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1. Epigenome-wide meta-analysis of blood DNA methylation in newborns and children identifies numerous loci related to gestational age.

Author

Merid, Simon Kebede, Novoloaca, Alexei, Sharp, Gemma C, Küpers, Leanne K, Kho, Alvin T, Roy, Ritu, Gao, Lu, Annesi-Maesano, Isabella, Jain, Pooja, Plusquin, Michelle, Kogevinas, Manolis, Allard, Catherine, Vehmeijer, Florianne O, Kazmi, Nabila, Salas, Lucas A, Rezwan, Faisal I, Zhang, Hongmei, Sebert, Sylvain, Czamara, Darina, Rifas-Shiman, Sheryl L, Melton, Phillip E, Lawlor, Debbie A, Pershagen, Göran, Breton, Carrie V, Huen, Karen, Baiz, Nour, Gagliardi, Luigi, Nawrot, Tim S, Corpeleijn, Eva, Perron, Patrice, Duijts, Liesbeth, Nohr, Ellen Aagaard, Bustamante, Mariona, Ewart, Susan L, Karmaus, Wilfried, Zhao, Shanshan, Page, Christian M, Herceg, Zdenko, Jarvelin, Marjo-Riitta, Lahti, Jari, Baccarelli, Andrea A, Anderson, Denise, Kachroo, Priyadarshini, Relton, Caroline L, Bergström, Anna, Eskenazi, Brenda, Soomro, Munawar Hussain, Vineis, Paolo, Snieder, Harold, Bouchard, Luigi, Jaddoe, Vincent W, Sørensen, Thorkild IA, Vrijheid, Martine, Arshad, S Hasan, Holloway, John W, Håberg, Siri E, Magnus, Per, Dwyer, Terence, Binder, Elisabeth B, DeMeo, Dawn L, Vonk, Judith M, Newnham, John, Tantisira, Kelan G, Kull, Inger, Wiemels, Joseph L, Heude, Barbara, Sunyer, Jordi, Nystad, Wenche, Munthe-Kaas, Monica C, Räikkönen, Katri, Oken, Emily, Huang, Rae-Chi, Weiss, Scott T, Antó, Josep Maria, Bousquet, Jean, Kumar, Ashish, Söderhäll, Cilla, Almqvist, Catarina, Cardenas, Andres, Gruzieva, Olena, Xu, Cheng-Jian, Reese, Sarah E, Kere, Juha, Brodin, Petter, Solomon, Olivia, Wielscher, Matthias, Holland, Nina, Ghantous, Akram, Hivert, Marie-France, Felix, Janine F, Koppelman, Gerard H, London, Stephanie J, and Melén, Erik

Subjects
Humans, Premature Birth, DNA, DNA Methylation, Fetal Development, Adolescent, Child, Child, Preschool, Infant, Newborn, Infant, Premature, Female, Male, Genetic Loci, Epigenome, Development, Epigenetics, Gestational age, Preterm birth, Transcriptomics, Preschool, Infant, Newborn, Premature, Genetics, Clinical Sciences
Abstract

BackgroundPreterm birth and shorter duration of pregnancy are associated with increased morbidity in neonatal and later life. As the epigenome is known to have an important role during fetal development, we investigated associations between gestational age and blood DNA methylation in children.MethodsWe performed meta-analysis of Illumina's HumanMethylation450-array associations between gestational age and cord blood DNA methylation in 3648 newborns from 17 cohorts without common pregnancy complications, induced delivery or caesarean section. We also explored associations of gestational age with DNA methylation measured at 4-18 years in additional pediatric cohorts. Follow-up analyses of DNA methylation and gene expression correlations were performed in cord blood. DNA methylation profiles were also explored in tissues relevant for gestational age health effects: fetal brain and lung.ResultsWe identified 8899 CpGs in cord blood that were associated with gestational age (range 27-42 weeks), at Bonferroni significance, P

Published
2020

2. Variants in the fetal genome near pro-inflammatory cytokine genes on 2q13 associate with gestational duration.

Author

Liu, Xueping, Helenius, Dorte, Skotte, Line, Beaumont, Robin N, Wielscher, Matthias, Geller, Frank, Juodakis, Julius, Mahajan, Anubha, Bradfield, Jonathan P, Lin, Frederick TJ, Vogelezang, Suzanne, Bustamante, Mariona, Ahluwalia, Tarunveer S, Pitkänen, Niina, Wang, Carol A, Bacelis, Jonas, Borges, Maria C, Zhang, Ge, Bedell, Bruce A, Rossi, Robert M, Skogstrand, Kristin, Peng, Shouneng, Thompson, Wesley K, Appadurai, Vivek, Lawlor, Debbie A, Kalliala, Ilkka, Power, Christine, McCarthy, Mark I, Boyd, Heather A, Marazita, Mary L, Hakonarson, Hakon, Hayes, M Geoffrey, Scholtens, Denise M, Rivadeneira, Fernando, Jaddoe, Vincent WV, Vinding, Rebecca K, Bisgaard, Hans, Knight, Bridget A, Pahkala, Katja, Raitakari, Olli, Helgeland, Øyvind, Johansson, Stefan, Njølstad, Pål R, Fadista, João, Schork, Andrew J, Nudel, Ron, Miller, Daniel E, Chen, Xiaoting, Weirauch, Matthew T, Mortensen, Preben Bo, Børglum, Anders D, Nordentoft, Merete, Mors, Ole, Hao, Ke, Ryckman, Kelli K, Hougaard, David M, Kottyan, Leah C, Pennell, Craig E, Lyytikainen, Leo-Pekka, Bønnelykke, Klaus, Vrijheid, Martine, Felix, Janine F, Lowe, William L, Grant, Struan FA, Hyppönen, Elina, Jacobsson, Bo, Jarvelin, Marjo-Riitta, Muglia, Louis J, Murray, Jeffrey C, Freathy, Rachel M, Werge, Thomas M, Melbye, Mads, Buil, Alfonso, and Feenstra, Bjarke

Subjects
Chromosomes, Human, Pair 2, Fetus, Humans, Premature Birth, Cytokines, Gestational Age, Pregnancy, Polymorphism, Single Nucleotide, Genome, Human, Infant, Newborn, Female, Genome-Wide Association Study, Chromosomes, Human, Pair 2, Polymorphism, Single Nucleotide, Genome, Infant, Newborn
Abstract

The duration of pregnancy is influenced by fetal and maternal genetic and non-genetic factors. Here we report a fetal genome-wide association meta-analysis of gestational duration, and early preterm, preterm, and postterm birth in 84,689 infants. One locus on chromosome 2q13 is associated with gestational duration; the association is replicated in 9,291 additional infants (combined P = 3.96 × 10-14). Analysis of 15,588 mother-child pairs shows that the association is driven by fetal rather than maternal genotype. Functional experiments show that the lead SNP, rs7594852, alters the binding of the HIC1 transcriptional repressor. Genes at the locus include several interleukin 1 family members with roles in pro-inflammatory pathways that are central to the process of parturition. Further understanding of the underlying mechanisms will be of great public health importance, since giving birth either before or after the window of term gestation is associated with increased morbidity and mortality.

