Using COVID-19 as a cover story

One aspect of the coronavirus is how it’s being used for economic upheavals that weren’t previously acceptable. The view from a Hong Kong analyst:

From March 2020 MMT is now a reality:

“Under cover of the ‘coronacrisis’, we are now witnessing the introduction of Modern Monetary Theory (MMT), which isn’t modern and isn’t a theory.

The dollars that the government will inject into the Fed’s Special Purpose Vehicles (SPVs) were previously created out of nothing when the Fed monetised Treasury securities. So, the Fed creates money out of nothing. This money then goes to the government. The government then deposits some of this money into the Fed’s new SPVs, and based on this injection of ‘capital’ the Fed creates a lot more money out of nothing.

No longer will governments feel constrained by their abilities to tax the population and borrow from bond investors. From now on they will act like they have unrestricted access to a bottomless pool of money.”

The Coming Great Inflation from October 2019 showed that current developments were already in the works:

“The difference between money and every other economic good is that money is on one side of almost every economic transaction. Consequently, there is no single number that can accurately represent the price (purchasing power) of money, meaning that even the most honest and rigorous attempt to calculate the ‘general price level’ will fail. This doesn’t imply that changes in the supply of money have no effect on money purchasing power, but it does imply that the effects of changes in the money supply can’t be explained or understood via a simple equation.

The economic effects of a money-supply increase driven by commercial banks making loans to their customers will be very different from the economic effects of a money-supply increase driven by central banks monetising assets. ‘Main Street’ is the first receiver of the new money in the former case and ‘Wall Street’ is the first receiver of the new money in the latter case. This alone goes a long way towards explaining why the QE programs of Q4-2008 onward had a much greater effect on financial asset prices than on the prices that get added together to form the Consumer Price Index.

Due to the combination of the false belief that large increases in the supply of money have only a minor effect on the purchasing power of money and the equally false belief that the economy would benefit from a bit more ‘price inflation’, it’s a good bet that central banks and governments will devise ways to inject a lot more money into the economy in reaction to future economic weakness.”

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”

Recover your sanity

“Men, it has been well said, think in herds; it will be seen that they go mad in herds, while they only recover their senses slowly, one by one.

During the great plague, which ravaged all Europe, between the years 1345 and 1350, it was generally considered that the end of the world was at hand.

‘But the facts, my dear fellow,’ said his friend, ‘the facts do not agree with your theory.’ ‘Don’t they?’ replied the philosopher, shrugging his shoulders, ‘then, tant pis pour les faits’ – so much the worse for the facts!”

Charles Mackay, 1841 “Memoirs of Extraordinary Popular Delusions and the Madness of Crowds”

Alfred Jacob Miller “Hunting buffalo” 1837

“Establishing the hidden communication networks in large self-organized groups facilitates a quantitative understanding of behavioral contagion.

An individual will be more likely to respond (is more susceptible) if it is strongly connected to the initiator (short path length), and if it has neighbors which are strongly connected to each other.”

Why is it so difficult to live your own life?

Wander into creativity?

This 2019 US study investigated the context of creative ideas:

“Creative inspiration routinely occurs during moments of mind wandering. Approximately 20% of ideas occurred in this manner.

Although ideas that occurred while participants were both on task and mind wandering did not differ in overall quality, there were several dimensions on which they did consistently differ. Ideas that occurred while mind wandering were reported to be experienced with a greater sense of ‘aha’ and were more likely to involve overcoming an impasse.

The present findings are consistent with the view that spontaneous task-independent mind wandering represents a source of the inventive ideas that individuals have each day. This potential function of mind wandering may help to explain why a mental state that can be associated with significant negative outcomes is nevertheless so ubiquitous.”

“Would you say the idea felt like an ‘aha!’ moment?” and “How creative do you feel the idea was?” were the closest items to emotional measures. “How important do you think this idea is?” and several months later “How important has the idea proven to be overall?” were used to measure importance. “When the Muses Strike: Creative Ideas of Physicists and Writers Routinely Occur During Mind Wandering”

I came across this study from its reference in How Productivity Apps Can Make Us Less Productive (And Less Happy).

