30 results on '"Jankipersadsing, Soesma"'
Search Results
2. Lifelines NEXT : a prospective birth cohort adding the next generation to the three-generation Lifelines cohort study
- Author
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Warmink-Perdijk, Willemijn D. B., Peters, Lilian L., Tigchelaar, Ettje F., Dekens, Jackie A. M., Jankipersadsing, Soesma A., Zhernakova, Alexandra, Bossers, Willem J. R., Sikkema, Jan, de Jonge, Ank, Reijneveld, Sijmen A., Verkade, Henkjan J., Koppelman, Gerard H., Wijmenga, Cisca, Kuipers, Folkert, and Scherjon, Sicco A.
- Published
- 2020
3. Gender differences in the mental health impact of the COVID-19 lockdown: Longitudinal evidence from the Netherlands
- Author
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Boezen, Marike H., Mierau, Jochen O., Franke, Lude, Dekens, Jackie, Deelen, Patrick, Lanting, Pauline, Vonk, Judith M., Nolte, Ilja, Ori, Anil P.S., Claringbould, Annique, Boulogne, Floranne, Dijkema, Marjolein X.L., Wiersma, Henry H., Warmerdam, Robert, Jankipersadsing, Soesma A., Vloo, A., Alessie, R.J.M., and Mierau, J.O.
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- 2021
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4. Shared DNA methylation signatures in childhood allergy: The MeDALL study
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Xu, Cheng-Jian, Gruzieva, Olena, Qi, Cancan, Esplugues, Ana, Gehring, Ulrike, Bergström, Anna, Mason, Dan, Chatzi, Leda, Porta, Daniela, Lodrup Carlsen, Karin C., Baïz, Nour, Madore, Anne-Marie, Alenius, Harri, van Rijkom, Bianca, Jankipersadsing, Soesma A., van der Vlies, Pieter, Kull, Inger, van Hage, Marianne, Bustamante, Mariona, Lertxundi, Aitana, Torrent, Matias, Santorelli, Gillian, Fantini, Maria Pia, Hovland, Vegard, Pesce, Giancarlo, Fyhrquist, Nanna, Laatikainen, Tiina, Nawijn, Martijn C., Li, Yang, Wijmenga, Cisca, Netea, Mihai G., Bousquet, Jean, Anto, Josep M., Laprise, Catherine, Haahtela, Tari, Annesi-Maesano, Isabella, Carlsen, Kai-Håkon, Gori, Davide, Kogevinas, Manolis, Wright, John, Söderhäll, Cilla, Vonk, Judith M., Sunyer, Jordi, Melén, Erik, and Koppelman, Gerard H.
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- 2021
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5. DNA methylation in childhood asthma: an epigenome-wide meta-analysis
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Xu, Cheng-Jian, Söderhäll, Cilla, Bustamante, Mariona, Baïz, Nour, Gruzieva, Olena, Gehring, Ulrike, Mason, Dan, Chatzi, Leda, Basterrechea, Mikel, Llop, Sabrina, Torrent, Maties, Forastiere, Francesco, Fantini, Maria Pia, Carlsen, Karin C Lødrup, Haahtela, Tari, Morin, Andréanne, Kerkhof, Marjan, Merid, Simon Kebede, van Rijkom, Bianca, Jankipersadsing, Soesma A, Bonder, Marc Jan, Ballereau, Stephane, Vermeulen, Cornelis J, Aguirre-Gamboa, Raul, de Jongste, Johan C, Smit, Henriette A, Kumar, Ashish, Pershagen, Göran, Guerra, Stefano, Garcia-Aymerich, Judith, Greco, Dario, Reinius, Lovisa, McEachan, Rosemary R C, Azad, Raf, Hovland, Vegard, Mowinckel, Petter, Alenius, Harri, Fyhrquist, Nanna, Lemonnier, Nathanaël, Pellet, Johann, Auffray, Charles, van der Vlies, Pieter, van Diemen, Cleo C, Li, Yang, Wijmenga, Cisca, Netea, Mihai G, Moffatt, Miriam F, Cookson, William O C M, Anto, Josep M, Bousquet, Jean, Laatikainen, Tiina, Laprise, Catherine, Carlsen, Kai-Håkon, Gori, Davide, Porta, Daniela, Iñiguez, Carmen, Bilbao, Jose Ramon, Kogevinas, Manolis, Wright, John, Brunekreef, Bert, Kere, Juha, Nawijn, Martijn C, Annesi-Maesano, Isabella, Sunyer, Jordi, Melén, Erik, and Koppelman, Gerard H
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- 2018
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6. Transmission and dynamics of mother-infant gut viruses during pregnancy and early life.
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Garmaeva, Sanzhima, Sinha, Trishla, Gulyaeva, Anastasia, Kuzub, Nataliia, Spreckels, Johanne E., Andreu-Sánchez, Sergio, Gacesa, Ranko, Vich Vila, Arnau, Brushett, Siobhan, Kruk, Marloes, Lifelines NEXT cohort study, Dotinga, Aafje, Gordijn, Sanne, Jankipersadsing, Soesma, de Jonge, Ank, de Kroon, Marlou L. A., Koppelman, Gerard H., Peters, Lilian L., Prins, Jelmer R., and Reijneveld, Sijmen A.
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INFECTIOUS disease transmission ,PREGNANCY ,BREASTFEEDING ,MOTHERS ,VIRUS-like particles ,SHOTGUN sequencing ,BIFIDOBACTERIUM ,INFANT development ,BACTERIOPHAGES - Abstract
Early development of the gut ecosystem is crucial for lifelong health. While infant gut bacterial communities have been studied extensively, the infant gut virome remains under-explored. To study the development of the infant gut virome over time and the factors that shape it, we longitudinally assess the composition of gut viruses and their bacterial hosts in 30 women during and after pregnancy and in their 32 infants during their first year of life. Using shotgun metagenomic sequencing applied to dsDNA extracted from Virus-Like Particles (VLPs) and bacteria, we generate 205 VLP metaviromes and 322 total metagenomes. With this data, we show that while the maternal gut virome composition remains stable during late pregnancy and after birth, the infant gut virome is dynamic in the first year of life. Notably, infant gut viromes contain a higher abundance of active temperate phages compared to maternal gut viromes, which decreases over the first year of life. Moreover, we show that the feeding mode and place of delivery influence the gut virome composition of infants. Lastly, we provide evidence of co-transmission of viral and bacterial strains from mothers to infants, demonstrating that infants acquire some of their virome from their mother's gut. Gut ecosystem colonization impacts lifelong health. Here, authors track mother-infant gut viruses over time, reveal feeding's influence on early viral colonization, and demonstrate the co-transmission of bacteriophages and bacteria from mothers to infants. [ABSTRACT FROM AUTHOR]
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- 2024
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7. DNA Methylation in Newborns and Maternal Smoking in Pregnancy: Genome-wide Consortium Meta-analysis
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Joubert, Bonnie R., Felix, Janine F., Yousefi, Paul, Bakulski, Kelly M., Just, Allan C., Breton, Carrie, Reese, Sarah E., Markunas, Christina A., Richmond, Rebecca C., Xu, Cheng-Jian, Küpers, Leanne K., Oh, Sam S., Hoyo, Cathrine, Gruzieva, Olena, Söderhäll, Cilla, Salas, Lucas A., Baïz, Nour, Zhang, Hongmei, Lepeule, Johanna, Ruiz, Carlos, Ligthart, Symen, Wang, Tianyuan, Taylor, Jack A., Duijts, Liesbeth, Sharp, Gemma C., Jankipersadsing, Soesma A., Nilsen, Roy M., Vaez, Ahmad, Fallin, M. Daniele, Hu, Donglei, Litonjua, Augusto A., Fuemmeler, Bernard F., Huen, Karen, Kere, Juha, Kull, Inger, Munthe-Kaas, Monica Cheng, Gehring, Ulrike, Bustamante, Mariona, Saurel-Coubizolles, Marie José, Quraishi, Bilal M., Ren, Jie, Tost, Jörg, Gonzalez, Juan R., Peters, Marjolein J., Håberg, Siri E., Xu, Zongli, van Meurs, Joyce B., Gaunt, Tom R., Kerkhof, Marjan, Corpeleijn, Eva, Feinberg, Andrew P., Eng, Celeste, Baccarelli, Andrea A., Benjamin Neelon, Sara E., Bradman, Asa, Merid, Simon Kebede, Bergström, Anna, Herceg, Zdenko, Hernandez-Vargas, Hector, Brunekreef, Bert, Pinart, Mariona, Heude, Barbara, Ewart, Susan, Yao, Jin, Lemonnier, Nathanaël, Franco, Oscar H., Wu, Michael C., Hofman, Albert, McArdle, Wendy, Van der Vlies, Pieter, Falahi, Fahimeh, Gillman, Matthew W., Barcellos, Lisa F., Kumar, Ashish, Wickman, Magnus, Guerra, Stefano, Charles, Marie-Aline, Holloway, John, Auffray, Charles, Tiemeier, Henning W., Smith, George Davey, Postma, Dirkje, Hivert, Marie-France, Eskenazi, Brenda, Vrijheid, Martine, Arshad, Hasan, Antó, Josep M., Dehghan, Abbas, Karmaus, Wilfried, Annesi-Maesano, Isabella, Sunyer, Jordi, Ghantous, Akram, Pershagen, Göran, Holland, Nina, Murphy, Susan K., DeMeo, Dawn L., Burchard, Esteban G., Ladd-Acosta, Christine, Snieder, Harold, Nystad, Wenche, Koppelman, Gerard H., Relton, Caroline L., Jaddoe, Vincent W.V., Wilcox, Allen, Melén, Erik, and London, Stephanie J.