Published
2019

3. Maternal body mass index, gestational weight gain, and the risk of overweight and obesity across childhood: An individual participant data meta-analysis

Author

Voerman, Ellis, Santos, Susana, Golab, Bernadeta Patro, Amiano, Pilar, Ballester, Ferran, Barros, Henrique, Bergström, Anna, Charles, Marie-Aline, Chatzi, Leda, Chevrier, Cécile, Chrousos, George P, Corpeleijn, Eva, Costet, Nathalie, Crozier, Sarah, Devereux, Graham, Eggesbø, Merete, Ekström, Sandra, Fantini, Maria Pia, Farchi, Sara, Forastiere, Francesco, Georgiu, Vagelis, Godfrey, Keith M, Gori, Davide, Grote, Veit, Hanke, Wojciech, Hertz-Picciotto, Irva, Heude, Barbara, Hryhorczuk, Daniel, Huang, Rae-Chi, Inskip, Hazel, Iszatt, Nina, Karvonen, Anne M, Kenny, Louise C, Koletzko, Berthold, Küpers, Leanne K, Lagström, Hanna, Lehmann, Irina, Magnus, Per, Majewska, Renata, Mäkelä, Johanna, Manios, Yannis, McAuliffe, Fionnuala M, McDonald, Sheila W, Mehegan, John, Mommers, Monique, Morgen, Camilla S, Mori, Trevor A, Moschonis, George, Murray, Deirdre, Chaoimh, Carol Ní, Nohr, Ellen A, Andersen, Anne-Marie Nybo, Oken, Emily, Oostvogels, Adriëtte JJM, Pac, Agnieszka, Papadopoulou, Eleni, Pekkanen, Juha, Pizzi, Costanza, Polanska, Kinga, Porta, Daniela, Richiardi, Lorenzo, Rifas-Shiman, Sheryl L, Ronfani, Luca, Santos, Ana C, Standl, Marie, Stoltenberg, Camilla, Thiering, Elisabeth, Thijs, Carel, Torrent, Maties, Tough, Suzanne C, Trnovec, Tomas, Turner, Steve, van Rossem, Lenie, von Berg, Andrea, Vrijheid, Martine, Vrijkotte, Tanja GM, West, Jane, Wijga, Alet, Wright, John, Zvinchuk, Oleksandr, Sørensen, Thorkild IA, Lawlor, Debbie A, Gaillard, Romy, and Jaddoe, Vincent WV

Subjects
Biomedical and Clinical Sciences, Public Health, Health Sciences, Nutrition and Dietetics, Reproductive Medicine, Nutrition, Prevention, Pediatric, Clinical Research, Obesity, Oral and gastrointestinal, Stroke, Reproductive health and childbirth, Cardiovascular, Generic health relevance, Metabolic and endocrine, Cancer, Australia, Body Mass Index, Cohort Studies, Data Analysis, Europe, Female, Gestational Weight Gain, Humans, North America, Overweight, Pediatric Obesity, Pregnancy, Risk Factors, Medical and Health Sciences, General & Internal Medicine, Biomedical and clinical sciences, Health sciences
Abstract

BackgroundMaternal obesity and excessive gestational weight gain may have persistent effects on offspring fat development. However, it remains unclear whether these effects differ by severity of obesity, and whether these effects are restricted to the extremes of maternal body mass index (BMI) and gestational weight gain. We aimed to assess the separate and combined associations of maternal BMI and gestational weight gain with the risk of overweight/obesity throughout childhood, and their population impact.Methods and findingsWe conducted an individual participant data meta-analysis of data from 162,129 mothers and their children from 37 pregnancy and birth cohort studies from Europe, North America, and Australia. We assessed the individual and combined associations of maternal pre-pregnancy BMI and gestational weight gain, both in clinical categories and across their full ranges, with the risks of overweight/obesity in early (2.0-5.0 years), mid (5.0-10.0 years) and late childhood (10.0-18.0 years), using multilevel binary logistic regression models with a random intercept at cohort level adjusted for maternal sociodemographic and lifestyle-related characteristics. We observed that higher maternal pre-pregnancy BMI and gestational weight gain both in clinical categories and across their full ranges were associated with higher risks of childhood overweight/obesity, with the strongest effects in late childhood (odds ratios [ORs] for overweight/obesity in early, mid, and late childhood, respectively: OR 1.66 [95% CI: 1.56, 1.78], OR 1.91 [95% CI: 1.85, 1.98], and OR 2.28 [95% CI: 2.08, 2.50] for maternal overweight; OR 2.43 [95% CI: 2.24, 2.64], OR 3.12 [95% CI: 2.98, 3.27], and OR 4.47 [95% CI: 3.99, 5.23] for maternal obesity; and OR 1.39 [95% CI: 1.30, 1.49], OR 1.55 [95% CI: 1.49, 1.60], and OR 1.72 [95% CI: 1.56, 1.91] for excessive gestational weight gain). The proportions of childhood overweight/obesity prevalence attributable to maternal overweight, maternal obesity, and excessive gestational weight gain ranged from 10.2% to 21.6%. Relative to the effect of maternal BMI, excessive gestational weight gain only slightly increased the risk of childhood overweight/obesity within each clinical BMI category (p-values for interactions of maternal BMI with gestational weight gain: p = 0.038, p < 0.001, and p = 0.637 in early, mid, and late childhood, respectively). Limitations of this study include the self-report of maternal BMI and gestational weight gain for some of the cohorts, and the potential of residual confounding. Also, as this study only included participants from Europe, North America, and Australia, results need to be interpreted with caution with respect to other populations.ConclusionsIn this study, higher maternal pre-pregnancy BMI and gestational weight gain were associated with an increased risk of childhood overweight/obesity, with the strongest effects at later ages. The additional effect of gestational weight gain in women who are overweight or obese before pregnancy is small. Given the large population impact, future intervention trials aiming to reduce the prevalence of childhood overweight and obesity should focus on maternal weight status before pregnancy, in addition to weight gain during pregnancy.

Published
2019

4. Gestational weight gain charts for different body mass index groups for women in Europe, North America, and Oceania

Author

Santos, Susana, Eekhout, Iris, Voerman, Ellis, Gaillard, Romy, Barros, Henrique, Charles, Marie-Aline, Chatzi, Leda, Chevrier, Cécile, Chrousos, George P, Corpeleijn, Eva, Costet, Nathalie, Crozier, Sarah, Doyon, Myriam, Eggesbø, Merete, Fantini, Maria Pia, Farchi, Sara, Forastiere, Francesco, Gagliardi, Luigi, Georgiu, Vagelis, Godfrey, Keith M, Gori, Davide, Grote, Veit, Hanke, Wojciech, Hertz-Picciotto, Irva, Heude, Barbara, Hivert, Marie-France, Hryhorczuk, Daniel, Huang, Rae-Chi, Inskip, Hazel, Jusko, Todd A, Karvonen, Anne M, Koletzko, Berthold, Küpers, Leanne K, Lagström, Hanna, Lawlor, Debbie A, Lehmann, Irina, Lopez-Espinosa, Maria-Jose, Magnus, Per, Majewska, Renata, Mäkelä, Johanna, Manios, Yannis, McDonald, Sheila W, Mommers, Monique, Morgen, Camilla S, Moschonis, George, Murínová, Ľubica, Newnham, John, Nohr, Ellen A, Andersen, Anne-Marie Nybo, Oken, Emily, Oostvogels, Adriëtte JJM, Pac, Agnieszka, Papadopoulou, Eleni, Pekkanen, Juha, Pizzi, Costanza, Polanska, Kinga, Porta, Daniela, Richiardi, Lorenzo, Rifas-Shiman, Sheryl L, Roeleveld, Nel, Santa-Marina, Loreto, Santos, Ana C, Smit, Henriette A, Sørensen, Thorkild IA, Standl, Marie, Stanislawski, Maggie, Stoltenberg, Camilla, Thiering, Elisabeth, Thijs, Carel, Torrent, Maties, Tough, Suzanne C, Trnovec, Tomas, van Gelder, Marleen MHJ, van Rossem, Lenie, von Berg, Andrea, Vrijheid, Martine, Vrijkotte, Tanja GM, Zvinchuk, Oleksandr, van Buuren, Stef, and Jaddoe, Vincent WV

