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Eight Subcellular Pathologies driving Chronic Metabolic Diseases – Methods for Mapping Bioelectronic Adjustable Measurements as potential new Therapeutics: Impact on Pharmaceuticals in Use

Eight Subcellular Pathologies driving Chronic Metabolic Diseases – Methods for Mapping Bioelectronic Adjustable Measurements as potential new Therapeutics: Impact on Pharmaceuticals in Use

Curators:

 

THE VOICE of Aviva Lev-Ari, PhD, RN

In this curation we wish to present two breaking through goals:

Goal 1:

Exposition of a new direction of research leading to a more comprehensive understanding of Metabolic Dysfunctional Diseases that are implicated in effecting the emergence of the two leading causes of human mortality in the World in 2023: (a) Cardiovascular Diseases, and (b) Cancer

Goal 2:

Development of Methods for Mapping Bioelectronic Adjustable Measurements as potential new Therapeutics for these eight subcellular causes of chronic metabolic diseases. It is anticipated that it will have a potential impact on the future of Pharmaceuticals to be used, a change from the present time current treatment protocols for Metabolic Dysfunctional Diseases.

According to Dr. Robert Lustig, M.D, an American pediatric endocrinologist. He is Professor emeritus of Pediatrics in the Division of Endocrinology at the University of California, San Francisco, where he specialized in neuroendocrinology and childhood obesity, there are eight subcellular pathologies that drive chronic metabolic diseases.

These eight subcellular pathologies can’t be measured at present time.

In this curation we will attempt to explore methods of measurement for each of these eight pathologies by harnessing the promise of the emerging field known as Bioelectronics.

Unmeasurable eight subcellular pathologies that drive chronic metabolic diseases

  1. Glycation
  2. Oxidative Stress
  3. Mitochondrial dysfunction [beta-oxidation Ac CoA malonyl fatty acid]
  4. Insulin resistance/sensitive [more important than BMI], known as a driver to cancer development
  5. Membrane instability
  6. Inflammation in the gut [mucin layer and tight junctions]
  7. Epigenetics/Methylation
  8. Autophagy [AMPKbeta1 improvement in health span]

Diseases that are not Diseases: no drugs for them, only diet modification will help

Image source

Robert Lustig, M.D. on the Subcellular Processes That Belie Chronic Disease

https://www.youtube.com/watch?v=Ee_uoxuQo0I

 

Exercise will not undo Unhealthy Diet

Image source

Robert Lustig, M.D. on the Subcellular Processes That Belie Chronic Disease

https://www.youtube.com/watch?v=Ee_uoxuQo0I

 

These eight Subcellular Pathologies driving Chronic Metabolic Diseases are becoming our focus for exploration of the promise of Bioelectronics for two pursuits:

  1. Will Bioelectronics be deemed helpful in measurement of each of the eight pathological processes that underlie and that drive the chronic metabolic syndrome(s) and disease(s)?
  2. IF we will be able to suggest new measurements to currently unmeasurable health harming processes THEN we will attempt to conceptualize new therapeutic targets and new modalities for therapeutics delivery – WE ARE HOPEFUL

In the Bioelecronics domain we are inspired by the work of the following three research sources:

  1. Biological and Biomedical Electrical Engineering (B2E2) at Cornell University, School of Engineering https://www.engineering.cornell.edu/bio-electrical-engineering-0
  2. Bioelectronics Group at MIT https://bioelectronics.mit.edu/
  3. The work of Michael Levin @Tufts, The Levin Lab
Michael Levin is an American developmental and synthetic biologist at Tufts University, where he is the Vannevar Bush Distinguished Professor. Levin is a director of the Allen Discovery Center at Tufts University and Tufts Center for Regenerative and Developmental Biology. Wikipedia
Born: 1969 (age 54 years), Moscow, Russia
Education: Harvard University (1992–1996), Tufts University (1988–1992)
Affiliation: University of Cape Town
Research interests: Allergy, Immunology, Cross Cultural Communication
Awards: Cozzarelli prize (2020)
Doctoral advisor: Clifford Tabin
Most recent 20 Publications by Michael Levin, PhD
SOURCE
SCHOLARLY ARTICLE
The nonlinearity of regulation in biological networks
1 Dec 2023npj Systems Biology and Applications9(1)
Co-authorsManicka S, Johnson K, Levin M
SCHOLARLY ARTICLE
Toward an ethics of autopoietic technology: Stress, care, and intelligence
1 Sep 2023BioSystems231
Co-authorsWitkowski O, Doctor T, Solomonova E
SCHOLARLY ARTICLE
Closing the Loop on Morphogenesis: A Mathematical Model of Morphogenesis by Closed-Loop Reaction-Diffusion
14 Aug 2023Frontiers in Cell and Developmental Biology11:1087650
Co-authorsGrodstein J, McMillen P, Levin M
SCHOLARLY ARTICLE
30 Jul 2023Biochim Biophys Acta Gen Subj1867(10):130440
Co-authorsCervera J, Levin M, Mafe S
SCHOLARLY ARTICLE
Regulative development as a model for origin of life and artificial life studies
1 Jul 2023BioSystems229
Co-authorsFields C, Levin M
SCHOLARLY ARTICLE
The Yin and Yang of Breast Cancer: Ion Channels as Determinants of Left–Right Functional Differences
1 Jul 2023International Journal of Molecular Sciences24(13)
Co-authorsMasuelli S, Real S, McMillen P
SCHOLARLY ARTICLE
Bioelectricidad en agregados multicelulares de células no excitables- modelos biofísicos
Jun 2023Revista Española de Física32(2)
Co-authorsCervera J, Levin M, Mafé S
SCHOLARLY ARTICLE
Bioelectricity: A Multifaceted Discipline, and a Multifaceted Issue!
1 Jun 2023Bioelectricity5(2):75
Co-authorsDjamgoz MBA, Levin M
SCHOLARLY ARTICLE
Control Flow in Active Inference Systems – Part I: Classical and Quantum Formulations of Active Inference
1 Jun 2023IEEE Transactions on Molecular, Biological, and Multi-Scale Communications9(2):235-245
Co-authorsFields C, Fabrocini F, Friston K
SCHOLARLY ARTICLE
Control Flow in Active Inference Systems – Part II: Tensor Networks as General Models of Control Flow
1 Jun 2023IEEE Transactions on Molecular, Biological, and Multi-Scale Communications9(2):246-256
Co-authorsFields C, Fabrocini F, Friston K
SCHOLARLY ARTICLE
Darwin’s agential materials: evolutionary implications of multiscale competency in developmental biology
1 Jun 2023Cellular and Molecular Life Sciences80(6)
Co-authorsLevin M
SCHOLARLY ARTICLE
Morphoceuticals: Perspectives for discovery of drugs targeting anatomical control mechanisms in regenerative medicine, cancer and aging
1 Jun 2023Drug Discovery Today28(6)
Co-authorsPio-Lopez L, Levin M
SCHOLARLY ARTICLE
Cellular signaling pathways as plastic, proto-cognitive systems: Implications for biomedicine
12 May 2023Patterns4(5)
Co-authorsMathews J, Chang A, Devlin L
SCHOLARLY ARTICLE
Making and breaking symmetries in mind and life
14 Apr 2023Interface Focus13(3)
Co-authorsSafron A, Sakthivadivel DAR, Sheikhbahaee Z
SCHOLARLY ARTICLE
The scaling of goals from cellular to anatomical homeostasis: an evolutionary simulation, experiment and analysis
14 Apr 2023Interface Focus13(3)
Co-authorsPio-Lopez L, Bischof J, LaPalme JV
SCHOLARLY ARTICLE
The collective intelligence of evolution and development
Apr 2023Collective Intelligence2(2):263391372311683SAGE Publications
Co-authorsWatson R, Levin M
SCHOLARLY ARTICLE
Bioelectricity of non-excitable cells and multicellular pattern memories: Biophysical modeling
13 Mar 2023Physics Reports1004:1-31
Co-authorsCervera J, Levin M, Mafe S
SCHOLARLY ARTICLE
There’s Plenty of Room Right Here: Biological Systems as Evolved, Overloaded, Multi-Scale Machines
1 Mar 2023Biomimetics8(1)
Co-authorsBongard J, Levin M
SCHOLARLY ARTICLE
Transplantation of fragments from different planaria: A bioelectrical model for head regeneration
7 Feb 2023Journal of Theoretical Biology558
Co-authorsCervera J, Manzanares JA, Levin M
SCHOLARLY ARTICLE
Bioelectric networks: the cognitive glue enabling evolutionary scaling from physiology to mind
1 Jan 2023Animal Cognition
Co-authorsLevin M
SCHOLARLY ARTICLE
Biological Robots: Perspectives on an Emerging Interdisciplinary Field
1 Jan 2023Soft Robotics
Co-authorsBlackiston D, Kriegman S, Bongard J
SCHOLARLY ARTICLE
Cellular Competency during Development Alters Evolutionary Dynamics in an Artificial Embryogeny Model
1 Jan 2023Entropy25(1)
Co-authorsShreesha L, Levin M
5

5 total citations on Dimensions.

