The epigenetics of perinatal stress

This 2019 McGill review discussed long-lasting effects of perinatal stress:

“Epigenetic processes are involved in embedding the impact of early-life experience in the genome and mediating between social environments and later behavioral phenotypes. Since these phenotypes are apparent a long time after the early experience, the changes in gene expression programming must be stable.

Although loss of methylation in a promoter is necessary for expression, it is not sufficient. Demethylation removes a barrier for expression, but expression might be realized at the right time or context when the needed factors or signals are present.

DNA methylation anticipates future transcriptional response to triggers. Comparing steady-state expression with DNA methylation does not capture the full meaning and scope of the regulatory roles of differential methylation.

A model for epigenetic programming by early life stress:

  1. Perinatal stress perceived by the brain triggers release of glucocorticoids (GC) from the adrenal in the mother prenatally or the newborn postnatally.
  2. GC activate nuclear glucocorticoid receptors across the body, which epigenetically program (demethylate) genes that are targets of GR in brain and white blood cells (WBC).
  3. The demethylation events are insufficient for activation of these genes. A brain specific factor (TF) is required for expression and will activate low expression of the gene in the brain but not in blood.
  4. During adulthood a stressful event transiently triggers a very high level of expression of the GR regulated gene specifically in the brain.

Horizontal arrow, transcription; circles, CpG sites; CH3 in circles, methylated sites; empty circles, unmethylated CpG sites; horizon[t]al curved lines, mRNA.”

Points discussed in the review:

  • “Epigenetic marks are laid down and maintained by enzymes that either add or remove epigenetic modifications and are therefore potentially reversible in contrast to genetic changes.
  • The response to early life stress and maternal behavior is also not limited to the brain and involves at least the immune system as well.
  • The placenta is also impacted by maternal social experience and early life stress.
  • Most studies are limited to peripheral tissues such as saliva and white blood cells, and the relevance to brain physiology and pathology is uncertain.
  • The low absolute differences in methylation seen in most human behavioral EWAS raise questions about their biological significance.

  • Although post-mortem studies examine epigenetic programming in physiologically relevant tissues, they represent only a final and single stage that does not capture the dynamic evolution of environments and epigenetic programming in living humans.”

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6952743/ “The epigenetics of perinatal stress”


Other reviewers try to ignore the times when we were all fetuses and newborns. For example, in the same journal issue was a Boston review of PTSD that didn’t mention anything about the earliest times of human lives! Those reviewers speculated around this obvious gap on their way to being paid by NIH.

Why would researchers ignore perinatal stress events that prime humans for later-life PTSD? Stress generally has a greater impact on fetuses and newborns than even infants, and a greater impact on infants than adults.

A blood plasma aging clock

This 2019 Stanford human study developed an aging clock using blood plasma proteins:

“We measured 2,925 plasma proteins from 4,331 young adults to nonagenarians [18 – 95] and developed a novel bioinformatics approach which uncovered profound non-linear alterations in the human plasma proteome with age. Waves of changes in the proteome in the fourth, seventh, and eighth decades of life reflected distinct biological pathways, and revealed differential associations with the genome and proteome of age-related diseases and phenotypic traits.

To determine whether the plasma proteome can predict chronological age and serve as a “proteomic clock,” we used 2,858 randomly selected subjects to fine-tune a predictive model that was tested on the remaining 1,473 subjects. We identified a sex-independent plasma proteomic clock consisting of 373 proteins. Subjects that were predicted younger than their chronologic age based on their plasma proteome performed better on cognitive and physical tests.

The 3 age-related crests were comprised of different proteins. Few proteins, such as GDF15, were among the top 10 differentially expressed proteins in each crest, consistent with its strong increase across lifespan. Other proteins, like chordin-like protein 1 (CHRDL1) or pleiotrophin (PTN), were significantly changed only at the last two crests, reflecting their exponential increase with age.

We observed a prominent shift in multiple biological pathways with aging:

  • At young age (34 years), we observed a downregulation of proteins involved in structural pathways such as the extracellular matrix. These changes were reversed in middle and old ages (60 and 78 years, respectively).
  • At age 60, we found a predominant role of hormonal activity, binding functions and blood pathways.
  • At age 78, key processes still included blood pathways but also bone morphogenetic protein signaling, which is involved in numerous cellular functions, including inflammation.

These results suggest that aging is a dynamic, non-linear process characterized by waves of changes in plasma proteins that are reflective of a complex shift in the activity of biological processes.”

https://www.biorxiv.org/content/10.1101/751115v1.full “Undulating changes in human plasma proteome across lifespan are linked to disease”


A non-critical review of the study was published by the Life Extension Advocacy Foundation. Frequent qualifiers like “could,” “may,” and “possible” were consistent with the confirmation biases of their advocacy.

