Going off the rails with the biomarker paradigm

This 2018 US government rodent study used extreme dosages to achieve its directed goals of demonizing nicotine and extolling the biomarker paradigm:

“This study examined whether adolescent nicotine exposure alters adult hippocampus-dependent learning, involving persistent changes in hippocampal DNA methylation and if choline, a dietary methyl donor, would reverse and mitigate these alterations.

Mice were chronically treated with nicotine (12.6mg/kg/day) starting at post-natal day 23 (pre-adolescent), p38 (late adolescent), or p54 (adult) for 12 days followed by a 30-day period during which they consumed either standard chow or chow supplemented with choline (9g/kg).

Our gene expression analyses support this model and point to two particular genes involved in chromatin remodeling, Smarca2 and Bahcc1. Both Smarca2 and Bahcc1 showed a similar inverse correlation pattern between promoter methylation and gene expression.

Our findings support a role for epigenetic modification of hippocampal chromatin remodeling genes in long-term learning deficits induced by adolescent nicotine and their amelioration by dietary choline supplementation.”


Let’s use the average weight of a US adult male – published by the US Centers for Disease Control as 88.8 kg – to compare the study’s dosages with human equivalents:

  1. Nicotine at “12.6mg/kg/day” x 88.8 kg = 1119 mg. The estimated lower limit of a lethal dose of nicotine has been reported as between 500 and 1000 mg!
  2. Choline at “9g/kg” x 88.8 kg = 799 g. The US National Institutes of Health published the Tolerable Upper Intake Levels for Choline as 3.5 g!!

The human-equivalent dosage of nicotine used in this study would probably kill an adult human. Who knows what exceeding the choline “Tolerable Upper Intake Level” BY 228 TIMES would do to an adult human.

Is the main purpose of animal studies to help humans? Or is it to promote an agenda?


A funding source of this study was National Institute on Drug Abuse (NIDA) Identification of Biomarkers for Nicotine Addiction award (T-DA-1002 MG). Has the biomarker paradigm been institutionalized to the point where research proposals that don’t have biomarkers as goals aren’t funded?

https://www.sciencedirect.com/science/article/pii/S107474271830193X “Choline ameliorates adult learning deficits and reverses epigenetic modification of chromatin remodeling factors related to adolescent nicotine exposure” (not freely available)

Advertisements

Measuring epigenetic changes at a single-cell level

This 2018 Canadian cell study described the development of a single-cell protocol to:

“Profile primitive hematopoietic cells of mouse and human origin to identify epigenetically distinct subpopulations. Deep sampling of the CpG content of individual HSCs allowed for the near complete reconstitution of regulatory states from epigenetically defined subpopulations of HSCs and revealed a high level of redundancy of CpG methylation states within these phenotypically defined hematopoietic cell types.

Hematopoietic stem cells (HSCs) are functionally defined cells that display evidence of extensive self-renewal of their ability to generate mature blood cells for the lifetime of the organism and following transplantation into myelosuppressed permissive hosts. Most of the epigenetic measurements underpinning these observations represent consensus values experimentally derived from thousands of cells partially enriched in HSCs or their progeny, thus failing to discern distinct epigenetic states within HSCs.

Current analytical strategies for single-cell DNA methylation measurements average DNA methylation in fixed genomic bins or over defined genomic regions. However, inference across cells (as well as sequence context) assumes homogeneity across cells, which is at cross-purposes with the generation of single-cell molecular measurements through the potential to mask rare subpopulations.

We identified donor as a significant source of consistent epigenetic heterogeneity, which was reduced but not eliminated by correcting for personal genetic variants. This observation is consistent with previous reports that showed genetic diversity as related to but not accountable for all DNA methylation differences and suggests that in utero environmental differences may be encoded within the HSC compartment.”


The study advanced science not only by measuring single-CpG methylation within each HSC but also by producing another data point “that in utero environmental differences may be encoded within the HSC compartment.”

The paragraph with “assumes homogeneity across cells” bold text provided another example of the statistical analysis flaw that gives individually inapplicable results per Group statistics don’t necessarily describe an individual. The above graphic of human hematopoietic phenotypes demonstrated that the researchers have potentially solved this problem by measuring individual cells.

The researchers discussed another aspect of the study that’s similar to the epigenetic clock methodology:

“Phenotype-specific methylation signatures are characterized by extensive redundancy such that distinct epigenetic states can be accurately described by only a small fraction of single-CpG methylation states. In support of such a notion, the unique components of a DNA methylation “age” signature are contained in ∼353 CpGs sites, presumably representing a random sample of a total age signature that involves many more sites not detected using the reduced representation strategies from which these signatures have been derived.”