Article has an altmetric score of 16
SCHOLARLY ARTICLE
1 Jan 2023BIOLOGICAL JOURNAL OF THE LINNEAN SOCIETY138(1):141
Co-authorsClawson WP, Levin M
SCHOLARLY ARTICLE
Future medicine: from molecular pathways to the collective intelligence of the body
1 Jan 2023Trends in Molecular Medicine
Co-authorsLagasse E, Levin M

THE VOICE of Dr. Justin D. Pearlman, MD, PhD, FACC

PENDING

THE VOICE of  Stephen J. Williams, PhD

Ten TakeAway Points of Dr. Lustig’s talk on role of diet on the incidence of Type II Diabetes

 

  1. 25% of US children have fatty liver
  2. Type II diabetes can be manifested from fatty live with 151 million  people worldwide affected moving up to 568 million in 7 years
  3. A common myth is diabetes due to overweight condition driving the metabolic disease
  4. There is a trend of ‘lean’ diabetes or diabetes in lean people, therefore body mass index not a reliable biomarker for risk for diabetes
  5. Thirty percent of ‘obese’ people just have high subcutaneous fat.  the visceral fat is more problematic
  6. there are people who are ‘fat’ but insulin sensitive while have growth hormone receptor defects.  Points to other issues related to metabolic state other than insulin and potentially the insulin like growth factors
  7. At any BMI some patients are insulin sensitive while some resistant
  8. Visceral fat accumulation may be more due to chronic stress condition
  9. Fructose can decrease liver mitochondrial function
  10. A methionine and choline deficient diet can lead to rapid NASH development

 

Read Full Post »

COVID-19 Sequel: Neurological Impact of Social isolation been linked to poorer physical and mental health

Reporter: Aviva Lev-Ari, PhD, RN

UPDATED on 4/13/2021

Toward Understanding COVID-19 Recovery: National Institutes of Health Workshop on Postacute COVID-19

 

Abstract

Over the past year, the SARS-CoV-2 pandemic has swept the globe, resulting in an enormous worldwide burden of infection and mortality. However, the additional toll resulting from long-term consequences of the pandemic has yet to be tallied. Heterogeneous disease manifestations and syndromes are now recognized among some persons after their initial recovery from SARS-CoV-2 infection, representing in the broadest sense a failure to return to a baseline state of health after acute SARS-CoV-2 infection. On 3 to 4 December 2020, the National Institute of Allergy and Infectious Diseases, in collaboration with other Institutes and Centers of the National Institutes of Health, convened a virtual workshop to summarize existing knowledge on postacute COVID-19 and to identify key knowledge gaps regarding this condition.

Over the past year, the SARS-CoV-2 pandemic has swept the globe, resulting in more than 113 million persons infected and 2.5 million deaths (1). However, the additional toll resulting from long-term consequences of the pandemic has yet to be tallied. Heterogeneous disease manifestations and syndromes are now recognized among some persons after their initial recovery from SARS-CoV-2 infection. Although a standardized case definition does not yet exist for these manifestations, in the broadest sense they represent a failure to return to a baseline state of health after acute SARS-CoV-2 infection. The various terms used to describe this condition have included postacute (or late) sequelae of COVID-19, post-COVID condition or syndrome, long COVID, and long-haul COVID. In this article, we use the general umbrella term of “postacute COVID-19” to refer to multiple disease processes that may have varying degrees of overlap (including but not limited to sequelae of critical illness and hospitalization in persons with COVID-19) and the entity of long COVID, which refers to prolonged health abnormalities in persons previously infected with SARS-CoV-2 who may or may not have required hospitalization. Of note, there is not yet a consensus on terminology, which will likely evolve with a better understanding of this condition.

Reported symptoms are wide-ranging and may involve nearly all organ systems, with fatigue, dyspnea, cognitive dysfunction, anxiety, and depression often described (2–5). Although abnormalities in imaging studies and functional testing have been reported, the long-term clinical significance of some of these findings is not yet clear (367). Postacute manifestations of COVID-19 have been seen in persons of all demographic groups and include reports of multisystem inflammatory syndrome in children (89). Although the epidemiology of the diverse manifestations of postacute COVID-19 is not yet known, the expansive global burden of SARS-CoV-2 infection suggests that the potential public health effects of postacute COVID-19 are significant if even a small proportion of persons with SARS-CoV-2 infection have prolonged recovery or do not return to their baseline health.

On 3 to 4 December 2020, the National Institute of Allergy and Infectious Diseases, in collaboration with other Institutes and Centers of the National Institutes of Health, convened a virtual workshop (available via videocast at https://videocast.nih.gov/watch=38878 and https://videocast.nih.gov/watch=38879) to summarize existing knowledge on postacute COVID-19 and to identify key knowledge gaps. The speakers and participants included epidemiologists, clinicians, clinical and basic scientists, and members of the affected community. The videocast was open to the general public and had more than 1200 registered participants.

SOURCE

UPDATED on 4/7/2021

‘Beyond a Reasonable Doubt’: COVID-19 Brain Health Fallout Is Real, Severe

Sarah Edmonds

April 07, 2021

Editor’s note: Find the latest COVID-19 news and guidance in Medscape’s Coronavirus Resource Center.

START QUOTE

COVID-19 survivors face a sharply elevated risk of developing psychiatric or neurologic disorders in the six months after they contract the virus — a danger that mounts with symptom severity, new research shows.

In what is purported to be the largest study of its kind to-date, results showed that among 236,379 COVID-19 patients, one third were diagnosed with at least one of 14 psychiatric or neurologic disorders within a 6-month span.

The rate of illnesses, which ranged from depression to stroke, rose sharply among those with COVID-19 symptoms acute enough to require hospitalization.  

“If we look at patients who were hospitalized, that rate increased to 39%, and then increased to about just under 1 in 2 patients who needed ICU admission at the time of the COVID-19 diagnosis,” Maxime Taquet, PhD, University of Oxford Department of Psychiatry, Oxford, United Kingdom, told a media briefing.

Incidence jumps to almost two thirds in patients with encephalopathy at the time of COVID-19 diagnosis, he added.

The study, which examined the brain health of 236,379 survivors of COVID-19 via a US database of 81 million electronic health records, was published online April 6 in The Lancet Psychiatry.

High Rate of Neurologic, Psychiatric Disorders

The research team looked at the first-time diagnosis or recurrence of 14 neurologic and psychiatric outcomes in patients with confirmed SARS-CoV-2 infections. They also compared the brain health of this cohort with a control group of those with influenza or with non-COVID respiratory infections over the same period. 

SOURCE

The Effects of Loneliness and Our Brain function: poorer physical and mental health

One review of the science of loneliness found that people with stronger social relationships have a 50 per cent increased likelihood of survival over a set period of time compared with those with weaker social connections. Other studies have linked loneliness to cardiovascular disease, inflammation, and depression.

For loneliness researchers the pandemic has provided an unprecedented natural experiment in the impact that social isolation might have on our brains. As millions of people across the world emerge from months of reduced social contact, a new neuroscience of loneliness is starting to figure out why social relationships are so crucial to our health.

Neural basis of Emotion

Desire for Social Interaction

Are there neurological differences between people who experience short-term isolation and those who have been isolated for long stretches of time? What kinds of social interactions satisfy our social cravings? Is a video call enough to quell our need for social contact, or do some people require an in-person connection to really feel satiated?

