semisupercentenarians, 884 centenarians, 343 nonagenarians, and 436 controls were prospectively followed for an average of 3 years (range 0–13 years). The older the age group, the later the onset of diseases such as cancer, cardiovascular disease, dementia, and stroke, as well as cognitive and functional decline. The hazard ratios for these individual diseases became progressively less with older and older age, and the relative time spent with disease was lower with increasing age group. The team observed a progressive delay in the onset of physical and cognitive function impairment, age-related diseases, and overall morbidity with increasing age of survivorship. As the limit of human lifespan was effectively approached with supercentenarians, compression of morbidity was generally observed.

An independent, but similar, report is in Willcox et al., studying 15 supercentenarians in Okinawa. The age at death ranged from 110 to 112 years. Most supercentenarians had minimal clinically apparent disease until late in life, with cataracts (42%), fractures (33%) and coronary heart disease (8%) being common, while stroke (8%), cancer (0%), and diabetes (0%) were rare or not evident on clinical examination. Functionally, most supercentenarians were ADL-free at age 100 years, and few were institutionalized before the age of 105 years. Most had normal clinical parameters at age 100, but by age 105, they exhibited multiple clinical markers of frailty coincident with a rapid ADL decline.

I also observed the reduction of cancer or diabetes records in the USA NBER (National Burreau of Economic Research) in the death certificates that belong to the centenarians and older groups (https://www.nber.org/research/data/mortality-data-vital-statistics-nchs-multiple-cause-death-data). Considering the data by Willcox et al., this is meaningful. Schoenhofen et al., Liu et al., and Clerencia-Sierra et al. also demonstrate healthier profiles in centenarians and supercentenarians, specifically better glucose metabolism and cancer survival. The same pattern as in NBER is visible in the Australian Cause of Death data (Grim Books, https://www.aihw.gov.au/reports/life-expectancy-deaths/grim-books/contents/grim-excel-workbooks).

All the facts above suggest that greater stability of energy metabolism and improved proliferation control (absence of cancer or its better survival) are the conditions for exceeding the centenarian benchmark. This conclusion also fits the general idea that the rigidity of the entire information template of the organism (that includes homeostatic and proliferation control stability) is a correlative of longevity, not only the epigenetic stability of heterochromatin. The disease-related genes are also aging-mediating genes; therefore, the conventional pharmaceuticals used to treat chronic disease are prime candidates to serve as potential anti-aging agents as well as prototypes for the chemical evolution process described in the “Protective Complexity” page. This conclusion agrees with the opinion of others (Blagosklonny, MV).

Can FDA-approved pharmaceuticals extend the lifespan of other mammals in controlled trials and studies?

The caveat here is that the animals must be genetically heterogeneous, long-living breeds, healthy, and normal. Only then can the result be attributed to the reversal of aging or an improvement in survival at the current aging rate. Direct treatment of acute symptoms (spikes of blood pressure, arrythmia) would certainly increase the lifespan of the symptomatic populations vs. untreated controls. The tested population must be randomized to the health factor; the study must be conducted with different breeds and strains and in multiple centers to minimize the effects of the variation shown in Liao et al., cited in the “Protective Complexity” page.

Martin-Montalvo et al. report that long-term treatment with metformin (0.1% w/w in diet) starting at middle age extends healthspan and lifespan in male mice, while a higher dose (1% w/w) was toxic. Treatment with metformin mimics some of the benefits of calorie restriction, such as improved physical performance, increased insulin sensitivity, and reduced low-density lipoprotein and cholesterol levels, without a decrease in caloric intake. At a molecular level, metformin increases AMP-activated protein kinase activity and increases antioxidant protection, resulting in reductions in both oxidative damage accumulation and chronic inflammation. Figure 1 shows ~ 5% extension of murine lifespan (~ 7-8 weeks for a ~150-week lifespan in ~ 150 male C57BL/6 mice). This is a great effect, equivalent to ~ 4-5 years if projected on humans.

Anisimov et al. report that treatment of female outbred SHR mice with metformin (100 mg/kg in drinking water) slightly modified food consumption but decreased the body weight after the age of 20 months, slowed down the age-related switch-off of estrous function, increased the mean life span by 37.8%, the mean life span of the last 10% of survivors by 20.8%, and the maximum life span by 2.8 months (+10.3%) in comparison with control mice. On the other side, treatment with metformin failed to influence blood estradiol concentration and spontaneous tumor incidence in female SHR mice. Thus, antidiabetic biguanide metformin dramatically extends life span, even without cancer prevention in this model.

The evidence concerning metformin points to a reduction in lifespan at higher doses, which can be neurotoxic. Also, the effect of mimicking caloric restriction may not interact well with decreasing energy production in older organisms. Perhaps the question of doses and combination partners is the key. Mohammed et al. present a critical review of the evidence that metformin is a putative anti-aging drug that enhances health and extends lifespan. The review concludes that metformin increases the lifespan of early mortality pockets of the population and improves health span, while its effect on maximal lifespan is controversial.

Spindler et al. describe combined statin and angiotensin-converting enzyme (ACE) inhibitor treatment as increasing the lifespan of long-lived F1 male mice. The simvastatin and ramipril combination therapy significantly increased the mean and median lifespan by 9%. In contrast, simvastatin, ramipril, or candesartan monotherapy was ineffective.

Varela et al. report that combined treatment with statins and aminobisphosphonates extends longevity in a mouse model of human premature aging. Biphosphonates were later discovered to stimulate the proliferation of killer T-cells, which can be easily translated into a lifespan effect, as observed in the patients with osteoporosis treated by these drugs. According to Varela et al., a combination of statins and aminobisphosphonates efficiently inhibits both farnesylation and geranylgeranylation of progerin and prelamin A and markedly improves the aging-like phenotypes of mice deficient in the metalloproteinase Zmpste24, including growth retardation, loss of weight, lipodystrophy, hair loss, and bone defects. Likewise, the longevity of these mice is substantially extended. Figure 4 demonstrates a spectacular 60% increase in lifespan from 155 to 225 days in this model, perhaps non-ideal for relating to healthy, normal humans.

Jacobs et al. addressed cholesterol, statins, and longevity in humans from age 70 to 90 years. The prevalence of high total cholesterol at ages 70, 78, and 85 was 75% (n = 344), 65% (n = 332), and 34% (n = 237), and statin use was 0%, 17.9%, and 45.4%, respectively. Survival was increased (not significantly) among subjects with high total cholesterol >200 mg/dL versus ≤200 mg/dL from ages 70 to 78, 78 to 85, and 85 to 90: 79.1% versus 73.3% (log rank P =.16), 68.7% versus 61.5% (P =.10), and 73.4% versus 70.3% (P =.45), respectively. Survival was significantly increased among subjects treated with statins versus no statins at ages 78 to 85 (74.7% vs. 64.3%, log rank P =.07) and 85 to 90 (76.2% vs. 67.4%, P =.01). After adjustment, total cholesterol (continuous or dichotomous) was not associated with mortality from 70 to 78, 78 to 85, or 85 to 90. In contrast, statins at age 85 were associated with decreased mortality from age 85 to 90 (adjusted HR 0.61, 95% confidence interval 0.42–0.89). In the book, I provide a similar analysis. High cholesterol is an indication for statin use, and the first part of the study tests the effect of phenotypic background before testing the effect of the drug.