Also, in The epigenetic clock theory of aging the originator of the epigenetic clock characterized HSCs as an effective intervention against epigenetic aging:

“In vivo, haematopoietic stem cell therapy resets the epigenetic age of blood of the recipient to that of the donor.”

https://www.cell.com/stem-cell-reports/article/S2213-6711(18)30308-4/fulltext “High-Resolution Single-Cell DNA Methylation Measurements Reveal Epigenetically Distinct Hematopoietic Stem Cell Subpopulations”

Epigenetic effects of breast cancer treatments

This 2018 UC San Diego review subject was the interplay between breast cancer treatments and their effects on aging:

“Although current breast cancer treatments are largely successful in producing cancer remission and extending lifespan, there is concern that these treatments may have long lasting detrimental effects on cancer survivors, in part, through their impact on non-tumor cells. It is unclear whether breast cancer and/or its treatments are associated with an accelerated aging phenotype.

In this review, we have highlighted five of nine previously described cellular hallmarks of aging that have been described in the context of cytotoxic breast cancer treatments:

  1. Telomere attrition;
  2. Mitochondrial dysfunction;
  3. Genomic instability;
  4. Epigenetic alterations; and
  5. Cellular senescence.”


The review was full of caveats weakening the above graphic’s associations. To their credit, these reviewers at least presented some of the contrary evidence, and didn’t continue on with a directed narrative as many other reviewers are prone to do:

  1. “Telomere attrition – Blood TL [telomere length] was not associated with chemotherapy in three out of four studies;
  2. Mitochondrial dysfunction – How cancer therapies affect cellular energetics as they relate to rate of aging is unclear;
  3. Genomic instability – Potentially contributing to accelerated aging;
  4. Epigenetic alterations – Although some of the key regulators of these processes have begun to be identified, including DNA and histone methylases and demethylases, histone acetylases and de-acetylases and chromatin remodelers, how they regulate the changes in aging through alteration of global transcriptional programs, remains to be elucidated; and
  5. Cellular senescence – Dysregulated pathways can be targeted by cytotoxic chemotherapies, resulting in preferential cell death of tumor cells, but how these treatments also affect normal cells with intact pathways is unclear.”

https://www.sciencedirect.com/science/article/pii/S1879406818301176 “Breast cancer treatment and its effects on aging” (not freely available)


The originator of the epigenetic clock methodology was a coauthor of the review. Only one of his works was cited in the Epigenetic alterations subsection:

https://link.springer.com/article/10.1007%2Fs10549-017-4218-4 “DNA methylation age is elevated in breast tissue of healthy women”

This freely-available 2017 study quoted below highlighted that epigenetic clock measurements as originally designed were tissue-specific:

“To our knowledge, this is the first study to demonstrate that breast tissue epigenetic age exceeds that of blood tissue in healthy female donors. In addition to validating our earlier finding of age elevation in breast tissue, we further demonstrate that the magnitude of the difference between epigenetic age of breast and blood is highest in the youngest women in our study (age 20–30 years) and gradually diminishes with advancing age. As women approach the age of the menopausal transition, we found that the epigenetic of age of blood approaches that of the breast.”

Additional caution was justified in both interpreting age measurements and extending them into “cellular hallmarks” when the tissue contained varying cell types:

“Our studies were performed on whole breast tissue. Diverse types of cells make up whole breast tissue, with the majority of cells being adipocytes. Other types of cells include epithelial cells, cuboidal cells, myoepithelial cells, fibroblasts, inflammatory cells, vascular endothelial cells, preadipocytes, and adipose tissue macrophages.

This raises the possibility that the magnitude of the effects we observe, of breast tissue DNAm age being greater than other tissues, might be an underestimation, since it is possible that not all of the cells of the heterogenous sample have experienced this effect. Since it is difficult to extract DNA from adipose tissue, we suspect that the majority of DNA extracted from our whole breast tissues was from epithelial and myoepithelial cells.”

Allergies and epigenetic histone modifications

This 2018 German review provided short summaries of 44 studies on the contribution of histone modifications to allergies. An overall summary of their search results was:

“There are at least two levels at which the role of histone modifications is manifested.

  • One is the regulation of cells that contribute to the allergic inflammation (T cells and macrophages) and those that participate in airway remodeling.
  • The other is the direct association between histone modifications and allergic phenotypes.

