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.”

Chronological age by itself is an outdated clinical measurement

This 2018 editorial in the New England Journal of Medicine concerned a clinical trial of an osteoporosis treatment:

“When measurement of bone density was first introduced 25 years ago, absolute bone mineral density (g per square centimeter) was considered as too onerous for clinicians to understand. Ultimately, these events led to a treatment gap in patients who had strong clinical risk factors for an osteoporotic fracture (particularly age) but had T scores in the osteopenic range.

The average age of the participants in the current trial was approximately 3.5 years older than that in the Fracture Intervention Trial. Owing to the interaction between age and bone mineral density, the results of the current trial should not be extrapolated to younger postmenopausal women (50 to 64 years of age) with osteopenia.

This trial reminds us that risk assessment and treatment decisions go well beyond bone mineral density and should focus particularly on age and a history of previous fractures.”


The time has passed for physicians and clinicians to consider only chronological age when evaluating a patient’s clinical age. More effective human age measurements covering the entire person as well as their body’s components include:

F2.large


This editorial provided the history of how a still-generally-accepted set of diagnostic measurements were selected for their relative convenience instead of chosen for their efficacy. Add chronological age to such ineffective measurements.

Let’s recognize better aging and diagnostic measurements, then incorporate them. How else will we advance past this uninformative averaging and unhelpful recommendation based on chronological age?

“The average age of the participants in the current trial was approximately 3.5 years older than that in the Fracture Intervention Trial. Owing to the interaction between age and bone mineral density, the results of the current trial should not be extrapolated to younger postmenopausal women (50 to 64 years of age) with osteopenia.”

https://www.nejm.org/doi/pdf/10.1056/NEJMe1812434 “A Not-So-New Treatment for Old Bones”

A slanted view of the epigenetic clock

The founder of the epigenetic clock technique was interviewed for MIT Technology Review:

“We need to find ways to keep people healthier longer,” he says. He hopes that refinements to his clock will soon make it precise enough to reflect changes in lifestyle and behavior.”


The journalist attempted to dumb the subject down “for the rest of us” with distortions such as the headline. The varying correlation of epigenetic age to chronological age was somewhat better reported in the story:

“The epigenetic clock is more accurate the younger a person is. It’s especially inaccurate for the very old.”

The journalist inappropriately used luck as a synonym for randomness/stochasticity:

“He estimates that about 40% of the ticking rate is determined by genetic inheritance, and the rest by lifestyle and luck.”

A third example of less-than-straightforward journalism started with:

“Such personalization raises questions about fairness. If your epigenetic clock is ticking faster through no fault of your own..”

Were MIT Technology Review readers unable to comprehend a straightforward story on the epigenetic clock? What was the purpose of slants and distortions in an introductory article?

https://www.technologyreview.com/s/612256/want-to-know-when-youre-going-to-die/ “Want to know when you’re going to die?”

Reductionism vs. reductionism

This 2004 essay by an evolutionary biologist reviewed his field’s direction in the current century:

“Science is impelled by two main factors, technological advance and a guiding vision (overview). A properly balanced relationship between the two is key to the successful development of a science.

Without the proper technological advances the road ahead is blocked. Without a guiding vision there is no road ahead; the science becomes an engineering discipline, concerned with temporal practical problems.

Empirical reductionism is in essence methodological; it is simply a mode of analysis, the dissection of a biological entity or system into its constituent parts in order better to understand it. Empirical reductionism makes no assumptions about the fundamental nature, an ultimate understanding, of living things.

Fundamentalist reductionism (the reductionism of 19th century classical physics), on the other hand, is in essence metaphysical. It is ipso facto a statement about the nature of the world: living systems (like all else) can be completely understood in terms of the properties of their constituent parts.

This is a view that flies in the face of what classically trained biologists tended to take for granted, the notion of emergent properties. Whereas emergence seems to be required to explain numerous biological phenomena, fundamentalist reductionism flatly denies its existence: in all cases the whole is no more than the sum of its parts.”

Regarding cellular evolution:

“Modern concepts of cellular evolution are effectively petrified versions of 19th century speculations. Try to imagine a biology released from the intellectual shackles of mechanism, reductionism, and determinism.

Evolution, as a complex dynamic process, will encounter critical points in its course, junctures that result in phase transitions (drastic changes in the character of the system as a whole). Human language is a development that has set Homo sapiens worlds apart from its otherwise very close primate relatives, adding new dimensions to the phase space within which human evolution occurs. Another good critical-point candidate is the advent of (eucaryotic) multicellularity.

Nowhere in thinking about a symbiotic origin of the eucaryotic cell has consideration been given to the fact that the process as envisioned would involve radical change in the designs of the cells involved. You can’t just tear cell designs apart and willy-nilly construct a new type of design from the parts.

The organization of the mitochondrial endosymbiont is radically changed during its evolution, but that change is a degeneration to a far simpler “cell-like” design. The mitochondrial design could never evolve back to the level of complexity that its free-living [bacterial] ancestor had.

A common thread that links language and multicellularity is communication (interaction at a distance). In each case a complex, sophisticated network of interactions forms the medium within which the new level of organization (entities) comes into existence.

Our experience with variation and selection in the modern context does not begin to prepare us for understanding what happened when cellular evolution was in its very early, rough-and-tumble phase(s) of spewing forth novelty. Cellular evolution began in a highly multiplex fashion, from many initial independent ancestral starting points, not just a single one.”

https://mmbr.asm.org/content/68/2/173 “A New Biology for a New Century”


I came across this review by it being referenced in this researcher’s blog post:

Chinese Longevity Herb
I often don’t agree with him, but I subscribe to his blog because it’s interesting.