Eat your oats

Here’s some motivation to replenish your oats supply.

From a 2013 Canadian human review:

“Review of human studies investigating the post-prandial blood-glucose lowering ability of oat and barley food products” https://www.nature.com/articles/ejcn201325

“Change in glycaemic response (expressed as incremental area under the post-prandial blood-glucose curve) was greater for intact grains than for processed foods. For processed foods, glycaemic response was more strongly related to the β-glucan dose alone than to the ratio of β-glucan to the available carbohydrate.”

The review found that people don’t have to eat a lot of carbohydrates to get the glycemic-response benefits of β-glucan. Also, eating ~3 grams of β-glucan in whole oats and barley will deliver the same glycemic-response benefits as eating ~4 grams of β-glucan in processed oats and barley.

The glycemic index used in the review is otherwise a very flawed measure, however. It doesn’t help healthy people to rank food desirability using an unhealthy-white-bread standard.


The reviewer somewhat redeemed herself by participating in a 2018 review:

“Processing of oat: the impact on oat’s cholesterol lowering effect” https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5885279/

“For a similar dose of β-glucan:

  1. Liquid oat-based foods seem to give more consistent, but moderate reductions in cholesterol than semi-solid or solid foods where the results are more variable;
  2. The quantity of β-glucan and the molecular weight at expected consumption levels (∼3 g day) play a role in cholesterol reduction; and
  3. Unrefined β-glucan-rich oat-based foods (where some of the plant tissue remains intact) often appear more efficient at lowering cholesterol than purified β-glucan added as an ingredient.”

The review’s sections 3. Degree of processing and functionality and 4. Synergistic action of oat constituents were informative:

“Both in vitro and in vivo studies clearly demonstrated the beneficial effect of oat on cholesterolemia, which is unlikely to be due exclusively to β-glucan, but rather to a combined and synergetic action of several oat compounds acting together to reduce blood cholesterol levels.”


Another use of β-glucan is to improve immune response. Here’s a 2016 Netherlands study where the researchers used β-glucan to get a dozen people well after making them sick with lipopolysaccharide as is often done in animal studies:

β-Glucan Reverses the Epigenetic State of LPS-Induced Immunological Tolerance” https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5927328/

“The innate immune “training stimulus” β-glucan can reverse macrophage tolerance ex vivo.”

I’ve curated other research on β-glucan’s immune-response benefits in:

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Adverse epigenetic effects of prenatal and perinatal anesthesia

This 2018 Chinese animal review subject was prenatal and perinatal anesthesia’s adverse epigenetic effects on a fetus/neonate:

“Accumulating evidence from rodent and primate studies has demonstrated that in utero or neonatal exposure to commonly used inhaled and intravenous general anesthetics is associated with neural degeneration and subsequent neurocognitive impairments, manifested in learning and memory disabilities.

So far, conflicting data exist about the effect of anesthetic agents on neurodevelopment in humans and no definite conclusion has been given yet.”

The inhibitors in the above graphic counter anesthesia’s effects on the fetus/neonate, summarized as:

“Epigenetic targeting of DNA methyltransferases and/or histone deacetylases may have some therapeutic value.”


Are there any physicians who take into consideration possible epigenetic alterations of a newborn’s chromatin structure and gene expression when they administer anesthesia to a human mother during childbirth?

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6079265/ “Epigenetic Alterations in Anesthesia-Induced Neurotoxicity in the Developing Brain”

A top-down view of biological goal-directed mechanisms

This 2016 US/Italy article was written from the perspective of regenerative bioengineering:

“Higher levels beyond the molecular can have their own unique dynamics that offer better (e.g. more parsimonious and potent) explanatory power than models made at lower levels. Biological systems may be best amenable to models that include information structures (organ shape, size, topological arrangements and complex anatomical metrics) not defined at the molecular or cellular level but nevertheless serving as the most causally potent ‘knobs’ regulating the large-scale outcomes.

Top-down models can be as quantitative as the familiar bottom-up systems biology examples, but they are formulated in terms of building blocks that cannot be defined at the level of gene expression and treat those elements as bona fide causal agents (which can be manipulated by interventions and optimization techniques). The near-impossibility of determining which low-level components must be tweaked in order to achieve a specific system-level outcome is a problem that plagues most complex systems.