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- 2016
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8. Population-based metagenomics analysis reveals markers for gut microbiome composition and diversity
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Zhernakova, Alexandra, Kurilshikov, Alexander, Bonder, Marc Jan, Tigchelaar, Ettje F., Schirmer, Melanie, Vatanen, Tommi, Mujagic, Zlatan, Vila, Arnau Vich, Falony, Gwen, Vieira-Silva, Sara, Wang, Jun, Imhann, Floris, Brandsma, Eelke, Jankipersadsing, Soesma A., Joossens, Marie, Cenit, Maria Carmen, Deelen, Patrick, Swertz, Morris A., study, LifeLines cohort, Weersma, Rinse K., Feskens, Edith J. M., Netea, Mihai G., Gevers, Dirk, Jonkers, Daisy, Franke, Lude, Aulchenko, Yurii S., Huttenhower, Curtis, Raes, Jeroen, Hofker, Marten H., Xavier, Ramnik J., Wijmenga, Cisca, and Fu, Jingyuan
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- 2016
9. Workplace impact on employees: A Lifelines Corona Research Initiative on the return to work
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Mobach, Mark P., Boezen, H. M, Mierau, Jochen O., Franke, H. Lude, Dekens, Jackie, Deelen, Patrick, Lanting, Pauline, Vonk, Judith M., Nolte, Ilja, Ori, Anil P.S., Claringbould, Annique, Boulogne, Floranne, Dijkema, Marjolein X.L., Wiersma, Henry H., Warmerdam, Robert, Jankipersadsing, Soesma A., van Blokland, Irene, de Bock, Geer Truida H., Rosmalen, Judith G.M., Wijmenga, Cisca, Life Course Epidemiology (LCE), Groningen Research Institute for Asthma and COPD (GRIAC), Value, Affordability and Sustainability (VALUE), Research programme EEF, Faculteit Medische Wetenschappen/UMCG, Stem Cell Aging Leukemia and Lymphoma (SALL), Groningen Institute for Gastro Intestinal Genetics and Immunology (3GI), Basic and Translational Research and Imaging Methodology Development in Groningen (BRIDGE), Damage and Repair in Cancer Development and Cancer Treatment (DARE), and Interdisciplinary Centre Psychopathology and Emotion regulation (ICPE)
- Abstract
A large proportion of the global workforce migrated home during the COVID-19 pandemic and subsequent lockdowns. It remains unclear what the exact differences between home workers and non-home workers were, especially during the pandemic when a return to work was imminent. How were building, workplace, and related facilities associated with workers’ perceptions and health? What are the lessons to be learned? Lifelines Corona Research Initiative was used to compare employees’ workplaces and related concerns, facilities, work quality, and health in a complete case analysis (N = 12,776) when return to work was imminent. Mann-Whitney U, logistic regression, and Wilcoxon matched-pairs were used for analyses. Notwithstanding small differences, the results show that home workers had less favourable scores for concerns about and facilities of on-site buildings and workplaces upon return to work, but better scores for work quality and health than non-home workers. However, additional analyses also suggest that building, workplace, and related facilities may have had the capacity to positively influence employees’ affective responses and work quality, but not always their health.
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- 2023
10. Symptoms and quality of life before, during, and after a SARS-CoV-2 PCR positive or negative test: data from Lifelines.
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Goërtz, Yvonne M. J., Spruit, Martijn A., Van Herck, Maarten, Dukers-Muijrers, Nicole, Lifelines Corona Research Initiative, Boezen, H. Marike, Mierau, Jochen O., Franke, H. Lude, Dekens, Jackie, Deelen, Patrick, Lanting, Pauline, Vonk, Judith M., Nolte, Ilja, Ori, Anil P. S., Claringbould, Annique, Boulogne, Floranne, Dijkema, Marjolein X. L., Wiersma, Henry H., Warmerdam, Robert, and Jankipersadsing, Soesma A.
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DIAGNOSTIC use of polymerase chain reaction ,SARS-CoV-2 ,COVID-19 testing ,POLYMERASE chain reaction ,SYMPTOMS - Abstract
This study evaluates to what extent symptoms are present before, during, and after a positive SARS-CoV-2 polymerase chain reaction (PCR) test, and to evaluate how the symptom burden and quality of Life (QoL) compares to those with a negative PCR test. Participants from the Dutch Lifelines COVID-19 Cohort Study filled-out as of March 2020 weekly, later bi-weekly and monthly, questions about demographics, COVID-19 diagnosis and severity, QoL, and symptoms. The study population included those with one positive or negative PCR test who filled out two questionnaires before and after the test, resulting in 996 SARS-CoV-2 PCR positive and 3978 negative participants. Nearly all symptoms were more often reported after a positive test versus the period before the test (p < 0.05), except fever. A higher symptom prevalence after versus before a test was also found for nearly all symptoms in negatives (p < 0.05). Before the test, symptoms were already partly present and reporting of nearly all symptoms before did not differ between positives and negatives (p > 0.05). QoL decreased around the test for positives and negatives, with a larger deterioration for positives. Not all symptoms after a positive SARS-CoV-2 PCR test might be attributable to the infection and symptoms were also common in negatives. [ABSTRACT FROM AUTHOR]
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- 2023
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11. The effect of host genetics on the gut microbiome
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Bonder, Marc Jan, Kurilshikov, Alexander, Tigchelaar, Ettje F, Mujagic, Zlatan, Imhann, Floris, Vila, Arnau Vich, Deelen, Patrick, Vatanen, Tommi, Schirmer, Melanie, Smeekens, Sanne P, Zhernakova, Daria V, Jankipersadsing, Soesma A, Jaeger, Martin, Oosting, Marije, Cenit, Maria Carmen, Masclee, Ad A M, Swertz, Morris A, Li, Yang, Kumar, Vinod, Joosten, Leo, Harmsen, Hermie, Weersma, Rinse K, Franke, Lude, Hofker, Marten H, Xavier, Ramnik J, Jonkers, Daisy, Netea, Mihai G, Wijmenga, Cisca, Fu, Jingyuan, and Zhernakova, Alexandra
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- 2016
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12. Impact of the COVID-19 pandemic on adults with moderate-to-severe atopic dermatitis in the Dutch general population