There were several misstatements of what the study did, including the innumerate:

  1. “used around half of the participant data to build a “proteomic clock”
  2. tested it on the other half of the participants
  3. a total of 3000 proteins”

Per the above study quotation, the numbers were actually:

  1. Closer to two thirds (2,858 ÷ 4,331), not “around half”;
  2. The other third (1,473 ÷ 4,331), not “the other half”; and
  3. 2,925 not 3000.

The final paragraph and other parts of the review bordered on woo. Did a review of the findings have to fit LEAF’s perspective?


In contrast, Josh Mitteldorf did his usual excellent job of providing contexts for the study with New Aging Clock based on Proteins in the Blood, emphasizing comparisons with epigenetic clock methodologies:

“For some of the proteins that feature prominently in the clock, we have a good understanding of their metabolic function, and for the most part they vindicate my belief that epigenetic changes are predominantly drivers of senescence rather than protective responses to damage.

Wyss-Coray compared the proteins in the new (human) proteome clock with the proteins that were altered in the (mouse) parabiosis experiments, and found a large overlap [46 proteins change in the same direction and define a conserved aging signature]. This may be the best evidence we have that the proteome changes are predominantly causal factors of senescence.

46 plasma proteins

Almost all the proteins identified as changing rapidly at age 78 are increasing. In contrast, a few of the fastest-changing proteins at age 60 are decreasing (though most are increasing). GDF15 deserves a story of its own.

The implication is that a more accurate clock can be constructed if it incorporates different information at different life stages. None of the Horvath clocks have been derived based on different CpG sites at different ages, and this suggests an opportunity for a potential improvement in accuracy.”

A commentator linked the below study:

https://www.sciencedirect.com/science/article/pii/S0092867419308323 “GDF15 Is an Inflammation-Induced Central Mediator of Tissue Tolerance” (not freely available)

which prompted his response:

“Thanks, Lee! This is just the kind of specific information that I was asking for. It would seem we should construct our clocks without GDF15, which otherwise might loom large.”

Organismal aging and cellular senescence

I’ll curate this 2019 German review through its figures:

“With the discovery of beneficial aspects of cellular senescence and evidence of senescence being not limited to replicative cellular states, a redefinition of our comprehension of aging and senescence appears scientifically overdue.

Figure 1. Current determinants and relevant open questions, marking the processes of aging and senescence as discussed in the text. Aspects represented in green are considered as broadly accepted or scientifically consolidated. Novel aspects that are yet unproven, or are under debate, are highlighted in red.

SASP = senescence-associated secretory phenotype. AASP = putative aging-associated secretory phenotype as suggested in the text.

Figure 2. Theories on the causality and purpose of aging. Graphically summarized are four contrasting concepts crystallized from current evidence addressing the inductive driving force of aging. Apart from a stochastic deleteriome, there are arguments for a pseudo-programmed, programmed or at least partially programmed nature of aging.

Figure 3. Comparative representation of the aging and senescence processes highlighting different levels of interaction and putative sites of interventions.

(1) As discussed in the text, causative mechanisms of aging are still not well understood, however, multiple factors including genetic, epigenetic and stress-related effects seem to have an orchestrated role in the progression of aging. Senescence on the other hand, is seen as a programmed response to different kinds of stressors, which proceed in defined stages. Whether, in analogy, aging also follows a defined program or sequential stages is not known.

(2) Senescence involves autocrine and paracrine factors, which are responsible for a ‘seno-infection’ or bystander effect in neighboring cells. There is currently no direct evidence for a similar factor composition propagating the aging process via a kind of ‘gero-infection’.

(3) Accumulation of senescent cells has been described as a hallmark of aging; however, whether they are a causative factor or a consequence of tissue and organismal aging is still unknown. As discussed in the text, it appears possible that aging and senescence mutually influence each other through positive feedback at this level, leading to accelerated tissue damage and aging.

(4,5) Clearance of senescent or aging cells might constitute putative targets for interventional approaches aimed to reduce or reverse the impact of aging and improve cell and tissue homeostasis by inducing a ‘rejuvenation’ process.

Figure 4. Pathological and beneficial functions of aging and senescence, according to current knowledge. In red are represented pathological consequences and in green beneficial functions of aging and senescence.

The impact of aging has mainly been described at the organismal level, since a complete cellular functional profile has not yet been established. Accordingly, whether beneficial consequences of the aging process exist at the cellular level is unclear.”