Santos et al. performed long-term treatment with the ACE inhibitor enalapril, observing a decrease in body weight gain and an increase in life span in normotensive Wistar rats. Long-term enalapril treatment decreases absolute food intake, serum leptin concentration, and body weight gain. Moreover, in adipose tissue, enalapril treatment led to decreased ACE activity and enhanced the expression of peroxisome proliferator-activated receptor gamma, adiponectin, hormone-sensitive lipase, fatty acid synthase, catalase, and superoxide dismutase, resulting in a prolonged life span. The results are shown in Figure 7. Life span was evaluated in rats receiving enalapril for 26 months. Over the course of the study, the mortality rate reached 80% in the vehicle-treated rats in both diets (Fig. 7A and B). On the other hand, enalapril reduced mortality to 45% in the group fed with a standard diet and 40% in the animals fed with a palatable hyperlipidic diet (p < 0.05 vs. vehicle-treated rats). Eleven rats fed with a standard diet and 12 fed with a palatable hyperlipidic diet (from 20 animals in each group) survived when treated with enalapril in comparison to four rats in the control groups.

Benigni et al. report that disruption of the Angiotensin II type 1 receptor promotes longevity in mice. The popular antihypertensive sartan drugs (telmisartan, candesartan, losartan, valsartan, etc.), as well as ACE inhibitors, either directly block this receptor or block activation of its peptide ligand angiotensin, which itself may have other pro-aging targets. Targeted disruption of the Agtr1a gene that encodes AT1A results in marked prolongation of life in mice. Agtr1a-/- mice developed less cardiac and vascular injury, and multiple organs from these mice displayed less oxidative damage than wild-type mice. The longevity phenotype was associated with an increased number of mitochondria and upregulation of the prosurvival genes nicotinamide phosphoribosyltransferase (Nampt) and sirtuin 3 (Sirt3) in the kidney. In cultured tubular epithelial cells, Ang II downregulated Sirt3 mRNA, and this effect was inhibited by an AT1 antagonist. Figure 1 demonstrates a dramatic increase in lifespan in male Agtr1a–/– animals vs. controls, from 29 to 36 months (~ 26%). For these experiments, male F1 (C57BL/6 × 129/SvEv) mice lacking AT1A for Ang II were obtained and bred in the animal facility of the Durham Veterans Affairs Medical Center under the NIH guidelines. The article was followed by useful comments by Nishiyama et al. agreeing that targeting the angiotensin/renin system may translate into human longevity. Elkahloun et al. report candesartan neuroprotection in rat primary neurons that negatively correlates with aging and senescence, according to transcriptomic analysis.

Strong et al. describe lifespan benefits for the combination of rapamycin plus acarbose and captopril in genetically heterogeneous mice. Mice were tested for possible lifespan benefits of (R/S)-1,3-butanediol (BD), captopril (Capt), leucine (Leu), the Nrf2-activating botanical mixture PB125, sulindac, syringaresinol, or the combination of rapamycin and acarbose at 9 or 16 months of age (RaAc9, RaAc16). In male mice, the combination of Rapa and Aca started at 9 months and led to a longer lifespan than in either of the two prior cohorts of mice treated with Rapa alone, suggesting that this drug combination was more potent than either of its components used alone (Figure 1, >20% lifespan effects). Captopryl led to a significant, though small (4% or 5%), increase in female lifespan. Combinations of anti-aging drugs may have effects that surpass the benefits produced by either drug used alone, and those additional studies of captopril over a wider range of doses are likely to be rewarding.

Neff et al. report that rapamycin extends murine lifespan but has limited effects on aging. While rapamycin did extend lifespan, it ameliorated a few studied aging phenotypes. These lifespan effects were not due to a modulation of aging but rather related to aging-independent drug effects. Neff’s data largely dissociate rapamycin’s longevity effects from its effects on aging itself.

Jiang et al. present a short-term treatment with a cocktail of rapamycin, acarbose, and phenylbutyrate, delaying aging phenotypes in mice. Combining drugs shown to extend lifespan and promote healthy aging in mice produced a greater impact than any individual drug. A cocktail diet containing 14 ppm rapamycin, 1000 ppm acarbose, and 1000 ppm phenylbutyrate was fed to 20-month-old C57BL/6 and HET3 4-way cross mice of both sexes for three months. Mice treated with the cocktail showed a sex- and strain-dependent phenotype consistent with healthy aging, including decreased body fat, improved cognition, increased strength and endurance, and decreased age-related pathology compared to mice treated with individual drugs or control. The severity of age-related lesions in the heart, lungs, liver, and kidney was consistently decreased in mice treated with the cocktail compared to mice treated with individual drugs or control, suggesting an interactive advantage of the three drugs. This study shows that a combination of three drugs, each previously shown to enhance lifespan and health span in mice, delays aging phenotypes in middle-aged mice more effectively than any individual drug in the cocktail over a 3-month treatment period.

Swindell et al. analyzed raw data from 29 survival studies of rapamycin- and control-treated mice with the goals of estimating summary statistics and identifying factors associated with effect size heterogeneity. Meta-analysis demonstrated significant heterogeneity across studies, with hazard ratio (HR) estimates ranging from 0.22 (95% confidence interval [CI]: 0.06–0.82) to 0.92 (95% CI: 0.65–1.28). Sex was the major factor accounting for effect size variation, and mortality decreased more in females (HR = 0.41; 95% CI: 0.35–0.48) as compared with males (HR = 0.63; 95% CI: 0.55–0.71). Rapamycin effects were also genotype-dependent, however, with stronger survivorship increases in hybrid mice (14.4%; 95% CI: 12.5–16.3%) relative to pure inbred strains (8.8%; 95% CI: 6.2–11.6%).

The studies above are just a few examples, and more animal data is available relating to targeting the genes involved in chronic disease and longevity. Barinda et al. address the repurposing effect of cardiovascular-metabolic drugs to increase lifespan by conducting a systematic review of animal studies and current clinical trial progress. The authors selected animal studies responding to the terms “animal,” “cardiometabolic drug,” and “lifespan.” All clinical trial registries were also searched on the WHO International Clinical Trial Registry Platform (ICTRP). Analysis of 49 animal trials and 10 clinical human trial registries shows that various cardiovascular and metabolic drugs have the potential to target lifespan. Metformin, acarbose, and aspirin are the three most studied drugs in animal trials. Aspirin and acarbose are the promising ones, whereas metformin exhibits mixed results. In clinical trial registries, metformin, omega-3 fatty acids, acarbose, and atorvastatin are currently cardiometabolic drugs that are repurposed to target aging. Published clinical trial results show great potential for omega-3 and metformin in health.

Thanapairoje et al. examined the anti-aging effects of FDA-approved medicines in a focused review. The review focused on describing the in vivo anti-aging activity of US-FDA-approved drugs and found that alogliptin, canagliflozin, and metformin might produce anti-aging activity via AMPK activation. Rapamycin and canagliflozin may inhibit mTOR to promote lifespan. Atracurium, carnitine, and statins act as DAF-16 activators, which potentially contribute to anti-aging activity (I am not so positive regarding any quaternary bases, like atracurium in humans, see Chapter IV of the book). Hydralazine, lisinopril, rosiglitazone, and zidovudine may help stabilize genomic integrity and prolong life expectancy. Other indirect mechanisms, including the insulin-lowering effect of acarbose and the calcium channel-blocking activity of verapamil, may also promote longevity. Interestingly, some drugs (i.e., canagliflozin, metformin, rapamycin, and acarbose) are likely to demonstrate a lifespan-promoting effect predominantly in male animals. 

