Deaths in Italy attributed to COVID-19

Why have so many coronavirus patients died in Italy? from the Telegraph today:

“According to Prof Walter Ricciardi, scientific adviser to Italy’s minister of health, the country’s mortality rate is far higher due to demographics – the nation has the second oldest population worldwide – and the manner in which hospitals record deaths.

‘The age of our patients in hospitals is substantially older – the median is 67, while in China it was 46,’ Prof Ricciardi says. ‘So essentially the age distribution of our patients is squeezed to an older age and this is substantial in increasing the lethality.

But Prof Ricciardi added that Italy’s death rate may also appear high because of how doctors record fatalities.

‘The way in which we code deaths in our country is very generous in the sense that all the people who die in hospitals with the coronavirus are deemed to be dying of the coronavirus.

On re-evaluation by the National Institute of Health, only 12 per cent of death certificates have shown a direct causality from coronavirus, while 88 per cent of patients who have died have at least one pre-morbidity – many had two or three,’ he says.”

Refactoring the current 4,825 deaths in Italy attributed to COVID-19 equals 579 (4,825 x .12). That number places Italy slightly above France’s 562 current total.

Evidence-based statements wouldn’t sufficiently frighten the herd, though. The article continued on to include now-obligatory, hyperbolic, unscientific WHO statements referencing a “miracle.”

Image from “Culture Audits: We Have Been Asking the Wrong Question”

Well done, WHO, carefully played

A follow up to The WHO has a financial incentive to declare COVID-19 a pandemic:

Today CNBC reported Investors in World Bank’s ‘pandemic bonds’ face big losses due to the coronavirus outbreak

“According to ratings agency DBRS Morningstar, investors who hold the riskier of the two bonds could be losing their entire principal amount soon, with the firm saying that the price should have dropped more than 80%.

According to the World Bank, the outbreak would need to last at least 12 weeks, and have more than 2,500 deaths for the riskier of the two bonds, and 250 deaths for the other. There must also be more than 20 deaths in a second country.

When all those conditions are fulfilled, it triggers a payout to selected countries in need of help to contain the outbreak, and investors lose some or all of their money. That date works out to be Mar. 24, going by the 12-week period, and the start date of the outbreak – Dec. 31, according to the WHO, said DBRS Morningstar.

The World Bank did not respond to CNBC’s request for comment.”

Regarding WHO terminology, from The Cyclical Nature of Disease:

“The World Health Organization (WHO) made the announcement on Wednesday, March 11, 2020, that the Coronavirus is a “pandemic” which is actually not as bad as an Epidemic which is a term that describes any problem that has grown out of control. An epidemic is therefore defined as “an outbreak of a disease that occurs over a wide geographic area and affects an exceptionally high proportion of the population.”

Consequently, an epidemic requires a high proportion of society to be infected and is an event in which a disease is actively spreading. That is clearly not the case with the coronavirus. since the proportion of society infected has not even reached 1/10th of one percent of the population.

In contrast, the term pandemic relates to geographic spread and is used to describe a disease that affects a whole country or the entire world. This has nothing to do with the proportion of the population that is infected.”

Regarding WHO timing, last week The Guardian reported:

First Covid-19 case happened in November, China government records show – report

“The first case of someone suffering from Covid-19 can be traced back to 17 November, according to media reports on unpublished Chinese government data.

The report, in the South China Morning Post, said Chinese authorities had identified at least 266 people who contracted the virus last year and who came under medical surveillance, and the earliest case was 17 November – weeks before authorities announced the emergence of the new virus.

The Chinese government was widely criticised over attempts to cover up the outbreak in the early weeks, including crackdowns on doctors who tried to warn colleagues about a new Sars-like virus which was emerging in the city of Wuhan in Hubei province.

The data obtained by the Post, which the Guardian has not been able to verify, said a 55-year-old from Hubei province could have been the first person to contract Covid-19. For about one month after that date there were one to five new cases reported each day, the report said, and by 20 December there were 60 confirmed cases.

Official statements by the Chinese government to the World Health Organisation reported that the first confirmed case had been diagnosed on 8 December.”

Looking past the headlines, we have time to ask cui bono questions while we’re sitting at home:

  • Who’s benefiting from (medical, economic, social, and political) reports on and actions taken with COVID-19?
  • Who’s suffering from these reports and actions?

An evolutionary view of transgenerational epigenetic inheritance

This 2020 Swiss/German review mainly cited weed, worm, and yeast studies:

“RNA interference-related mechanisms can mediate the deposition and transgenerational inheritance of specific chromatin modifications in a truly epigenetic fashion.

Epigenetics was initially defined as any heritable change in gene expression patterns without changes in the DNA sequence. Now, epigenetic phenomena are often characterized as ‘gene expression changes that are mutation independent and heritable in the absence of the triggering event’, a definition we will follow in this review. We note that this definition can be expanded to include protein only-based inheritance mechanisms that do not necessarily cause changes in gene expression.