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Zhang, Junfen, Loman, Laura, Kamphuis, Esmé, Schuttelaar, Marie L A, Boezen, H. M., Mierau, Jochen, Franke, H. Lude, Dekens, Jackie, Deelen, Patrick, Lanting, Pauline, Vonk, Judith M., Nolte, Ilja M., Ori, Anil P.S., Claringbould, Annique, Boulogne, Floranne, Dijkema, Marjolein X L, Wiersma, Henry H., Warmerdam, Robert, Jankipersadsing, Soesma A., van Blokland, Irene, Public Health Research (PHR), Life Course Epidemiology (LCE), Groningen Research Institute for Asthma and COPD (GRIAC), Value, Affordability and Sustainability (VALUE), Research programme EEF, Stem Cell Aging Leukemia and Lymphoma (SALL), and Groningen Institute for Gastro Intestinal Genetics and Immunology (3GI)
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atopic dermatitis ,RL1-803 ,atopic eczema ,COVID-19 ,disease severity ,epidemiology ,Dermatology ,general population ,Article - Published
- 2022
13. Environmental factors shaping the gut microbiome in a Dutch population
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Gacesa, Ranko, Kurilshikov, Alexander, Vich Vila, Arnau, Sinha, Trishla, Klaassen, M, Bolte, L.A., Andreu-Sánchez, S., Chen, L., Collij, V., Hu, S., Dekens-Konter, J.A.M., Lenters, Virissa, Björk, J.R., Swarte, J.C., Swertz, Morris A., Jansen, B.H.R., Gelderloos-Arends, J., Jankipersadsing, Soesma A, Hofker, M., Vermeulen, Roel, Sanna, S., Harmsen, H J M, Wijmenga, Cisca, Fu, J., Zhernakova, A., Weersma, Rinse K., IRAS OH Epidemiology Chemical Agents, Groningen Institute for Gastro Intestinal Genetics and Immunology (3GI), Microbes in Health and Disease (MHD), Center for Liver, Digestive and Metabolic Diseases (CLDM), Translational Immunology Groningen (TRIGR), and IRAS OH Epidemiology Chemical Agents
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Multidisciplinary ,Bacteria ,GENETICS ,ASSOCIATION ,Environment ,METAGENOMICS ,ENTEROTYPES ,Diet ,Gastrointestinal Microbiome ,ALIGNMENT ,Socioeconomic Factors ,POLLUTION ,MARKERS ,USE REGRESSION-MODELS ,AREAS ,Humans ,INTESTINAL MICROBIOME ,Life Style ,Netherlands - Abstract
The gut microbiome is associated with diverse diseases(1-3), but a universal signature of a healthy or unhealthy microbiome has not been identified, and there is a need to understand how genetics, exposome, lifestyle and diet shape the microbiome in health and disease. Here we profiled bacterial composition, function, antibiotic resistance and virulence factors in the gut microbiomes of 8,208 Dutch individuals from a three-generational cohort comprising 2,756 families. We correlated these to 241 host and environmental factors, including physical and mental health, use of medication, diet, socioeconomic factors and childhood and current exposome. We identify that the microbiome is shaped primarily by the environment and cohabitation. Only around 6.6% of taxa are heritable, whereas the variance of around 48.6% of taxa is significantly explained by cohabitation. By identifying 2,856 associations between the microbiome and health, we find that seemingly unrelated diseases share a common microbiome signature that is independent of comorbidities. Furthermore, we identify 7,519 associations between microbiome features and diet, socioeconomics and early life and current exposome, with numerous early-life and current factors being significantly associated with microbiome function and composition. Overall, this study provides a comprehensive overview of gut microbiome and the underlying impact of heritability and exposures that will facilitate future development of microbiome-targeted therapies.
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- 2022
14. MICROBIOME: Population-based metagenomics analysis reveals markers for gut microbiome composition and diversity
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Zhernakova, Alexandra, Kurilshikov, Alexander, Bonder, Marc Jan, Tigchelaar, Ettje F., Schirmer, Melanie, Vatanen, Tommi, Mujagic, Zlatan, Vila, Arnau Vich, Falony, Gwen, Vieira-Silva, Sara, Wang, Jun, Imhann, Floris, Brandsma, Eelke, Jankipersadsing, Soesma A., Joossens, Marie, Cenit, Maria Carmen, Deelen, Patrick, Swertz, Morris A., Weersma, Rinse K., Feskens, Edith J. M., Netea, Mihai G., Gevers, Dirk, Jonkers, Daisy, Franke, Lude, Aulchenko, Yurii S., Huttenhower, Curtis, Raes, Jeroen, Hofker, Marten H., Xavier, Ramnik J., Wijmenga, Cisca, and Fu, Jingyuan
- Published
- 2016
15. Improving coeliac disease risk prediction by testing non-HLA variants additional to HLA variants
- Author
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Romanos, Jihane, Rosén, Anna, Kumar, Vinod, Trynka, Gosia, Franke, Lude, Szperl, Agata, Gutierrez-Achury, Javier, van Diemen, Cleo C, Kanninga, Roan, Jankipersadsing, Soesma A, Steck, Andrea, Eisenbarth, Georges, van Heel, David A, Cukrowska, Bozena, Bruno, Valentina, Mazzilli, Maria Cristina, Núñez, Concepcion, Bilbao, Jose Ramon, Mearin, M Luisa, Barisani, Donatella, Rewers, Marian, Norris, Jill M, Ivarsson, Anneli, Boezen, H Marieke, Liu, Edwin, Wijmenga, Cisca, Scerri, Cristian, Koltai, Tunde, Kolaček, Sanja, Mišak, Zrinka, Abdović, Slaven, Koletzko, Sibylle, Osiander, Gertraud, Werkstetter, Katharina, Mummert, Eckart, Korponay-Szabo, Ilma R, Gyimesi, Judit, Shamir, Raanan, Hartman, Corina, Bravi, Enzo, Poles, Marco, Auricchio, Renata, Limongelli, G Gianna Giovamma, Greco, Luigi, Troncone, Riccardo, Villanacci, Vincenzo, Bindels, Jacques G, Brand, Ronald, Kupper, Bibi Funke, Esch, Caroline E Hogen, Hopman, Erica G, Koning, Frits, Kooy-Winkelaar, Yvonne, te Marvelde, Chantal, Putter, Hein, Stoopman, Els, Vriezinga, Sabine, Sollid, Ludvig M, Ráki, Melinda, Chmielewska, Ania, Dziechciarz, Piotr, Pieścik-Lech, Małgorzata, Szajewska, Hania, Szaflarska-Szczepanik, Anna, Castillejo, Gemma, Capilla, Amalia, Varea, Vicente, Ribes-Koninckx, Carmen, Lopez, Anna, Crespo, Paula, Martinez, Eva, Polanco, Isabel, Högberg, Lotta, Stenhammar, Lars, Carlsson, Annelie, Webb, Charlotta, Hammarroth, Solveig, Hernell, Olle, Lagerqvist, Carina, Myléus, Anna, Nordyke, Katrina, Norström, Fredrik, Sandström, Olof, Wall, Stig, and Karlsson, Eva
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- 2014
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16. Sex and Gender-Related Differences in COVID-19 Diagnoses and SARS-CoV-2 Testing Practices During the First Wave of the Pandemic: The Dutch Lifelines COVID-19 Cohort Study.
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Ballering, Aranka Viviënne, Oertelt-Prigione, Sabine, olde Hartman, Tim C., Rosmalen, Judith G.M., Boezen, Marike, Mierau, Jochen O., Franke, Lude H., Dekens, Jackie, Deelen, Patrick, Lanting, Pauline, Vonk, Judith M., Nolte, Ilja, Ori, Anil P.S., Claringbould, Annique, Boulogne, Floranne, Dijkema, Marjolein X.L., Wiersma, Henry H., Warmerdam, Robert, and Jankipersadsing, Soesma A.