The study’s design missed opportunities to discover sources of creative ideas and feelings of importance. It focused on effects and intentionally disregarded causes, despite asserting that “mind wandering represents a source of inventive ideas.”

Experiments were subjectively biased for a framework that considered ideas as originating solely from a person’s thinking brain. A framework that demonstrated how ideas may arise as defenses against feelings wasn’t considered, although relevant.

Let’s use the finding “Ideas that occurred while mind wandering were more likely to involve overcoming an impasse” as an example for the alternative framework’s view:

  1. A person who has a seemingly unsolvable work problem probably encounters feelings of helplessness.
  2. Staying busy with tasks can distract them from these feelings.
  3. During times of less cognitive activity, though, these feelings can have more impetus.
  4. The resultant discomfort will trigger ideas to help ward off helpless feelings.

Regarding importance judgments, there are many needs a person develops and tries to satisfy as substitutes for real needs that weren’t fulfilled. I’ve focused on the need to feel important in blog posts such as Your need to feel important will run your life, and you’ll never feel satisfied.

A blood plasma aging clock

This 2019 Stanford human study developed an aging clock using blood plasma proteins:

“We measured 2,925 plasma proteins from 4,331 young adults to nonagenarians [18 – 95] and developed a novel bioinformatics approach which uncovered profound non-linear alterations in the human plasma proteome with age. Waves of changes in the proteome in the fourth, seventh, and eighth decades of life reflected distinct biological pathways, and revealed differential associations with the genome and proteome of age-related diseases and phenotypic traits.

To determine whether the plasma proteome can predict chronological age and serve as a “proteomic clock,” we used 2,858 randomly selected subjects to fine-tune a predictive model that was tested on the remaining 1,473 subjects. We identified a sex-independent plasma proteomic clock consisting of 373 proteins. Subjects that were predicted younger than their chronologic age based on their plasma proteome performed better on cognitive and physical tests.

The 3 age-related crests were comprised of different proteins. Few proteins, such as GDF15, were among the top 10 differentially expressed proteins in each crest, consistent with its strong increase across lifespan. Other proteins, like chordin-like protein 1 (CHRDL1) or pleiotrophin (PTN), were significantly changed only at the last two crests, reflecting their exponential increase with age.

We observed a prominent shift in multiple biological pathways with aging:

  • At young age (34 years), we observed a downregulation of proteins involved in structural pathways such as the extracellular matrix. These changes were reversed in middle and old ages (60 and 78 years, respectively).
  • At age 60, we found a predominant role of hormonal activity, binding functions and blood pathways.
  • At age 78, key processes still included blood pathways but also bone morphogenetic protein signaling, which is involved in numerous cellular functions, including inflammation.

These results suggest that aging is a dynamic, non-linear process characterized by waves of changes in plasma proteins that are reflective of a complex shift in the activity of biological processes.” “Undulating changes in human plasma proteome across lifespan are linked to disease”

A non-critical review of the study was published by the Life Extension Advocacy Foundation. Frequent qualifiers like “could,” “may,” and “possible” were consistent with the confirmation biases of their advocacy.

There were several misstatements of what the study did, including the innumerate:

  1. “used around half of the participant data to build a “proteomic clock”
  2. tested it on the other half of the participants
  3. a total of 3000 proteins”

Per the above study quotation, the numbers were actually:

  1. Closer to two thirds (2,858 ÷ 4,331), not “around half”;
  2. The other third (1,473 ÷ 4,331), not “the other half”; and
  3. 2,925 not 3000.

The final paragraph and other parts of the review bordered on woo. Did a review of the findings have to fit LEAF’s perspective?

In contrast, Josh Mitteldorf did his usual excellent job of providing contexts for the study with New Aging Clock based on Proteins in the Blood, emphasizing comparisons with epigenetic clock methodologies:

“For some of the proteins that feature prominently in the clock, we have a good understanding of their metabolic function, and for the most part they vindicate my belief that epigenetic changes are predominantly drivers of senescence rather than protective responses to damage.