The current paradigm in biology of exclusively tracking physical measurable and ignoring internal representation and information structures in patterning contexts quite resemble the ultimately unsuccessful behaviourist programme in psychology and neuroscience. For example, even if stem cell biologists knew how to make any desired cell type from an undifferentiated progenitor, the task of assembling them into a limb would be quite intractable.

The current state of the art in the field of developmental bioelectricity is that it is known, at the cellular level, how resting potentials are transduced into downstream gene cascades, as well as which transcriptional and epigenetic targets are sensitive to change in developmental bioelectrical signals. What is largely missing however is a quantitative understanding of how the global dynamics of bioelectric circuits make decisions that orchestrate large numbers of individual cells, spread out over considerable anatomical distances, towards specific pattern outcomes.”


Regenerative research is gathering evidence for goal-directed memory and learning that doesn’t meet current definitions. For example:

salamander

“A tail grafted to the flank of a salamander slowly remodels to a limb, a structure more appropriate for its new location, illustrating shape homeostasis towards a normal amphibian body plan. Even the tail tip cells (in red) slowly become fingers, showing that the remodelling is not driven by only local information.”

The reviewers compared their findings to several existing research and real-world-operations domains. Other models may also benefit from the concepts of:

“Quantitative, predictive, mechanistic understanding of goal-directed morphogenesis.”


I came across this article as a result of its citation in The Body Electric blog post.

“Levin drops a hint that there are photo-sensitive drugs that can control ion gates that can be used to translate a projected geometric image into a pattern of membrane potentials. He argues that the patterns encode ‘blueprints’ rather than a ‘construction manual’ based on the fact that the program is adaptive in the face of physical barriers and disruptions.”

https://royalsocietypublishing.org/doi/full/10.1098/rsif.2016.0555 “Top-down models in biology: explanation and control of complex living systems above the molecular level”

Epigenetic clock statistics and methods

This 2018 Chinese study was a series of statistical and methodological counter-arguments to a previous epigenetic clock study finding that:

“Only [CpG] sites mapping to the ELOVL2 promoter constitute cell and tissue-type independent aDMPs [age-associated differentially methylated positions].”

The study used external data sets and the newer epigenetic clock’s fibroblast data in its analyses to find:

“While we agree that specific sites mapping to ELOVL2 are special aDMPs in the sense that their effect sizes are particularly large across a number of different tissue-types, our analysis suggests that most aDMPs are valid across multiple different tissue types, suggesting that shared aDMPs are common.”

The details of each of the study’s counter-arguments were compelling. For example:

“We analyzed Illumina 850k data from an EWAS profiling blood, buccal and cervical samples from a common set of 263 women. Because blood is a complex mixture of many immune-cell subtypes, and buccal and cervical samples are highly contaminated by immune cells, we identified aDMPs in each tissue after adjustment for batch effects and cell-type heterogeneity.

Using either an FDR [false discovery rate] < 0.05 or Bonferroni adjusted P-value < 0.05 thresholds, the overlap of aDMPs between the 3 tissues was highly significant, mimicking the result obtained on blood cell subtypes. We observed a total of 2200 aDMPs in common between blood, buccal and cervix, an overlap which cannot be explained by random chance.”

The study’s Discussion section provided qualifications and limitations such as:

“It is important to point out that even if age-associated DNAm changes are widespread across the genome, downstream functional effects may be rare. While specific aDMPs may be shared between tissue-types, it is only in specific tissues or cell-types that any associated functional deregulation may be of biological and clinical significance.

https://www.aging-us.com/article/101666/text “Cell and tissue type independent age-associated DNA methylation changes are not rare but common”


The November 2018 issue of Aging also contained other articles of interest:

https://www.aging-us.com/article/101626/text “Accelerated DNA methylation age and the use of antihypertensive medication among older adults”

“DNAmAge and AA [age acceleration] may not be able to capture the preventive effects of AHMs [antihypertensive medications] that reduce cardiovascular risks and mortality.”

https://www.aging-us.com/article/101633/text “Azithromycin and Roxithromycin define a new family of senolytic drugs that target senescent human fibroblasts”

“Azithromycin preferentially targets senescent cells, removing approximately 97% of them with great efficiency. This represents a near 25-fold reduction in senescent cells.”

https://www.aging-us.com/article/101647/text “Disease or not, aging is easily treatable”

“Aging consists of progression from (pre)-pre-diseases (early aging) to diseases (late aging associated with functional decline). Aging is NOT a risk factor for these diseases, as aging consists of these diseases: aging and diseases are inseparable.”