The originator of the 2013 epigenetic clock improved its coverage with this 2018 UCLA human study:
“We present a new DNA methylation-based biomarker (based on 391 CpGs) that was developed to accurately measure the age of human fibroblasts, keratinocytes, buccal cells, endothelial cells, skin and blood samples. We also observe strong age correlations in sorted neurons, glia, brain, liver, and bone samples.
The skin & blood clock outperforms widely used existing biomarkers when it comes to accurately measuring the age of an individual based on DNA extracted from skin, dermis, epidermis, blood, saliva, buccal swabs, and endothelial cells. Thus, the biomarker can also be used for forensic and biomedical applications involving human specimens.
The biomarker applies to the entire age span starting from newborns, e.g. DNAm of cord blood samples correlates with gestational week.
Furthermore, the skin & blood clock confirms the effect of lifestyle and demographic variables on epigenetic aging. Essentially it highlights a significant trend of accelerated epigenetic aging with sub-clinical indicators of poor health.
Conversely, reduced aging rate is correlated with known health-improving features such as physical exercise, fish consumption, high carotenoid levels. As with the other age predictors, the skin & blood clock is also able to predict time to death.
Collectively, these features show that while the skin & blood clock is clearly superior in its performance on skin cells, it crucially retained all the other features that are common to other existing age estimators.”
An introduction to the study highlighted several items:
“Although the skin-blood clock was derived from significantly less samples (~900) than Horvath’s clock (~8000 samples), it was found to more accurately predict chronological age, not only across fibroblasts and skin, but also across blood, buccal and saliva tissue. A potential factor driving this improved accuracy in blood could be related to the approximate 18-fold increase in genomic coverage afforded by using Illumina 450k/850k beadarrays.
It serves as a roadmap for future clock studies, pointing towards the importance of constructing tissue or cell-type specific epigenetic clocks, to more accurately measure biological aging in the given tissue/cell-type, and therefore with the potential to be more informative of disease-risk or the success of disease interventions in the tissue or cell-type of interest.”
This 2018 Chinese study electronically modeled the brain’s circuits to evaluate memory transfer mechanisms:
“During non-rapid-eye-movement (NREM) sleep, thalamo-cortical spindles and hippocampal sharp wave-ripples have been implicated in declarative memory consolidation. Evidence suggests that long-term memory consolidation is coordinated by the generation of:
enabling memory transfer from the hippocampus to the cortex.
Consolidation has also been demonstrated in other brain tasks, such as:
In the acquisition of motor skills, where there is a shift from activity in prefrontal cortex to premotor, posterior parietal, and cerebellar structures; and
In the transfer of conscious to unconscious tasks, where activity in initial unskilled tasks and activity in skilled performance are located in different regions, the so-called ‘scaffolding-storage’ framework.
By separating a neural circuit into a feedforward chain of gating populations and a second chain coupled to the gating chain (graded chain), graded information (i.e. information encoded in firing rate amplitudes) may be faithfully propagated and processed as it flows through the circuit. The neural populations in the gating chain generate pulses, which push populations in the graded chain above threshold, thus allowing information to flow in the graded chain.
In this paper, we will describe how a set of previously learned synapses may in turn be copied to another module with a pulse-gated transmission paradigm that operates internally to the circuit and is independent of the learning process.”
The study has neither been peer-reviewed, nor have the mechanisms been tested in living beings.
“Genome-wide technology has facilitated epigenome-wide association studies (EWAS), permitting ‘hypothesis-free’ examinations in relation to adversity and/or mental health problems. Results of EWAS are in fact conditional on several a priori hypotheses:
EWAS coverage is sufficient for complex psychiatric problems;
Peripheral tissue is meaningful for mental health problems; and
1. CpG sites were chosen as potentially biologically informative based on consultation with a consortium of DNA methylation experts. Selection was, in part, based on data from a number of phenotypes (some medical in nature such as cancer), and thus is not specifically targeted to brain-based, stress-related complex mental health phenotypes.
