Archive for the ‘Evolution of Biology Through Culture’ Category

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

Posted in Evolution of Biology Through Culture, Gene Regulation, Gene Regulation and Evolution, Genome Biology, Population genetics, Population Health Management, Population Health Management, Genetics & Pharmaceutical, tagged adaptation, health disparities, human variation, polygenic scores, Population genetics on August 29, 2020| Leave a Comment »

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

• L. Duncan,
• H. Shen,
• B. Gelaye,
• J. Meijsen,
• K. Ressler,
• M. Feldman,
• R. Peterson &
• B. Domingue

Nature Communications volume 10, Article number: 3328 (2019) Cite this article

• 20k Accesses

• 43 Citations

• 112 Altmetric

• Metricsdetails

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:

 

Volume 2019

Issue 1

2019

Article Contents

• Abstract
• INTRODUCTION
• COMPLEX PHENOTYPES AND POLYGENIC SCORES
• GENETIC BASIS OF POPULATION DIFFERENCES IN COMPLEX PHENOTYPES
• POLYGENIC ADAPTATION
• THE CASE OF HEALTH DISPARITIES
• PROSPECTS
• ACKNOWLEDGEMENTS
• REFERENCES

• < Previous
• Next >

EDITOR’S CHOICE

Interpreting polygenic scores, polygenic adaptation, and human phenotypic differences

Noah A Rosenberg, Michael D Edge, Jonathan K Pritchard, Marcus W Feldman

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

https://academic.oup.com/emph/article/2019/1/26/5262222

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.

adaptation, health disparities, human variation, polygenic scores, population genetics

Topic:

• phenotype
• genetics
• health disparity

Subject

Public Health and EpidemiologyEvolutionary Biology

Issue Section:

Commentary

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.

SOURCE

https://academic.oup.com/emph/article/2019/1/26/5262222

 

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

Posted in Biomarkers: Inflammation, coronavirus, Coronavirus Gene Expression, COVID-19, Curation, Curation methodology, Economic Impact of Coronavirus Pandemic, Evidence-based decision-making, Evolution of Biology Through Culture, Immune-Mediation (independent immunopathology: lung and reticuloendothelial system), number of asymptomatic infections, Population Health Management, Population Health Management, Genetics & Pharmaceutical, SAR-Cov-2 a vasculotropic (blood vessels) RNA Virus, SARS-CoV-2, SARS-COV2 Hijacking the Complement and Coagulation Systems, Seasonality & Environmental Factors in Resurgence, Serology tests for coronavirus antibodies, Treatment Protocols for COVID-19, Vaccinology, Virus Infective Acute Respiratory Syndrome: SARS-CoV, tagged China, coronavirus, Ebola virus, Epidemiology, Pandemic, Population Health Management, SARS-CoV-2 on May 15, 2020| Leave a Comment »

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.

Geographic Regions of China. Source: https://www.unicef.cn/en/figure-11-geographic-regions-china

 

 

 

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.

Source: https://www.indexmundi.com/china/age_structure.html

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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Special COVID-19 Christopher Magazine

Posted in Cardiovascular and Vascular Systems, coronavirus, Coronavirus Gene Expression, COVID-19, Evolution of Biology Through Culture, Immune-Mediation (independent immunopathology: lung and reticuloendothelial system), Interviews with Key Opinion Leaders (KOLs), Interviews with Scientific Leaders, Population genetics, Population Health Management, Population Health Management, Genetics & Pharmaceutical, Population Health Management, Nutrition and Phytochemistry, SAR-Cov-2 a vasculotropic (blood vessels) RNA Virus, SARS-CoV-2, SARS-COV2 Hijacking the Complement and Coagulation Systems, Seasonality & Environmental Factors in Resurgence, Serology tests for coronavirus antibodies, Vaccinology, Virus Infective Acute Respiratory Syndrome: SARS-CoV, tagged #COVID-19, coronavirus, diagnostic testing, key opinion leaders, SARS-CoV-2, scientific leaders on May 10, 2020| Leave a Comment »

via Special COVID-19 Christopher Magazine

 

Special COVID-19 Christopher Magazine

MARCH 21, 2020 / KARLSMALLEY

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

Posted in Epigenetics and Environmental Factors, Evolution of Biology Through Culture, Population genetics, Population Health Management, Population Health Management, Genetics & Pharmaceutical, Population Health Management, Nutrition and Phytochemistry, Reproductive Andrology, Embryology, Genomic Endocrinology, Preimplantation Genetic Diagnosis and Reproductive Genomics, Reproductive Biology & Bio Instrumentation, Statistical Methods for Research Evaluation, tagged environment, lifestyle, sperm concentration, sperm count, Stress, toxicity on August 24, 2017| Leave a Comment »

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

There Are Two Americas Now: One With a B.A. and One Without’

Oct. 5, 2022

By Thomas B. Edsall

“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.

A 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 Autor, David 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 Sehgal, Dahai Yue, Elle Pope, Ren 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

https://www.nytimes.com/2022/10/05/opinion/mortality-penalty-inequality-education.html?smid=nytcore-ios-share&referringSource=articleShare

 

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

By Shanna H. Swan and Stacey Colino

Hardcover

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.

What Are Sperm Telling Us?

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

By Nicholas Kristof

Opinion Columnist

• Feb. 20, 2021, 3:00 p.m. EST

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 Swan. Human 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.

https://pharmaceuticalintelligence.com/2017/07/27/trends-in-sperm-count/

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

Lauren Wichman Saigopal Somasundaram Christine Breindel Dana M. ValerioJohn R. McCarrey Craig A. Hodges Ahmad M Khalil, Biology of Reproduction, iox084, https://doi.org/10.1093/biolre/iox084

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 Graham, Sergio 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 Graham, Sergio 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.

SOURCE 

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

 

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