Subjects
Reproductive Medicine, Biomedical and Clinical Sciences, Health Sciences, Conditions Affecting the Embryonic and Fetal Periods, Pediatric, Nutrition, Obesity, Clinical Research, Prevention, Perinatal Period - Conditions Originating in Perinatal Period, Metabolic and endocrine, Reproductive health and childbirth, Good Health and Well Being, Adult, Body Mass Index, Europe, Female, Gestational Weight Gain, Humans, North America, Oceania, Pregnancy, Pregnancy Complications, Pregnancy Outcome, Risk Factors, Weight gain, Charts, References, Medical and Health Sciences, General & Internal Medicine, Biomedical and clinical sciences, Health sciences
Abstract

BackgroundGestational weight gain differs according to pre-pregnancy body mass index and is related to the risks of adverse maternal and child health outcomes. Gestational weight gain charts for women in different pre-pregnancy body mass index groups enable identification of women and offspring at risk for adverse health outcomes. We aimed to construct gestational weight gain reference charts for underweight, normal weight, overweight, and grades 1, 2 and 3 obese women and to compare these charts with those obtained in women with uncomplicated term pregnancies.MethodsWe used individual participant data from 218,216 pregnant women participating in 33 cohorts from Europe, North America, and Oceania. Of these women, 9065 (4.2%), 148,697 (68.1%), 42,678 (19.6%), 13,084 (6.0%), 3597 (1.6%), and 1095 (0.5%) were underweight, normal weight, overweight, and grades 1, 2, and 3 obese women, respectively. A total of 138, 517 women from 26 cohorts had pregnancies with no hypertensive or diabetic disorders and with term deliveries of appropriate for gestational age at birth infants. Gestational weight gain charts for underweight, normal weight, overweight, and grade 1, 2, and 3 obese women were derived by the Box-Cox t method using the generalized additive model for location, scale, and shape.ResultsWe observed that gestational weight gain strongly differed per maternal pre-pregnancy body mass index group. The median (interquartile range) gestational weight gain at 40 weeks was 14.2 kg (11.4-17.4) for underweight women, 14.5 kg (11.5-17.7) for normal weight women, 13.9 kg (10.1-17.9) for overweight women, and 11.2 kg (7.0-15.7), 8.7 kg (4.3-13.4) and 6.3 kg (1.9-11.1) for grades 1, 2, and 3 obese women, respectively. The rate of weight gain was lower in the first half than in the second half of pregnancy. No differences in the patterns of weight gain were observed between cohorts or countries. Similar weight gain patterns were observed in mothers without pregnancy complications.ConclusionsGestational weight gain patterns are strongly related to pre-pregnancy body mass index. The derived charts can be used to assess gestational weight gain in etiological research and as a monitoring tool for weight gain during pregnancy in clinical practice.

Published
2018

5. Influence of maternal obesity on the association between common pregnancy complications and risk of childhood obesity: an individual participant data meta-analysis

Author

Golab, Bernadeta Patro, Santos, Susana, Voerman, Ellis, Lawlor, Debbie A, Jaddoe, Vincent WV, Gaillard, Romy, Authors, MOCO Study Group, Barros, Henrique, Bergström, Anna, Charles, Marie-Aline, Chatzi, Leda, Chevrier, Cécile, Chrousos, George P, Corpeleijn, Eva, Costet, Nathalie, Crozier, Sarah, Devereux, Graham, Eggesbø, Merete, Ekström, Sandra, Fantini, Maria P, Farchi, Sara, Forastiere, Francesco, Georgiu, Vagelis, Godfrey, Keith M, Gori, Davide, Hanke, Wojciech, Hertz-Picciotto, Irva, Heude, Barbara, Hryhorczuk, Daniel, Inskip, Hazel, Ibarluzea, Jesus, Kenny, Louise C, Küpers, Leanne K, Lagström, Hanna, Lehmann, Irina, Lenters, Virissa, Llop, Sabrina Llop, Magnus, Per, Majewska, Renata, Mäkelä, Johanna, Manios, Yannis, McAuliffe, Fionnuala M, McDonald, Sheila W, Mehegan, John, Mommers, Monique, Morgen, Camilla S, Moschonis, George, Murray, Deirdre, Chaoimh, Carol Ní, Nøhr, Ellen A, Andersen, Anne-Marie Nybo, Oken, Emily, Oostvogels, Adriëtte JJM, Pac, Agnieszka, Papadopoulou, Eleni, Pizzi, Costanza, Polanska, Kinga, Porta, Daniela, Richiardi, Lorenzo, Rifas-Shiman, Sheryl L, Rusconi, Franca, Santos, Ana C, Smit, Henriette A, Sørensen, Thorkild IA, Standl, Marie, Stoltenberg, Camilla, Sunyer, Jordi, Taylor, Michelle, Thiering, Elisabeth, Thijs, Carel, Torrent, Maties, Tough, Suzanne C, Trnovec, Tomas, Turner, Steve, van Rossem, Lenie, von Berg, Andrea, Vrijheid, Martine, Vrijkotte, Tanja, West, Jane, Wright, John, and Zvinchuk, Oleksandr

Subjects
Nutrition, Contraception/Reproduction, Pediatric, Obesity, Prevention, Clinical Research, Cardiovascular, Perinatal Period - Conditions Originating in Perinatal Period, Diabetes, Aetiology, 2.1 Biological and endogenous factors, Metabolic and endocrine, Reproductive health and childbirth, Adolescent, Animals, Body Mass Index, Child, Child, Preschool, Diabetes, Gestational, Europe, Female, Humans, Hypertension, Pregnancy-Induced, Life Style, North America, Pediatric Obesity, Pre-Eclampsia, Pregnancy, Pregnancy Complications, Risk Factors, MOCO Study Group Authors
Abstract