START QUOTE

Julianne Holt-Lunstad, a psychology professor at Brigham Young University in the US and the author of two major studies on social isolation and health. “We have a lot of data that very robustly shows that both isolation and loneliness put us at increased risk for premature mortality—and conversely, that being socially connected is protective and reduces our risk,” she says.

START QUOTE

“Trying to investigate isolation or loneliness is not as straightforward in humans. In humans, being lonely is not necessarily correlated with how many people are around you,” says Tomova. She is particularly interested in the impact that the pandemic might have had on young people whose cognitive and social skills are still developing. “I think we will see potentially some differences in how their social behavior developed or things like that,” she says. But as is always the case in the uncertain world of loneliness research, the opposite could be true. “It could also be that most people are fine, because maybe social media does fulfill our social needs really well.”

SOURCE

https://www.wired.co.uk/article/lockdown-loneliness-neuroscience

The Weird Science of Loneliness and Our Brains – Social isolation as been linked to poorer physical and mental health, but scientists are finally starting to understand its neurological impact

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Sex Differences in Immune Responses that underlie COVID-19 Disease Outcomes

Reporter: Aviva Lev-Ari, PhD, RN and Irina Robu, PhD COVID-19 is a non-discriminatory virus, it can infect anyone from young to old, but it seems that older men are twice more susceptible to it and most likely to become severely sick and die in comparison to women of the same age. Researchers from Yale university, published an article suggesting that men, particularly those over the age of 60 may need to depend more on vaccines to protect themselves from infection. According to their research published in Nature in August 2020, known sex differences between men and women pose challenges to the immune system. Women mount faster and stronger immune responses, possibly because their bodies are equipped to fight pathogens that threaten unborn or newborn children. Over time, an immune system in a constant state of high alert can be harmful. The findings underline the necessity for companies developing coronavirus vaccines to analyze their data by sex and may influence decisions about dosing. Dr. Iwasaki’s team from Yale  analyzed immune responses in 17 men and 22 women who were admitted to the hospital soon after they were infected with the coronavirus. The investigators collected blood, nasopharyngeal swabs, saliva, urine and stool from the patients every three to seven days. The researchers also analyzed data from an additional 59 men and women who did not meet those criteria. Over all, the scientists found, the women’s bodies produced more T cells, which can kill and stop the infection from spreading. Men on the other hand presented  a much weaker activation of T cells and that delay was linked to how sick the men became. The older the men, the weaker their T cell responses. Even though the study provided some more information about why men become sicker when diagnosed with coronavirus than women,  it did not offer a clear reason for the differences between men and women. SOURCE https://www.nature.com/articles/s41586-020-2700-3
Article

This is an unedited manuscript that has been accepted for publication. Nature Research are providing this early version of the manuscript as a service to our authors and readers. The manuscript will undergo copyediting, typesetting and a proof review before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers apply.

Sex differences in immune responses that underlie COVID-19 disease outcomes

Abstract

A growing body of evidence indicates sex differences in the clinical outcomes of coronavirus disease 2019 (COVID-19)1–5. However, whether immune responses against SARS-CoV-2 differ between sexes, and whether such differences explain male susceptibility to COVID-19, is currently unknown. In this study, we examined sex differences in
  • viral loads,
  • SARS-CoV-2-specific antibody titers,
  • plasma cytokines, as well as
  • blood cell phenotyping in COVID-19 patients.
By focusing our analysis on patients with moderate disease who had not received immunomodulatory medications, our results revealed that
  • male patients had higher plasma levels of innate immune cytokines such as IL-8 and IL-18 along with more robust induction of non-classical monocytes. In contrast,
  • female patients mounted significantly more robust T cell activation than male patients during SARS-CoV-2 infection, which was sustained in old age.
  • Importantly, we found that a poor T cell response negatively correlated with patients’ age and was associated with worse disease outcome in male patients, but not in female patients.
  • Conversely, higher innate immune cytokines in female patients associated with worse disease progression, but not in male patients.
  • These findings reveal a possible explanation underlying observed sex biases in COVID-19, and provide an important basis for the development of
  • a sex-based approach to the treatment and care of men and women with COVID-19.

Author information

Affiliations

Consortia

Corresponding author

Correspondence to Akiko Iwasaki.

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Most significant article published in the Society of Evolution, Medicine and Public Health won Prize: polygenic scores, polygenic adaptation, and human phenotypic differences

Reporter: Aviva Lev-Ari, PhD, RN 

 

UPDATED on 8/30/2020

Analysis of polygenic risk score usage and performance in diverse human populations

Abstract

A historical tendency to use European ancestry samples hinders medical genetics research, including the use of polygenic scores, which are individual-level metrics of genetic risk. We analyze the first decade of polygenic scoring studies (2008–2017, inclusive), and find that 67% of studies included exclusively European ancestry participants and another 19% included only East Asian ancestry participants. Only 3.8% of studies were among cohorts of African, Hispanic, or Indigenous peoples. We find that predictive performance of European ancestry-derived polygenic scores is lower in non-European ancestry samples (e.g. African ancestry samples: t = −5.97, df = 24, p = 3.7 × 10−6), and we demonstrate the effects of methodological choices in polygenic score distributions for worldwide populations. These findings highlight the need for improved treatment of linkage disequilibrium and variant frequencies when applying polygenic scoring to cohorts of non-European ancestry, and bolster the rationale for large-scale GWAS in diverse human populations.

SOURCE

https://www.nature.com/articles/s41467-019-11112-0

The Voice of Prof. Marcus W. Feldman

You might be interested in the paper “interpreting polygenic scores, polygenic adaptation, and human phenotypic differences” by N. Rosenberg, M. Edge, J. Pritchard, and M. Feldman, published in Evolution, Medicine and Public Health  (2019).    Rosenberg and Pritchard are my former PhD students, both full professors at Stanford, and M.Edge is a student of Rosenberg.

 

On Aug 28, 2020, at 4:36 PM, Horowitz, Barbara Natterson <natterson-horowitz@fas.harvard.edu> wrote:

Dear Dr. Rosenberg,

It is my pleasure in my role as President of the International Society for Evolution, Medicine and Public Health to inform you that your 2019 EMPH article, “Interpreting polygenic scores, polygenic adaptation, and human phenotypic differences” has won The George C. Williams Prize which is awarded each year to the first author of the most significant article published in the Society’s flagship journal, Evolution, Medicine and Public Health.  

The Prize recognizes the contributions of George C. Williams to evolutionary medicine and aims to encourage and highlight important research in this growing field. It includes $5,000 and an invitation to present at the online lecture series, Club EvMed. The Prize is made possible by donations from Doris Williams, Randolph Nesse, and other supporters of EMPH.

The winning article:

 

Interpreting polygenic scores, polygenic adaptation, and human phenotypic differences

Evolution, Medicine, and Public Health, Volume 2019, Issue 1, 2019, Pages 26–34, https://doi.org/10.1093/emph/eoy036
Published:
27 December 2018

Article history

SOURCE

Abstract

Recent analyses of polygenic scores have opened new discussions concerning the genetic basis and evolutionary significance of differences among populations in distributions of phenotypes. Here, we highlight limitations in research on polygenic scores, polygenic adaptation and population differences. We show how genetic contributions to traits, as estimated by polygenic scores, combine with environmental contributions so that differences among populations in trait distributions need not reflect corresponding differences in genetic propensity. Under a null model in which phenotypes are selectively neutral, genetic propensity differences contributing to phenotypic differences among populations are predicted to be small. We illustrate this null hypothesis in relation to health disparities between African Americans and European Americans, discussing alternative hypotheses with selective and environmental effects. Close attention to the limitations of research on polygenic phenomena is important for the interpretation of their relationship to human population differences.

INTRODUCTION

We are currently witnessing a surge in public interest in the intersection of evolutionary genetics with such topics as cognitive phenotypes, disease, race and heritability of human traits [1–7]. This attention emerges partly from recent advances in genomics, including the introduction of polygenic scores—the aggregation of estimated effects of genome-wide variants to predict the contribution of a person’s genome to a phenotypic trait [8–10]—and a new focus on polygenic adaptations, namely adaptations that have occurred by natural selection on traits influenced by many genes [11–13].

