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1. Maternal and Infant Research Electronic Data Analysis (MIREDA): A protocol for creating a common data model for federated analysis of UK birth cohorts and the life course.

Author

Seaborne, MJ, primary, Jones, HE, additional, Cockburn, N, additional, Durbaba, S, additional, Giles, TC, additional, González-Izquierdo, A, additional, Hough, A, additional, Mason, D, additional, Mendez-Villalon, A, additional, Sanchez-Soriano, C., additional, Orton, C., additional, Ford, D, additional, Quinlan, P, additional, Nirantharakumar, K, additional, Poston, L., additional, Reynolds, RM, additional, Santorelli, G, additional, and Brophy, S, additional

Published
2024
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2. External validation of prognostic models to predict stillbirth using International Prediction of Pregnancy Complications (IPPIC) Network database: individual participant data meta-analysis

Author

Allotey, J, Whittle, R, Snell, K, Smuk, M, Townsend, R, von Dadelszen, P, Heazell, A, Magee, L, Smith, G, Sandall, J, Thilaganathan, B, Zamora, J, Riley, R, Khalil, A, Thangaratinam, S, Coomarasamy, A, Kwong, A, Savitri, A, Salvesen, K, Bhattacharya, S, Uiterwaal, C, Staff, A, Andersen, L, Olive, E, Redman, C, Sletner, L, Daskalakis, G, Macleod, M, Abdollahain, M, Ramirez, J, Masse, J, Audibert, F, Magnus, P, Jenum, A, Baschat, A, Ohkuchi, A, Mcauliffe, F, West, J, Askie, L, Mone, F, Farrar, D, Zimmerman, P, Smits, L, Riddell, C, Kingdom, J, van de Post, J, Illanes, S, Holzman, C, van Kuijk, S, Carbillon, L, Villa, P, Eskild, A, Chappell, L, Prefumo, F, Velauthar, L, Seed, P, van Oostwaard, M, Verlohren, S, Poston, L, Ferrazzi, E, Vinter, C, Nagata, C, Brown, M, Vollebregt, K, Takeda, S, Langenveld, J, Widmer, M, Saito, S, Haavaldsen, C, Carroli, G, Olsen, J, Wolf, H, Zavaleta, N, Eisensee, I, Vergani, P, Lumbiganon, P, Makrides, M, Facchinetti, F, Sequeira, E, Gibson, R, Ferrazzani, S, Frusca, T, Norman, J, Figueiro, E, Lapaire, O, Laivuori, H, Lykke, J, Conde-Agudelo, A, Galindo, A, Mbah, A, Betran, A, Herraiz, I, Trogstad, L, Steegers, E, Salim, R, Huang, T, Adank, A, Zhang, J, Meschino, W, Browne, J, Allen, R, Costa, F, Klipstein-Grobusch Browne, K, Crowther, C, Jorgensen, J, Forest, J, Rumbold, A, Mol, B, Giguere, Y, Kenny, L, Ganzevoort, W, Odibo, A, Myers, J, Yeo, S, Goffinet, F, Mccowan, L, Pajkrt, E, Teede, H, Haddad, B, Dekker, G, Kleinrouweler, E, Lecarpentier, E, Roberts, C, Groen, H, Skrastad, R, Heinonen, S, Eero, K, Anggraini, D, Souka, A, Cecatti, J, Monterio, I, Pillalis, A, Souza, R, Hawkins, L, Gabbay-Benziv, R, Crovetto, F, Figuera, F, Jorgensen, L, Dodds, J, Patel, M, Aviram, A, Papageorghiou, A, Khan, K, Allotey J., Whittle R., Snell K. I. E., Smuk M., Townsend R., von Dadelszen P., Heazell A. E. P., Magee L., Smith G. C. S., Sandall J., Thilaganathan B., Zamora J., Riley R. D., Khalil A., Thangaratinam S., Coomarasamy A., Kwong A., Savitri A. I., Salvesen K. A., Bhattacharya S., Uiterwaal C. S. P. M., Staff A. C., Andersen L. B., Olive E. L., Redman C., Sletner L., Daskalakis G., Macleod M., Abdollahain M., Ramirez J. A., Masse J., Audibert F., Magnus P. M., Jenum A. K., Baschat A., Ohkuchi A., McAuliffe F. M., West J., Askie L. M., Mone F., Farrar D., Zimmerman P. A., Smits L. J. M., Riddell C., Kingdom J. C., van de Post J., Illanes S. E., Holzman C., van Kuijk S. M. J., Carbillon L., Villa P. M., Eskild A., Chappell L., Prefumo F., Velauthar L., Seed P., van Oostwaard M., Verlohren S., Poston L., Ferrazzi E., Vinter C. A., Nagata C., Brown M., Vollebregt K. C., Takeda S., Langenveld J., Widmer M., Saito S., Haavaldsen C., Carroli G., Olsen J., Wolf H., Zavaleta N., Eisensee I., Vergani P., Lumbiganon P., Makrides M., Facchinetti F., Sequeira E., Gibson R., Ferrazzani S., Frusca T., Norman J. E., Figueiro E. A., Lapaire O., Laivuori