Inhibitors of histone-modifying enzymes may potentially be used as anti-allergic drugs. Furthermore, epigenetic patterns may provide novel tools in the diagnosis of allergic disorders.”


This type of search is what’s expected of researchers who will perform either:

  • A meta-analysis of studies selected from the search results; or
  • Their own study.

These reviewers didn’t indicate that they were proceeding along either path.

The review was fine for the purpose of presenting current studies of the subject. But the review was just the preparatory stage of research.

https://aacijournal.biomedcentral.com/articles/10.1186/s13223-018-0259-4 “Histone modifications and their role in epigenetics of atopy and allergic diseases”

Epigenetic variations in metabolism

This 2018 German review was comprehensive for its subject, epigenetic control of variation and stochasticity in metabolic disease. I’ll focus on one aspect, phenotypic variation:

“Phenotypic [Mendelian] variation can result both from gain- and loss-of-function mutations. Because of the extreme interconnectivity of cell regulatory networks, even at the cellular level, predicting the impact of a sequence variant is difficult as the resultant variation acts:

  • In the context of all other variants and
  • Their potential additive, synergistic and antagonistic interactions.

This phenomenon is known as epistasis.

∼98.5% of our genome is non-protein-coding: it is pervasively transcribed, and its transcripts can support regulatory function. Among the best functionally characterized non-coding RNAs (ncRNAs) arising from these sequences are microRNAs (miRNAs)

Environmental [non-Mendelian] variation or ‘stimuli’ occurring during critical windows of susceptibility can elicit lifelong alterations in an individual’s phenotype. Intergenerational metabolic reprogramming [in fruit flies] results from global alterations in chromatin state integrity, particularly from reduced H3K27me3 and H3K9me3 [histone] domains.

The broad variation of fingerprints in humans is thought to depend to a large degree on stochastic variation in mechanical forces. These clear examples of inducible multi-stable or stochastic variation highlight how little we know about the landscape of potential phenotypic variation itself.

Consensus estimates of heritability for obesity and T2D are ∼70% and ∼35% respectively. The remaining, unexplained component is known to involve gene–environment interactions as well as non-Mendelian players.”


Although the above graphic displays transgenerational inheritance for humans, the reviewers didn’t cite any human studies that adequately demonstrated causes for and effects of transgenerational epigenetic inheritance.

I’ve read the cited Swedish and Dutch studies. Their designs, methods, and “correlate with” / “was associated with” results didn’t provide incontrovertible evidence from the F0 great-grandparents, F1 grandparents, F2 parents, and F3 children. It’s necessary to thoroughly study each generation to confirm definitive transgenerational epigenetic inheritance causes and effects.

As noted in How to hijack science: Ignore its intent and focus on the 0.0001%, there aren’t any such published studies to cite. Researchers urgently need to do this human research, and stop using these poor substitutes [1] to pretend there are already adequately evidenced transgenerational epigenetic inheritance human results.

I downgraded the review for treating research of this and other subjects as faits accomplis. It’s opposite ends of the evidential spectrum to state “how little we know about the landscape of potential phenotypic variation,” and in the same review, speciously extrapolate animal experiments into putative human results.

https://www.sciencedirect.com/science/article/pii/S2212877818301984 “Epigenetic control of variation and stochasticity in metabolic disease”


[1] As an example of the poor substitutes for evidence, a researcher referred me to the 2013 “Transgenerational effects of prenatal exposure to the 1944–45 Dutch famine” which is freely available at https://obgyn.onlinelibrary.wiley.com/doi/full/10.1111/1471-0528.12136 as a study finding human transgenerational epigenetic inheritance.

The methods section showed:

  • The study’s non-statistical data was almost all self-reported by a self-selected sample of the F2 grandchildren, average age 37.
  • No detailed physical measurements or samples were taken of them, or of their F1 parents, or of their F0 grandparents, all of which are required as baselines for any transgenerational epigenetic inheritance.
  • No detailed physical measurements or samples were taken of their F3 children, which is the generation that may provide evidence for transgenerational findings if the previous generations also have detailed physical baselines.

The study’s researchers drew enough participants (360) such that their statistics package allowed them to impute and assume into existence a LOT of data. But the scientific method constrained them to make factual statements of what the evidence actually showed. They admitted:

“In conclusion, we did not find a transgenerational effect of prenatal famine exposure on the health of grandchildren in this study.”

Yet this study is somehow cited for evidence of human transgenerational epigenetically inherited causes and effects.

