Epidemiology

Adiposity may confound the association between vitamin D and disease risk – a lifecourse Mendelian randomization study

1. Tom G Richardson
2. Grace M Power
3. George Davey Smith

• Curated by eLife

  Evaluation Summary:

  This manuscript is of broad interest to readers in the fields of vitamin D and obesity. It utilises a Mendelian randomization framework to separate the genetically predicted effects of adiposity at two timepoints in the lifecourse, childhood and adulthood. The key claims of the manuscript are well supported by the data. Higher childhood body size had a direct effect on lower vitamin D levels in early life, while in midlife, childhood body size impacted on adult obesity to result in lower vitamin D levels.

  (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #1 agreed to share their name with the authors.)

Reviewed by eLife

The influence of biological, epidemiological, and treatment factors on the establishment and spread of drug-resistant Plasmodium falciparum

1. Thiery Masserey
2. Tamsin Lee
3. Monica Golumbeanu
4. Andrew J Shattock
5. Sherrie L Kelly
6. Ian M Hastings
7. Melissa A Penny

• Curated by eLife

  Evaluation Summary:

  This manuscript explores the establishment and spread of antimalarial drug-resistant P. falciparum parasites using a combination of transmission modeling and model emulation. The authors add an important component to the broader understanding by jointly considering multiple factors driving drug resistance.

  (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. The reviewers remained anonymous to the authors.)

Reviewed by eLife

Validation of a multi-ancestry polygenic risk score and age-specific risks of prostate cancer: A meta-analysis within diverse populations

1. Fei Chen
2. Burcu F Darst
3. Ravi K Madduri
4. Alex A Rodriguez
5. Xin Sheng
6. Christopher T Rentsch
7. Caroline Andrews
8. Wei Tang
9. Adam S Kibel
10. Anna Plym
11. Kelly Cho
12. Mohamed Jalloh
13. Serigne Magueye Gueye
14. Lamine Niang
15. Olufemi J Ogunbiyi
16. Olufemi Popoola
17. Akindele O Adebiyi
18. Oseremen I Aisuodionoe-Shadrach
19. Hafees O Ajibola
20. Mustapha A Jamda
21. Olabode P Oluwole
22. Maxwell Nwegbu
23. Ben Adusei
24. Sunny Mante
25. Afua Darkwa-Abrahams
26. James E Mensah
27. Andrew Anthony Adjei
28. Halimatou Diop
29. Joseph Lachance
30. Timothy R Rebbeck
31. Stefan Ambs
32. J Michael Gaziano
33. Amy C Justice
34. David V Conti
35. Christopher A Haiman

• Curated by eLife

  Evaluation Summary:

  This paper is mainly for an audience of genetic epidemiologists interested in the evaluation and portability of polygenic scores. The authors show that a polygenic risk score to predict prostate cancer risk is very informative for individuals that are classified on three different ancestry categories. The authors show that the polygenic risk score can be used to predict the risk to develop prostate cancer as a function of age. This paper provides evidence that genetic information could be used to provide guidance to clinicians on when to perform screenings to detect prostate cancer in patients.

  (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #2 agreed to share their name with the authors.)

Reviewed by eLife

Societal COVID-19 epidemic counter measures and activities associated with SARS-CoV-2 infection in an adult unvaccinated population – a case-control study in Denmark, June 2021

1. Pernille Kold Munch
2. Laura Espenhain
3. Christian Holm Hansen
4. Tyra Grove Krause
5. Steen Ethelberg

Reviewed by ScreenIT

One Million and Counting: Estimates of Deaths in the United States from Ancestral SARS-CoV-2 and Variants

1. Jo Walker
2. Nathan D. Grubaugh
3. Gregg Gonsalves
4. Virginia Pitzer
5. Zain Rizvi

Reviewed by ScreenIT

Pregnancy during COVID-19: social contact patterns and vaccine coverage of pregnant women from CoMix in 19 European countries

