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New data and an old puzzle: the negative association between schizophrenia and rheumatoid arthritis

Overview of attention for article published in International Journal of Epidemiology, August 2015
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (82nd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (59th percentile)

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17 X users

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55 Dimensions

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123 Mendeley
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Title
New data and an old puzzle: the negative association between schizophrenia and rheumatoid arthritis
Published in
International Journal of Epidemiology, August 2015
DOI 10.1093/ije/dyv136
Pubmed ID
Authors

S Hong Lee, Enda M Byrne, Christina M Hultman, Anna Kähler, Anna AE Vinkhuyzen, Stephan Ripke, Ole A Andreassen, Thomas Frisell, Alexander Gusev, Xinli Hu, Robert Karlsson, Vasilis X Mantzioris, John J McGrath, Divya Mehta, Eli A Stahl, Qiongyi Zhao, Kenneth S Kendler, Patrick F Sullivan, Alkes L Price, Michael O’Donovan, Yukinori Okada, Bryan J Mowry, Soumya Raychaudhuri, Naomi R Wray, William Byerley, Wiepke Cahn, Rita M Cantor, Sven Cichon, Paul Cormican, David Curtis, Srdjan Djurovic, Valentina Escott-Price, Pablo V Gejman, Lyudmila Georgieva, Ina Giegling, Thomas F Hansen, Andrés Ingason, Yunjung Kim, Bettina Konte, Phil H Lee, Andrew McIntosh, Andrew McQuillin, Derek W Morris, Markus M Nöthen, Colm O’Dushlaine, Ann Olincy, Line Olsen, Carlos N Pato, Michele T Pato, Benjamin S Pickard, Danielle Posthuma, Henrik B Rasmussen, Marcella Rietschel, Dan Rujescu, Thomas G Schulze, Jeremy M Silverman, Srinivasa Thirumalai, Thomas Werge, Ingrid Agartz, Farooq Amin, Maria H Azevedo, Nicholas Bass, Donald W Black, Douglas H R Blackwood, Richard Bruggeman, Nancy G Buccola, Khalid Choudhury, Robert C Cloninger, Aiden Corvin, Nicholas Craddock, Mark J Daly, Susmita Datta, Gary J Donohoe, Jubao Duan, Frank Dudbridge, Ayman Fanous, Robert Freedman, Nelson B Freimer, Marion Friedl, Michael Gill, Hugh Gurling, Lieuwe De Haan, Marian L Hamshere, Annette M Hartmann, Peter A Holmans, René S Kahn, Matthew C Keller, Elaine Kenny, George K Kirov, Lydia Krabbendam, Robert Krasucki, Jacob Lawrence, Todd Lencz, Douglas F Levinson, Jeffrey A Lieberman, Dan-Yu Lin, Don H Linszen, Patrik KE Magnusson, Wolfgang Maier, Anil K Malhotra, Manuel Mattheisen, Morten Mattingsdal, Steven A McCarroll, Helena Medeiros, Ingrid Melle, Vihra Milanova, Inez Myin-Germeys, Benjamin M Neale, Roel A Ophoff, Michael J Owen, Jonathan Pimm, Shaun M Purcell, Vinay Puri, Digby J Quested, Lizzy Rossin, Douglas Ruderfer, Alan R Sanders, Jianxin Shi, Pamela Sklar, David St. Clair, T Scott Stroup, Jim Van Os, Peter M Visscher, Durk Wiersma, Stanley Zammit, S Louis Bridges, Hyon K Choi, Marieke JH Coenen, Niek de Vries, Philippe Dieud, Jeffrey D Greenberg, Tom WJ Huizinga, Leonid Padyukov, Katherine A Siminovitch, Paul P Tak, Jane Worthington, Philip L De Jager, Joshua C Denny, Peter K Gregersen, Lars Klareskog, Xavier Mariette, Robert M Plenge, Mart van Laar, Piet van Riel

Abstract

A long-standing epidemiological puzzle is the reduced rate of rheumatoid arthritis (RA) in those with schizophrenia (SZ) and vice versa. Traditional epidemiological approaches to determine if this negative association is underpinned by genetic factors would test for reduced rates of one disorder in relatives of the other, but sufficiently powered data sets are difficult to achieve. The genomics era presents an alternative paradigm for investigating the genetic relationship between two uncommon disorders. We use genome-wide common single nucleotide polymorphism (SNP) data from independently collected SZ and RA case-control cohorts to estimate the SNP correlation between the disorders. We test a genotype X environment (GxE) hypothesis for SZ with environment defined as winter- vs summer-born. We estimate a small but significant negative SNP-genetic correlation between SZ and RA (-0.046, s.e. 0.026, P = 0.036). The negative correlation was stronger for the SNP set attributed to coding or regulatory regions (-0.174, s.e. 0.071, P = 0.0075). Our analyses led us to hypothesize a gene-environment interaction for SZ in the form of immune challenge. We used month of birth as a proxy for environmental immune challenge and estimated the genetic correlation between winter-born and non-winter born SZ to be significantly less than 1 for coding/regulatory region SNPs (0.56, s.e. 0.14, P  = 0.00090). Our results are consistent with epidemiological observations of a negative relationship between SZ and RA reflecting, at least in part, genetic factors. Results of the month of birth analysis are consistent with pleiotropic effects of genetic variants dependent on environmental context.

X Demographics

X Demographics

The data shown below were collected from the profiles of 17 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 123 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Japan 1 <1%
United Kingdom 1 <1%
Unknown 121 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 19 15%
Student > Ph. D. Student 15 12%
Professor 15 12%
Student > Master 10 8%
Other 9 7%
Other 25 20%
Unknown 30 24%
Readers by discipline Count As %
Medicine and Dentistry 30 24%
Agricultural and Biological Sciences 13 11%
Biochemistry, Genetics and Molecular Biology 11 9%
Psychology 9 7%
Neuroscience 7 6%
Other 10 8%
Unknown 43 35%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 9. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 01 February 2021.
All research outputs
#3,848,115
of 25,629,945 outputs
Outputs from International Journal of Epidemiology
#1,800
of 5,923 outputs
Outputs of similar age
#47,707
of 278,159 outputs
Outputs of similar age from International Journal of Epidemiology
#32
of 76 outputs
Altmetric has tracked 25,629,945 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,923 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 22.0. This one has gotten more attention than average, scoring higher than 67% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 278,159 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 82% of its contemporaries.
We're also able to compare this research output to 76 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 59% of its contemporaries.