BackgroundGestational diabetes and gestational hypertensive disorders are associated with offspring obesity, but the role of maternal adiposity in these associations remains unclear. We aimed to investigate whether these pregnancy complications affect the odds of offspring obesity independently of maternal obesity.MethodsWe did an individual participant data (IPD) meta-analysis of mother-offspring pairs from prospective birth cohort studies that had IPD on mothers with singleton liveborn children born from 1989 onwards and had information available about maternal gestational diabetes, gestational hypertension or pre-eclampsia, and childhood body-mass index (BMI). We applied multilevel mixed-effects models to assess associations of gestational diabetes, gestational hypertension, and pre-eclampsia with BMI SD scores and the odds of overweight and obesity throughout childhood, adjusting for lifestyle characteristics (offspring's sex, maternal age, educational level, ethnicity, parity, and smoking during pregnancy). We then explored the extent to which any association was explained by maternal pre-pregnancy or early-pregnancy BMI.Findings160 757 mother-offspring pairs from 34 European or North American cohorts were analysed. Compared with uncomplicated pregnancies, gestational diabetes was associated with increased odds of overweight or obesity throughout childhood (odds ratio [OR] 1·59 [95% CI 1·36 to 1·86] for early childhood [age 2·0-4·9 years], 1·41 [1·26 to 1·57] for mid childhood [5·0-9·9 years], and 1·32 [0·97 to 1·78] for late childhood [10·0-17·9 years]); however, these associations attenuated towards the null following adjustment for maternal BMI (OR 1·35 [95% CI 1·15 to 1·58] for early childhood, 1·12 [1·00 to 1·25] for mid childhood, and 0·96 [0·71 to 1·31] for late childhood). Likewise, gestational hypertension was associated with increased odds of overweight throughout childhood (OR 1·19 [95% CI 1·01 to 1·39] for early childhood, 1·23 [1·15 to 1·32] for mid childhood, and 1·49 [1·30 to 1·70] for late childhood), but additional adjustment for maternal BMI largely explained these associations (1·01 [95% CI 0·86 to 1·19] for early childhood, 1·02 [0·95 to 1·10] for mid childhood, and 1·18 [1·03 to 1·36] for late childhood). Pre-eclampsia was associated with decreased BMI in early childhood only (difference in BMI SD score -0·05 SD score [95% CI -0·09 to -0·01]), and this association strengthened following additional adjustment for maternal BMI.InterpretationAlthough lowering maternal risk of gestational diabetes, gestational hypertension, and pre-eclampsia is important in relation to maternal and fetal pregnancy outcomes, such interventions are unlikely to have a direct impact on childhood obesity. Preventive strategies for reducing childhood obesity should focus on maternal BMI rather than on pregnancy complications.FundingEU's Horizon 2020 research and innovation programme (LifeCycle Project).

Published
2018

6. Maternal BMI at the start of pregnancy and offspring epigenome-wide DNA methylation: findings from the pregnancy and childhood epigenetics (PACE) consortium

Author

Sharp, Gemma C, Salas, Lucas A, Monnereau, Claire, Allard, Catherine, Yousefi, Paul, Everson, Todd M, Bohlin, Jon, Xu, Zongli, Huang, Rae-Chi, Reese, Sarah E, Xu, Cheng-Jian, Baïz, Nour, Hoyo, Cathrine, Agha, Golareh, Roy, Ritu, Holloway, John W, Ghantous, Akram, Merid, Simon K, Bakulski, Kelly M, Küpers, Leanne K, Zhang, Hongmei, Richmond, Rebecca C, Page, Christian M, Duijts, Liesbeth, Lie, Rolv T, Melton, Phillip E, Vonk, Judith M, Nohr, Ellen A, Williams-DeVane, ClarLynda, Huen, Karen, Rifas-Shiman, Sheryl L, Ruiz-Arenas, Carlos, Gonseth, Semira, Rezwan, Faisal I, Herceg, Zdenko, Ekström, Sandra, Croen, Lisa, Falahi, Fahimeh, Perron, Patrice, Karagas, Margaret R, Quraishi, Bilal M, Suderman, Matthew, Magnus, Maria C, Jaddoe, Vincent WV, Taylor, Jack A, Anderson, Denise, Zhao, Shanshan, Smit, Henriette A, Josey, Michele J, Bradman, Asa, Baccarelli, Andrea A, Bustamante, Mariona, Håberg, Siri E, Pershagen, Göran, Hertz-Picciotto, Irva, Newschaffer, Craig, Corpeleijn, Eva, Bouchard, Luigi, Lawlor, Debbie A, Maguire, Rachel L, Barcellos, Lisa F, Smith, George Davey, Eskenazi, Brenda, Karmaus, Wilfried, Marsit, Carmen J, Hivert, Marie-France, Snieder, Harold, Fallin, M Daniele, Melén, Erik, Munthe-Kaas, Monica C, Arshad, Hasan, Wiemels, Joseph L, Annesi-Maesano, Isabella, Vrijheid, Martine, Oken, Emily, Holland, Nina, Murphy, Susan K, Sørensen, Thorkild IA, Koppelman, Gerard H, Newnham, John P, Wilcox, Allen J, Nystad, Wenche, London, Stephanie J, Felix, Janine F, and Relton, Caroline L

Subjects
Biological Sciences, Genetics, Perinatal Period - Conditions Originating in Perinatal Period, Contraception/Reproduction, Pediatric, Human Genome, Obesity, 2.1 Biological and endogenous factors, Aetiology, Reproductive health and childbirth, Adult, Body Mass Index, Cohort Studies, DNA Methylation, Epigenesis, Genetic, Epigenomics, Female, Humans, Infant, Newborn, Male, Maternal Inheritance, Mothers, Pregnancy, Pregnancy Outcome, Prenatal Exposure Delayed Effects, Medical and Health Sciences, Genetics & Heredity
Abstract

Pre-pregnancy maternal obesity is associated with adverse offspring outcomes at birth and later in life. Individual studies have shown that epigenetic modifications such as DNA methylation could contribute. Within the Pregnancy and Childhood Epigenetics (PACE) Consortium, we meta-analysed the association between pre-pregnancy maternal BMI and methylation at over 450,000 sites in newborn blood DNA, across 19 cohorts (9,340 mother-newborn pairs). We attempted to infer causality by comparing the effects of maternal versus paternal BMI and incorporating genetic variation. In four additional cohorts (1,817 mother-child pairs), we meta-analysed the association between maternal BMI at the start of pregnancy and blood methylation in adolescents. In newborns, maternal BMI was associated with small (

Published
2017

9. A maternal serum metabolite ratio predicts fetal growth restriction at term

Author

Sovio, Ulla, Goulding, Neil, McBride, Nancy, Cook, Emma, Gaccioli, Francesca, Charnock-Jones, D. Stephen, and Lawlor, Debbie A.

Subjects
Fetus -- Growth, Metabolites -- Measurement -- Health aspects, Prenatal influences -- Health aspects, Biological sciences, Health
Abstract

