This 2018 presentation by the founder of the epigenetic clock method described the state of the art up through July 2018. The webinar was given on the release day of The epigenetic clock now includes skin study.
This 2018 Austrian human study subject was various associations of prenatal testosterone levels to fetal development:
“The available evidence suggests, albeit not conclusively, that prenatal testosterone levels may be one cause for the association of sexual orientation with handedness. Associations among women were consistent with predictions of the Geschwind–Galaburda theory (GGT), whereas those among men were consistent with predictions of the callosal hypothesis. However, research on the associations between sexual orientation and handedness appears to be compromised by various methodological and interpretational problems which need to be overcome to arrive at a clearer picture.
The GGT posits that high prenatal testosterone levels cause a delay in the fetal development of the left cerebral hemisphere which results in a right-hemisphere dominance and hence in a tendency for left-handedness. According to the GGT, high prenatal testosterone levels entail not only a masculinization of the female fetus, but also a feminization of the male fetus (contrary to neurohormonal theory). Overall, the male fetus is subjected to higher levels of intrauterine testosterone than the female fetus. The GGT is thus consistent with the higher prevalence of left-handedness among men than among women.
The callosal hypothesis applies to men only and assumes, in line with neurohormonal theory, that low prenatal testosterone levels are associated with later homosexuality. According to the CH, high prenatal testosterone enhances processes of cerebral lateralization through mechanisms of axonal pruning, thereby resulting in stronger left-hemisphere dominance and a smaller corpus callosum. Consistent with this, women have a larger corpus callosum than men.”
The study’s Limitations section included the following:
- “Limitations of the current study pertain to the self-report nature of our data. Behavioral data may provide differing results from those obtained here.
- Assessment of sexual orientation relied on a single-item measure. Utilization of rating scales (e.g., the Kinsey Sexual Orientation Scale) or of multi-item scales, and assessing different components of sexual orientation, would have allowed for a more fine-grained analysis and for a cross-validation of sexual orientation ratings with sexual attraction.
- Albeit both our samples were large, the proportions of bisexual and homosexual individuals were, expectedly, only small, as were effects of lateral preferences. Thus, in analysis we could not differentiate bisexual from homosexual individuals. Bisexual and homosexual individuals may differ with regard to the distribution of lateral preferences.
- Some effect tests in this study have been underpowered. Independent replications with even larger samples are still needed.”
The largest unstated limitation was no fetal measurements. When a fetus’ epigenetic responses and adaptations aren’t considered, not only can the two competing hypotheses not be adequately compared, but causes for the studied phenotypic programming and other later-life effects will also be missed.
https://link.springer.com/article/10.1007/s10508-018-1346-9 “Associations of Bisexuality and Homosexuality with Handedness and Footedness: A Latent Variable Analysis Approach”
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:
- 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;
- The quantity of β-glucan and the molecular weight at expected consumption levels (∼3 g day) play a role in cholesterol reduction; and
- 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:
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”
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.”
“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.”
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:
- The current epigenetic clock which uses 391 CpGs “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” per The epigenetic clock now includes skin.
- The 2016 Using an epigenetic clock with older adults which used a frailty index of 34 parameters to establish “clinically relevant aging-related phenotypes.”
- The 2015 A study of biological aging in young adults with limited findings which used 10 biologic age markers of subjects at age 38 to find that their biological ages ranged from 28 to 61.
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”
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?”