A conclusion can be drawn that indeed, pharmaceuticals targeting chronic conditions such as inflammation, diabetes, and cardiovascular disease are effective modulators of the genes controlling aging rates, which are also common drug targets. Targeting symptoms also means modifying causes in the self-accelerating aging process in mammals. Binding the modulators alters the signaling role of these drug targets (information hubs) to the state that existed in the past and the effect reorganizes the global information landscape, which is the measure of aging or youth. If the landscape has a youthful pattern at any chronological age, the organism is biologically younger. The epigenetic component is just one projection of this complex aging hyperspace. Multiple changes in the signaling by key targets bound by the modulators alter this information hyperspace and change the final aging vector (Figure 1, 6). The results of gene list analysis, animal experiments, and the expert opinions of others all converge. Furthermore, all agents mentioned above occupy high Z-score ranks in the analysis described in the next section.

Observational human data.

In the absence of clinical trials of anti-aging multimodalities, observational results corrected for possible biases by a background model remain the only option. On the positive side, the observational analysis allows a glimpse into the future, when multiple mechanisms of aging control will be combined in cocktails prescribed to healthy people regardless of diagnosed disease (Sharma et al., Rosenfeld et al., Ladiges et al., and many more).

What are the limits and surprises of this approach, the closest to the anti-aging remedy we have for now, in addition to a healthy lifestyle and mental attitude? I used publicly available data, with the benefit of easy verification by everybody willing to put effort into independent research.

The sources included, among others:  NACC (National Alzheimer’s Coordination Center, https://naccdata.org/). The Center freely shares data after registration at this point. NACC cohorts are selected from individuals referred to the local centers, with the controls consisting of close relatives. While not representative, the dataset is sufficient as a start. The findings in NACC were validated in a representative dataset, CLHLS (https://agingcenter.duke.edu/CLHLS) and NSHAP (https://www.norc.org/research/projects/national-social-life-health-and-aging-project.html). The data are available at ICPSR (https://www.icpsr.umich.edu/web/pages/). In this blog, only NACC data is provided; the rest is in the book. The research methodology includes several steps:

1. Identification of statistically significant effects of lifespan modulation in the databases of electronic medical records. This was done by producing a regression background model of dementia and mortality, clustering the databases (NACC, NSHAP, and CLHLS) by exposure to each factor, and computing a Z score for each factor. Z = (predicted effect by the regression model minus observed effect) / variation. The Z scores for mortality and Z scores for dementia are not always identical, but the processes are strongly related, and both outcomes produce a more robust metric for the detection of longevity effects. The background multivariate linear regression model includes all confounders: age, gender, education, cognitive state, health state, specific comorbidity levels, ADL, the presence of cholinesterase inhibitors, opiates, antidepressants, antipsychotic medicines, diuretics, sedatives, anti-cancer drugs in the prescriptions, total polypharmacy, supplementation levels, cardiovascular disease correlating with mortality, BMI, and arthritis.
2. Validation of the initial Z scores in animal experiments, COVID-19 and dementia prevention clinical trials, anti-depression clinical trials, observational studies of survival in sepsis, trauma, stroke, and myocardial infarction. The agents that found support in such evidence and received high rankings in similar analyses by other authors were accepted for tracking in the patient profiles.

3. Each positive factor’s presence in the patient profile was marked by +1, a negative by -1. The combined number forms the protective complexity score. The patients were ranked by their protective complexity scores.
4. The effects of drugs and supplements must be separated from confounding by the underlying diagnoses. The book describes disconnects between the observed lifespans and “aging markers.” These markers may include the onset of chronic disease, and the book shows that mortality rates can be inversely proportional to the number of chronic diseases (Preuss et al.), predicting centenarian longevity for the top ranks formed by selected multi-morbidities. Whatever the mechanism is, these effects were accounted for.
5. For practical recommendations, the author aligned the observational evidence for each factor with related clinical trial data and developed correction coefficients that aim to “shrink” the size of the expected effects if a random person decides to incorporate a factor in his or her lifestyle. For example, 10,000 steps a day may correlate with a 10-year increase in lifespan, but the latter is not entirely caused by physical activity. The genetically slower aging rate and better health are factors to consider. The “pure effect” (RCT equivalent) of 10000 daily steps for a random beginner will be ~ 4 years, and the correction coefficient is ~ 0.4. The coefficients for most foods and supplements are 0.2–0.3 and amount to only a few months of lifespan increments. Nevertheless, Table 14 of the book includes 47 literature-validated factors, already diminished by the RCT equivalence analysis. The average effect of 1 factor is +1.2 years of lifespan increment, below +2.4 years expected from extrapolation of animal experimental data to humans. By exercising willpower daily to control the lifestyle, attitude, and agents allowed in our organism, we can gain decades of median lifespan. Figure 13 presents the observational data in NACC.

Figure 13: Dependence of annual probability of mortality H(T) and survival on the ages and protective complexity of the NACC patients, N = 134000. The protective mechanisms were limited solely to cardiometabolic, anti-Parkinson, anti-ADHD, selective antiallergy, selective antidepressant, selective anxiolytic, selective antimigraine, selective anti-inflammatory, and selective OTC supplement agents.

13A. H(T) as a function of age and the number of protective mechanisms. The number of protective mechanisms is increasing in the direction of the hollow arrow. The third curve in the direction of the hollow arrow (triangles) corresponds to the average exposure of NACC patients. The curve 10 in the direction of the hollow arrow is maximal exposure (squares).

13B. Fraction of the population entering the hypothetical prospective study at the baseline age of 50 years. At 50 years of age, each group exposed to N protective mechanisms includes 100% of its members. The percentage of survivors decreases with age, following the annual mortality probabilities H(T) of Figure 13A. For each year, S(T) = (1-H(T)) x S (T -1), where 1-H(T) is the probability of survival for a given year and S (T-1) is the fraction of survival at the end of the previous year. Line 3, in the direction of the hollow arrow, corresponds to the average NACC population.

Figure 13A shows the trends in the annual probability of mortality. The probabilities are vanishingly small for N = 9 or 10 but begin to rapidly rise after 90 years, and at the age of 105 years, one can observe a crossing point for the family of H(T, N) curves with N in the range between 0 and 10. Figure 13B represents survival curves following Figure 13A. The survival rate of 50% is reached at 77 years for N = 2 (the average population of NACC), 104 years for N = 10, or 100 years for N = 9. The maximal delta reaches 27 years of additional lifespan for individuals exposed to 10-component cocktails of protective agents. Maximal lifespan can be defined as an age of reaching survival of <10%, < 5%, or < 2%. The maximal lifespans for the points for N = 2 and N = 10 are 101 and 112 (<10%), 107 and 114 (<5%), and 113 and 116, respectively (<2%). The maximal lifespans for 0% survival are equal, and both are 120 years. The 50% survival age of 77–78 years for the average NACC population matches the average US life expectancy. In this context, one can marvel that exposure to 10 protective mechanisms allows a survival rate of 71% at 100 years! Typically, 1.5% of all decedents are registered in NBER for this age, and a possible 40-fold increase in the chances of reaching the 100-year milestone is just tantalizing. It is especially interesting that this increase is traceable to specific molecules or procedures.