1. Kerry L. M. Wong
2. Amy Gimma
3. Enny S. Paixao
4. CoMix Europe Working Group
5. Daniela Paolotti
6. André Karch
7. Veronika Jäger
8. Joaquin Baruch
9. Tanya Melillo
10. Henrieta Hudeckova
11. Magdalena Rosinska
12. Marta Niedzwiedzka-Stadnik
13. Krista Fischer
14. Sigrid Vorobjov
15. Hanna Sõnajalg
16. Christian Althaus
17. Nicola Low
18. Martina Reichmuth
19. Kari Auranen
20. Markku Nurhonen
21. Goranka Petrović
22. Zvjezdana Lovric Makaric
23. Sónia Namorado
24. Constantino Caetano
25. Ana João Santos
26. Gergely Röst
27. Beatrix Oroszi
28. Márton Karsai
29. Mario Fafangel
30. Petra Klepac
31. Natalija Kranjec
32. Cristina Vilaplana
33. Jordi Casabona
34. Christel Faes
35. Philippe Beutels
36. Niel Hens
37. Christopher I. Jarvis
38. W. John Edmunds

Reviewed by ScreenIT

WAVES (Web-based tool for Analysis and Visualization of Environmental Samples)—a web application for visualization of wastewater pathogen sequencing results

1. Petr Triska
2. Fabian Amman
3. Lukas Endler
4. Andreas Bergthaler

Reviewed by ScreenIT

Vaccine effectiveness against SARS-CoV-2 reinfection during periods of Alpha (B.1.1.7), Delta (B.1.617.2) or Omicron (B.1.1.529) dominance: A Danish nationwide study

1. Katrine Finderup Nielsen
2. Ida Rask Moustsen-Helms
3. Astrid Blicher Schelde
4. Mie Agermose Gram
5. Hanne-Dorthe Emborg
6. Jens Nielsen
7. Christian Holm Hansen
8. Michael Asger Andersen
9. Marianna Meaidi
10. Jan Wohlfahrt
11. Palle Valentiner-Branth

Reviewed by ScreenIT

Response of wastewater-based epidemiology predictor for the second wave of COVID-19 in Ahmedabad, India: A long-term data Perspective

1. Manish Kumar
2. Madhvi Joshi
3. Guangming Jiang
4. Rintaro Yamada
5. Ryo Honda
6. Vaibhav Srivastava
7. Jürgen Mahlknecht
8. Damia Barcelo
9. Sabarathinam Chidambram
10. Anwar Khursheed
11. David W Graham
12. Ritusmita Goswami
13. Keisuke Kuroda
14. Ananda Tiwari
15. Chaitanya Joshi

Reviewed by ScreenIT

Lineage BA.2 dominated the Omicron SARS-CoV-2 epidemic wave in the Philippines

1. Yao-Tsun Li
2. Francisco Gerardo M Polotan
3. Gerald Ivan S Sotelo
4. Anne Pauline A Alpino
5. Ardiane Ysabelle M Dolor
6. Ma. Angelica A Tujan
7. Ma. Ricci R Gomez
8. Othoniel Jan T Onza
9. Angela Kae T Chang
10. Criselda T Bautista
11. June C Carandang
12. Maria Sofia L Yangzon
13. Elcid Aaron R Pangilinan
14. Renato Jacinto Mantaring
15. Alyssa Joyce E Telles
16. John Michael C Egana
17. Joshua Jose S Endozo
18. Rianna Patricia S Cruz
19. Francis A Tablizo
20. Jan Michael C Yap
21. Benedict A Maralit
22. Marc Edsel C Ayes
23. Eva Marie C de la Paz
24. Cynthia P Saloma
25. Dodge R Lim
26. Lei Lanna M Dancel
27. Mayan Uy-Lumandas
28. Inez Andrea P Medado
29. Timothy John R Dizon
30. Katie Hampson
31. Simon Daldry
32. Joseph Hughes
33. Kirstyn Brunker

Reviewed by ScreenIT