Gene silencing can persist over multiple generations in the germline of C. elegans. Gene repression is typically maintained without the initial trigger for three to seven generations and occasionally for tens of generations. In contrast, silencing of somatically expressed genes mostly affects only the subsequent generation through nonepigenetic parental effects.

In the presence of an ‘enabling’ mutation, primary siRNAs [small interfering RNAs] can trigger an RNAe [RNA-induced epigenetic silencing] response. Secondary siRNA amplification is required for transgenerational inheritance.

The fitness of a population in a dynamic environment strongly depends on the ability of individuals to adapt to the new condition as well as to remember, inherit, and forget such adaptation:

  • (A) A well-adapted population (grey) is at its maximal density (dotted line) in a given niche until an environmental change (1st stress) creates a bottleneck. Only few individuals can adapt through mutations and repopulate the niche. After the environment changes back to the initial blue state, only individuals that acquire rare counteracting mutations survive, often leading to extinction of the population.
  • (B) Individuals of a population in the red state can gain beneficial epimutations through siRNAs and repopulate the niche. When exposed again to the blue state, the epimutations can be quickly reversed and the population rapidly reaches maximal density. After recurrence of the red state, organisms establish de novo epimutations with the same low frequency as when they first encountered this state.
  • (C) In contrast, organisms that can maintain the memory of a beneficial silencing event can quickly re-establish beneficial epimutations and grow to full density. Such memory can be maintained by phenotypically neutral epimutations, marked by the continuously high production of siRNAs without substantial reductions in the expression of a gene. A population that can adapt through phenotypically plastic epimutations is predicted to have a maximal fitness advantage in a dynamic environment.”

The Concluding Remarks section included:

“RNA-mediated epigenetic responses could contribute to adaptation.

Even though RNAe may yield significant adaptive advantages, a high induction frequency could cause silencing of multiple essential genes and therefore be detrimental. Hence, it is plausible that mechanisms would have coevolved that counteract silencing.

Similarly, if constituting a bet-hedging strategy to cope with ever-changing environments, permanent fixation of an acquired silencing response would not constitute a selective advantage and mechanisms that modify and limit the duration of RNAe would be predicted.” “Small RNAs in the Transgenerational Inheritance of Epigenetic Information”

The review’s arguments were based on evolutionary selective advantages and less-complex organisms. It predicted that there would be an endpoint generation as in the (A) case of the above graphic.

Were the mechanisms in the (B) case necessarily transgenerational throughout? The review further explained:

“Epimutations tend to occur in hot spots (e.g., in stress-related or nutritional pathway genes) and can potentially silence several homologous genes simultaneously. Incomplete penetrance of a beneficial epimutation by stochastic loss of siRNAs [59] can result in loss of adaptation in a given environment (red state), but can be beneficial if the previous blue state is re-established. However, when the environment changes back to the red state, epimutations must initiate de novo, at the same low frequency as when the population first encountered this state.”

The study cited at 59 found:

“A feedback between siRNAs and RNAi genes determines heritable silencing duration”

but not “Incomplete penetrance of a beneficial epimutation by stochastic loss of siRNAs.” Hmm.

In any event, the review stated:

“Evidence for naturally occurring RNAe-related phenomena in other animals is scarce and we should be cautious about inferring RNAe as a widely conserved phenomenon.”

It’s encouraging to read studies that find benefits to epigenetic transgenerational inheritance, albeit in organisms that are less complex than rodents and humans.


Do epigenetic clocks measure causes or effects?

The founder of the PhenoAge epigenetic clock methodology authored this 2020 article:

“The Ge[r]oscience paradigm suggests that targeting the aging process could delay or prevent the risk of multiple major age-related diseases. We need clinically valid measures of the underlying biological process and/or classification criteria for what it means to be biologically, rather than chronologically, “aged”.

The majority of aging biomarkers, including the first-generation epigenetic clocks, are developed using cross-sectional data, in which the researchers take a variable that proxies aging (e.g. chronological age) and apply supervised machine learning, or deep learning, approaches to predict that variable using tens to hundreds of thousands of input variables. The problem with this approach is that it doesn’t account for mortality selection. This biases the algorithm to select markers that are not causal, but instead correlative with aging.

When considering individuals of the same chronological age, do those with higher epigenetic age look phenotypically older on average (e.g. have higher mortality rates, greater disease burden, and worse physical and cognitive functioning)? FEV1 [forced expiratory volume in one second] declined at a faster rate for individuals with higher baseline GrimAge and/or PhenoAge. A similar finding was observed for the decline in grip strength as a function of GrimAge; however, the rate of change for any of the epigenetic clocks was not associated with rate of change in any performance measure.