Figure 13B shows comorbidity compression; the populations initially rescued from mortality at younger ages demonstrate accelerated extinction at later ages. This result limits concerns regarding overpopulation. If these effects are attributable to the combined effects of modern pharmaceuticals, they seem to change the median lifespan and seem unable to extend the maximal lifespan of ~ 120 years suggested for humans by our data. Apparently, some members of the same pharmacological classes have more benign pleiotropic effects and longevity profiles than others. Learning these specifics would allow prescribing only such members of the pharmacological classes that postpone dementia and mortality (if provided to healthy volunteers not relying on the primary drug prescription for health). In the book, I developed a chemoinformatic approach to this objective.

The animal experiments mentioned above demonstrate the bona fide anti-aging effects of drug cocktails, especially of stronger prescription drugs. If so, why do the NACC data not show the shifts in the maximal lifespan? One explanation is that human aging is less plastic than murine. After all, it takes an extra 8 mechanisms to achieve a 27-year extension over the median 78-year lifespan, or 34.6%. This result is equivalent to ~ 4.2% of lifespan extension per mechanism, despite possible interaction or even synergy between the components. The effects in mice are typically greater, with 5% at a minimum. The human NACC data is not randomized, and the background corrective model cannot account for all confounding. The effects in a randomized setting are typically 0.25–0.3 of the observational, so ~ 1.5% of the mean lifespan increment per single mechanism is a better measure of the real effect of each drug. Fortunately, the effects of physical activity, dietary choices, personality, socialization, and beliefs are all important and converge in the common information template of the organism, affecting each other (single and compounded placebos are one example). I counted at least 47 factors impacting lifespan. If each of them contributes on average 1-2% to the total, the data in Figure 13 are still correct; simply, the effects of pharmaceuticals combine with the effects of confounders (which promote longevity too, in their own way).

Reproducibility of results in Figure 13 in other datasets.

Considering that the normalization/background model accounts for the expected mortality rate for a given group, reduced mortality in the group forms a high Z score, and these Z scores form a rank. The reproducibility is measured by the reappearance of the ranking structure for the pharmaceuticals, demonstrating the disproportionate improvement in survival. The ranking structure observed in the NACC was also observed in the NSHAP (https://www.norc.org/research/projects/national-social-life-health-and-aging-project.html) for similar pharmaceuticals. Appleby et al. published a review addressing the treatment of Alzheimer’s disease by repurposed agents. Tables 1–4 of Appleby are enriched with pharmaceuticals that correlate with reduced mortality in Figure 13, while cognitive decline (brain aging) is the closest statistical correlate of mortality in humans, exceeded only by pancreatic cancer or sudden cardiopulmonary arrest. Bauzon et al. consider repurposed agents in the Alzheimer’s disease drug development pipeline, including Phase I-III clinical trials. Amlodipine, Atorvastatin, DM+Bupropion, Icosapent ethyl, Losartan, Metformin, Methylphenidate, and Zolpidem scored high in the NACC data and are included in the Phase III dementia prevention trials. The pharmaceuticals included in the Phase II trials and highly scoring in the NACC are Candesartan, Cilostazol, Dapagliflozin, Dasatinib + Quercetin, Montelukast, Nicotinamide, Prazosin, Riluzole, Telmisartan, and Valacyclovir. It is rather remarkable how the NACC data align so well with the findings of Bauzon et al. and suggest a universal reproducible ranking structure for pharmaceuticals.

The work of Zang et. al. considers a new method: high-throughput target trial emulation for Alzheimer’s disease drug repurposing with real-world data. For each target drug, the researchers electronically emulated one hundred trials by constructing different comparison groups by selecting patients exposed to either a random alternative drug or a similar drug within the same therapeutic class (e.g., the second-level Anatomical Therapeutic Chemical classification). All patients were followed up to five years in the primary analyses, and the two-year follow-up results were provided in the sensitivity analyses. The comparison within the same mechanistic group is especially helpful in eliminating prescription biases when agents are prescribed at a certain level of frailty by traditional decision-making among the doctors. With the best-performed model, five top-ranked drugs emerged (summarized in Fig. 3), including pantoprazole, gabapentin, atorvastatin, fluticasone, and omeprazole, all among the higher ranks in the NACC as well. Tracking the pharmaceutical use mentioned in this paragraph in the NACC patients would predict their ability to reach 105 years with a 50% probability. Does that mean we identified a short list of real lifespan extenders that only need to be combined at a respective diversity level?

Aging paradox is a major confounder.

Initially, it was just an “obesity paradox” described, for example, in Yeo et al., presenting the better survival of obese elderly people in sepsis. Natale et al. report that obesity in late life is a protective factor against dementia and dementia-related mortality, and I completely agree based on the analysis of multiple datasets. I dedicate an entire chapter in the book called “Diseases extending lifespan” to show how many other conditions beyond obesity are protective in the elderly (>70), but when the same symptoms are observed prematurely (in the 40s and 50s), the effect is clearly negative. I called this fact the “early onset effect.” It is not present in all conditions that demonstrate protective effects in old age. Can it be that the effects attributed to pharmaceuticals are in fact the combined protective effects of underlying diseases in the observational data?

The animal (murine) studies introduced earlier are randomized and multicenter. At least some murine experiments are translatable to humans, and they show the effects of drugs per se, not the underlying phenotypes. In the book, I deconvoluted the drug and underlying phenotype effects in the NACC and estimated that ~ 50% of the lifespan effect may result from the combination of protective phenotypical features developing as adaptations to old age. I describe how reaching centenarian ages can be predicted not only by analyzing a person’s current age and respective list of consumed pharmaceuticals but also by lists of underlying diagnoses, some of which form patterns predicting the ability to reach supercentenarian ages.

I explained the aging paradox as a release of regulatory tension developing in a runaway systemic deregulation (that is what aging is) through coupling of the organ aging via the informational function of blood serum. When aging leaders are linked through the exchange of mediators via serum, they are also rejuvenated by the aging laggards. The leaders do not reach the critical breakdown parameters too early. This may take place when the coupling between organ aging and systemic mediation is weak. In this case, an aging leader can reach critical condition early. High systemic coupling means that aging is evenly distributed among multiple sub-systems (multimorbidity) instead of concentrating in one. The pattern of stronger systemic coupling accompanies certain combinations of symptoms. The “early onset” effect means that the system is overall unstable; therefore, a serious early diagnosis may serve as a marker of earlier mortality. The role of comorbidity changes in advanced age categories. At older ages, comorbidity is both a source of damage and a release valve for systemic tension, all at the same time. In the chapter “Diseases extending lifespan,” I describe cohorts living 10 years longer than the average population and developing final comorbidity rates by ~ 100% higher. There is a repeating pattern of disconnect between the typical markers of health and actual lifespans, as highlighted in the cited literature. This chapter is an invitation to reconsider the nature of the chronic disease of aging.

What is practically important? Human trials of life-extending pharmaceuticals depend on normalization and randomization. Trial randomization should include the combinations of diagnoses as independent variables, each a proxy of a particular genotype defining a trajectory of age-dependent decline. Without considering the “aging paradox” as a major confounder our efforts will be wasteful. Below is a review of some of the clinical trials addressing biomarker reversals or delays in humans.

Human clinical trials of FDA-approved pharmaceuticals,
diets and lifestyles characterized by biological clocks and conducted in healthy individuals.