Group statistics don’t necessarily describe an individual

I’m curating this 2018 UC Berkeley/Drexel/Netherlands analysis of human studies via its press coverage. The authors:

“Collaborated to analyze data on hundreds of adults – some mentally or physically sound, others suffering from various conditions such as depression, anxiety, or post-traumatic stress disorder. Participants had completed surveys about their mental health and had their heart rates monitored via electrocardiogram.

Researchers used the data to conduct six different experiments. They sought to find out whether the conclusions of each study would successfully apply to participants individually.

One study that focused on how frequently depression sufferers reported feeling worried. Results tallied from the pool of participants showed that depressed people worry a significant amount.

But when the analysis was applied individually, the results were all over the map. Some participants worried hardly at all, while others were notably beyond the group average.

Another experiment that centered around the link between fear and avoidance showed a strong correlation when measured as a group. Yet a significant number of participants who experienced fear had no issues with avoiding various activities.

Across all six experiments, the authors could not show that what was concluded for the group applied to most individuals.”


Other studies such as the below have addressed problems with statistical analysis techniques. The issues aren’t limited to human studies:

The current study highlighted the fact that people aren’t interchangeable. Assuming ergodicity is a statistical analysis flaw that produces individually inapplicable results for many measurements of fruit flies, cells, humans, you name the organism.

When this presumption makes a study’s statistics useless for an individual, researchers can’t cure the analysis by invoking an “individual differences” meme. Neither is the flaw fixed by spinning a tale about “This is how we can truly personalize medicine.” The current study needed to provide evidence for its proposed solution.


Regarding worrying, Dr. Arthur Janov said it best as I quoted in How well can catastrophes be predicted?:

“Worrying is not a problem, it is the symptom of something that is occurring physiologically within the brain. What causes the worrying is the problem.

The constant worry is anticipating catastrophe. But what we don’t realize is that the catastrophe already has happened; we simply have no access to it.

We are actually worried about the past, not the future.”

http://www.pnas.org/content/early/2018/06/15/1711978115 “Lack of group-to-individual generalizability is a threat to human subjects research” (not freely available)

Preventing prostate cancer with a broccoli sprouts diet

This 2018 Oregon rodent study fed a 15% broccoli sprout diet beginning at four weeks of age to a mouse strain with a near-100% chance of developing prostate cancer:

“Broccoli sprouts reduced prostate cancer incidence and progression to invasive cancer. Broccoli sprout consumption also decreased histone H3 lysine 9 trimethylation in the ventral lobe (age 12 wk), and decreased histone H3 lysine 18 acetylation in all prostate lobes (age 28 wk).

The TRAMP model of prostate cancer was utilized because the tumors occur in the prostate epithelium and the tumor tissue histopathology closely mimics human disease. Additional advantages include that the tumors arise spontaneously and appear in ∼100% of mice.”


Like in utero prevention of breast cancer by a broccoli sprouts diet, this study had a problem measuring sulforaphane dosage. The relevant statements were:

“This 15% broccoli sprout diet had 400 mg SFN [sulforaphane]/kg diet, which was chosen because it is equivalent to 1 mg SFN/d which has been used in previous studies.

Food consumption was measured over the course of the study and no difference was found in the intake of food between the control and broccoli sprout–fed groups.”

To be “equivalent to 1 mg SFN/d” at a .4 mg sulforaphane/gram rate, the animals would need to eat 2.5 grams per day. “Food consumption was measured” but not disclosed.

Also, looking at the sulforaphane references, the study cited at http://cancerpreventionresearch.aacrjournals.org/content/early/2015/02/21/1940-6207.CAPR-14-0386.full-text.pdf for the “1 mg SFN/d” dosage was actually:

“4 week old male TRAMP mice were treated with PBS [phosphate-buffered saline] (control) or 1 mg SFN in PBS three times/week for 15-18 weeks.”

not “1 mg SFN/d.”

I downgraded the study’s rating because the researchers didn’t sufficiently quantify their findings to help humans, which is the basic purpose of any animal study. The study’s sulforaphane dosage was undefined, so no human equivalent dosage could be derived.

https://academic.oup.com/cdn/article/2/3/nzy002/4803105 “Broccoli Sprouts Delay Prostate Cancer Formation and Decrease Prostate Cancer Severity with a Concurrent Decrease in HDAC3 Protein Expression in Transgenic Adenocarcinoma of the Mouse Prostate (TRAMP) Mice”