- Subjects
STATISTICS ,EVALUATION of medical care ,REVERSE transcriptase polymerase chain reaction ,COVID-19 ,CONFIDENCE intervals ,MULTIPLE regression analysis ,SEX distribution ,EMPLOYMENT ,DESCRIPTIVE statistics ,COVID-19 testing ,POLYMERASE chain reaction ,SMOKING ,ODDS ratio ,COVID-19 pandemic ,LONGITUDINAL method ,COMORBIDITY - Abstract
Background: Although sex differences are described in Coronavirus Disease 2019 (COVID-19) diagnoses and testing, many studies neglect possible gender-related influences. Additionally, research is often performed in clinical populations, while most COVID-19 patients are not hospitalized. Therefore, we investigated associations between sex and gender-related variables, and COVID-19 diagnoses and testing practices in a large general population cohort during the first wave of the pandemic when testing capacity was limited. Methods: We used data from the Lifelines COVID-19 Cohort (N = 74,722; 60.8% female). We applied bivariate and multiple logistic regression analyses. The outcomes were a COVID-19 diagnosis (confirmed by SARS-CoV-2 PCR testing or physician's clinical diagnosis) and PCR testing. Independent variables included among others participants' sex, age, somatic comorbidities, occupation, and smoking status. Sex-by-comorbidity and sex-by-occupation interaction terms were included to investigate sex differences in associations between the presence of comorbidities or an occupation with COVID-19 diagnoses or testing practices. Results: In bivariate analyses female sex was significantly associated with COVID-19 diagnoses and testing, but significance did not persist in multiple logistic regression analyses. However, a gender-related variable, being a health care worker, was significantly associated with COVID-19 diagnoses (OR = 1.68; 95%CI = 1.30–2.17) and testing (OR = 12.5; 95%CI = 8.55–18.3). Female health care workers were less often diagnosed and tested than male health care workers (OR
interaction = 0.54; 95%CI = 0.32–0.92, ORinteraction = 0.53; 95%CI = 0.29–0.97, respectively). Conclusions: We found no sex differences in COVID-19 diagnoses and testing in the general population. Among health care workers, a male preponderance in COVID-19 diagnoses and testing was observed. This could be explained by more pronounced COVID-19 symptoms in males or by gender inequities. [ABSTRACT FROM AUTHOR]- Published
- 2021
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17. Author Correction: Symptoms and quality of life before, during, and after a SARS‑CoV‑2 PCR positive or negative test: data from Lifelines.
- Author
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Goërtz, Yvonne M. J., Spruit, Martijn A., Van Herck, Maarten, Dukers-Muijrers, Nicole, Boezen, H. Marike, Mierau, Jochen O., Franke, H. Lude, Dekens, Jackie, Deelen, Patrick, Lanting, Pauline, Vonk, Judith M., Nolte, Ilja, Ori, Anil P. S., Claringbould, Annique, Boulogne, Floranne, Dijkema, Marjolein X. L., Wiersma, Henry H., Warmerdam, Robert, Jankipersadsing, Soesma A., and van Blokland, Irene
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SARS-CoV-2 ,QUALITY of life ,SYMPTOMS ,CONSORTIA - Abstract
This document is a correction notice for an article titled "Symptoms and quality of life before, during, and after a SARS-CoV-2 PCR positive or negative test: data from Lifelines" published in Scientific Reports. The correction states that H. Marike Boezen, one of the authors, is deceased. Additionally, there was an error in the Consortium list, where Judith G. M. Rosmalen was incorrectly listed as an author. The correction has been made to the original article. The authors of the article are Yvonne M. J. Goërtz, Martijn A. Spruit, Maarten Van Herck, Nicole Dukers-Muijrers, H. Marike Boezen, Jochen O. Mierau, H. Lude Franke, Jackie Dekens, Patrick Deelen, Pauline Lanting, Judith M. Vonk, Ilja Nolte, Anil P. S. Ori, Annique Claringbould, Floranne Boulogne, Marjolein X. L. Dijkema, Henry H. Wiersma, Robert Warmerdam, Soesma A. Jankipersadsing, Irene van Blokland, Geertruida H. de Bock, Cisca Wijmenga, Carla J. H. van der Kallen, Chris Burtin, and Daisy J. A. Janssen. [Extracted from the article]
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- 2024
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18. Letter: A GWAS meta-analysis suggests roles for xenobiotic metabolism and ion channel activity in the biology of stool frequency in GUT, vol 66, issue 4, pp 756-758
- Author
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Jankipersadsing, Soesma A., Hadizadeh, Fatemeh, Jan Bonder, Marc, Tigchelaar, Ettje F., Deelen, Patrick, Fu, Jingyuan, Andreasson, Anna, Agreus, Lars, Walter, Susanna, Wijmenga, Cisca, Hysi, Pirro, DAmato, Mauro, and Zhernakova, Alexandra
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Klinisk medicin ,Clinical Medicine - Abstract
n/a Funding Agencies|Top Institute Food and Nutrition, Wageningen [GH001]; Netherlands Organization for Scientific Research [NWO-VIDI 864.13.013]; Swedish Research Council (VR); University of Groningen
- Published
- 2017
19. The emerging landscape of dynamic DNA methylation in early childhood
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Xu, Cheng-Jian, Bonder, Marc Jan, Söderhäll, Cilla, Bustamante, Mariona, Baïz, Nour, Gehring, Ulrike, Jankipersadsing, Soesma A, Van Der Vlies, Pieter, van Diemen, Cleo C, van Rijkom, Bianca, Just, Jocelyne, Kull, Inger, Kere, Juha, Antó, Josep Maria, Bousquet, Jean, Zhernakova, Alexandra, Wijmenga, Cisca, Annesi-Maesano, Isabella, Sunyer, Jordi, Melén, Erik, Li, Yang, Postma, Dirkje S, Koppelman, Gerard H, LS IRAS EEPI ME (Milieu epidemiologie), dIRAS RA-2, Institut Pierre Louis d'Epidémiologie et de Santé Publique (iPLESP), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM), CHU Trousseau [APHP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier), Contre les MAladies Chroniques pour un VIeillissement Actif en Languedoc-Roussillon (MACVIA-LR), Université Montpellier 1 (UM1)-Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre Hospitalier Universitaire de Nîmes (CHU Nîmes)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-European Innovation Partnership on Active and Healthy Ageing Reference Site (EIP on AHA), Commission Européenne-Commission Européenne-Organisation Mondiale de la Santé / World Health Organization Office (OMS / WHO), Vieillissement et Maladies chroniques : approches épidémiologique et de santé publique (VIMA), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut National de la Santé et de la Recherche Médicale (INSERM), Centre de l'Asthme et des Allergies [CHU Trousseau], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-CHU Trousseau [APHP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU), Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier)-Université Montpellier 1 (UM1)-Centre Hospitalier Universitaire de Nîmes (CHU Nîmes)-European Innovation Partnership on Active and Healthy Ageing Reference Site (EIP on AHA), Commission Européenne-Commission Européenne-Organisation Mondiale de la Santé / World Health Organization Office (OMS / WHO)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Montpellier (UM), Research Programs Unit, Juha Kere / Principal Investigator, LS IRAS EEPI ME (Milieu epidemiologie), dIRAS RA-2, Groningen Institute for Gastro Intestinal Genetics and Immunology (3GI), Translational Immunology Groningen (TRIGR), and Groningen Research Institute for Asthma and COPD (GRIAC)
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Epigenomics ,0301 basic medicine ,Aging ,Methylation quatitative trait loci ,ADN ,DISEASE ,Methylation quantitative trait loci ,Epigenesis, Genetic ,Child Development ,Pregnancy ,Non-U.S. Gov't ,Child ,POPULATION ,Maternal smoking ,Genetics ,DNA methylation ,NEWBORNS ,Research Support, Non-U.S. Gov't ,Smoking ,1184 Genetics, developmental biology, physiology ,Environmental exposure ,Methylation ,3. Good health ,D-ASPARTATE ,Maternal Exposure ,Child, Preschool ,Prenatal Exposure Delayed Effects ,Female ,Metilació ,PROJECT ,Research Article ,Infància ,Biotechnology ,Quantitative Trait Loci ,Genomics ,Quantitative trait locus ,Biology ,Research Support ,03 medical and health sciences ,AGE ,Genetic ,MICROARRAY ,Journal Article ,Humans ,Genetic Predisposition to Disease ,COHORT ,Epigenetics ,Preschool ,Gene ,METAANALYSIS ,[SDV.MHEP.PED]Life Sciences [q-bio]/Human health and pathology/Pediatrics ,[SDV.GEN.GPO]Life Sciences [q-bio]/Genetics/Populations and Evolution [q-bio.PE] ,Infant, Newborn ,Genetic Variation ,Infant ,DNA ,DNA Methylation ,Newborn ,Childhood ,030104 developmental biology ,[SDV.GEN.GH]Life Sciences [q-bio]/Genetics/Human genetics ,ASTHMA ,CpG Islands ,3111 Biomedicine ,Epigenesis ,Genome-Wide Association Study - Abstract
Background: DNA methylation has been found to associate with disease, aging and environmental exposure, but it is unknown how genome, environment and disease influence DNA methylation dynamics in childhood. Results: By analysing 538 paired DNA blood samples from children at birth and at 4–5 years old and 726 paired samples from children at 4 and 8 years old from four European birth cohorts using the Illumina Infinium Human Methylation 450 k chip, we have identified 14,150 consistent age-differential methylation sites (a-DMSs) at epigenome-wide significance of p
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- 2017
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20. Analysis of 1135 gut metagenomes identifies sex-specific resistome profiles.