2. The assumption is often that distinct peripheral tissues are interchangeable and equally suited for biomarker detection, when in fact it is highly probable that peripheral tissues themselves correspond differently to environmental adversity and/or disease state.
3. Analyses result in general statements such as ‘neurodevelopment’ or the ‘immune system’ being involved in the aetiology of a given phenotype. Whether these broad categories play indeed a substantial role in the aetiology of the mental health problem is often hard to determine given the post hoc nature of the interpretation.”
The reviewers mentioned in item #2 the statistical flaw of assuming that measured entities are interchangeable with one another. They didn’t mention that the problem also affected item #1 methodologies of averaging CpG methylation measurements in fixed genomic bins or over defined genomic regions, as discussed in:
The reviewers offered suggestions for reducing the impacts of these three hypotheses. But will doing more of the same, only better, advance science?
Was it too much to ask of researchers whose paychecks and reputations depended on a framework’s paradigm – such as the “biomarker” mentioned a dozen and a half times – to admit the uselessness of gathering data when the framework in which the data operated wasn’t viable? They already knew or should have known this.
“Blood-based EWAS may yield limited information relating to underlying pathological processes for disorders where brain is the primary tissue of interest.”
The truth about complex traits and GWAS added another example of how this framework and many of its paradigms haven’t produced effective explanations of “the aetiology of the mental health problem”
“The most investigated candidate gene hypotheses of schizophrenia are not well supported by genome-wide association studies, and it is likely that this will be the case for other complex traits as well.”
Researchers need to reevaluate their framework if they want to make a difference in their fields. Recasting GWAS as EWAS won’t make it more effective.
This 2018 Loma Linda review subject was gestational hypoxia:
“Of all the stresses to which the fetus and newborn infant are subjected, perhaps the most important and clinically relevant is that of hypoxia. This review explores the impact of gestational hypoxia on maternal health and fetal development, and epigenetic mechanisms of developmental plasticity with emphasis on the uteroplacental circulation, heart development, cerebral circulation, pulmonary development, and the hypothalamic-pituitary-adrenal axis and adipose tissue.
An understanding of the specific hypoxia-induced environmental and epigenetic adaptations linked to specific organ systems will enhance the development of target-specific inhibition of DNA methylation, histone modifications, and noncoding RNAs that underlie hypoxia-induced phenotypicprogramming of disease vulnerability later in life.
A potential stumbling block to these efforts, however, relates to timing of the intervention. The greatest potential effect would be accomplished at the critical period in development for which the genomic plasticity is at its peak, thus ameliorating the influence of hypoxia or other stressors.
With future developments, it may even become possible to intervene before conception, before the genetic determinants of the risk of developing programmed disease are established.”
Table 3 “Antenatal hypoxia and developmental plasticity” column titles were Species | Offspring Phenotypes of Disorders and Diseases | Reference Nos.
This review was really an ebook, with 94 pages and 1,172 citations in the pdf file. As I did with Faith-tainted epigenetics, I read it with caution toward recognizing 1) the influence of the sponsor’s biases, 2) any directed narrative that ignored evidence contradicting the narrative, and 3) any storytelling.
One review topic that was misconstrued was transgenerational epigenetic inheritance of hypoxic effects. The “transgenerational” term was used inappropriately by several of the citations, and no cited study provided evidence for gestational hypoxic effects through the F2 grandchild and F3 great-grandchild generations.
“One substance that fetuses are frequently exposed to is caffeine, which is a non-selective adenosine receptor antagonist. We discovered that in utero alteration in adenosine action leads to adverse effects on embryonic and adult murine hearts. We find that cardiac A1ARs [a type of adenosine receptor] protect the embryo from in utero hypoxic stress, a condition that causes an increase in adenosine levels.
After birth in mice, we observed that in utero caffeine exposure leads to abnormal cardiac function and morphology in adults, including an impaired response to β-adrenergic stimulation. Recently, we observed that in utero caffeine exposure induces transgenerational effects on cardiac morphology, function, and gene expression.”