Human trials of conventional pharmaceuticals administered to test longevity effects in asymptomatic populations are known. Barzilai reports a trial called “Targeting Aging with Metformin (TAME),”  which by 2024 absorbed an investment of $50,000,000.  TAME is a placebo-controlled, multi-center study in ~3000 elderly adults aged 65–79, with the novel primary outcome of delaying the incidence of a composite of multiple age-related diseases. Additional outcomes relate to geriatric syndromes and functional health (https://www.afar.org/tame-trial). IL-6, TNFα-receptor I or II, CRP, GDF15, insulin, IGF1, cystatin C, NT-proBNP, and hemoglobin A1c biomarkers will be utilized in this investigation, as they have been demonstrated to be the most accurate predictors of numerous biological aging processes.

Fahy et al. report a reversal of epigenetic aging and immunosenescent trends in humans. Using a protocol intended to regenerate the thymus, Fahy et al. observed protective immunological changes, improved risk indices for many age-related diseases, and a mean 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 months to −6.5 year/year from 9–12 months. 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. During the first week of the trial, rhGH (recombinant human growth hormone) alone (0.015 mg/kg) was administered to obtain an initial insulin response, and during the second week, rhGH was combined with 50 mg of DHEA to evaluate insulin suppression by DHEA alone. During the third week, the same doses of rhGH and DHEA were combined with 500 mg of metformin. Beginning in the fourth week, all doses were individualized based on each volunteer’s particular responses. The original small trial of Fahy et al. is currently extended (https://clinicaltrials.gov/study/NCT04375657); a greater trial is named TRIIM-X, with the enrollment of 85 individuals and a putative completion date of 11/2024, https://www.youtube.com/watch?v=kNWX8SU47yU).

Kulkarni et al. report metformin regulation of metabolic and nonmetabolic pathways in the skeletal muscle and subcutaneous adipose tissues of older adults. Designed as a crossover study, the Metformin in Longevity Study (MILES) is a double-blinded study where the subjects act as their own placebo control group. MILES (https://clinicaltrials.gov/ct2/show/NCT02432287) commenced in October 2014 and was conducted on 14 elderly participants with impaired glucose tolerance to determine whether metformin (1700 mg/day) can cause physiological and transcriptomic changes in muscle and adipose tissues after 6 weeks of treatment and also to determine which pathways are affected by metformin and outline possible molecular intermediates involved in metformin’s mechanism of action. Data from the MILES trial indicate that metformin modified multiple pathways associated with aging, including metabolic pathways, collagen trimerization and extracellular matrix (ECM) remodeling, adipose tissue and fatty acid metabolism, mitochondria, and the MutS genes, MSH2 and MSH3, which play a role in DNA mismatch repair, a process that declines with age.

Statins for Extension of Disability-Free Survival and Primary Prevention of Cardiovascular Events Among Older People (STAREE) is also an intriguing study in the aging field. This investigation comprises the STAREE-HEART and STAREE-MIND sub studies (Barinda et al.). STAREE-HEART will examine the incidence of atrial fibrillation and global longitudinal strain (GLS) in healthy older adults taking 20 mg of atorvastatin daily over a 3-year follow-up period. Following a 4-year follow-up period, STAREE-MIND will assess brain aging parameters and investigate the correlation between changes in brain imaging and cognitive impairment. Hopefully, the findings of this research will shed light on the impact of atorvastatin on healthy subjects’ cardiovascular and neurological aging.

Fitzgerald et al. revisit the potential reversal of epigenetic age using diet and lifestyle interventions in the form of a pilot randomized clinical trial. This and earlier publications report a randomized controlled clinical trial conducted among 43 healthy adult males between the ages of 50 and 72. The 8-week treatment program included diet, sleep, exercise and relaxation guidance, and supplemental probiotics and phytonutrients. The control group received no intervention. Genome-wide DNA methylation analysis was conducted on saliva samples using the Illumina Methylation Epic Array, and DNAmAge was calculated using the online Horvath DNAmAge clock (2013). The diet and lifestyle treatment were associated with a 3.23-year decrease in DNAmAge compared with controls (p = 0.018). The biological age of those in the treatment group decreased by an average of 1.96 years by the end of the program compared to the same individuals at the beginning, with a strong trend towards significance (p = 0.066). Changes in blood biomarkers were significant for mean serum 5-methyltetrahydrofolate (+15%, p = 0.004) and mean triglycerides (-25%, p = 0.009). According to Firzgerald et al., as of 2021, this is the first randomized controlled study to suggest that specific diet and lifestyle interventions may reverse Horvath DNAmAge (2013) epigenetic aging in healthy adult males. Larger-scale and longer-duration clinical trials are needed to confirm these findings, as well as investigations in other human populations.

The results, like Fitzgerald, produced by lifestyle interventions are mentioned in Yaskolka Meir et al. (2021), Yaskolka Meir et al. (2023), Kong et al., Fiorito et al., Kim et al., Zhao et al., and Galkin et al.

Apparently, the factors that generate higher Z scores in NACC and NSHAP (and shape the pattern of Figure 13) are also the factors that delay and even reverse the progression of epigenetic clocks in the literature cited above (the list is far from being complete). Both murine experiments demonstrate organ morphology changes in response to rejuvenating combinations of pharmaceuticals, as well as shifts in aging clocks in humans in response to high Z-score lifestyle factors, and rule out confounding as the only source of Figure 13 patterns. Such a confounding is possible due to the complexity of “aging paradox” effects, but it cannot be the only component explaining Figure 13. The multivariate regression of NACC data attributes ~ 50% of the longevity effect to the deliberately applied factors, mostly pharmacology and supplements. This means that the lifespan effect per chemical factor is ~ 1.6 years.

Principle of opposition.

If the TRIIMX trial succeeds and prior results (Fahy et al.) reproduce, this success will mean a significant advance in understanding aging. The main agent, growth hormone, reaches its peak at 18, and its presence and profile are clearly programmatic in nature. The levels of GH subside by 25 years and continue the slow downward trend for the rest of life. Dehydroepiandrosterone sulfate (DHEA-S) begins its downward trend at ~ 40 years and continues to decline. A pathway partner of growth hormone, insulin-like growth factor 1 (IGF1), increases gradually with age, rising sharply after reaching peak levels in mid-adolescence, then decreasing slowly to the levels of young adults and continuing to rapidly drop in the second part of life, opposing inflammation. Apparently, the supplementation of important regulators that typically decline with age and the inhibition of important regulators that increase with age can delay aging. This observation is consistent with equations (1)–(19), postulating a runaway component. Each current state of aging sets the stage for the next state. The consequences of senescence become its causes due to its autocatalytic character. Opposing the consequences not only helps survival but also fights the causes.

The reversal of cardiometabolic parameters by the respective pharmaceuticals produces a signal causing a reassessment of the current aging state, setting up an updated biological age. An increase in mobility is a trademark of younger ages; thus, the reversal of age-related sedentarism causes a shift in the methylation clock. The Mediterranean diet causes a reduction of chronic inflammation markers and perhaps a reduction of amino acid levels associated with sarcopenia or protein hyper-synthesis; therefore, it also produces a delay in the progression of epigenetic clock. Geriatric depression is a typical terminal state; reversing depression correlates with setting the epigenetic clock to an earlier time. Longevity is traditionally associated with multifaceted activities. But why? What is the meaning of stimulation? The meaning is imitation of the youthful pattern, and the perception of the youthful pattern resets biological age. A morbidly obese individual will reduce biological age together with losing adipose tissue; an individual losing muscle mass will rejuvenate by muscle training; and an individual lacking enough vitamin D or magnesium will respond with epigenetic shifts after supplementation. Active compensation of age-driven changes is a signal defining the interpretation of the current aging status by the inherent sensors, perhaps of hypothalamic localization.