Fetal growth restriction (FGR) is the major single cause of stillbirth.sup.1 and is also associated with neonatal morbidity and mortality.sup.2,3, impaired health and educational achievement in childhood.sup.4,5 and with a range of diseases in later life.sup.6. Effective screening and intervention for FGR is an unmet clinical need. Here, we performed ultrahigh performance liquid chromatography-tandem mass spectroscopy (UPLC-MS/MS) metabolomics on maternal serum at 12, 20 and 28 weeks of gestational age (wkGA) using 175 cases of term FGR and 299 controls from the Pregnancy Outcome Prediction (POP) study, conducted in Cambridge, UK, to identify predictive metabolites. Internal validation using 36 wkGA samples demonstrated that a ratio of the products of the relative concentrations of two positively associated metabolites (1-(1-enyl-stearoyl)-2-oleoyl-GPC (P-18:0/18:1) and 1,5-anhydroglucitol) to the product of the relative concentrations of two negatively associated metabolites (5[alpha]-androstan-3[alpha],17[alpha]-diol disulfate and N1,N12-diacetylspermine) predicted FGR at term. The ratio had approximately double the discrimination as compared to a previously developed angiogenic biomarker.sup.7, the soluble fms-like tyrosine kinase 1:placental growth factor (sFLT1:PlGF) ratio (AUC 0.78 versus 0.64, P = 0.0001). We validated the predictive performance of the metabolite ratio in two sub-samples of a demographically dissimilar cohort, Born in Bradford (BiB), conducted in Bradford, UK (P = 0.0002). Screening and intervention using this metabolite ratio in conjunction with ultrasonic imaging at around 36 wkGA could plausibly prevent adverse events through enhanced fetal monitoring and targeted induction of labor. The relative concentrations of four metabolites in maternal blood at 36 weeks of gestation predict fetal growth restriction in infants subsequently born at term, enabling enhanced fetal monitoring in pregnancies at risk., Author(s): Ulla Sovio [sup.1] [sup.2] , Neil Goulding [sup.3] [sup.4] [sup.5] , Nancy McBride [sup.3] [sup.4] [sup.5] , Emma Cook [sup.1] , Francesca Gaccioli [sup.1] [sup.2] , D. Stephen Charnock-Jones [...]

Published
2020
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10. HMG-coenzyme A reductase inhibition, type 2 diabetes, and bodyweight: evidence from genetic analysis and randomised trials

Author

Swerdlow, Daniel I, Preiss, David, Kuchenbaecker, Karoline B, Holmes, Michael V, Engmann, Jorgen EL, Shah, Tina, Sofat, Reecha, Stender, Stefan, Johnson, Paul CD, Scott, Robert A, Leusink, Maarten, Verweij, Niek, Sharp, Stephen J, Guo, Yiran, Giambartolomei, Claudia, Chung, Christina, Peasey, Anne, Amuzu, Antoinette, Li, KaWah, Palmen, Jutta, Howard, Philip, Cooper, Jackie A, Drenos, Fotios, Li, Yun R, Lowe, Gordon, Gallacher, John, Stewart, Marlene CW, Tzoulaki, Ioanna, Buxbaum, Sarah G, van der A, Daphne L, Forouhi, Nita G, Onland-Moret, N Charlotte, van der Schouw, Yvonne T, Schnabel, Renate B, Hubacek, Jaroslav A, Kubinova, Ruzena, Baceviciene, Migle, Tamosiunas, Abdonas, Pajak, Andrzej, Topor-Madry, Romanvan, Stepaniak, Urszula, Malyutina, Sofia, Baldassarre, Damiano, Sennblad, Bengt, Tremoli, Elena, de Faire, Ulf, Veglia, Fabrizio, Ford, Ian, Jukema, J Wouter, Westendorp, Rudi GJ, de Borst, Gert Jan, de Jong, Pim A, Algra, Ale, Spiering, Wilko, der Zee, Anke H Maitland-van, Klungel, Olaf H, de Boer, Anthonius, Doevendans, Pieter A, Eaton, Charles B, Robinson, Jennifer G, Duggan, David, DIAGRAM Consortium, MAGIC Consortium, Kjekshus, John, Downs, John R, Gotto, Antonio M, Keech, Anthony C, Marchioli, Roberto, Tognoni, Gianni, Sever, Peter S, Poulter, Neil R, Waters, David D, Pedersen, Terje R, Amarenco, Pierre, Nakamura, Haruo, McMurray, John JV, Lewsey, James D, Chasman, Daniel I, Ridker, Paul M, Maggioni, Aldo P, Tavazzi, Luigi, Ray, Kausik K, Seshasai, Sreenivasa Rao Kondapally, Manson, JoAnn E, Price, Jackie F, Whincup, Peter H, Morris, Richard W, Lawlor, Debbie A, Smith, George Davey, Ben-Shlomo, Yoav, Schreiner, Pamela J, Fornage, Myriam, Siscovick, David S, Cushman, Mary, Kumari, Meena, Wareham, Nick J, Verschuren, WM Monique, Redline, Susan, Patel, Sanjay R, Whittaker, John C, and Hamsten, Anders

Subjects
Epidemiology, Biomedical and Clinical Sciences, Health Sciences, Clinical Trials and Supportive Activities, Clinical Research, Genetics, Nutrition, Diabetes, 6.1 Pharmaceuticals, Evaluation of treatments and therapeutic interventions, Metabolic and endocrine, Aged, Body Mass Index, Body Weight, Cholesterol, HDL, Cholesterol, LDL, Diabetes Mellitus, Type 2, Female, Genetic Testing, Humans, Hydroxymethylglutaryl CoA Reductases, Hydroxymethylglutaryl-CoA Reductase Inhibitors, Male, Middle Aged, Polymorphism, Single Nucleotide, Randomized Controlled Trials as Topic, Risk Factors, DIAGRAM Consortium, MAGIC Consortium, InterAct Consortium, Medical and Health Sciences, General & Internal Medicine, Biomedical and clinical sciences, Health sciences
Abstract

BackgroundStatins increase the risk of new-onset type 2 diabetes mellitus. We aimed to assess whether this increase in risk is a consequence of inhibition of 3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR), the intended drug target.MethodsWe used single nucleotide polymorphisms in the HMGCR gene, rs17238484 (for the main analysis) and rs12916 (for a subsidiary analysis) as proxies for HMGCR inhibition by statins. We examined associations of these variants with plasma lipid, glucose, and insulin concentrations; bodyweight; waist circumference; and prevalent and incident type 2 diabetes. Study-specific effect estimates per copy of each LDL-lowering allele were pooled by meta-analysis. These findings were compared with a meta-analysis of new-onset type 2 diabetes and bodyweight change data from randomised trials of statin drugs. The effects of statins in each randomised trial were assessed using meta-analysis.FindingsData were available for up to 223 463 individuals from 43 genetic studies. Each additional rs17238484-G allele was associated with a mean 0·06 mmol/L (95% CI 0·05-0·07) lower LDL cholesterol and higher body weight (0·30 kg, 0·18-0·43), waist circumference (0·32 cm, 0·16-0·47), plasma insulin concentration (1·62%, 0·53-2·72), and plasma glucose concentration (0·23%, 0·02-0·44). The rs12916 SNP had similar effects on LDL cholesterol, bodyweight, and waist circumference. The rs17238484-G allele seemed to be associated with higher risk of type 2 diabetes (odds ratio [OR] per allele 1·02, 95% CI 1·00-1·05); the rs12916-T allele association was consistent (1·06, 1·03-1·09). In 129 170 individuals in randomised trials, statins lowered LDL cholesterol by 0·92 mmol/L (95% CI 0·18-1·67) at 1-year of follow-up, increased bodyweight by 0·24 kg (95% CI 0·10-0·38 in all trials; 0·33 kg, 95% CI 0·24-0·42 in placebo or standard care controlled trials and -0·15 kg, 95% CI -0·39 to 0·08 in intensive-dose vs moderate-dose trials) at a mean of 4·2 years (range 1·9-6·7) of follow-up, and increased the odds of new-onset type 2 diabetes (OR 1·12, 95% CI 1·06-1·18 in all trials; 1·11, 95% CI 1·03-1·20 in placebo or standard care controlled trials and 1·12, 95% CI 1·04-1·22 in intensive-dose vs moderate dose trials).InterpretationThe increased risk of type 2 diabetes noted with statins is at least partially explained by HMGCR inhibition.FundingThe funding sources are cited at the end of the paper.