The reviewers’ position on Figure 2 was:

“In our view, recent evidence that

  • Senescence is based on an unterminated developmental growth program and the finding that
  • The concept of post-mitotic senescence requires the activation of expansion, or ‘growth’ factors as a second hit,

favor the assumption that aging underlies a grating of genetic determination similarly to what is summarized above under the pseudo-programmed causative approach.”

Their position on Figure 4’s beneficial effects of aging began with the sentence:

“If we assume that aging already starts before birth, it can be considered simply a developmental stage, required to complete the evolutionary program associated with species-intrinsic biological functions such as reproduction, survival, and selection.”

Cited studies included:

https://www.mdpi.com/2073-4409/8/11/1446 “Dissecting Aging and Senescence-Current Concepts and Open Lessons”

Epigenetic transgenerational inheritance extends to the great-great-grand offspring

This 2019 rodent study by the Washington State University labs of Dr. Michael Skinner continued to F4 generation great-great-grand offspring, and demonstrated that epigenetic inheritance mechanisms are similar to imprinted genes:

“Epigenetic transgenerational inheritance potentially impacts disease etiology, phenotypic variation, and evolution. An increasing number of environmental factors from nutrition to toxicants have been shown to promote the epigenetic transgenerational inheritance of disease.

Imprinted genes are a special class of genes since their DNA methylation patterns are unchanged over the generation and are not affected by the methylation erasure occurring early in development. The transgenerational epigenetic alterations in the germline appear to be permanently reprogrammed like imprinted genes, and appear protected from this DNA methylation erasure and reprogramming at fertilization in the subsequent generations. Similar to imprinted genes, the epigenetic transgenerational germline epimutations appear to have a methylation erasure in the primordial germ cells involving an epigenetic molecular memory.

Comparison of the transgenerational F3 generation, with the outcross to the F4 generation through the paternal or maternal lineages, allows an assessment of parent-of-origin transmission of disease or pathology. Observations provided examples of the following:

  1. Pathology that required combined contribution of both paternal and maternal alleles to promote disease [testis and ovarian disease];
  2. Pathology that is derived from the opposite sex allele such as father to daughter [kidney disease] or mother to son [prostate disease];
  3. Pathology that is derived from either parent-of-origin alleles independently [obesity];
  4. Pathology that is transmitted within the same sex, such as maternal to daughter [mammary tumor development]; and
  5. Pathology that is observed only following a specific parent-of-origin outcross [both F4 male obesity and F4 female kidney disease in the vinclozolin lineage].”

The study showed that epigenetically inherited legacies extend to the fifth generation. Do any of us know our ancestors’ medical histories back to our great-great-grandparents?

Will toxicologists take their jobs seriously, catch up to the current science, and investigate possible effects in at least the F3 generation that had no direct toxicant exposure?

https://www.sciencedirect.com/science/article/pii/S0012160619303471 “Epigenetic transgenerational inheritance of parent-of-origin allelic transmission of outcross pathology and sperm epimutations”

Do genes or maternal environments shape fetal brains?

This 2019 Singapore human study used Diffusion Tensor Imaging on 5-to-17-day old infants to find:

“Our findings showed evidence for region-specific effects of genotype and GxE on individual differences in human fetal development of the hippocampus and amygdala. Gene x Environment models outcompeted models containing genotype or environment only, to best explain the majority of measures but some, especially of the amygdaloid microstructure, were best explained by genotype only.

Models including DNA methylation measured in the neonate umbilical cords outcompeted the Gene and Gene x Environment models for the majority of amygdaloid measures and minority of hippocampal measures. The fact that methylation models outcompeted gene x environment models in many instances is compatible with the idea that DNA methylation is a product of GxE.

A genome-wide association study of SNP [single nucleotide polymorphism] interactions with the prenatal environments (GxE) yielded genome wide significance for 13 gene x environment models. The majority (10) explained hippocampal measures in interaction with prenatal maternal mental health and SES [socioeconomic status]. The three genome-wide significant models predicting amygdaloid measures, explained right amygdala volume in interaction with maternal depression.

The transcription factor CUX1 was implicated in the genotypic variation interaction with prenatal maternal health to shape the amygdala. It was also a central node in the subnetworks formed by genes mapping to the CpGs in neonatal umbilical cord DNA methylation data associating with both amygdala and hippocampus structure and substructure.

Our results implicated the glucocorticoid receptor (NR3C1) in population variance of neonatal amygdala structure and microstructure.

Estrogen in the hippocampus affects learning, memory, neurogenesis, synapse density and plasticity. In the brain testosterone is commonly aromatized to estradiol and thus the estrogen receptor mediates not only the effects of estrogen, but also that of testosterone.”

https://onlinelibrary.wiley.com/doi/full/10.1111/gbb.12576 “Neonatal amygdalae and hippocampi are influenced by genotype and prenatal environment, and reflected in the neonatal DNA methylome” (not freely available)

Reversal of aging and immunosenescent trends

The title of this post is essentially the same as the 2019 human clinical trial:

“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 mean epigenetic age approximately 1.5 years less than baseline after 1 year of 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.