For example, the onset of puberty is delayed, even when the infants are capable of it and possess GH-sensitive neurons. Infants go through a brief activation of the hypothalamic-pituitary-gonadal (HPG) axis after birth, which is known as “mini-puberty.” But, as a testament to the programmatic part of human development, the transition is curtailed at this point. At a later stage, the integrated signal from body mass, metabolic cues, accumulated aging damage, epigenetic shifts, circadian clocks, endogenous protein oscillations, and other ingredients combine in a puberty-inducing multimodal clock (Sisk et al., Binder et al., Tolson et al.). A similar multimodal perception must exist for aging, explaining how attitudes and personality characteristics may alter the aging rate (Chapman et al., Terraciano et al.). Like pubertal development, aging consists of a sequence of programs, one flowing into the next after completion (Lehallier et al., Kang et al.). The partial imposition of the youthful value on the integrated clock by external influences is interpreted as rejuvenation by the inherent sensors.

The pharmaceuticals included in the analysis of Figure 13
are the pharmaceuticals compensating progeroid phenotype.

The age of onset for degenerative diseases needs an explanation. In certain cases, like Tai-Sachs disease or Werner syndrome, the damage accumulates gradually, and this explains a lag before the onset. But in cases of laminopathies (progeria), the damage is inherently present. During the entire gestation and first year of life, the nucleus incorporates the dominant negative mutant Lamin A. Nevertheless, the progeroid infants are indistinguishable from normal when compared at birth. The “pressure of youth” restrains cancer and makes it relatively rare until the age of onset in the 50s and above. In the case of cancer, the source of this “pressure” is morphogenic activity imposing proliferation control or apoptosis/senescence when the first line of defense fails. A similar “pressure of youth” staves off degenerative disease (HPGS) by ~ 2 years. The deficiency of intracellular information processing in progeria is compensated by the systemic influx of corrective information into each cell.Can a pharmaceutical regime emulate the natural “pressure of youth” staving off the genetic disease? Such a regime will also be rejuvenating in natural aging process. A response normalizing a progeroid cell can be an interesting assay for anti-aging methods and compounds. For example, Kill et al. report 5-6 faster loss of the share of proliferating fibroblasts in the culture isolated from Werner syndrome patients compared to normal cells. The share of proliferating fibroblasts is detectable by immunostaining. Normalization of such fibroblasts by a combination of pharmaceuticals, ASO, miRNA, or whole extracts would produce a rapid anti-aging assay. Buchwalter et al. discuss nucleolar expansion and elevated protein translation in premature aging (HGPS). The authors report a widespread increase in protein turnover in HGPS-derived cells compared to normal cells. Global protein synthesis is elevated because of activated nucleoli and enhanced ribosome biogenesis in HGPS-derived fibroblasts. Depleting normal lamin A or inducing mutant lamin A are each sufficient to drive nucleolar expansion. Nucleolar size correlates with donor age in primary fibroblasts derived from healthy individuals, and ribosomal RNA production increases with age, indicating that nucleolar size and activity can serve as aging biomarkers. Limiting ribosome biogenesis extends lifespan in several systems, the report shows that increased ribosome biogenesis and activity are a hallmark of premature aging. Buchwalter et al. consider two mechanisms: one that lamin A is a regulator of heterochromatin integrity, and the disruption of the lamin A network by the progerin mutant opens the blocked ribosomal RNA genes for expression. In this case, elevated protein synthesis is a marker of general information loss in the nucleus. The second pro-aging mechanism is excessive protein synthesis, depleting the ATP resources of the cell and competing with all other processes (runaway dynamics, equations (1)-(19)). Nucleolus size and protein turnover are increased in normal aging, meaning a greater probability for misfolding or incorrect post-translational modification. The problems are more severe in non-dividing cells, without the possibility of defect deletion. A potential anti-aging assay can be morphological, the observation of the size and structure of the nucleolus, or measurement of the ratio of protein turnover rate to the available ATP pool. It is possible that paracrine maintenance of heterochromatin integrity and systemic inhibition of the ribosomal synthesis are the main components of the” youth’s pressure” staving off the onset of HGPS.

Considering how important the lamin A balance turned out to be, its pharmacological modification can be another anti-aging mechanism and anti-aging assay.

Clements et al. analyzed the distribution of prelamin A, lamin A, lamin A/C, progerin, lamin B1, and B2 in the nuclei of HGPS cells before and after treatments with farnesyltransferase inhibitors (FTI) and a geranylgeranyltransferase inhibitor (GGTI) and [FTI with pravastatin and zoledronic acid] in combination. Confirming other studies, prelamin A, lamin A, progerin, and lamin B2 staining was different between control and HGPS fibroblasts. The drugs that reduced progerin staining were FTI, pravastatin, zoledronic acid, and rapamycin (all known candidates for longevity studies, see the review by Center et al. covering zoledronic acid, while statins and rapamycin were presented earlier).

I noticed that pravastatin and zolendronic acid (bisphosphonate drugs) are scoring high in the NACC analysis and combined with other partners would create the pattern of Figure 13.

The drugs affecting the mevalonate pathway increased prelamin A, with only FTI reducing internal prelamin A foci. The distribution of lamin A in HGPS cells was improved with treatments of FTI, pravastatin, and FTI+GGTI. All treatments reduced the number of cells displaying internal speckles of lamin A/C and lamin B2. Drugs targeting the mevalonate pathway worked best for progerin reduction, with zoledronic acid removing internal progerin speckles. Rapamycin and NAC, which impact the MTOR pathway, both reduced both pools of progerin without increasing prelamin A in HGPS cell nuclei.

I continued the literature search in the same direction, assessing the ability of the drugs included in the analysis of Figure 13 to delay or reverse the progression of premature aging syndromes.

Guilbert et al. mentions successful alleviation of HGPS hallmarks by retinoids, vitamin A, metformin in combination. Metformin could lead to a decrease in SRSF1 and progerin in both iPS cell-derived MSCs and HGPS fibroblasts, and metformin is also included in the agents analyzed in Figure 13. Guilbert et al. addresses the absence of progerin expression in neural cells, explaining normal neurological development of the patients. The group isolated miR-9, a miRNA predominantly expressed in neural cells, and capable to target the 3’UTR of progerin and decrease its expression in neurons. Will this sequence be effective as a systemically applied ASO suppressing the progerin component of normal aging? Senolytic drugs show promise in the treatment of HGPS, and I observed high Z score for desatinib, the partner of quercetin mentioned in Guilbert et al. Another prospective antiprogeric drug -TUDCA (Tauroursodeoxycholic acid) represents a class of ursodeoxycholic acids scoring high in NACC – and probably elsewhere too. The alignment between progeria and normal aging is explored in Aliper et al. who addressed the question of whether HGPS recapitulates the normal aging process, or simply mimics the aging phenotype. The group analyzed publicly available microarray datasets for fibroblasts undergoing cellular aging in culture, as well as fibroblasts derived from young, middle-age, and old-age individuals, and patients with HGPS. Using GeroScope pathway analysis and drug discovery platform, the group analyzed the activation states of 65 major cellular signaling pathways. According to the study, signaling pathway activation states in cells derived from chronologically young patients with HGPS strongly resemble cells taken from normal middle-aged and old individuals. This clearly indicates that HGPS may truly represent accelerated aging, rather than being just a simulacrum. The data also points to potential pathways that could be targeted to develop drugs and drug combinations for both HGPS and normal aging.