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Sinha, Trishla, Vich Vila, Arnau, Garmaeva, Sanzhima, Jankipersadsing, Soesma A., Imhann, Floris, Collij, Valerie, Bonder, Marc Jan, Jiang, Xiaofang, Gurry, Thomas, Alm, Eric J., D'Amato, Mauro, Weersma, Rinse K., Scherjon, Sicco, Wijmenga, Cisca, Fu, Jingyuan, Kurilshikov, Alexander, and Zhernakova, Alexandra
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- 2019
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21. The emerging landscape of dynamic DNA methylation in early childhood.
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Cheng-Jian Xu, Bonder, Marc Jan, Söderhäll, Cilla, Bustamante, Mariona, Baïz, Nour, Gehring, Ulrike, Jankipersadsing, Soesma A., van der Vlies, Pieter, van Diemen, Cleo C., van Rijkom, Bianca, Just, Jocelyne, Kull, Inger, Kere, Juha, Antó, Josep Maria, Bousquet, Jean, Zhernakova, Alexandra, Wijmenga, Cisca, Annesi-Maesano, Isabella, Sunyer, Jordi, and Melén, Erik
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DNA methylation ,GENETIC transcription ,GENE expression ,PHYSIOLOGICAL effects of tobacco ,EPIGENETICS ,CHILD development - Abstract
Background: DNA methylation has been found to associate with disease, aging and environmental exposure, but it is unknown how genome, environment and disease influence DNA methylation dynamics in childhood. Results: By analysing 538 paired DNA blood samples from children at birth and at 4-5 years old and 726 paired samples from children at 4 and 8 years old from four European birth cohorts using the Illumina Infinium Human Methylation 450 k chip, we have identified 14,150 consistent age-differential methylation sites (a-DMSs) at epigenomewide significance of p <1.14 x 10
-7 . Genes with an increase in age-differential methylation were enriched in pathways related to 'development', and were more often located in bivalent transcription start site (TSS) regions, which can silence or activate expression of developmental genes. Genes with a decrease in age-differential methylation were involved in cell signalling, and enriched on H3K27ac, which can predict developmental state. Maternal smoking tended to decrease methylation levels at the identified da-DMSs. We also found 101 a-DMSs (0.71%) that were regulated by genetic variants using cis-differential Methylation Quantitative Trait Locus (cis-dMeQTL) mapping. Moreover, a-DMS-associated genes during early development were significantly more likely to be linked with disease. Conclusion: Our study provides new insights into the dynamic epigenetic landscape of the first 8 years of life. [ABSTRACT FROM AUTHOR]- Published
- 2017
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22. Epigenome-Wide Meta-Analysis of Methylation in Children Related to Prenatal NO2 Air Pollution Exposure.
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Gruzieva, Olena, Xu, Cheng-Jian, Breton, Carrie V., Annesi-Maesano, Isabella, Antó, Josep M., Auffray, Charles, Ballereau, Stéphane, Bellander, Tom, Bousquet, Jean, Bustamante, Mariona, Charles, Marie-Aline, Kluizenaar, Yvonne de, Dekker, Herman T. den, Duijts, Liesbeth, Felix, Janine F., Gehring, Ulrike, Guxens, Mònica, Jaddoe, Vincent V. W., Jankipersadsing, Soesma A., and Merid, Simon Kebede
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AIR pollution ,HEALTH ,CHILDREN'S health ,NITROGEN dioxide & the environment ,DNA methylation ,PRENATAL influences ,MATERNAL exposure ,HUMAN genome ,MOTHER-child relationship ,CORD blood ,GENE expression ,META-analysis ,NITROGEN oxides ,PROBABILITY theory ,ENVIRONMENTAL exposure ,MULTIPLE regression analysis ,DATA analysis software ,DESCRIPTIVE statistics ,EPIGENOMICS ,FETUS - Abstract
BACKGROUND: Prenatal exposure to air pollution is considered to be associated with adverse effects on child health. This may partly be mediated by mechanisms related to DNA methylation. OBJECTIVES: We investigated associations between exposure to air pollution, using nitrogen dioxide (NO
2 ) as marker, and epigenome-wide cord blood DNA methylation. METHODS: We meta-analyzed the associations between NO2 exposure at residential addresses during pregnancy and cord blood DNA methylation (Illumina 450K) in four European and North American studies (n = 1,508) with subsequent look-up analyses in children ages 4 (n = 733) and 8 (n = 786) years. Additionally, we applied a literature-based candidate approach for antioxidant and anti-inflammatory genes. To assess influence of exposure at the transcriptornics level, we related mRNA expression in blood cells to NO2 exposure in 4- (n = 111) and 16-year-olds (n = 239). RESULTS: We found epigenome-wide significant associations [false discovery rate (FDR) p < 0.05] between maternal NO2 exposure during pregnancy and DNA methylation in newborns for 3 CpG sites in mitochondria-related genes: cgl2283362 (LONP1), cg24l72570 (3.8 kbp upstream of HIBADH), and cg08973675 (SLC25A28). The associations with cg08973675 methylation were also significant in the older children. Further analysis of antioxidant and anti-inflammatory genes revealed differentially methylated CpGs in CAT and TPO in newborns (FDR p < 0.05). NO2 exposure at the time of biosampling in childhood had a significant impact on CAT and TPO expression. CONCLUSIONS: NO2 exposure during pregnancy was associated with differential offspring DNA methylation in mitochondria-related genes. Exposure to NO2 was also linked to differential methylation as well as expression of genes involved in antioxidant defense pathways. CITATION: Gruzieva O, Xu CJ, Breton CV, Annesi-Maesano I, Antó JM, Auffray C, Ballereau S, Bellander T, Bousquet J, Bustamante M, Charles MA, de Kluizenaar Y, den Dekker HT, Duijts L, Felix JF, Gehring U, Guxens M, Jaddoe VV, Jankipersadsing SA, Merid SK, Kere J, Kumar A, Lemonnier N, Lepeule J, Nystad W, Page CM, Panasevich S, Postma D, Slama R, Sunyer J, Söderhäll C, Yao J, London SJ, Pershagen G, Koppelman GH, Melen E. 2017. Epigenome-wide meta-analysis of methylation in children related to prenatal NO2 air pollution exposure. Environ Health Perspect 125:104-110; http://dx.doi.org/10.1289/EHP36 [ABSTRACT FROM AUTHOR]- Published
- 2017
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23. Proton pump inhibitors affect the gut microbiome.