Analysis of CyTOF‐defined immune cell populations revealed the most robust changes to be decreases in total and CD38‐positive monocytes and resulting increases in the lymphocyte‐to‐monocyte ratio (LMR). The changes in mean monocyte populations persisted 6 months after discontinuation of treatment, and the increase in LMR remained highly significant at 18 months as well.

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”

https://onlinelibrary.wiley.com/doi/full/10.1111/acel.13028 “Reversal of epigenetic aging and immunosenescent trends in humans”


Here’s a 2017 interview with the clinical trial lead author:

“You might also say that what also happened was to just postpone death from infectious diseases to after 60-65 years of age, which means that the same basic problem still remains.”


The popular press botched the facts as they usually do. I won’t link the UK Independent article because they couldn’t be bothered to even define epigenetic clock correctly.

A science journal article did a better job of explaining the study to readers. However, they often used hyperbole instead of trying to promote understanding.

Josh Mitteldorf’s blog post 1st Age Reversal Results—Is it HGH or Something Else? provided the most informative explanations:

“In 2015, Fahy finally had funding and regulatory approval to replicate his one-man trial in a still-tiny sample of ten men, aged 51-65. That it took so long is an indictment of everything about the way aging research is funded in this country; and not just aging – all medical research is prioritized according to projected profits rather than projected health benefits.”

Take care reading the post’s comments. Both non-scientist (such as Mark, Adrian, and others) and scientist commentators (such as Gustavo, Jeff, and others) attempted to hijack the discussion into their pet theories of reality in which they imagined themselves to be the definitive authorities. My discussion comment – with respect to a Mayo Clinic warning about DHEA – was: “19 instances of the word ‘might’ doesn’t lend itself to credibility.”

Developmental disorders and the epigenetic clock

This 2019 UK/Canada/Germany human study investigated thirteen developmental disorders to identify genes that changed aspects of the epigenetic clock:

“Sotos syndrome accelerates epigenetic aging [+7.64 years]. Sotos syndrome is caused by loss-of-function mutations in the NSD1 gene, which encodes a histone H3 lysine 36 (H3K36) methyltransferase.

This leads to a phenotype which can include:

  • Prenatal and postnatal overgrowth,
  • Facial gestalt,
  • Advanced bone age,
  • Developmental delay,
  • Higher cancer predisposition, and, in some cases,
  • Heart defects.

Many of these characteristics could be interpreted as aging-like, identifying Sotos syndrome as a potential human model of accelerated physiological aging.

This research will shed some light on the different processes that erode the human epigenetic landscape during aging and provide a new hypothesis about the mechanisms behind the epigenetic aging clock.”

“Proposed model that highlights the role of H3K36 methylation maintenance on epigenetic aging:

  • The H3K36me2/3 mark allows recruiting de novo DNA methyltransferases DNMT3A (in green) and DNMT3B (not shown).
  • DNA methylation valleys (DMVs) are conserved genomic regions that are normally found hypomethylated.
  • During aging, the H3K36 methylation machinery could become less efficient at maintaining the H3K36me2/3 landscape.
  • This would lead to a relocation of de novo DNA methyltransferases from their original genomic reservoirs (which would become hypomethylated) to other non-specific regions such as DMVs (which would become hypermethylated and potentially lose their normal boundaries),
  • With functional consequences for the tissues.”

The researchers improved methodologies of several techniques:

  1. “Previous attempts to account for technical variation have used the first 5 principal components estimated directly from the DNA methylation data. However, this approach potentially removes meaningful biological variation. For the first time, we have shown that it is possible to use the control probes from the 450K array to readily correct for batch effects in the context of the epigenetic clock, which reduces the error associated with the predictions and decreases the likelihood of reporting a false positive.
  2. We have confirmed the suspicion that Horvath’s model underestimates epigenetic age for older ages and assessed the impact of this bias in the screen for epigenetic age acceleration.
  3. Because of the way that the Horvath epigenetic clock was trained, it is likely that its constituent 353 CpG sites are a low-dimensional representation of the different genome-wide processes that are eroding the epigenome with age. Our analysis has shown that these 353 CpG sites are characterized by a higher Shannon entropy when compared with the rest of the genome, which is dramatically decreased in the case of Sotos patients.”

https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1753-9 “Screening for genes that accelerate the epigenetic aging clock in humans reveals a role for the H3K36 methyltransferase NSD1”