Theories involving natural selection have long been applied in the scientific literature to explain mean phenotypic differences among human populations [14–16]. Although new tools for statistical analysis of polygenic variation and polygenic adaptation provide opportunities for studying human evolution and the genetic basis of traits, they also generate potential for misinterpretation. In the past, public attention to research on human variation and its possible evolutionary basis has often been accompanied by claims that are not justified by the research findings [17]. Recognizing pitfalls in the interpretation of new research on human variation is therefore important for advancing discussions on associated sensitive and controversial topics.

The contribution of polygenic score distributions to phenotype distributions. Two populations are considered, populations 1 (red) and 2 (blue). Each population has a distribution of genetic propensities, which are treated as accurately estimated in the form of polygenic scores (left). The genetic propensity distribution and an environment distribution sum to produce a phenotype distribution (right). All plots have the same numerical scale. (A) Environmental differences amplify an underlying difference in genetic propensities. (B) Populations differ in their phenotypes despite having no differences in genetic propensity distributions. (C) Environmental differences obscure a difference in genetic propensities opposite in direction to the difference in phenotype means. (D) Similarity in phenotype distributions is achieved despite a difference in genetic propensity distributions by an intervention that reduces the environmental contribution for individuals with polygenic scores above a threshold. (E) Within populations, heritability is high, so that genetic variation explains the majority of phenotypic variation; however, the difference between populations is explained by an environmental difference. Panels (A–C and E) present independent normal distributions for genotype and environment that sum to produce normal distributions for phenotype. In (D), (genotype, environment) pairs are simulated from independent normal distributions and a negative constant—reflecting the effect of a medication or other intervention—is added to environmental contributions associated with simulated genotypic values that exceed a threshold

Summary

These limitations illustrate that much of the complexity embedded in use of polygenic scores—the effects of the environment on phenotype and its relationship to genotype, the proportion of variance explained, and the peculiarities of the underlying GWAS data that have been used to estimate effect sizes—is obscured by the apparent simplicity of the single values computed for each individual for each phenotype. Consequently, in using polygenic scores to describe genomic contributions to traits, particularly traits for which the total contribution of genetic variation to trait variation, as measured by heritability, is low—but even if it is high (Fig. 1E)—a difference in polygenic scores between populations provides little information about potential genetic bases for trait differences between those populations.

Unlike heritability, which ranges from 0 to 1 and therefore makes it obvious that the remaining contribution to phenotypic variation is summarized by its difference from 1, the limited explanatory role of genetics is not embedded in the nature of the polygenic scores themselves. Although polygenic scores encode knowledge about specific genetic correlates of trait variation, they do not change the conceptual framework for genetic and environmental contribution to population differences. Attributions of phenotypic differences among populations to genetic differences should therefore be treated with as much caution as similar genetic attributions from heritability in the pre-genomic era.

 

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The Wide Variability in Reported COVID-19 Epidemiologic Data May Suggest That Personalized Omic Testing May Be Needed to Identify At-Risk Populations

Curator: Stephen J. Williams, PhD

I constantly check the Youtube uploads from Dr. John Campbell, who is a wonderful immunologist and gives daily reports on new findings on COVID-19 from the scientific literature.  His reporting is extremely insightful and easily understandable.  This is quite a feat as it seems the scientific field has been inundated with a plethora of papers, mostly reported clinical data from small retrospective studies, and many which are being put on preprint servers, and not peer reviewed.

It has become a challenge for many scientists, already inundated with expanding peer reviewed literature in their own fields, as well as the many requests to review papers, to keep up with all these COVID related literature.  Especially when it is up to the reader to do their own detailed peer review. So many thanks to people like Dr. Campbell who is an expert in his field for doing this.

However the other day he had posted a video which I found a bit disturbing, as a central theme of the video was that many expert committee could not find any reliable epidemiologic study concerning transmission or even incidence of the disease.  In all studies, as Dr. Campell alluded to, there is such a tremendous variability in the reported statistics, whether one is looking at percentage of people testing positive who are symptomatic, the percentage of asymptomatic which may be carriers, the transmission of the disease, and even the percentage of people who recover.

With all the studies being done it would appear that, even if a careful meta analysis were done using all available studies, and assuming their validity before peer review, that there would be a tighter consensus on some of these metrics of disease spread, incidence and prevalence.

Below is the video from Dr. Campbell and the topic is on percentage of asymptomatic carriers of the COVID-19 virus.  This was posted last week but later in this post there will be updated information and views by the WHO on this matter as well as other literature (which still shows to my point that this wide variability in reported data may be adding to the policy confusion with respect to asymptomatic versus symptomatic people and why genetic testing might be needed to further discriminate these cohorts of people.

 

Below is the video: 

From the Oxford Center for Evidence Based Medicine: COVID-19 Portal at https://www.cebm.net/oxford-covid-19-evidence-service/

“There is not a single reliable study to determine the number of asymptomatic infections”

And this is very troubling as this means there is no reliable testing resulting in any meaningful data.

As Dr. Campell says

” This is not good enough.  There needs to be some sort of coordinated research program it seems all ad hoc”

A few other notes from post and Oxford Center for Evidence Based Medicine:

  • Symptom based screening will miss a lot of asymptomatic and presymptomatic cases
  • Some asymptomatic cases will become symptomatic over next week (these people were technically presymptomatic but do we know the %?)
  • We need a population based antibody screening program
  • An Italian study of all 3,000 people in city of Vo’Euganeo revealed that 50-75% of those who tested positive were asymptomatic and authors concluded that asymptomatic represents “a formidable source of infection”; Dr. Campbell feels this was a reliable study
  • Another study from a Washington state nursing facility showed while 56% of positive cases were asymptomatic, 75% of these asymptomatic developed symptoms within a week. Symptom based screening missed half of cases.
  • Other studies do not follow-up on the positive cases to determine in presymptomatic
  • It also appears discrepancies between data from different agencies (like CDC, WHO) on who is shedding virus as different tests used (PCR vs antibody)

 

Recent Studies Conflict Concerning Asymptomatic, Presymtomatic and Viral Transmission

‘We don’t actually have that answer yet’: WHO clarifies comments on asymptomatic spread of Covid-19

From StatNews

A top World Health Organization official clarified on Tuesday that scientists have not determined yet how frequently people with asymptomatic cases of Covid-19 pass the disease on to others, a day after suggesting that such spread is “very rare.”

The clarification comes after the WHO’s original comments incited strong pushback from outside public health experts, who suggested the agency had erred, or at least miscommunicated, when it said people who didn’t show symptoms were unlikely to spread the virus.

Maria Van Kerkhove, the WHO’s technical lead on the Covid-19 pandemic, made it very clear Tuesday that the actual rates of asymptomatic transmission aren’t yet known.

Some of the confusion boiled down to the details of what an asymptomatic infection actually is, and the different ways the term is used. While some cases of Covid-19 are fully asymptomatic, sometimes the word is also used to describe people who haven’t started showing symptoms yet, when they are presymptomatic. Research has shown that people become infectious before they start feeling sick, during that presymptomatic period.

At one of the WHO’s thrice-weekly press briefings Monday, Van Kerkhove noted that when health officials review cases that are initially reported to be asymptomatic, “we find out that many have really mild disease.” There are some infected people who are “truly asymptomatic,” she said, but countries that are doing detailed contact tracing are “not finding secondary transmission onward” from those cases. “It’s very rare,” she said.

Source: https://www.statnews.com/2020/06/09/who-comments-asymptomatic-spread-covid-19/

 

Therefore the problems have been in coordinating the testing results, which types of tests conducted, and the symptomology results.  As Dr. Campbell previously stated it appears more ‘ad hoc’ than coordinated research program.  In addition, defining the presymptomatic and measuring this group have been challenging.

However, an alternative explanation to the wide variability in the data may be we need to redefine the cohorts of patients we are evaluating and the retrospective data we are collecting.  It is feasible that sub groups, potentially defined by genetic background may be identified and data re-evaluated based on personalized omic data, in essence creating new cohorts based on biomarker data.