Wyss-Coray compared the proteins in the new (human) proteome clock with the proteins that were altered in the (mouse) parabiosis experiments, and found a large overlap [46 proteins change in the same direction and define a conserved aging signature]. This may be the best evidence we have that the proteome changes are predominantly causal factors of senescence.

46 plasma proteins

Almost all the proteins identified as changing rapidly at age 78 are increasing. In contrast, a few of the fastest-changing proteins at age 60 are decreasing (though most are increasing). GDF15 deserves a story of its own.

The implication is that a more accurate clock can be constructed if it incorporates different information at different life stages. None of the Horvath clocks have been derived based on different CpG sites at different ages, and this suggests an opportunity for a potential improvement in accuracy.”

A commentator linked the below study: “GDF15 Is an Inflammation-Induced Central Mediator of Tissue Tolerance” (not freely available)

which prompted his response:

“Thanks, Lee! This is just the kind of specific information that I was asking for. It would seem we should construct our clocks without GDF15, which otherwise might loom large.”

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 GWAS meta-analysis of two epigenetic clocks

This 2019 UK human study conducted a meta-analysis of genome-wide association studies of two epigenetic clocks using 13,493 European-ancestry individuals aged between ten and 98 years:

“Horvath-EAA, described in previous publications as ‘intrinsic’ epigenetic age acceleration (IEAA), can be interpreted as a measure of cell-intrinsic ageing that exhibits preservation across multiple tissues, appears unrelated to lifestyle factors, and probably indicates a fundamental cell ageing process that is largely conserved across cell types.

In contrast, Hannum-EAA, referred to in previous studies as ‘extrinsic’ epigenetic age acceleration (EEAA), can be considered a biomarker of immune system ageing, explicitly incorporating aspects of immune system decline such as age-related changes in blood cell counts, correlating with lifestyle and health-span related characteristics, and thus yielding a stronger predictor of all-cause mortality.

The meta-analysis of Horvath-EAA identified ten independent associated SNPs [single nucleotide polymorphisms], doubling the number reported to date, and highlighted 21 genes involved in Horvath-based epigenetic ageing. Four of the ten Horvath-EAA-associated SNPs are mQTL [methylation quantitative trait loci] for CpGs used in the Horvath/Hannum epigenetic clocks. A possible interpretation of this is that the functional mechanism by which these SNPs influence the rate of biological ageing is via altering methylation levels.

Father’s age at death, a rough proxy for lifespan, was nominally significantly correlated with both EAA measures, and parents’ age at death was additionally correlated with Hannum-EAA. Aside from these, genetic correlations with age-related traits were surprisingly few: it is possible that this could reflect an overly conservative correction for the multiple tests carried out, or low statistical power, rather than a genuine lack of correlations.

Genetic correlation analysis should be restricted to GWAS with a heritability Z-score of 4 or more, on the grounds of interpretability and power, so the Horvath-based results particularly should be interpreted with caution.”

A non-apologetic way to explain the above graphic is that NONE of these 218 “health and behavioral traits” were any more associated with the studied genetic measurements than would be expected by chance!

Fervent believers in the GWAS methodology’s capability to exactly predict individual phenotypes eventually become victims of the scientific method. These GWAS researchers griped about “overly conservative correction, or low statistical power” and other predictable shortfalls, and ended a long limitations statement with:

“While we have identified a number of SNPs and genes significantly associated with EAA, including genes already known to be related to ageing, the analyses presented here fall short of providing a mechanistic explanation for how these variants and genes act to influence biological age.”

Outside of beliefs, it’s hard to understand why research money keeps pouring into the GWAS dead end. If these researchers and their employing institution and sponsors want to make a difference in human lives, they need to get busy in other areas.

These researchers were employed by the same institution that couldn’t be bothered to scrape together six more weeks of funds to study the transgenerational damaging effects of acetaminophen – an analgesic available to billions of people – in Epigenetics research that was designed to fall one step short of wonderful. “A meta-analysis of genome-wide association studies of epigenetic age acceleration”