Published
2015

14. Prevalence of obesity, hypertension, and diabetes, and cascade of care in sub-Saharan Africa: a cross-sectional, population-based study in rural and urban Malawi

Author

Price, Alison J, Crampin, Amelia C, Amberbir, Alemayehu, Kayuni-Chihana, Ndoliwe, Musicha, Crispin, Tafatatha, Terence, Branson, Keith, Lawlor, Debbie A, Mwaiyeghele, Elenaus, Nkhwazi, Lawrence, Smeeth, Liam, Pearce, Neil, Munthali, Elizabeth, Mwagomba, Beatrice M, Mwansambo, Charles, Glynn, Judith R, Jaffar, Shabbar, and Nyirenda, Moffat

Published
2018
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27. Exploring regression dilution bias using repeat measurements of 2858 variables in ≤49 000 UK Biobank participants

Author

Rutter, Charlotte E, Millard, Louise A C, Borges, Maria C, and Lawlor, Debbie A

Abstract

BackgroundMeasurement error in exposures and confounders can bias exposure–outcome associations but is rarely considered. We aimed to assess random measurement error of all continuous variables in UK Biobank and explore approaches to mitigate its impact on exposure–outcome associations.MethodsRandom measurement error was assessed using intraclass correlation coefficients (ICCs) for all continuous variables with repeat measures. Regression calibration was used to correct for random error in exposures and confounders, using the associations of red blood cell distribution width (RDW), C-reactive protein (CRP) and 25-hydroxyvitamin D [25(OH)D] with mortality as illustrative examples.ResultsThe 2858 continuous variables with repeat measures varied in sample size from 109 to 49 121. They fell into three groups: (i) baseline visit [529 variables; median (interquartile range) ICC = 0.64 (0.57, 0.83)]; (ii) online diet by 24-h recall [22 variables; 0.35 (0.30, 0.40)] and (iii) imaging measures [2307 variables; 0.85 (0.73, 0.94)]. Highest ICCs were for anthropometric and medical history measures, and lowest for dietary and heart magnetic resonance imaging.The ICCs (95% confidence interval) for RDW, CRP and 25(OH)D were 0.52 (0.51, 0.53), 0.29 (0.27, 0.30) and 0.55 (0.54, 0.56), respectively. Higher RDW and levels of CRP were associated with higher risk of all-cause mortality, and higher concentration of 25(OH)D with lower risk. After correction for random measurement error in the main exposure, the associations all strengthened. Confounder correction did not influence estimates.ConclusionsRandom measurement error varies widely and is often non-negligible. For UK Biobank we provide relevant statistics and adaptable code to help other researchers explore and correct for this.

Published
2023

30. Life expectancy associated with different ages at diagnosis of type 2 diabetes in high-income countries : 23 million person-years of observation

Author

Kaptoge, Stephen, Sun, Luanluan, Walker, Matthew, Spackman, Sarah, Pennells, Lisa, Seshasai, Sreenivasa Rao Kondapally, Bolton, Thomas, Ataklte, Feven, Willeit, Peter, Bell, Steven, Burgess, Steven, Altay, Servet, Assmann, Gerd, Ben-Shlomo, Yoav, Best, Lyle G., Bjorkelund, Cecilia, Blazer, Dan G., Brenner, Hermann, Brunner, Eric J., Dagenais, Gilles R., Cooper, Jackie A., Cooper, Cyrus, Crespo, Carlos J., Cushman, Mary, D'Agostino, Ralph B., Sr., Daimon, Makoto, Daniels, Lori B., Dankner, Rachel, Davidson, Karina W., de Jongh, Renate T., Donfrancesco, Chiara, Ducimetiere, Pierre, Elders, Petra J. M., Engstrom, Gunnar, Ford, Ian, Gallacher, John, Bakker, Stephan J. L., Goldbourt, Uri, de la Camara, Agustin Gomez, Grimsgaard, Sameline, Gudnason, Vilmundur, Hansson, Per-Olof, Imano, Hironori, Jukema, J. Wouter, Kabrhel, Christopher, Kauhanen, Jussi, Kavousi, Maryam, Kiechl, Stefan, Knuiman, Matthew W., Kromhout, Daan, Krumholz, Harlan M., Kuller, Lewis H., Laatikainen, Tiina, Lawlor, Debbie A., Meyer, Haakon E., Mukamal, Kenneth, Nietert, Paul J., Ninomiya, Toshiharu, Nitsch, Dorothea, Nordestgaard, Borge G., Palmieri, Luigi, Price, Jackie F., Ridker, Paul M., Sun, Qi, Rosengren, Annika, Roussel, Ronan, Sakurai, Masaru, Salomaa, Veikko, Schottker, Ben, Shaw, Jonathan E., Strandberg, Timo E., Sundström, Johan, Tolonen, Hanna, Tverdal, Aage, Verschuren, W. M. Monique, Volzke, Henry, Wagenknecht, Lynne, Wallace, Robert B., Wannamethee, S. Goya, Wareham, Nicholas J., Wassertheil-Smoller, Sylvia, Yamagishi, Kazumasa, Yeap, Bu B., Harrison, Seamus, Thompson, Simon G., Inouye, Michael, Griffin, Simon, Butterworth, Adam S., Wood, Angela M., Sattar, Naveed, Danesh, John, Di Angelantonio, Emanuele, Kaptoge, Stephen, Sun, Luanluan, Walker, Matthew, Spackman, Sarah, Pennells, Lisa, Seshasai, Sreenivasa Rao Kondapally, Bolton, Thomas, Ataklte, Feven, Willeit, Peter, Bell, Steven, Burgess, Steven, Altay, Servet, Assmann, Gerd, Ben-Shlomo, Yoav, Best, Lyle G., Bjorkelund, Cecilia, Blazer, Dan G., Brenner, Hermann, Brunner, Eric J., Dagenais, Gilles R., Cooper, Jackie A., Cooper, Cyrus, Crespo, Carlos J., Cushman, Mary, D'Agostino, Ralph B., Sr., Daimon, Makoto, Daniels, Lori B., Dankner, Rachel, Davidson, Karina W., de Jongh, Renate T., Donfrancesco, Chiara, Ducimetiere, Pierre, Elders, Petra J. M., Engstrom, Gunnar, Ford, Ian, Gallacher, John, Bakker, Stephan J. L., Goldbourt, Uri, de la Camara, Agustin Gomez, Grimsgaard, Sameline, Gudnason, Vilmundur, Hansson, Per-Olof, Imano, Hironori, Jukema, J. Wouter, Kabrhel, Christopher, Kauhanen, Jussi, Kavousi, Maryam, Kiechl, Stefan, Knuiman, Matthew W., Kromhout, Daan, Krumholz, Harlan M., Kuller, Lewis H., Laatikainen, Tiina, Lawlor, Debbie A., Meyer, Haakon E., Mukamal, Kenneth, Nietert, Paul J., Ninomiya, Toshiharu, Nitsch, Dorothea, Nordestgaard, Borge G., Palmieri, Luigi, Price, Jackie F., Ridker, Paul M., Sun, Qi, Rosengren, Annika, Roussel, Ronan, Sakurai, Masaru, Salomaa, Veikko, Schottker, Ben, Shaw, Jonathan E., Strandberg, Timo E., Sundström, Johan, Tolonen, Hanna, Tverdal, Aage, Verschuren, W. M. Monique, Volzke, Henry, Wagenknecht, Lynne, Wallace, Robert B., Wannamethee, S. Goya, Wareham, Nicholas J., Wassertheil-Smoller, Sylvia, Yamagishi, Kazumasa, Yeap, Bu B., Harrison, Seamus, Thompson, Simon G., Inouye, Michael, Griffin, Simon, Butterworth, Adam S., Wood, Angela M., Sattar, Naveed, Danesh, John, and Di Angelantonio, Emanuele