From a Perspective in The Lancet about a worldwide proteomic effort (COVID-19 MS Coalition) to discover biomarkers related to COVID19 infection risk, by identifying COVID-related antigens.

The COVID-19 MS Coalition is a collective mass spectrometry effort that will provide molecular level information on SARS-CoV-2 in the human host and reveal pathophysiological and structural information to treat and minimise COVID-19 infection. Collaboration with colleagues at pace involves sharing of optimised methods for sample collection and data generation, processing and formatting for maximal information gain. Open datasets will enable ready access to this valuable information by the computational community to help understand antigen response mechanisms, inform vaccine development, and enable antiviral drug design. As countries across the world increase widespread testing to confirm SARS-CoV-2 exposure and assess immunity, mass spectrometry has a significant role in fighting the disease. Through collaborative actions, and the collective efforts of the COVID-19 MS Coalition, a molecular level quantitative understanding of SARS-CoV-2 and its effect will benefit all.

 

In an ACS Perspective below, Morteza Mahmoudi suggests a few possible nanobased technologies (i.e., protein corona sensor array and magnetic levitation) that could discriminate COVID-19-infected people at high risk of death while still in the early stages of infection.

Emerging Biomolecular Testing to Assess the Risk of Mortality from COVID-19 Infection

Morteza Mahmoudi*

Publication Date:May 7, 2020

 

Please see other articles on COVID-19 on our Coronavirus Portal at

An Epidemiological Approach Stephen J. Williams, PhD and Aviva Lev-Ari, PhD, RN Lead Curators – e–mail Contacts: sjwilliamspa@comcast.net and avivalev-ari@alum.berkeley.edu

https://pharmaceuticalintelligence.com/coronavirus-portal/an-epidemiological-approach-stephen-j-williams-phd-and-aviva-lev-ari-phd-rn-lead-curators-e-mail-contacts-sjwilliamspacomcast-net-and-avivalev-arialum-berkeley-edu/

and

https://pharmaceuticalintelligence.com/coronavirus-portal/

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COVID-19’s seasonal cycle to be estimated at Lawrence Berkeley National Laboratory (Berkeley Lab) by Artificial Intelligence and Machine Learning Algorithms: Will A Fall and Winter resurgence be Likely??

Reporter: Aviva Lev-Ari, PhD, RN

Using machine learning to estimate COVID-19’s seasonal cycle

Woman walks down empty city street wearing a mask

Credit: Ivan Marc/Shutterstock

Berkeley Lab researchers have launched a project to determine if the novel coronavirus might be seasonal, waning in summer months and resurging in fall and winter.

One of the many unanswered scientific questions about COVID-19 is whether it is seasonal like the flu — waning in warm summer months then resurging in the fall and winter.

Now scientists at Lawrence Berkeley National Laboratory (Berkeley Lab) are launching a project to apply machine-learning methods to a plethora of health and environmental datasets, combined with high-resolution climate models and seasonal forecasts, to tease out the answer.

“Environmental variables, such as temperature, humidity, and UV [ultraviolet radiation] exposure, can have an effect on the virus directly, in terms of its viability. They can also affect the transmission of the virus and the formation of aerosols,” said Berkeley Lab scientist Eoin Brodie, the project lead. “We will use state-of-the-art machine-learning methods to separate the contributions of social factors from the environmental factors to attempt to identify those environmental variables to which disease dynamics are most sensitive.

The research team will take advantage of an abundance of health data available at the county level — such as the severity, distribution and duration of the COVID-19 outbreak, as well as what public health interventions were implemented when — along with demographics, climate and weather factors, and, thanks to smartphone data, population mobility dynamics. The initial goal of the research is to predict — for each county in the United States — how environmental factors influence the transmission of the SARS-CoV-2 virus, which causes COVID-19.

Multidisciplinary team for a complex problem

Untangling environmental factors from social and health factors is a knotty problem with a large number of variables, all interacting in different ways. On top of that, climate and weather affect not only the virus but also human physiology and behavior. For example, people may spend more or less time indoors, depending on the weather; and their immune systems may also change with the seasons.

It’s a complex data problem similar to others tackled by Berkeley Lab’s researchers studying systems like watersheds and agriculture; the challenge involves integrating data across scales to make predictions at the local level. “Downscaling of climate information is something that we routinely do to understand how climate impacts ecosystem processes,” Brodie said. “It involves the same types of variables — temperature, humidity, solar radiation.”

Brodie, deputy director of Berkeley Lab’s Climate and Ecosystem Sciences Division, is leading a cross-disciplinary team of Lab scientists with expertise in climate modeling, data analytics, machine learning, and geospatial analytics. Ben Brown, a computational biologist in Berkeley Lab’s Biosciences Area, is leading the machine-learning analysis. One of their main aims is to understand how climate and weather interact with societal factors.

“We don’t necessarily expect climate to be a massive or dominant effect in and of itself. It’s not going to trump which city shut down when,” Brown said. “But there may be some really important interactions [between the variables]. Looking at New York and California for example, even accounting for the differences between the timing of state-instituted interventions, the death rate in New York may be four times higher than in California — though additional testing on random samples of the population is needed to know for sure. Understanding the environmental interactions may help explain why these patterns appear to be emerging. This is a quintessential problem for machine learning and AI [artificial intelligence].”

The computing work will be conducted at the National Energy Research Scientific Computing Center (NERSC), a DOE Office of Science user facility located at Berkeley Lab.

Signs of climatic influences

map of the worldwide incidence rate of COVID-19
The worldwide incidence rate of COVID-19.
Credit: Center for Systems Science and Engineering at Johns Hopkins University

Already, geographical differences in how the disease behaves have been reported, the researchers point out. Temperature, humidity, and the UV Index have all been statistically associated with rates of COVID-19 transmission — although contact rates are still the dominant influence on the spread of disease. In the southern hemisphere, for example, where it’s currently fall, disease spread has been slower than in the northern hemisphere. “There’s potentially other factors associated with that,” Brodie said. “The question is, when the southern hemisphere moves into winter, will there be an increase in transmission rate, or will fall and winter 2020 lead to a resurgence across the U.S. in the absence of interventions?”

India is another place where COVID-19 does not yet appear to be as virulent. “There are cities where it behaves as if it’s the most infectious disease in recorded history. Then there are cities where it behaves more like influenza,” Brown said. “It is really critical to understand why we see those massive differences.”

Brown notes other experiments suggesting the SARS-CoV-2 virus could be seasonal. In particular, the National Biodefense Analysis and Countermeasures Center (NBACC) assessed the longevity of the virus on various surfaces. “Under sunlight and humidity, they found that the virus loses viability in under 60 minutes,” Brown said. “But in darkness and low temperatures it’s stable for eight days. There’s some really serious differences that need investigating.”

The Berkeley Lab team believes that enough data may now be available to determine what environmental factors may influence the virulence of the virus. “Now we should have enough data from around the world to really make an assessment,” Brown said.

The team hopes to have the first phase of their analysis available by late summer or early fall. The next phase will be to make projections under different scenarios, which could aid in public health decisions.

“We would use models to project forward, with different weather scenarios, different health intervention scenarios — such as continued social distancing or whether there are vaccines or some level of herd immunity — in different parts of the country. For example, we hope to be able to say, if you have kids going back to school under this type of environment, the climate and weather in this zone will influence the potential transmission by this amount,” Brodie explained. “That will be a longer-term task for us to accomplish.”

This research is supported by Berkeley Lab’s Laboratory Directed Research and Development (LDRD) program. Other team members include Dan Feldman, Zhao Hao, Chaincy Kuo, Haruko Wainwright, and Nicola Falco. Berkeley Lab mobilized quickly to provide LDRD funding for several research projects to address the COVID-19 pandemic, including one on text mining scientific literature and another on indoor transmission of the virus.

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Crowdsourcing Difficult-to-Collect Epidemiological Data in Pandemics: Lessons from Ebola to the current COVID-19 Pandemic

 

Curator: Stephen J. Williams, Ph.D.