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Imhann, Floris, Bonder, Marc Jan, Vila, Arnau Vich, Jingyuan Fu, Mujagic, Zlatan, Vork, Lisa, Tigchelaar, Ettje F., Jankipersadsing, Soesma A., Cenit, Maria Carmen, Harmsen, Hermie J. M., Dijkstra, Gerard, Franke, Lude, Xavier, Ramnik J., Jonkers, Daisy, Wijmenga, Cisca, Weersma, Rinse K., and Zhernakova, Alexandra
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PROTON pump inhibitors ,CLOSTRIDIOIDES difficile ,GUT microbiome ,META-analysis ,STREPTOCOCCUS ,STAPHYLOCOCCUS - Abstract
Background and aims Proton pump inhibitors (PPIs) are among the top 10 most widely used drugs in the world. PPI use has been associated with an increased risk of enteric infections, most notably Clostridium difficile. The gut microbiome plays an important role in enteric infections, by resisting or promoting colonisation by pathogens. In this study, we investigated the influence of PPI use on the gut microbiome. Methods The gut microbiome composition of 1815 individuals, spanning three cohorts, was assessed by tag sequencing of the 16S rRNA gene. The difference in microbiota composition in PPI users versus non-users was analysed separately in each cohort, followed by a meta-analysis. Results 211 of the participants were using PPIs at the moment of stool sampling. PPI use is associated with a significant decrease in Shannon's diversity and with changes in 20% of the bacterial taxa (false discovery rate <0.05). Multiple oral bacteria were over-represented in the faecal microbiome of PPI-users, including the genus Rothia (p=9.8×10
-38 ). In PPI users we observed a significant increase in bacteria: genera Enterococcus, Streptococcus, Staphylococcus and the potentially pathogenic species Escherichia coli. Conclusions The differences between PPI users and non-users observed in this study are consistently associated with changes towards a less healthy gut microbiome. These differences are in line with known changes that predispose to C. difficile infections and can potentially explain the increased risk of enteric infections in PPI users. On a population level, the effects of PPI are more prominent than the effects of antibiotics or other commonly used drugs. [ABSTRACT FROM AUTHOR]- Published
- 2016
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24. DNA methylation mediates the effect of maternal smoking during pregnancy on birthweight of the offspring.
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Küpers, Leanne K., Xiaojing Xu, Jankipersadsing, Soesma A., Vaez, Ahmad, Gemert, Sacha la Bastide-van, Scholtens, Salome, Nolte, Ilja M., Richmond, Rebecca C., Relton, Caroline L., Felix, Janine F., Duijts, Liesbeth, van Meurs, Joyce B., Tiemeier, Henning, Jaddoe, Vincent W., Xiaoling Wang, Corpeleijn, Eva, Snieder, Harold, Xu, Xiaojing, la Bastide-van Gemert, Sacha, and Wang, Xiaoling
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HEALTH ,SMOKING ,PREGNANT women ,WOMEN'S tobacco use ,BIRTH weight ,DNA methylation ,CORD blood ,CYTOSINE ,COHORT analysis ,GENES ,LONGITUDINAL method ,REGRESSION analysis ,RESEARCH funding ,TRANSCRIPTION factors ,DNA-binding proteins ,PRENATAL exposure delayed effects ,SEQUENCE analysis ,MATERNAL exposure - Abstract
Background: We examined whether the effect of maternal smoking during pregnancy on birthweight of the offspring was mediated by smoking-induced changes to DNA methylation in cord blood.Methods: First, we used cord blood of 129 Dutch children exposed to maternal smoking vs 126 unexposed to maternal and paternal smoking (53% male) participating in the GECKO Drenthe birth cohort. DNA methylation was measured using the Illumina HumanMethylation450 Beadchip. We performed an epigenome-wide association study for the association between maternal smoking and methylation followed by a mediation analysis of the top signals [false-discovery rate (FDR) < 0.05]. We adjusted both analyses for maternal age, education, pre-pregnancy BMI, offspring's sex, gestational age and white blood cell composition. Secondly, in 175 exposed and 1248 unexposed newborns from two independent birth cohorts, we replicated and meta-analysed results of eight cytosine-phosphate-guanine (CpG) sites in the GFI1 gene, which showed the most robust mediation. Finally, we performed functional network and enrichment analysis.Results: We found 35 differentially methylated CpGs (FDR < 0.05) in newborns exposed vs unexposed to smoking, of which 23 survived Bonferroni correction (P < 1 × 10(-7)). These 23 CpGs mapped to eight genes: AHRR, GFI1, MYO1G, CYP1A1, NEUROG1, CNTNAP2, FRMD4A and LRP5. We observed partial confirmation as three of the eight CpGs in GFI1 replicated. These CpGs partly mediated the effect of maternal smoking on birthweight (Sobel P < 0.05) in meta-analysis of GECKO and the two replication cohorts. Differential methylation of these three GFI1 CpGs explained 12-19% of the 202 g lower birthweight in smoking mothers. Functional enrichment analysis pointed towards activation of cell-mediated immunity.Conclusions: Maternal smoking during pregnancy was associated with cord blood methylation differences. We observed a potentially mediating role of methylation in the association between maternal smoking during pregnancy and birthweight of the offspring. Functional network analysis suggested a role in activating the immune system. [ABSTRACT FROM AUTHOR]- Published
- 2015
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25. A GWAS meta-analysis suggests roles for xenobiotic metabolism and ion channel activity in the biology of stool frequency.
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Jankipersadsing, Soesma A., Hadizadeh, Fatemeh, Bonder, Marc Jan, Tigchelaar, Ettje F., Deelen, Patrick, Jingyuan Fu, Andreasson, Anna, Agreus, Lars, Walter, Susanna, Wijmenga, Cisca, Hysi, Pirro, D'Amato, Mauro, and Zhernakova, Alexandra
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XENOBIOTICS ,METABOLISM ,ION channels ,FECES ,GENOMES - Published
- 2017
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26. Gut microbiota composition and functional changes in inflammatory bowel disease and irritable bowel syndrome.
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Vich Vila, Arnau, Imhann, Floris, Collij, Valerie, Jankipersadsing, Soesma A., Gurry, Thomas, Mujagic, Zlatan, Kurilshikov, Alexander, Bonder, Marc Jan, Jiang, Xiaofang, Tigchelaar, Ettje F., Dekens, Jackie, Peters, Vera, Voskuil, Michiel D., Visschedijk, Marijn C., van Dullemen, Hendrik M., Keszthelyi, Daniel, Swertz, Morris A., Franke, Lude, Alberts, Rudi, and Festen, Eleonora A. M.