Kreienkamp et al. report vitamin D receptor signaling improving Hutchinson-Gilford progeria syndrome cellular phenotypes. HGPS cells reduce expression of vitamin D receptor (VDR) and DNA repair factors BRCA1 and 53BP1 with progerin accumulation, and reconstituting VDR signaling via 1α,25-dihydroxyvitamin D₃ (1,25D) treatment improves HGPS phenotypes, including nuclear morphological abnormalities, DNA repair defects, and premature senescence. Importantly, the 1,25D/VDR axis regulates LMNA gene expression, as well as expression of DNA repair factors. 1,25D dramatically reduces progerin production in HGPS cells, while stabilizing BRCA1 and 53BP1, two key factors for genome integrity. Vitamin D and especially Vitamin D – calcium supplements produce leading Z scores in all analyses, leading to Figure 13.

Pellegrini report all-trans retinoic acid and rapamycin that normalize Hutchinson Gilford progeria fibroblast phenotype. Since the LMNA promoter contains a retinoic acid responsive element, Pellegrini et al. investigated if all-trans retinoic acid administration could lower progerin levels in cultured fibroblasts. They also evaluated the effect of associating rapamycin, which induces autophagic degradation of progerin and prelamin A. As a result, all-trans retinoic acid acts synergistically with low-dosage rapamycin reducing progerin and prelamin A, via transcriptional downregulation associated with protein degradation, and increasing the lamin A to progerin ratio. These effects rescue cell dynamics and cellular proliferation through recovery of DNA damage response factor PARP1 and chromatin-associated nuclear envelope proteins LAP2α and BAF. The combined all-trans retinoic acid-rapamycin treatment is dramatically efficient, highly reproducible, represents a promising new approach in Hutchinson-Gilford Progeria therapy and deserves investigation in ageing-associated disorders.

The interesting part of retinoic acid treatment is that it is effective in normalization of cancer (leukemias, breast cancer), acting through a similar mechanism – re-imposition of differentiation patterns lost to heterochromatin entropy (Jan et al.). Intravenously administered, retinoid activating nanoparticles increase lifespan and reduce neurodegeneration in the SOD1G93A mouse model of ALS (Medina et al.). The team encapsulated retinoid adapalene within nanoparticles (Adap-NPs) composed of poly(lactic acid)-poly(ethylene glycol) (PLA-PEG) and achieved a favorable biodistribution profile, a typical challenge for retinoids. The intravenous administration of Adap-NPs robustly activated retinoid signaling in the CNS. Chronic administration of Adap-NPs resulted in improved motor performance, prolonged lifespan (~ 5%-7%, Figure 3), and neuroprotection in SOD1^G93A mice. The study highlights retinoid signaling as a valuable therapeutic approach and presents a novel nanoparticle platform for the treatment of amyotrophic lateral sclerosis.

In all cases – loss of proliferation control due to chromatin chaotization in cancer, loss of protein synthesis control due to chromatin chaotization in progeria and loss of DNA integrity due to mutation in superoxide dismutase in ALS  – retinoid signaling induces the rigidity of information template, the earlier stated condition of extended lifespan. Retinoids (tretinoin, topical tretinoin) show high Z scores, when cancer is included in the normalizing model.

The benefits of working with the progeroid cells are in the fast rate of aging cell generation, and in the possibility of multiple assays, covering either morphological changes or specific aging markers such as distribution of lamin forms. Aging of human cells produced by reprogramming may be time consuming, and instead switching on progerin expression by a doxycycline-controlled promoter may become a method of rapidly aging the normal primary cells of different lineages. The reversal of the changes in HTS screening will lead to a pool of prospective rejuvenators, including miRNA candidates, or even genome-wide ASO libraries. Microfluidics would allow computerized observation of very small groups of cells, making the screening process even more cost-effective.

The translation of results obtained in cell culture to the level of the entire organism is not obvious. In this section we demonstrated that the observational data of Figure 13 correlate with both clinical trial data and with the progeria-corrective assays. This means that progeria-corrective assays are predictive of the future clinical trial success of anti-aging pharmaceuticals and should be included in the panel of rapid HTS for gero-protectants.

What kind of anti-aging therapy does Figure 13 suggest?

The longevity effects form “shadows” of the FDA approved pharmaceuticals. When such “shadows” are positive and act in complex combinations, synergies are likely. The dosages supporting the longevity effects can be smaller or much smaller than the typically prescribed dosages required to reverse the symptoms of already developed disease. For example, metformin shows positive lifespan effect at lower dosages and neurotoxic at higher levels. On the other hand, the effects of the drugs are allopathic, not homeopathic. If used at too low dosages, the lifespan effects will not develop. Imposing complex long-term polypharmacy on clinically healthy people to delay the onset of chronic disease is a matter of long, massive series of clinical trials, more achievable with government support (below). Perhaps testing the promising approved FDA drug cocktails within a Dog Aging Project or in the herds of domestic animals might be an intermediate step, but the promise of staving off chronic disease and longer life might also attract numerous human volunteers. The challenge with humans is the duration of the experiments and the need to bypass this obstacle by development of precise aging marker correlates (see the book). Hepatotoxicity, cancer, and damage to germline need to be carefully considered when dealing with the novel cocktails applied to healthier and younger people.

The danger of cancer—removing this roadblock to longevity.

Working with the data of Figure 13, I noticed that the cancer rate increases between the cohorts with lower and higher counts of protectants (HR = 1.35 ± 0.27), while cancer mortality decreases in parallel with all forms of mortality.

A terrifying side effect of longevity efforts can be development of malignancies and teratomas. A fundamental anti-cancer mechanism exists in early embryogenesis, proven by reprogramming of mutated malignant cells into normal embryonic cells by implantations in the embryos. Similarly potent systemic compensation exists for genetic diseases with the onsets sometime after birth. The molecular defects are present (progeria cases), but they are temporarily silenced. This silencing factor for cancer is a systemic morphogenic field, incredibly powerful in rapidly developing embryos and regeneration sites in amphibians. The survival of given species depends on the correctness of development in the pre-reproductive period, and compensatory safeguards are established to direct it along the right path for as long as possible. The rigidity of systemic regulation in the formative periods of life gradually gives way with time, allowing hereditary or acquired mutations to prevail and impose either a degenerative or hyper-proliferative phenotype.

But what is the exact mechanism of deferral of pathological phenotypes? A classic theory of carcinogenesis postulates progressive accumulation of mutations disabling tumor suppressors and activating oncogenes until the balance shifts in favor of uncontrolled proliferation and genomic instability. The latter leads to clonal progression, producing increasingly resistant and further mutating clones. Yet a cancer cell can be normalized amid clonal progression with accumulated mutations. The review by Pensotti et al. discusses the phenotypic reversion of cancer and experimental evidence of cancer reversibility through epigenetic mechanisms.