Loci that show consistent trends with chronological age, even at higher ages, are likely not causal. By using a cross-sectional study design for biomarker development there was a propensity away from selecting causal loci, to the point where fewer causal loci were selected than if loci had been chosen at random.

The power of these measures as diagnostic and prognostic may stem from the use of longitudinal data in training them. Rather than continuing to train chronological age predictors using diverse data, it may be more advantageous to retrain some of the existing measures by predicting longitudinal outcomes.” “Assessment of Epigenetic Clocks as Biomarkers of Aging in Basic and Population Research” (not freely available)

A cited 2019 study modeled corrections to “account for mortality selection.” It modified datasets “by incorporating correlates of mortality identified from longitudinal studies, allowing cross-sectional studies to effectively identify the causal factors of aging.” “Biomarkers for Aging Identified in Cross-sectional Studies Tend to Be Non-causative” (not freely available)

The article didn’t present a complete case to determine whether an individual’s epigenetic clock measurements over time may show causes of biological aging.

Other viewpoints include:

1. An epigenetic clock review by committee particularly in:

  • Challenge 3 “Integration of epigenetics into large and diverse longitudinal population studies”;
  • Challenge 5 “Single-cell analysis of aging changes and disease”; and
  • Table 1 “Major biological and analytic issues with epigenetic DNA methylation clocks” with single-cell analysis as the solution to five Significant issues.

2. A blood plasma aging clock presented evidence with its 46-protein conserved aging signature that some causes of biological aging are under genetic control. If the principle of this finding applies to CpG DNA methylation, the statement:

Loci that show consistent trends with chronological age, even at higher ages, are likely not causal.

may not hold. Such epigenetic changes could be among both the causes of senescence and the effects of evolution’s selection mechanisms.

Clearing out the 2019 queue of interesting papers

I’m clearing out the below queue of 27 studies and reviews I’ve partially read this year but haven’t taken the time to curate. I have a pesky full-time job that demands my presence elsewhere during the day. :-\

Should I add any of these back in? Let’s be ready for the next decade!

Early life “Early Behavioral Alterations and Increased Expression of Endogenous Retroviruses Are Inherited Across Generations in Mice Prenatally Exposed to Valproic Acid” (not freely available) “Consolidation of an aversive taste memory requires two rounds of transcriptional and epigenetic regulation in the insular cortex” (not freely available) “Intergenerational transmission of depression: clinical observations and molecular mechanisms” (not freely available)

mother “Epigenomics and Transcriptomics in the Prediction and Diagnosis of Childhood Asthma: Are We There Yet?” epigenetic clocks: estimating gestational age using placental DNA methylation levels” “Mismatched Prenatal and Postnatal Maternal Depressive Symptoms and Child Behaviours: A Sex-Dependent Role for NR3C1 DNA Methylation in the Wirral Child Health and Development Study” “Environmental influences on placental programming and offspring outcomes following maternal immune activation” “5-Hydroxymethylcytosine in cord blood and associations of DNA methylation with sex in newborns” (not freely available) “Paternal diet impairs F1 and F2 offspring vascular function through sperm and seminal plasma specific mechanisms in mice” “Sex differences in the epigenetic regulation of chronic visceral pain following unpredictable early life stress” (not freely available) “Genome-wide DNA methylation data from adult brain following prenatal immune activation and dietary intervention” in depression vulnerability and resilience: novel targets for preventive strategies”

Later life “Effect of Flywheel Resistance Training on Balance Performance in Older Adults. A Randomized Controlled Trial” “Eccentric Overload Flywheel Training in Older Adults” “Epigenetic regulation of the innate immune response to infection” (not freely available) “Hair Cell Regeneration” (not freely available) Modifications as an Intersection Between Diet and Longevity” “Serotonin transporter gene methylation predicts long-term cortisol concentrations in hair” (not freely available) “Frailty biomarkers in humans and rodents: Current approaches and future advances” (not freely available) “Neural mechanisms underlying adaptive and maladaptive consequences of stress: Roles of dopaminergic and inflammatory responses “In Search of Panacea—Review of Recent Studies Concerning Nature-Derived Anticancer Agents” “Reversal of oxycodone conditioned place preference by oxytocin: Promoting global DNA methylation in the hippocampus” (not freely available) “Different epigenetic clocks reflect distinct pathophysiological features of multiple sclerosis” “The Beige Adipocyte as a Therapy for Metabolic Diseases” “Bone adaptation: safety factors and load predictability in shaping skeletal form” (not freely available) “Successful treatment of post-traumatic stress disorder reverses DNA methylation marks” (not freely available) “Editing the Epigenome to Tackle Brain Disorders” (not freely available)

An epigenetic clock review by committee

This 2019 worldwide review of epigenetic clocks was a semi-anonymous mishmash of opinions, facts, hypotheses, unwarranted extrapolations, and beliefs. The diversity of viewpoints among the 21 coauthors wasn’t evident.