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GUT microbiome ,INFLAMMATORY bowel diseases ,IRRITABLE colon ,METAGENOMICS ,GASTROINTESTINAL diseases ,BACTERIA classification - Abstract
Differences in gut microbiota composition and function were observed between patients with inflammatory bowel disease or irritable bowel syndrome. Distinguishing two similar gut disorders: Inflammatory bowel disease (IBD) and irritable bowel syndrome (IBS) are two of the most common diseases of the gastrointestinal tract. In new work, Vich Vila and colleagues have characterized the gut microbiota composition of both disorders using shotgun metagenomic sequencing of stool samples from 1792 individuals. Analyses involving bacterial taxonomy, metabolic functions, antibiotic resistance genes, virulence factors, and bacterial growth rates showed key differences between these two gut disorders. On the basis of gut microbiota composition differences, patients with IBD could be distinguished from those with IBS. Changes in the gut microbiota have been associated with two of the most common gastrointestinal diseases, inflammatory bowel disease (IBD) and irritable bowel syndrome (IBS). Here, we performed a case-control analysis using shotgun metagenomic sequencing of stool samples from 1792 individuals with IBD and IBS compared with control individuals in the general population. Despite substantial overlap between the gut microbiome of patients with IBD and IBS compared with control individuals, we were able to use gut microbiota composition differences to distinguish patients with IBD from those with IBS. By combining species-level profiles and strain-level profiles with bacterial growth rates, metabolic functions, antibiotic resistance, and virulence factor analyses, we identified key bacterial species that may be involved in two common gastrointestinal diseases. [ABSTRACT FROM AUTHOR]
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- 2018
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27. DNA methylation in childhood asthma
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Mariona Bustamante, Francesco Forastiere, Henriette A. Smit, Petter Mowinckel, Marjan Kerkhof, Tari Haahtela, Martijn C. Nawijn, Raul Aguirre-Gamboa, Dan Mason, Mihai G. Netea, Cisca Wijmenga, Raf Azad, Vegard Hovland, John Wright, Josep M. Antó, Cilla Söderhäll, Pieter van der Vlies, William O.C.M. Cookson, Bianca van Rijkom, Lovisa E. Reinius, Soesma A Jankipersadsing, Leda Chatzi, Nour Baïz, Erik Melén, Daniela Porta, Olena Gruzieva, Juha Kere, Isabella Annesi-Maesano, Maties Torrent, Charles Auffray, Cleo C. van Diemen, Manolis Kogevinas, Davide Gori, Johann Pellet, Jose Ramon Bilbao, Harri Alenius, Göran Pershagen, Sabrina Llop, Miriam F. Moffatt, Nathanaël Lemonnier, Ashok Kumar, Simon Kebede Merid, Nanna Fyhrquist, Stephane Ballereau, Tiina Laatikainen, Cheng-Jian Xu, Johan C. de Jongste, Marc Jan Bonder, Judith Garcia-Aymerich, Karin C. Lødrup Carlsen, J Sunyer, Mikel Basterrechea, Dario Greco, Yang Li, Jean Bousquet, Ulrike Gehring, Catherine Laprise, Maria Pia Fantini, Rosemary R. C. McEachan, Bert Brunekreef, Stefano Guerra, Gerard H. Koppelman, Cornelis J. Vermeulen, Andréanne Morin, Carmen Iñiguez, Kai-Håkon Carlsen, Center for Research in Environmental Epidemiology (CREAL), Universitat Pompeu Fabra [Barcelona] (UPF)-Catalunya ministerio de salud, CIBER de Epidemiología y Salud Pública (CIBERESP), IMIM-Hospital del Mar, Generalitat de Catalunya, Universitat Pompeu Fabra [Barcelona] (UPF), Karolinska Institutet [Stockholm], University of Helsinki, King‘s College London, Institut Pierre Louis d'Epidémiologie et de Santé Publique (iPLESP), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU), Stockholm County Council, Aging Research Center [Karolinska Institutet] (ARC ), Stockholm University-Karolinska Institutet [Stockholm], Keck School of Medicine [Los Angeles], University of Southern California (USC), Azienda Sanitaria Locale [ROMA] (ASL), McGill University and Genome Quebec Innovation Centre, Département des Sciences Fondamentales [Chicoutimi] (DSF), Université du Québec à Chicoutimi (UQAC), European Institute for Systems Biology and Medicine (EISBM), Arizona Respiratory Center, Radboud University Medical Center [Nijmegen], Contre les MAladies Chroniques pour un VIeillissement Actif en Languedoc-Roussillon (MACVIA-LR), Université Montpellier 1 (UM1)-Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre Hospitalier Universitaire de Nîmes (CHU Nîmes)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-European Innovation Partnership on Active and Healthy Ageing Reference Site (EIP on AHA), Commission Européenne-Commission Européenne-Organisation Mondiale de la Santé / World Health Organization Office (OMS / WHO), Department of Dermatology, Allergology and Venereology, Clinicum, Medicum, Department of Bacteriology and Immunology, HUS Inflammation Center, One Health Chemisch, dIRAS RA-2, Pediatrics, RS: NUTRIM - R3 - Respiratory & Age-related Health, Complexe Genetica, RS: NUTRIM - R4 - Gene-environment interaction, Groningen Research Institute for Asthma and COPD (GRIAC), Groningen Institute for Gastro Intestinal Genetics and Immunology (3GI), Xu, Cheng-Jian, Söderhäll, Cilla, Bustamante, Mariona, Baïz, Nour, Gruzieva, Olena, Gehring, Ulrike, Mason, Dan, Chatzi, Leda, Basterrechea, Mikel, Llop, Sabrina, Torrent, Matie, Forastiere, Francesco, Fantini, Maria Pia, Carlsen, Karin C Lødrup, Haahtela, Tari, Morin, Andréanne, Kerkhof, Marjan, Merid, Simon Kebede, van Rijkom, Bianca, Jankipersadsing, Soesma A., Bonder, Marc Jan, Ballereau, Stephane, Vermeulen, Cornelis J., Aguirre-Gamboa, Raul, de Jongste, Johan C., Smit, Henriette A., Kumar, Ashish, Pershagen, Göran, Guerra, Stefano, Garcia-Aymerich, Judith, Greco, Dario, Reinius, Lovisa, McEachan, Rosemary R.C., Azad, Raf, Hovland, Vegard, Mowinckel, Petter, Alenius, Harri, Fyhrquist, Nanna, Lemonnier, Nathanaël, Pellet, Johann, Auffray, Charle, van der Vlies, Pieter, van Diemen, Cleo C., Li, Yang, Wijmenga, Cisca, Netea, Mihai G., Moffatt, Miriam F., Cookson, William O.C.M., Anto, Josep M., Bousquet, Jean, Laatikainen, Tiina, Laprise, Catherine, Carlsen, Kai-Håkon, Gori, Davide, Porta, Daniela, Iñiguez, Carmen, Bilbao, Jose Ramon, Kogevinas, Manoli, Wright, John, Brunekreef, Bert, Kere, Juha, Nawijn, Martijn C., Annesi-Maesano, Isabella, Sunyer, Jordi, Melén, Erik, and Koppelman, Gerard H.
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Male ,0301 basic medicine ,Allergy ,Cytotoxic ,T-Lymphocytes ,[SDV]Life Sciences [q-bio] ,Respiratory System ,lnfectious Diseases and Global Health Radboud Institute for Molecular Life Sciences [Radboudumc 4] ,CHILDREN ,Immunoglobulin E ,Epigenesis, Genetic ,Child ,POPULATION ,education.field_of_study ,biology ,Methylation ,3. Good health ,CpG site ,Child, Preschool ,DNA methylation ,Female ,BIOS Consortium ,Life Sciences & Biomedicine ,Pulmonary and Respiratory Medicine ,Population ,PHENOTYPES ,IMMUNITY ,03 medical and health sciences ,Critical Care Medicine ,Genetic ,General & Internal Medicine ,medicine ,Humans ,COHORT ,Epigenetics ,IGE ,EXPOSURE ,Preschool ,education ,Asthma ,Science & Technology ,business.industry ,RHINITIS ,DNA ,DNA Methylation ,medicine.disease ,Eosinophils ,030104 developmental biology ,3121 General medicine, internal medicine and other clinical medicine ,Immunology ,biology.protein ,GENOMEWIDE ASSOCIATION ,CpG Islands ,business ,COLLECTION ,T-Lymphocytes, Cytotoxic ,Epigenesis ,Genome-Wide Association Study - Abstract
Background: DNA methylation profiles associated with childhood asthma might provide novel insights into disease pathogenesis. We did an epigenome-wide association study to assess methylation profiles associated with childhood asthma. Methods: We did a large-scale epigenome-wide association study (EWAS) within the Mechanisms of the Development of ALLergy (MeDALL) project. We examined epigenome-wide methylation using Illumina Infinium Human Methylation450 BeadChips (450K) in whole blood in 207 children with asthma and 610 controls at age 4–5 years, and 185 children with asthma and 546 controls at age 8 years using a cross-sectional case-control design. After identification of differentially methylated CpG sites in the discovery analysis, we did a validation study in children (4–16 years; 247 cases and 2949 controls) from six additional European cohorts and meta-analysed the results. We next investigated whether replicated CpG sites in cord blood predict later asthma in 1316 children. We subsequently investigated cell-type-specific methylation of the identified CpG sites in eosinophils and respiratory epithelial cells and their related gene-expression signatures. We studied cell-type specificity of the asthma association of the replicated CpG sites in 455 respiratory epithelial cell samples, collected by nasal brushing of 16-year-old children as well as in DNA isolated from blood eosinophils (16 with asthma, eight controls [age 2–56 years]) and compared this with whole-blood DNA samples of 74 individuals with asthma and 93 controls (age 1–79 years). Whole-blood transcriptional profiles associated with replicated CpG sites were annotated using RNA-seq data of subsets of peripheral blood mononuclear cells sorted by fluorescence-activated cell sorting. Findings: 27 methylated CpG sites were identified in the discovery analysis. 14 of these CpG sites were replicated and passed genome-wide significance (p
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- 2018
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28. Lifelines COVID-19 cohort: investigating COVID-19 infection and its health and societal impacts in a Dutch population-based cohort.