The review begins by mentioning spontaneous reversions of cancer and the concept of morphogenic “pressure” containing the disease until certain ages when the pressure subsides. The review further considers the reversions of easier cases such as teratomas and virally induced malignancies, where the cellular “hardware” controlling proliferation is not damaged. This is not the case for solid tumors developing through the accumulation of variable mutations and experiencing irreversible microevolution of the genome. Pensotti et al. cite Livraghi et al. In this report, Livraghi et al. developed a product containing stem cell differentiation stage factors (SCDSF) that inhibits tumor growth in vivo and in vitro, including non-treatable advanced hepatocarcinoma (HCC). The product was tested in a randomized study. The aim of this open randomized study (N = 179) was to assess its efficacy in patients with HCC that are not suitable for resection, transplantation, ablation therapy, or arterial chemoembolization. Randomization was stopped at the second interim analysis (6 months) of the first 32 patients recruited when the inspection detected a significant difference in favor of treatment (p = 0.037). The responses to the therapy obtained in 154 additional patients confirmed previous results. Evaluation of survival showed a significant difference between the group of patients who responded to treatment versus the group with progression of disease (p < 0.001). Of the 23 treated patients with a performance status (PS) of 1, 19 changed to 0. The study indicated the efficacy of SCDSF treatment in patients with intermediate-advanced HCC. Later articles by the same group present a complete response in 5 out of 38 patients treated by similar stem cell extracts. In the book, I cite more literature covering the high melanoma cure rate using the amphibian oocyte extracts.

Pensotti et al. further consider molecular targets causing cancer normalization by the external environment. She cites the work of Telerman et al. Telerman et al. infected various tumor cell lines with parvovirus H-1, selectively eliminating only malignant tumor cells. Virus-resistant tumor cells were enriched and have lost their malignant potential. To confirm, the reprogrammed cells were subsequently implanted in immunocompromised mice to observe the stable reduction of tumorigenicity. The differential studies between the revertants and original cancer cell lines isolated nearly 300 genes involved in the reversion process. Telerman was able to identify the main ones, namely, seven in absentia gene (SIAH1), presenilin 1 (PS1), tumor suppressor-activated pathway (6TSAP6), and translationally controlled tumor protein (TCTP), where SIAH1 and TSAP6 are upregulated in the reverting tumor cells, while PS1 and TCTP are instead repressed.

According to Telerman, TCTP is the hub of this reprogramming function, capable of bypassing the role of wild-type or mutant p53. Mutated solid tumor cells with damaged tumor suppressor machinery can still be forced into the state of apoptosis, another marker of normalcy of the cells in a multicellular organism. These functions are blocked by presenilin 1 and TCTP, and elimination of TCTP by antisense construct leads to reversion of malignant phenotype. The review of Pensotti et al. further discusses the reversion of aggressive melanoma and breast cell lines by the embryonic stem cell extracts, acting via inhibition of the protein NODAL, or without mentioning a specific mechanism (Table 1).

The review of Table 1 of Pensotti et al. points to the correlation between regenerative ability and the ability to attenuate/revert malignant phenotype. In general, amphibian oocytes and embryos demonstrate the greatest reversion potential, even against cancer types harboring multiple mutations. Amphibians are known to regenerate limbs and tails, while mammals cannot. Amphibian regeneration of lost limbs starts with rapidly proliferating blastema, requiring shaping and control, provided by amphibian morphogenic fields. Early embryonic mammalian extracts are also more potent than the extracts from the stable lineages, such as adult marrow stem cells, placental, or umbilical cord cells. A dynamic morphogenic environment imposes normalcy on the tumor phenotype of the same lineage, and molecular mediators are waiting for isolation. Pensotti et al. mention fractionation experiments isolating thermostable and low molecular weight components from early embryonic extracts. Such components can be miRNA, short signal peptides and small molecular hormones, or perhaps very small miRNA-carrying exosomes. The normalizing effects on teratomas by healthy tissues of animals and plants require lesser intensity of morphogenic fields, and are also frequently observed.

The practical result of this research can yield a combinatorial cocktail of miRNA/ASO isolated from regenerative environments that would suppress multiple tumorigenic genes like TCTP or PS1. The pro-apoptotic effects of the regenerative extracts do not extend to normal fibroblasts (Pensotti et al.); they are tumor selective. Incorporation of cancer preventive sub-cocktails in the longevity cocktails would allow bolder moves in activation of stemness activity and in utilization of a wider range of pro-survival pathways. Increasing stem-cell activity many-fold without causing teratomas may fundamentally change the inherent rejuvenating potential of our organism which I introduced earlier and may qualitatively change the overall dynamic of aging. The mastery of morphogenic control is at the core of controlling human biology.

It is time to set up an International Antiaging Initiative.

More than 300 theories are proposed to explain aging. That means we probably do not have a theory. Aging can be considered a black box, and its understanding may come in the process of conquering it. A similar trend exists in the study of nuclear fusion, which was, until recently, a frustrating subject. Numerous practical experiments advanced the understanding of magnetic drifts, which in turn improved plasma containment methods. Regarding aging, we only recently developed the meme that the process of our demise is malleable at all. Until the arrival of this new meme, our collective will had been paralyzed by pessimism. Now, it is time to adopt a government-sponsored program of studies that would result in health benefits immeasurably exceeding anything tried before.

Even without a full understanding of aging, we can identify the approved pharmaceuticals and OTC supplements that demonstrate aging delays and reversals. We can propose analogues and derivatives that either produce stronger effects or are safer and easier to combine with other agents. We can bring in a single longevity system: diversified exercise, diet, supplementation, tested pharmaceuticals, embryonic stem cell extracts, miRNA and ASO cocktails, activities, attitudes, and the opposition principle of addressing aging-related changes.

This is why a government-led, of even international program of longevity cooperation is needed (Van Der Pol et al.). How would such a program benefit a wider society? Figure 13 provides a guide. The period of ~ 100% survival (95–100%) is the period when an individual has a capacity to contribute, including one’s Social Security and Medicaid plans. The period from ~ 90% survival and below can be defined as a period of limited ability or complete dependence. The ratio of these periods can be estimated as:

R(N) = A [50 + years of decline from 100 to 95% survival] / [tangent at 50% survival] (20)

Where R(N) is the ratio of accumulating and consuming periods of a person’s profile, A is a coefficient of proportionality, and N is the number of protective factors applied to the lifestyle. Figure 13 shows that R(10)/R(3) > 4. That means the coming “silver tide” of long lifespans and low birthrates can be managed without chaos and abandonment of humanistic norms, just by timely preparing the necessary research base for longevity and regenerative medicine. Managing old age and managing the decline of birthrates are, in my view, two of the most burning issues of society, more immediate than the already-managed climate change and energy crises.

The economic benefits of the extended period of competence and independence and the reduced period of disability can be so great that they will fund the programs of birthrate stimulation.

The existing measures are insufficient; more radical approaches are needed. In the current divided state, nations are probably incapable of such reforms, but determination will rise when the situation turns dire. Managing the process of aging with the combinational methods outlined in this blog would at least provide the resources for adaptation to historical changes.

The existing international cooperations in nuclear fusion, space exploration, epidemiology, and Interpol should grow into common projects of pandemic preparedness, asteroid defense, longevity research, regenerative medicine, control of human biology, colonization of the Moon, Mars, and other bodies of the solar system, and improved agriculture independent of climate and pollinators.

I think these were the common dreams of all educated people over the last 100 years—across all oceans and creeds.

References

Fernandes, Maria, Cen Wan, Robi Tacutu, Diogo Barardo, Ashish Rajput, Jingwei Wang, Harikrishnan Thoppil et al. “Systematic analysis of the gerontome reveals links between aging and age-related diseases.” Human molecular genetics 25, no. 21 (2016): 4804-4818.

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