 

At the onset of the COVID-19 pandemic, epidemiological data from the origin of the Sars-Cov2 outbreak, notably from the Wuhan region in China, was sparse.  In fact, official individual patient data rarely become available early on in an outbreak, when that data is needed most. Epidemiological data was just emerging from China as countries like Italy, Spain, and the United States started to experience a rapid emergence of the outbreak in their respective countries.  China, made of 31 geographical provinces, is a vast and complex country, with both large urban and rural areas.

 

 

 

As a result of this geographical diversity and differences in healthcare coverage across the country, epidemiological data can be challenging.  For instance, cancer incidence data for regions and whole country is difficult to calculate as there are not many regional cancer data collection efforts, contrasted with the cancer statistics collected in the United States, which is meticulously collected by cancer registries in each region, state and municipality.  Therefore, countries like China must depend on hospital record data and autopsy reports in order to back-extrapolate cancer incidence data.  This is the case in some developed countries like Italy where cancer registry is administered by a local government and may not be as extensive (for example in the Napoli region of Italy).

 

 

 

 

 

 

Population density China by province. Source https://www.unicef.cn/en/figure-13-population-density-province-2017

 

 

 

Epidemiologists, in areas in which data collection may be challenging, are relying on alternate means of data collection such as using devices connected to the internet-of-things such as mobile devices, or in some cases, social media is becoming useful to obtain health related data.  Such as effort to acquire pharmacovigilance data, patient engagement, and oral chemotherapeutic adherence using the social media site Twitter has been discussed in earlier posts: (see below)

Twitter is Becoming a Powerful Tool in Science and Medicine at https://pharmaceuticalintelligence.com/2014/11/06/twitter-is-becoming-a-powerful-tool-in-science-and-medicine/

 

 

 

 

 

Now epidemiologists are finding crowd-sourced data from social media and social networks becoming useful in collecting COVID-19 related data in those countries where health data collection efforts may be sub-optimal.  In a recent paper in The Lancet Digital Health [1], authors Kaiyuan Sun, Jenny Chen, and Cecile Viboud present data from the COVID-19 outbreak in China using information collected over social network sites as well as public news outlets and find strong correlations with later-released government statistics, showing the usefulness in such social and crowd-sourcing strategies to collect pertinent time-sensitive data.  In particular, the authors aim was to investigate this strategy of data collection to reduce the time delays between infection and detection, isolation and reporting of cases.

The paper is summarized below:

Kaiyuan Sun, PhD Jenny Chen, BScn Cécile Viboud, PhD . (2020).  Early epidemiological analysis of the coronavirus disease 2019 outbreak based on crowdsourced data: a population-level observational study.  The Lancet: Digital Health; Volume 2, Issue 4, E201-E208.

Summary

Background

As the outbreak of coronavirus disease 2019 (COVID-19) progresses, epidemiological data are needed to guide situational awareness and intervention strategies. Here we describe efforts to compile and disseminate epidemiological information on COVID-19 from news media and social networks.

Methods

In this population-level observational study, we searched DXY.cn, a health-care-oriented social network that is currently streaming news reports on COVID-19 from local and national Chinese health agencies. We compiled a list of individual patients with COVID-19 and daily province-level case counts between Jan 13 and Jan 31, 2020, in China. We also compiled a list of internationally exported cases of COVID-19 from global news media sources (Kyodo News, The Straits Times, and CNN), national governments, and health authorities. We assessed trends in the epidemiology of COVID-19 and studied the outbreak progression across China, assessing delays between symptom onset, seeking care at a hospital or clinic, and reporting, before and after Jan 18, 2020, as awareness of the outbreak increased. All data were made publicly available in real time.

Findings

We collected data for 507 patients with COVID-19 reported between Jan 13 and Jan 31, 2020, including 364 from mainland China and 143 from outside of China. 281 (55%) patients were male and the median age was 46 years (IQR 35–60). Few patients (13 [3%]) were younger than 15 years and the age profile of Chinese patients adjusted for baseline demographics confirmed a deficit of infections among children. Across the analysed period, delays between symptom onset and seeking care at a hospital or clinic were longer in Hubei province than in other provinces in mainland China and internationally. In mainland China, these delays decreased from 5 days before Jan 18, 2020, to 2 days thereafter until Jan 31, 2020 (p=0·0009). Although our sample captures only 507 (5·2%) of 9826 patients with COVID-19 reported by official sources during the analysed period, our data align with an official report published by Chinese authorities on Jan 28, 2020.

Interpretation

News reports and social media can help reconstruct the progression of an outbreak and provide detailed patient-level data in the context of a health emergency. The availability of a central physician-oriented social network facilitated the compilation of publicly available COVID-19 data in China. As the outbreak progresses, social media and news reports will probably capture a diminishing fraction of COVID-19 cases globally due to reporting fatigue and overwhelmed health-care systems. In the early stages of an outbreak, availability of public datasets is important to encourage analytical efforts by independent teams and provide robust evidence to guide interventions.

A Few notes on Methodology:

  • The authors used crowd-sourced reports from DXY.cn, a social network for Chinese physicians, health-care professionals, pharmacies and health-care facilities. This online platform provides real time coverage of the COVID-19 outbreak in China
  • More data was curated from news media, television and includes time-stamped information on COVID-19 cases
  • These reports are publicly available, de-identified patient data
  • No patient consent was needed and no ethics approval was required
  • Data was collected between January 20, 2020 and January 31,2020
  • Sex, age, province of identification, travel history, dates of symptom development was collected
  • Additional data was collected for other international sites of the pandemic including Cambodia, Canada, France, Germany, Hong Kong, India, Italy, Japan, Malaysia, Nepal, Russia, Singapore, UK, and USA
  • All patients in database had laboratory confirmation of infection

 

Results

  • 507 patient data was collected with 153 visited and 152 resident of Wuhan
  • Reported cases were skewed toward males however the overall population curve is skewed toward males in China
  • Most cases (26%) were from Beijing (urban area) while an equal amount were from rural areas combined (Shaanzi and Yunnan)
  • Age distribution of COVID cases were skewed toward older age groups with median age of 45 HOWEVER there were surprisingly a statistically high amount of cases less than 5 years of age
  • Outbreak progression based on the crowd-sourced patient line was consistent with the data published by the China Center for Disease Control
  • Median reporting delay in the authors crowd-sourcing data was 5 days
  • Crowd-sourced data was able to detect apparent rapid growth of newly reported cases during the collection period in several provinces outside of Hubei province, which is consistent with local government data

The following graphs show age distribution for China in 2017 and predicted for 2050.

projected age distribution China 2050. Source https://chinapower.csis.org/aging-problem/

 

 

 

 

 

 

 

 

 

 

 

 

The authors have previously used this curation of news methodology to analyze the Ebola outbreak[2].

A further use of the crowd-sourced database was availability of travel histories for patients returning from Wuhan and onset of symptoms, allowing for estimation of incubation periods.

The following published literature has also used these datasets:

Backer JA, Klinkenberg D, Wallinga J: Incubation period of 2019 novel coronavirus (2019-nCoV) infections among travellers from Wuhan, China, 20-28 January 2020. Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin 2020, 25(5).

Lauer SA, Grantz KH, Bi Q, Jones FK, Zheng Q, Meredith HR, Azman AS, Reich NG, Lessler J: The Incubation Period of Coronavirus Disease 2019 (COVID-19) From Publicly Reported Confirmed Cases: Estimation and Application. Annals of internal medicine 2020, 172(9):577-582.

Li Q, Guan X, Wu P, Wang X, Zhou L, Tong Y, Ren R, Leung KSM, Lau EHY, Wong JY et al: Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus-Infected Pneumonia. The New England journal of medicine 2020, 382(13):1199-1207.

Dataset is available on the Laboratory for the Modeling of Biological and Socio-technical systems website of Northeastern University at https://www.mobs-lab.org/.

References

  1. Sun K, Chen J, Viboud C: Early epidemiological analysis of the coronavirus disease 2019 outbreak based on crowdsourced data: a population-level observational study. The Lancet Digital health 2020, 2(4):e201-e208.
  2. Cleaton JM, Viboud C, Simonsen L, Hurtado AM, Chowell G: Characterizing Ebola Transmission Patterns Based on Internet News Reports. Clinical infectious diseases : an official publication of the Infectious Diseases Society of America 2016, 62(1):24-31.