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Mc Intyre K, Lanting P, Deelen P, Wiersma HH, Vonk JM, Ori APS, Jankipersadsing SA, Warmerdam R, van Blokland I, Boulogne F, Dijkema MXL, Herkert JC, Claringbould A, Bakker O, Lopera Maya EA, Bültmann U, Zhernakova A, Reijneveld SA, Zijlstra E, Swertz MA, Brouwer S, van Ooijen R, Angelini V, Dekker LH, Sijtsma A, Scherjon SA, Wijmenga C, Dekens JAM, Mierau J, Boezen HM, and Franke L
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- Adult, Anxiety, Communicable Disease Control, Female, Humans, Loneliness, Male, Middle Aged, Netherlands epidemiology, Prospective Studies, Quality of Life, Surveys and Questionnaires, COVID-19 psychology, Pandemics
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Purpose: The Lifelines COVID-19 cohort was set up to assess the psychological and societal impacts of the COVID-19 pandemic and investigate potential risk factors for COVID-19 within the Lifelines prospective population cohort., Participants: Participants were recruited from the 140 000 eligible participants of Lifelines and the Lifelines NEXT birth cohort, who are all residents of the three northern provinces of the Netherlands. Participants filled out detailed questionnaires about their physical and mental health and experiences on a weekly basis starting in late March 2020, and the cohort consists of everyone who filled in at least one questionnaire in the first 8 weeks of the project. FINDINGS TO DATE: >71 000 unique participants responded to the questionnaires at least once during the first 8 weeks, with >22 000 participants responding to seven questionnaires. Compiled questionnaire results are continuously updated and shared with the public through the Corona Barometer website. Early results included a clear signal that younger people living alone were experiencing greater levels of loneliness due to lockdown, and subsequent results showed the easing of anxiety as lockdown was eased in June 2020., Future Plans: Questionnaires were sent on a (bi)weekly basis starting in March 2020 and on a monthly basis starting July 2020, with plans for new questionnaire rounds to continue through 2020 and early 2021. Questionnaire frequency can be increased again for subsequent waves of infections. Cohort data will be used to address how the COVID-19 pandemic developed in the northern provinces of the Netherlands, which environmental and genetic risk factors predict disease susceptibility and severity and the psychological and societal impacts of the crisis. Cohort data are linked to the extensive health, lifestyle and sociodemographic data held for these participants by Lifelines, a 30-year project that started in 2006, and to data about participants held in national databases., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY. Published by BMJ.)
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- 2021
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29. The emerging landscape of dynamic DNA methylation in early childhood.
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Xu CJ, Bonder MJ, Söderhäll C, Bustamante M, Baïz N, Gehring U, Jankipersadsing SA, van der Vlies P, van Diemen CC, van Rijkom B, Just J, Kull I, Kere J, Antó JM, Bousquet J, Zhernakova A, Wijmenga C, Annesi-Maesano I, Sunyer J, Melén E, Li Y, Postma DS, and Koppelman GH
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- Child, Child, Preschool, CpG Islands, Female, Genetic Predisposition to Disease, Genetic Variation, Genome-Wide Association Study, Humans, Infant, Infant, Newborn, Maternal Exposure adverse effects, Pregnancy, Prenatal Exposure Delayed Effects, Quantitative Trait Loci, Smoking adverse effects, Child Development, DNA Methylation, Epigenesis, Genetic, Epigenomics methods
- Abstract
Background: DNA methylation has been found to associate with disease, aging and environmental exposure, but it is unknown how genome, environment and disease influence DNA methylation dynamics in childhood., Results: By analysing 538 paired DNA blood samples from children at birth and at 4-5 years old and 726 paired samples from children at 4 and 8 years old from four European birth cohorts using the Illumina Infinium Human Methylation 450 k chip, we have identified 14,150 consistent age-differential methylation sites (a-DMSs) at epigenome-wide significance of p < 1.14 × 10
-7 . Genes with an increase in age-differential methylation were enriched in pathways related to 'development', and were more often located in bivalent transcription start site (TSS) regions, which can silence or activate expression of developmental genes. Genes with a decrease in age-differential methylation were involved in cell signalling, and enriched on H3K27ac, which can predict developmental state. Maternal smoking tended to decrease methylation levels at the identified da-DMSs. We also found 101 a-DMSs (0.71%) that were regulated by genetic variants using cis-differential Methylation Quantitative Trait Locus (cis-dMeQTL) mapping. Moreover, a-DMS-associated genes during early development were significantly more likely to be linked with disease., Conclusion: Our study provides new insights into the dynamic epigenetic landscape of the first 8 years of life.- Published
- 2017
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30. Epigenome-Wide Meta-Analysis of Methylation in Children Related to Prenatal NO2 Air Pollution Exposure.
- Author
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Gruzieva O, Xu CJ, Breton CV, Annesi-Maesano I, Antó JM, Auffray C, Ballereau S, Bellander T, Bousquet J, Bustamante M, Charles MA, de Kluizenaar Y, den Dekker HT, Duijts L, Felix JF, Gehring U, Guxens M, Jaddoe VV, Jankipersadsing SA, Merid SK, Kere J, Kumar A, Lemonnier N, Lepeule J, Nystad W, Page CM, Panasevich S, Postma D, Slama R, Sunyer J, Söderhäll C, Yao J, London SJ, Pershagen G, Koppelman GH, and Melén E
- Subjects
- Child, Female, Humans, Infant, Newborn, London, Pregnancy, Air Pollutants analysis, Air Pollution statistics & numerical data, DNA Methylation, Maternal Exposure statistics & numerical data, Nitrogen Dioxide analysis, Prenatal Exposure Delayed Effects epidemiology
- Abstract
Background: Prenatal exposure to air pollution is considered to be associated with adverse effects on child health. This may partly be mediated by mechanisms related to DNA methylation., Objectives: We investigated associations between exposure to air pollution, using nitrogen dioxide (NO2) as marker, and epigenome-wide cord blood DNA methylation., Methods: We meta-analyzed the associations between NO2 exposure at residential addresses during pregnancy and cord blood DNA methylation (Illumina 450K) in four European and North American studies (n = 1,508) with subsequent look-up analyses in children ages 4 (n = 733) and 8 (n = 786) years. Additionally, we applied a literature-based candidate approach for antioxidant and anti-inflammatory genes. To assess influence of exposure at the transcriptomics level, we related mRNA expression in blood cells to NO2 exposure in 4- (n = 111) and 16-year-olds (n = 239)., Results: We found epigenome-wide significant associations [false discovery rate (FDR) p < 0.05] between maternal NO2 exposure during pregnancy and DNA methylation in newborns for 3 CpG sites in mitochondria-related genes: cg12283362 (LONP1), cg24172570 (3.8 kbp upstream of HIBADH), and cg08973675 (SLC25A28). The associations with cg08973675 methylation were also significant in the older children. Further analysis of antioxidant and anti-inflammatory genes revealed differentially methylated CpGs in CAT and TPO in newborns (FDR p < 0.05). NO2 exposure at the time of biosampling in childhood had a significant impact on CAT and TPO expression., Conclusions: NO2 exposure during pregnancy was associated with differential offspring DNA methylation in mitochondria-related genes. Exposure to NO2 was also linked to differential methylation as well as expression of genes involved in antioxidant defense pathways. Citation: Gruzieva O, Xu CJ, Breton CV, Annesi-Maesano I, Antó JM, Auffray C, Ballereau S, Bellander T, Bousquet J, Bustamante M, Charles MA, de Kluizenaar Y, den Dekker HT, Duijts L, Felix JF, Gehring U, Guxens M, Jaddoe VV, Jankipersadsing SA, Merid SK, Kere J, Kumar A, Lemonnier N, Lepeule J, Nystad W, Page CM, Panasevich S, Postma D, Slama R, Sunyer J, Söderhäll C, Yao J, London SJ, Pershagen G, Koppelman GH, Melén E. 2017. Epigenome-wide meta-analysis of methylation in children related to prenatal NO2 air pollution exposure. Environ Health Perspect 125:104-110; http://dx.doi.org/10.1289/EHP36., Competing Interests: The authors declare they have no actual or potential competing financial interests.
- Published
- 2017
- Full Text
- View/download PDF
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