Why was this review and its studies omitted? It was on target for both gestational hypoxia and transgenerational epigenetic inheritance of hypoxic effects!
It was alright to review smoking, cocaine, methamphetamine, etc., but the most prevalent drug addiction – caffeine – couldn’t be a review topic?
The Loma Linda review covered a lot, but I had a quick trigger due to the sponsor’s bias. I started to lose “faith” in the reviewers after reading the citation for the review’s last sentence that didn’t support the statement.
My “faith” disappeared after not understanding why a few topics were misconstrued and omitted. Why do researchers and sponsors ignore, misrepresent, and not continue experiments through the F3 generation to produce evidence for and against transgenerational epigenetic inheritance? Where was the will to follow evidence trails regardless of socially acceptable beverage norms?
The review acquired the taint of storytelling with the reviewers’ assertion:
“..timing of the intervention. The greatest potential effect would be accomplished at the critical period in development for which the genomic plasticity is at its peak, thus ameliorating the influence of hypoxia or other stressors.”
Contradictory evidence was in the omitted caffeine study’s graphic above which described two gestational critical periods where an “intervention” had opposite effects, all of which were harmful to the current fetus’ development and/or to following generations. Widening the PubMed link’s search parameters to “caffeine hypoxia” and “caffeine pregnancy” returned links to human early life studies that used caffeine in interventions, ignoring possible adverse effects on future generations.
This is my final curation of any paper sponsored by this institution.
“The proposed epigenetic clock theory of ageing views biological ageing as an unintended consequence of both developmental programmes and maintenance programmes, the molecular footprints of which give rise to DNAm [DNA methylation] age estimators.
It is best to interpret epigenetic age estimates as a higher-order property of a large number of CpGs much in the same way that the temperature of a gas is a higher-order property that reflects the average kinetic energy of the underlying molecules. This interpretation does not imply that DNAm age simply measures entropy across the entire genome.
To date, the most effective in vitro intervention against epigenetic ageing is achieved through expression of Yamanaka factors, which convert somatic cells into pluripotent stem cells, thereby completely resetting the epigenetic clock. In vivo, haematopoietic stem cell therapy resets the epigenetic age of blood of the recipient to that of the donor.
Future epidemiological studies should consider other sources of DNA (for example, buccal cells), because more powerful estimates of organismal age can be obtained by evaluating multiple tissues. Other types of epigenetic modifications such as adenine methylation or histone modifications may lend themselves for developing epigenetic age estimators.”
I’ve previously curated four other papers cited in this review:
The challenge is: do you want your quality of life to be under or over this curve?
What are you doing to reverse epigenetic processes and realize what you want? Do you have ideas and/or behaviors that interfere with taking constructive actions to change your phenotype?
If you aren’t doing anything, are you honest with yourself about the personal roots of beliefs in fate/feelings of helplessness? Do beliefs in technological or divine interventions provide justifications for inactions?
“All brain regions have similar DNAm ages in subjects younger than 80, but brain region becomes an increasingly significant determinant of age acceleration in older subjects. The cerebellum has a lower epigenetic age than other brain regions in older subjects.
To study age acceleration effects in non-brain tissues as well, we profiled a total of 30 tissues of a 112 year old woman. The cerebellum exhibited the lowest (negative) age acceleration effect compared to the remaining 29 other regions. In contrast, bone, bone marrow, and blood exhibit relatively older DNAm ages.”
“While the epigenetic age of blood has been shown to relate to biological age, the same cannot yet be said about brain tissue.
Cellular heterogeneity may confound these results since the cerebellum involves distinct cell types.
This cross-sectional analysis does not lend itself for dissecting cause and effect relationships.”
The study didn’t determine why the cerebellum was relatively younger. Some hypotheses were:
“Our findings suggest that cerebellar DNA is epigenetically more stable and requires less ‘maintenance work.’
The cerebellum has a lower metabolic rate than cortex.
It has far fewer mitochondrial DNA (mtDNA) deletions than cortex especially in older subjects, and it accumulates less oxidative damage to both mtDNA and nuclear DNA than does cortex.”