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Reporter: Stephen J. Williams, PhD

via Special COVID-19 Christopher Magazine

 

Special COVID-19 Christopher Magazine

Article ID #277: Special COVID-19 Christopher Magazine. Published on 5/10/2020

WordCloud Image Produced by Adam Tubman

Christopher-cover

Antonio Giordano, MD, PhD. explains what COVID is and how to contain the infection, pointing also to what will require attention next.

Please see this special release at http://online.fliphtml5.com/qlnw/zgau/#p=1

 

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Dr. Giordano Featured in Forbes Article on COVID-19 Antibody Tests in Italy and USA

Reporter: Stephen J. Williams, PhD

Article ID #276: Dr. Giordano Featured in Forbes Article on COVID-19 Antibody Tests in Italy and USA. Published on 5/10/2020

WordCloud Image Produced by Adam Tubman

via Dr. Giordano Featured in Forbes Article on COVID-19 Antibody Tests in Italy and USA

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Decline in Sperm Count – Epigenetics, Well-being and the Significance for Population Evolution and Demography

Dr. Marc Feldman, Expert Opinion on the significance of Sperm Count Decline on the Future of Population Evolution and Demography

Dr. Sudipta Saha, Effects of Sperm Quality and Quantity on Human Reproduction

Dr. Aviva Lev-Ari, Psycho-Social Effects of Poverty, Unemployment and Epigenetics on Male Well-being, Physiological Conditions affecting Sperm Quality and Quantity

 

Updated on 10/6/2022

“Red states are now less healthy than blue states, a reversal of what was once the case,” Anne Case and Angus Deaton, economists at Princeton, argue in a paper they published in April, “The Great Divide: Education, Despair, and Death.”

Carol Graham, a senior fellow at Brookings, described the erosion of economic and social status for whites without college degrees in a 2021 paper:

From 2005 to 2019, an average of 70,000 Americans died annually from deaths of despair (suicide, drug overdose, and alcohol poisoning). These deaths are concentrated among less than college educated middle-aged whites, with those out of the labor force disproportionately represented. Low-income minorities are significantly more optimistic than whites and much less likely to die of these deaths. This despair reflects the decline of the white working class. Counties with more respondents reporting lost hope in the years before 2016 were more likely to vote for Trump.

2010 Pew Research Center study that examined the effects of the Great Recession on Black and white Americans reported that Black Americans consistently suffered more in terms of unemployment, work cutbacks and other measures, but remained far more optimistic about the future than whites. Twice as many Black as white Americans were forced during the 2008 recession to work fewer hours, to take unpaid leave or switch to part-time, and Black unemployment rose from 8.9 to 15.5 percent from April 2007 to April 2009, compared with an increase from 3.7 to 8 percent for whites.

Despite experiencing more hardship, 81 percent of Black Americans agreed with the statement “America will always continue to be prosperous and make economic progress,” compared with 59 percent of whites; 45 percent of Black Americans said the country was still in recession compared with 57 percent of whites

In “Trends in Extreme Distress in the United States, 1993-2019,” David G. Blanchflower and Andrew J. Oswald, economists at Dartmouth and the University of Warwick in Britain, note that “the proportion of the U.S. population in extreme distress rose from 3.6 percent in 1993 to 6.4 percent in 2019. Among low-education midlife white persons, the percentage more than doubled, from 4.8 percent to 11.5 percent.”

In her 2020 paper, “Trends in U.S. Working-Age Non-Hispanic White Mortality: Rural-Urban and Within-Rural Differences,” Shannon M. Monnat, a professor of sociology at Syracuse University’s Maxwell School, explained that “between 1990-92 and 2016-18, the mortality rates among non-Hispanic whites increased by 9.6 deaths per 100,000 population among metro males and 30.5 among metro females but increased by 70.1 and 65.0 among nonmetro (rural and exurban) males and females, respectively.”

Three economists, David AutorDavid Dorn and Gordon Hanson of M.I.T., the University of Zurich and Harvard, reported in their 2018 paper, “When Work Disappears: Manufacturing Decline and the Falling Marriage Market Value of Young Men,” on the debilitating consequences for working-class men of the “China shock”

There is some evidence that partisanship correlates with mortality rates.

In their June 2022 paper, “The Association Between Covid-19 Mortality and the County-Level Partisan Divide in the United States,” Neil Jay SehgalDahai YueElle PopeRen Hao Wang and Dylan H. Roby, public health experts at the University of Maryland, found in their study of county-level Covid-19 mortality data from Jan. 1, 2020, to Oct. 31, 2021, that “majority Republican counties experienced 72.9 additional deaths per 100,000 people.”

Anne Case wrote in her email, that the United States is fast approaching a point where

Education divides everything, including connection to the labor market, marriage, connection to institutions (like organized religion), physical and mental health, and mortality. It does so for whites, Blacks and Hispanics. There has been a profound (not yet complete) convergence in life expectancy by education. There are two Americas now: one with a B.A. and one without.

SOURCE

 

UPDATED on 2/20/2021

Count Down

How Our Modern World Is Threatening Sperm Counts, Altering Male and Female Reproductive Development, and Imperiling the Future of the Human Race

https://www.simonandschuster.com/books/Count-Down/Shanna-H-Swan/9781982113667

Aside from the decline in sperm counts, growing numbers of sperm appear defective — there’s a boom in two-headed sperm — while others loll aimlessly in circles, rather than furiously swimming in pursuit of an egg. And infants who have had greater exposures to a kind of endocrine disruptor called phthalates have smaller penises, Swan found.

Still, the Endocrine Society, the Pediatric Endocrine Society, the President’s Cancer Panel and the World Health Organization have all warned about endocrine disruptors, and Europe and Canada have moved to regulate them.

Scientists are concerned by falling sperm counts and declining egg quality. Endocrine-disrupting chemicals may be the problem.

Opinion Columnist

https://www.nytimes.com/2021/02/20/opinion/sunday/endocrine-disruptors-sperm.html?campaign_id=45&emc=edit_nk_20210220&instance_id=27333&nl=nicholas-kristof&regi_id=65713389&segment_id=52055&te=1&user_id=edf020ada5f25f6d6c4b0b32ac4a1ee9

 

UPDATED on 2/3/2018

Nobody Really Knows What Is Causing the Overdose Epidemic, But Here Are A Few Theories

https://www.buzzfeed.com/danvergano/whats-causing-the-opioid-crisis?utm_term=.kbJPMgaQo4&utm_source=BrandeisNOW%2BWeekly&utm_campaign=58ada49a84-EMAIL_CAMPAIGN_2018_01_29&utm_medium=email#.uugW6mx1dG

 

Recent studies concluded via rigorous and comprehensive analysis found that Sperm Count (SC) declined 52.4% between 1973 and 2011 among unselected men from western countries, with no evidence of a ‘leveling off’ in recent years. Declining mean SC implies that an increasing proportion of men have sperm counts below any given threshold for sub-fertility or infertility. The high proportion of men from western countries with concentration below 40 million/ml is particularly concerning given the evidence that SC below this threshold is associated with a decreased monthly probability of conception.

1.Temporal trends in sperm count: a systematic review and meta-regression analysis 

Hagai Levine, Niels Jørgensen, Anderson Martino‐Andrade, Jaime Mendiola, Dan Weksler-Derri, Irina Mindlis, Rachel Pinotti, Shanna H SwanHuman Reproduction Update, July 25, 2017, doi:10.1093/humupd/dmx022.

Link: https://academic.oup.com/humupd/article-lookup/doi/10.1093/humupd/dmx022.

2. Sperm Counts Are Declining Among Western Men – Interview with Dr. Hagai Levine

https://news.afhu.org/news/sperm-counts-are-declining-among-western-men?utm_source=Master+List&utm_campaign=dca529d919-EMAIL_CAMPAIGN_2017_07_27&utm_medium=email&utm_term=0_343e19a421-dca529d919-92801633

3. Trends in Sperm Count – Biological Reproduction Observations

Reporter and Curator: Dr. Sudipta Saha, Ph.D.