Abstract

Background The prevalence of type 2 diabetes is increasing rapidly, particularly among younger age groups. Estimates suggest that people with diabetes die, on average, 6 years earlier than people without diabetes. We aimed to provide reliable estimates of the associations between age at diagnosis of diabetes and all-cause mortality, cause-specific mortality, and reductions in life expectancy. Methods For this observational study, we conducted a combined analysis of individual-participant data from 19 high income countries using two large-scale data sources: the Emerging Risk Factors Collaboration (96 cohorts, median baseline years 1961-2007, median latest follow-up years 1980-2013) and the UK Biobank (median baseline year 2006, median latest follow-up year 2020). We calculated age-adjusted and sex-adjusted hazard ratios (HRs) for all-cause mortality according to age at diagnosis of diabetes using data from 1 515 718 participants, in whom deaths were recorded during 23 center dot 1 million person-years of follow-up. We estimated cumulative survival by applying age-specific HRs to age-specific death rates from 2015 for the USA and the EU. Findings For participants with diabetes, we observed a linear dose-response association between earlier age at diagnosis and higher risk of all-cause mortality compared with participants without diabetes. HRs were 2 center dot 69 (95% CI 2 center dot 43-2 center dot 97) when diagnosed at 30-39 years, 2 center dot 26 (2 center dot 08-2 center dot 45) at 40-49 years, 1 center dot 84 (1 center dot 72-1 center dot 97) at 50-59 years, 1 center dot 57 (1 center dot 47-1 center dot 67) at 60-69 years, and 1 center dot 39 (1 center dot 29-1 center dot 51) at 70 years and older. HRs per decade of earlier diagnosis were similar for men and women. Using death rates from the USA, a 50-year-old individual with diabetes died on average 14 years earlier when diagnosed aged 30 years, 10 years earlier when diagnosed aged 40 years, or 6 years earlier

Published
2023
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32. Plasma urate concentration and risk of coronary heart disease: a Mendelian randomisation analysis

Author

White, Jon, Sofat, Reecha, Hemani, Gibran, Shah, Tina, Engmann, Jorgen, Dale, Caroline, Shah, Sonia, Kruger, Felix A, Giambartolomei, Claudia, Swerdlow, Daniel I, Palmer, Tom, McLachlan, Stela, Langenberg, Claudia, Zabaneh, Delilah, Lovering, Ruth, Cavadino, Alana, Jefferis, Barbara, Finan, Chris, Wong, Andrew, Amuzu, Antoinette, Ong, Ken, Gaunt, Tom R, Warren, Helen, Davies, Teri-Louise, Drenos, Fotios, Cooper, Jackie, Ebrahim, Shah, Lawlor, Debbie A, Talmud, Philippa J, Humphries, Steve E, Power, Christine, Hypponen, Elina, Richards, Marcus, Hardy, Rebecca, Kuh, Diana, Wareham, Nicholas, Ben-Shlomo, Yoav, Day, Ian N, Whincup, Peter, Morris, Richard, Strachan, Mark W J, Price, Jacqueline, Kumari, Meena, Kivimaki, Mika, Plagnol, Vincent, Whittaker, John C, Smith, George Davey, Dudbridge, Frank, Casas, Juan P, Holmes, Michael V, and Hingorani, Aroon D

Published
2016
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35. Hypertensive Disorders of Pregnancy and DNA Methylation in Newborns: Findings From the Pregnancy and Childhood Epigenetics Consortium

Author

Kazmi, Nabila, Sharp, Gemma C., Reese, Sarah E., Vehmeijer, Florianne O., Lahti, Jari, Page, Christian M., Zhang, Weiming, Rifas-Shiman, Sheryl L., Rezwan, Faisal I., Simpkin, Andrew J., Burrows, Kimberley, Richardson, Tom G., Santos Ferreira, Diana L., Fraser, Abigail, Harmon, Quaker E., Zhao, Shanshan, Jaddoe, Vincent W.V., Czamara, Darina, Binder, Elisabeth B., Magnus, Maria C., Håberg, Siri E., Nystad, Wenche, Nohr, Ellen A., Starling, Anne P., Kechris, Katerina J., Yang, Ivana V., DeMeo, Dawn L., Litonjua, Augusto A., Baccarelli, Andrea, Oken, Emily, Holloway, John W., Karmaus, Wilfried, Arshad, Syed H., Dabelea, Dana, Sørensen, Thorkild I.A., Laivuori, Hannele, Raikkonen, Katri, Felix, Janine F., London, Stephanie J., Hivert, Marie-France, Gaunt, Tom R., Lawlor, Debbie A., and Relton, Caroline L.

Published
2019
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37. Meta-analysis of epigenome-wide association studies in neonates reveals widespread differential DNA methylation associated with birthweight

Author

Küpers, Leanne K., Monnereau, Claire, Sharp, Gemma C., Yousefi, Paul, Salas, Lucas A., Ghantous, Akram, Page, Christian M., Reese, Sarah E., Wilcox, Allen J., Czamara, Darina, Starling, Anne P., Novoloaca, Alexei, Lent, Samantha, Roy, Ritu, Hoyo, Cathrine, Breton, Carrie V., Allard, Catherine, Just, Allan C., Bakulski, Kelly M., Holloway, John W., Everson, Todd M., Xu, Cheng-Jian, Huang, Rae-Chi, van der Plaat, Diana A., Wielscher, Matthias, Merid, Simon Kebede, Ullemar, Vilhelmina, Rezwan, Faisal I., Lahti, Jari, van Dongen, Jenny, Langie, Sabine A. S., Richardson, Tom G., Magnus, Maria C., Nohr, Ellen A., Xu, Zongli, Duijts, Liesbeth, Zhao, Shanshan, Zhang, Weiming, Plusquin, Michelle, DeMeo, Dawn L., Solomon, Olivia, Heimovaara, Joosje H., Jima, Dereje D., Gao, Lu, Bustamante, Mariona, Perron, Patrice, Wright, Robert O., Hertz-Picciotto, Irva, Zhang, Hongmei, Karagas, Margaret R., Gehring, Ulrike, Marsit, Carmen J., Beilin, Lawrence J., Vonk, Judith M., Jarvelin, Marjo-Riitta, Bergström, Anna, Örtqvist, Anne K., Ewart, Susan, Villa, Pia M., Moore, Sophie E., Willemsen, Gonneke, Standaert, Arnout R. L., Håberg, Siri E., Sørensen, Thorkild I. A., Taylor, Jack A., Räikkönen, Katri, Yang, Ivana V., Kechris, Katerina, Nawrot, Tim S., Silver, Matt J., Gong, Yun Yun, Richiardi, Lorenzo, Kogevinas, Manolis, Litonjua, Augusto A., Eskenazi, Brenda, Huen, Karen, Mbarek, Hamdi, Maguire, Rachel L., Dwyer, Terence, Vrijheid, Martine, Bouchard, Luigi, Baccarelli, Andrea A., Croen, Lisa A., Karmaus, Wilfried, Anderson, Denise, de Vries, Maaike, Sebert, Sylvain, Kere, Juha, Karlsson, Robert, Arshad, Syed Hasan, Hämäläinen, Esa, Routledge, Michael N., Boomsma, Dorret I., Feinberg, Andrew P., Newschaffer, Craig J., Govarts, Eva, Moisse, Matthieu, Fallin, M. Daniele, Melén, Erik, Prentice, Andrew M., Kajantie, Eero, Almqvist, Catarina, Oken, Emily, Dabelea, Dana, Boezen, H. Marike, Melton, Phillip E., Wright, Rosalind J., Koppelman, Gerard H., Trevisi, Letizia, Hivert, Marie-France, Sunyer, Jordi, Munthe-Kaas, Monica C., Murphy, Susan K., Corpeleijn, Eva, Wiemels, Joseph, Holland, Nina, Herceg, Zdenko, Binder, Elisabeth B., Davey Smith, George, Jaddoe, Vincent W. V., Lie, Rolv T., Nystad, Wenche, London, Stephanie J., Lawlor, Debbie A., Relton, Caroline L., Snieder, Harold, and Felix, Janine F.