1. Citations of the coauthors’ works seemed excessive, and they apologized for omissions. However:

  • Challenge 5 was titled “Single-cell analysis of aging changes and disease” and
  • Table 1 “Major biological and analytic issues with epigenetic DNA methylation clocks” had single-cell analysis as the Proposed solution to five Significant issues.

Yet studies such as High-Resolution Single-Cell DNA Methylation Measurements Reveal Epigenetically Distinct Hematopoietic Stem Cell Subpopulations were unmentioned.

2. Some coauthors semi-anonymously expressed faith that using current flawed methodologies in the future – only more thoroughly, with newer equipment, etc. – would yield better results. If the 21 coauthors were asked their viewpoints of Proposed solutions to the top three Significant issues of epigenetic clocks, what would they emphasize when quoted?

3. Techniques were praised:

“Given the precision with which DNA methylation clock age can be estimated and evolving measures of biological, phenotype-, and disease-related age (e.g., PhenoAge, GrimAge)..”

Exactly why these techniques have at times produced inexplicable results wasn’t examined, though. Two examples:

  • In Reversal of aging and immunosenescent trends, the Levine PhenoAge methodology estimated that the 51-65 year old subjects’ biological ages at the beginning of the study averaged 17.5 years less than their chronological age. Comparing that to the Horvath average biological age of 3.95 years less raised the question: exactly why did PhenoAge show such a large difference?
  • The paper mentioned the GrimAge methodology findings about “smoking-related changes.” But it didn’t explain why the GrimAge methylation findings most closely associated with smoking history also accurately predicted future disease risk with non-smokers.

Eluding explanations for these types of findings didn’t help build confidence in the methodologies.

4. A more readable approach to review by committee could have coauthors – in at least one section – answer discussion questions, as Reversing epigenetic T cell exhaustion did with 18 experts. “DNA methylation aging clocks: challenges and recommendations”

A review of fetal adverse events

This 2019 Australian review subject was fetal adversities:

“Adversity during the perinatal period is a significant risk factor for the development of neurodevelopmental disorders long after the causative event. Despite stemming from a variety of causes, perinatal compromise appears to have similar effects on the developing brain, thereby resulting in behavioural disorders of a similar nature.

These behavioural disorders occur in a sex‐dependent manner, with males affected more by externalizing behaviours such as attention deficit hyperactivity disorder (ADHD) and females by internalizing behaviours such as anxiety. The term ‘perinatal compromise’ serves as an umbrella term for intrauterine growth restriction, maternal immune activation, prenatal stress, early life stress, premature birth, placental dysfunction, and perinatal hypoxia.

The above conditions are associated with imbalanced excitatory-inhibitory pathways resulting from reduced GABAergic signalling. Methylation of the GAD1/GAD67 gene, which encodes the key glutamate‐to‐GABA synthesizing enzyme Glutamate Decarboxylase 1, resulting in increased levels of glutamate is one epigenetic mechanism that may account for a tendency towards excitation in disorders such as ADHD.

The posterior cerebellum’s role in higher executive functioning is becoming well established due to its connections with the prefrontal cortex, association cortices, and limbic system. It is now suggested that disruptions to cerebellar development, which can occur due to late gestation compromises such as preterm birth, can have a major impact on the region of the brain to which it projects.

Activation of the maternal hypothalamic-pituitary adrenal (HPA) axis and placental protection. Psychological stress is perceived by the maternal HPA axis, which stimulates cortisol release from the maternal adrenal gland.

High levels of maternal cortisol are normally prevented from reaching the fetus by the 11β-hydroxysteroid dehydrogenase 2 (HSD11B2) enzyme, which converts cortisol to the much less active cortisone. Under conditions of high maternal stress, this protective mechanism can be overwhelmed, with the gene encoding the enzyme becoming methylated, which reduces its expression allowing cortisol to cross the placenta and reach the fetus.”

The reviewers extrapolated many animal study findings to humans, although most of their own work was with guinea pigs. The “suggest” and “may” qualifiers were used often – 22 and 37 times, respectively. More frequent use of the “appears,” “hypothesize,” “propose,” and “possible” terms was justified.

As a result, many reviewed items such as the above graphic and caption should be viewed as hypothetical for humans rather than reflecting solid evidence from quality human studies.

The reviewers focused on the prenatal (before birth) period more than the perinatal (last trimester of pregnancy to one month after birth) period. There were fewer mentions of birth and early infancy adversities. “Perinatal compromise contributes to programming of GABAergic and Glutamatergic systems leading to long-term effects on offspring behaviour” (not freely available)