4. Long, mysterious strips of RNA contribute to low sperm count – Long non-coding RNAs can be added to the group of possible non-structural effects, possibly epigenetic, that might regulate sperm counts.

http://casemed.case.edu/cwrumed360/news-releases/release.cfm?news_id=689

https://scienmag.com/long-mysterious-strips-of-rna-contribute-to-low-sperm-count/

Dynamic expression of long non-coding RNAs reveals their potential roles in spermatogenesis and fertility

Published: 29 July 2017
Thus, we postulated that some lncRNAs may also impact mammalian spermatogenesis and fertility. In this study, we identified a dynamic expression pattern of lncRNAs during murine spermatogenesis. Importantly, we identified a subset of lncRNAs and very few mRNAs that appear to escape meiotic sex chromosome inactivation (MSCI), an epigenetic process that leads to the silencing of the X- and Y-chromosomes at the pachytene stage of meiosis. Further, some of these lncRNAs and mRNAs show strong testis expression pattern suggesting that they may play key roles in spermatogenesis. Lastly, we generated a mouse knock out of one X-linked lncRNA, Tslrn1 (testis-specific long non-coding RNA 1), and found that males carrying a Tslrn1 deletion displayed normal fertility but a significant reduction in spermatozoa. Our findings demonstrate that dysregulation of specific mammalian lncRNAs is a novel mechanism of low sperm count or infertility, thus potentially providing new biomarkers and therapeutic strategies.

This article presents two perspectives on the potential effects of Sperm Count decline.

One Perspective identifies Epigenetics and male well-being conditions

  1. as a potential explanation to the Sperm Count decline, and
  2. as evidence for decline in White male longevity in certain geographies in the US since the mid 80s.

The other Perspective, evaluates if Sperm Count Decline would have or would not have a significant long term effects on Population Evolution and Demography.

The Voice of Prof. Marc Feldman, Stanford University – Long term significance of Sperm Count Decline on Population Evolution and Demography

Poor sperm count appears to be associated with such demographic statistics as life expectancy (1), infertility (2), and morbidity (3,4). The meta-analysis by Levine et al. (5) focuses on the change in sperm count of men from North America, Europe, Australia, and New Zealand, and shows a more than 50% decline between 1973 and 2011. Although there is no analysis of potential environmental or lifestyle factors that could contribute to the estimated decline in sperm count, Levine et al. speculate that this decline could be a signal for other negative changes in men’s health.

Because this study focuses mainly on Western men, this remarkable decline in sperm count is difficult to associate with any change in actual fertility, that is, number of children born per woman. The total fertility rate in Europe, especially Italy, Spain, and Germany, has slowly declined, but age at first marriage has increased at the same time, and this increase may be more due to economic factors than physiological changes.

Included in Levine et al.’s analysis was a set of data from “Other” countries from South America, Asia, and Africa. Sperm count in men from these countries did not show significant trends, which is interesting because there have been strong fertility declines in Asia and Africa over the same period, with corresponding increases in life expectancy (once HIV is accounted for).

What can we say about the evolutionary consequences for humans of this decrease? The answer depends on the minimal number of sperm/ml/year that would be required to maintain fertility (per woman) at replacement level, say 2.1 children, over a woman’s lifetime. Given the smaller number of ova produced per woman, a change in the ovulation statistics of women would be likely to play a larger role in the total fertility rate than the number of sperm/ejaculate/man. In other words, sperm count alone, absent other effects on mortality during male reproductive years, is unlikely to tell us much about human evolution.

Further, the major declines in fertility over the 38-year period covered by Levine et al. occurred in China, India, and Japan. Chinese fertility has declined to less than 1.5 children per woman, and in Japan it has also been well below 1.5 for some time. These declines have been due to national policies and economic changes, and are therefore unlikely to signal genetic changes that would have evolutionary ramifications. It is more likely that cultural changes will continue to be the main drivers of fertility change.

The fastest growing human populations are in the Muslim world, where fertility control is not nearly as widely practiced as in the West or Asia. If this pattern were to continue for a few more generations, the cultural evolutionary impact would swamp any effects of potentially declining sperm count.

On the other hand, if the decline in sperm count were to be discovered to be associated with genetic and/or epigenetic phenotypic effects on fetuses, newborns, or pre-reproductive humans, for example, due to stress or obesity, then there would be cause to worry about long-term evolutionary problems. As Levine et al. remark, “decline in sperm count might be considered as a ‘canary in the coal mine’ for male health across the lifespan”. But to date, there is little evidence that the evolutionary trajectory of humans constitutes such a “coal mine”.

References

  1. Jensen TK, Jacobsen R, Christensen K, Nielsen NC, Bostofte E. 2009. Good semen quality and life expectancy: a cohort study of 43,277 men. Am J Epidemiol 170: 559-565.
  2. Eisenberg ML, Li S, Behr B, Cullen MR, Galusha D, Lamb DJ, Lipshultz LI. 2014. Semen quality, infertility and mortality in the USA. Hum Reprod 29: 1567-1574.
  3. Eisenberg ML, Li S, Cullen MR, Baker LC. 2016. Increased risk of incident chronic medical conditions in infertile men: analysis of United States claims data. Fertil Steril 105: 629-636.
  4. Latif T, Kold Jensen T, Mehlsen J, Holmboe SA, Brinth L, Pors K, Skouby SO, Jorgensen N, Lindahl-Jacobsen R. Semen quality is a predictor of subsequent morbidity. A Danish cohort study of 4,712 men with long-term follow-up. Am J Epidemiol. Doi: 10.1093/aje/kwx067. (Epub ahead of print]
  5. Levine H, Jorgensen N, Martino-Andrade A, Mendiola J, Weksler-Derri D, Mindlis I, Pinotti R, Swan SH. 2017. Temporal trends in sperm count: a systematic review and meta-regression analysis. Hum Reprod Update pp. 1-14. Doi: 10.1093/humupd/dmx022.

SOURCE

From: Marcus W Feldman <mfeldman@stanford.edu>

Date: Monday, July 31, 2017 at 8:10 PM

To: Aviva Lev-Ari <aviva.lev-ari@comcast.net>

Subject: Fwd: text of sperm count essay

Psycho-Social Effects of Poverty, Unemployment and Epigenetics on Male Well-being, Physiological Conditions as POTENTIAL effects on Sperm Quality and Quantity and Evidence of its effects on Male Longevity

The Voice of Carol GrahamSergio Pinto, and John Juneau II , Monday, July 24, 2017, Report from the Brookings Institute

  1. The IMPACT of Well-being, Stress induced by Worry, Pain, Perception of Hope related to Employment and Lack of employment on deterioration of Physiological Conditions as evidence by Decrease Longevity

  2. Epigenetics and Environmental Factors

The geography of desperation in America

Carol GrahamSergio Pinto, and John Juneau II Monday, July 24, 2017, Report from the Brookings Institute

In recent work based on our well-being metrics in the Gallup polls and on the mortality data from the Centers for Disease Control and Prevention, we find a robust association between lack of hope (and high levels of worry) among poor whites and the premature mortality rates, both at the individual and metropolitan statistical area (MSA) levels. Yet we also find important differences across places. Places come with different economic structures and identities, community traits, physical environments and much more. In the maps below, we provide a visual picture of the differences in in hope for the future, worry, and pain across race-income cohorts across U.S. states. We attempted to isolate the specific role of place, controlling for economic, socio-demographic, and other variables.

One surprise is the low level of optimism and high level of worry in the minority dense and generally “blue” state of California, and high levels of pain and worry in the equally minority dense and “blue” states of New York and Massachusetts. High levels of income inequality in these states may explain these patterns, as may the nature of jobs that poor minorities hold.

We cannot answer many questions at this point. What is it about the state of Washington, for example, that is so bad for minorities across the board? Why is Florida so much better for poor whites than it is for poor minorities? Why is Nevada “good” for poor white optimism but terrible for worry for the same group? One potential issue—which will enter into our future analysis—is racial segregation across places. We hope that the differences that we have found will provoke future exploration. Readers of this piece may have some contributions of their own as they click through the various maps, and we welcome their input. Better understanding the role of place in the “crisis” of despair facing our country is essential to finding viable solutions, as economic explanations, while important, alone are not enough.

https://www.brookings.edu/research/the-geography-of-desperation-in-america/?utm_medium=social&utm_source=facebook&utm_campaign=global

 

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