Published
2019
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41. Variation in the SLC23A1 gene does not influence cardiometabolic outcomes to the extent expected given its association with l-ascorbic acid

Author

Wade, Kaitlin H, Forouhi, Nita G, Cook, Derek G, Johnson, Paul, McConnachie, Alex, Morris, Richard W, Rodriguez, Santiago, Ye, Zheng, Ebrahim, Shah, Padmanabhan, Sandosh, Watt, Graham, Bruckdorfer, K Richard, Wareham, Nick J, Whincup, Peter H, Chanock, Stephen, Sattar, Naveed, Lawlor, Debbie A, Davey Smith, George, and Timpson, Nicholas J

Published
2015
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46. Effects of Simulated Interventions to Improve School Entry Academic Skills on Socioeconomic Inequalities in Educational Achievement

Author

Chittleborough, Catherine R., Mittinty, Murthy N., and Lawlor, Debbie A.

Abstract

Randomized controlled trial evidence shows that interventions before age 5 can improve skills necessary for educational success; the effect of these interventions on socioeconomic inequalities is unknown. Using trial effect estimates, and marginal structural models with data from the Avon Longitudinal Study of Parents and Children (n = 11,764, imputed), simulated effects of plausible interventions to improve school entry academic skills on socioeconomic inequality in educational achievement at age 16 were examined. Progressive universal interventions (i.e., more intense intervention for those with greater need) to improve school entry academic skills could raise population levels of educational achievement by 5% and reduce absolute socioeconomic inequality in poor educational achievement by 15%.

Published
2014
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49. Associations between insomnia and pregnancy and perinatal outcomes: Evidence from Mendelian randomization and multivariable regression analyses

Author

Yang, Qian, Borges, Maria C, Sanderson, Eleanor C M, Magnus, Maria, Kilpi, Fanny C, Collings, Paul J, Goncalves Soares, Ana Luiza, West, Jane, Magnus, Per, Wright, John, Håberg, Siri Eldevik, Tilling, Kate M, and Lawlor, Debbie A

Abstract

BackgroundInsomnia is common and associated with adverse pregnancy and perinatal outcomes in observational studies. However, those associations could be vulnerable to residual confounding or reverse causality. Our aim was to estimate the association of insomnia with stillbirth, miscarriage, gestational diabetes (GD), hypertensive disorders of pregnancy (HDP), perinatal depression, preterm birth (PTB), and low/high offspring birthweight (LBW/HBW).Methods and findingsWe used 2-sample mendelian randomization (MR) with 81 single-nucleotide polymorphisms (SNPs) instrumenting for a lifelong predisposition to insomnia. Our outcomes included ever experiencing stillbirth, ever experiencing miscarriage, GD, HDP, perinatal depression, PTB (gestational age 4,500 grams). We used data from women of European descent (N = 356,069, mean ages at delivery 25.5 to 30.0 years) from UK Biobank (UKB), FinnGen, Avon Longitudinal Study of Parents and Children (ALSPAC), Born in Bradford (BiB), and the Norwegian Mother, Father and Child Cohort (MoBa). Main MR analyses used inverse variance weighting (IVW), with weighted median and MR-Egger as sensitivity analyses. We compared MR estimates with multivariable regression of insomnia in pregnancy on outcomes in ALSPAC (N = 11,745). IVW showed evidence of an association of genetic susceptibility to insomnia with miscarriage (odds ratio (OR): 1.60, 95% confidence interval (CI): 1.18, 2.17, p = 0.002), perinatal depression (OR 3.56, 95% CI: 1.49, 8.54, p = 0.004), and LBW (OR 3.17, 95% CI: 1.69, 5.96, p < 0.001). IVW results did not support associations of insomnia with stillbirth, GD, HDP, PTB, and HBW, with wide CIs including the null. Associations of genetic susceptibility to insomnia with miscarriage, perinatal depression, and LBW were not observed in weighted median or MR-Egger analyses. Results from these sensitivity analyses were directionally consistent with IVW results for all outcomes, with the exception of GD, perinatal depression, and PTB in MR-Egger. Multivariable regression showed associations of insomnia at 18 weeks of gestation with perinatal depression (OR 2.96, 95% CI: 2.42, 3.63, p < 0.001), but not with LBW (OR 0.92, 95% CI: 0.69, 1.24, p = 0.60). Multivariable regression with miscarriage and stillbirth was not possible due to small numbers in index pregnancies. Key limitations are potential horizontal pleiotropy (particularly for perinatal depression) and low statistical power in MR, and residual confounding in multivariable regression.ConclusionsIn this study, we observed some evidence in support of a possible causal relationship between genetically predicted insomnia and miscarriage, perinatal depression, and LBW. Our study also found observational evidence in support of an association between insomnia in pregnancy and perinatal depression, with no clear multivariable evidence of an association with LBW. Our findings highlight the importance of healthy sleep in women of reproductive age, though replication in larger studies, including with genetic instruments specific to insomnia in pregnancy are important.

Published
2022

50. Metabolomic Consequences of Genetic Inhibition of PCSK9 Compared With Statin Treatment

Author

Sliz, Eeva, Kettunen, Johannes, Holmes, Michael V., Williams, Clare Oliver, Boachie, Charles, Wang, Qin, Männikkö, Minna, Sebert, Sylvain, Walters, Robin, Lin, Kuang, Millwood, Iona Y., Clarke, Robert, Li, Liming, Rankin, Naomi, Welsh, Paul, Delles, Christian, Jukema, J. Wouter, Trompet, Stella, Ford, Ian, Perola, Markus, Salomaa, Veikko, Järvelin, Marjo-Riitta, Chen, Zhengming, Lawlor, Debbie A., Ala-Korpela, Mika, Danesh, John, Davey Smith, George, Sattar, Naveed, Butterworth, Adam, and Würtz, Peter

Published
2018
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