H., Lykke J. A., Conde-Agudelo A., Galindo A., Mbah A., Betran A. P., Herraiz I., Trogstad L., Smith G. G. S., Steegers E. A. P., Salim R., Huang T., Adank A., Zhang J., Meschino W. S., Browne J. L., Allen R. E., Costa F. D. S., Klipstein-Grobusch Browne K., Crowther C. A., Jorgensen J. S., Forest J. -C., Rumbold A. R., Mol B. W., Giguere Y., Kenny L. C., Ganzevoort W., Odibo A. O., Myers J., Yeo S. A., Goffinet F., McCowan L., Pajkrt E., Teede H. J., Haddad B. G., Dekker G., Kleinrouweler E. C., LeCarpentier E., Roberts C. T., Groen H., Skrastad R. B., Heinonen S., Eero K., Anggraini D., Souka A., Cecatti J. G., Monterio I., Pillalis A., Souza R., Hawkins L. A., Gabbay-Benziv R., Crovetto F., Figuera F., Jorgensen L., Dodds J., Patel M., Aviram A., Papageorghiou A., Khan K., Allotey, J, Whittle, R, Snell, K, Smuk, M, Townsend, R, von Dadelszen, P, Heazell, A, Magee, L, Smith, G, Sandall, J, Thilaganathan, B, Zamora, J, Riley, R, Khalil, A, Thangaratinam, S, Coomarasamy, A, Kwong, A, Savitri, A, Salvesen, K, Bhattacharya, S, Uiterwaal, C, Staff, A, Andersen, L, Olive, E, Redman, C, Sletner, L, Daskalakis, G, Macleod, M, Abdollahain, M, Ramirez, J, Masse, J, Audibert, F, Magnus, P, Jenum, A, Baschat, A, Ohkuchi, A, Mcauliffe, F, West, J, Askie, L, Mone, F, Farrar, D, Zimmerman, P, Smits, L, Riddell, C, Kingdom, J, van de Post, J, Illanes, S, Holzman, C, van Kuijk, S, Carbillon, L, Villa, P, Eskild, A, Chappell, L, Prefumo, F, Velauthar, L, Seed, P, van Oostwaard, M, Verlohren, S, Poston, L, Ferrazzi, E, Vinter, C, Nagata, C, Brown, M, Vollebregt, K, Takeda, S, Langenveld, J, Widmer, M, Saito, S, Haavaldsen, C, Carroli, G, Olsen, J, Wolf, H, Zavaleta, N, Eisensee, I, Vergani, P, Lumbiganon, P, Makrides, M, Facchinetti, F, Sequeira, E, Gibson, R, Ferrazzani, S, Frusca, T, Norman, J, Figueiro, E, Lapaire, O, Laivuori, H, Lykke, J, Conde-Agudelo, A, Galindo, A, Mbah, A, Betran, A, Herraiz, I, Trogstad, L, Steegers, E, Salim, R, Huang, T, Adank, A, Zhang, J, Meschino, W, Browne, J, Allen, R, Costa, F, Klipstein-Grobusch Browne, K, Crowther, C, Jorgensen, J, Forest, J, Rumbold, A, Mol, B, Giguere, Y, Kenny, L, Ganzevoort, W, Odibo, A, Myers, J, Yeo, S, Goffinet, F, Mccowan, L, Pajkrt, E, Teede, H, Haddad, B, Dekker, G, Kleinrouweler, E, Lecarpentier, E, Roberts, C, Groen, H, Skrastad, R, Heinonen, S, Eero, K, Anggraini, D, Souka, A, Cecatti, J, Monterio, I, Pillalis, A, Souza, R, Hawkins, L, Gabbay-Benziv, R, Crovetto, F, Figuera, F, Jorgensen, L, Dodds, J, Patel, M, Aviram, A, Papageorghiou, A, Khan, K, Allotey J., Whittle R., Snell K. I. E., Smuk M., Townsend R., von Dadelszen P., Heazell A. E. P., Magee L., Smith G. C. S., Sandall J., Thilaganathan B., Zamora J., Riley R. D., Khalil A., Thangaratinam S., Coomarasamy A., Kwong A., Savitri A. I., Salvesen K. A., Bhattacharya S., Uiterwaal C. S. P. M., Staff A. C., Andersen L. B., Olive E. L., Redman C., Sletner L., Daskalakis G., Macleod M., Abdollahain M., Ramirez J. A., Masse J., Audibert F., Magnus P. M., Jenum A. K., Baschat A., Ohkuchi A., McAuliffe F. M., West J., Askie L. M., Mone F., Farrar D., Zimmerman P. A., Smits L. J. M., Riddell C., Kingdom J. C., van de Post J., Illanes S. E., Holzman C., van Kuijk S. M. J., Carbillon L., Villa P. M., Eskild A., Chappell L., Prefumo F., Velauthar L., Seed P., van Oostwaard M., Verlohren S., Poston L., Ferrazzi E., Vinter C. A., Nagata C., Brown M., Vollebregt K. C., Takeda S., Langenveld J., Widmer M., Saito S., Haavaldsen C., Carroli G., Olsen J., Wolf H., Zavaleta N., Eisensee I., Vergani P., Lumbiganon P., Makrides M., Facchinetti F., Sequeira E., Gibson R., Ferrazzani S., Frusca T., Norman J. E., Figueiro E. A., Lapaire O., Laivuori H., Lykke J. A., Conde-Agudelo A., Galindo A., Mbah A., Betran A. P., Herraiz I., Trogstad L., Smith G. G. S., Steegers E. A. P., Salim R., Huang T., Adank A., Zhang J., Meschino W. S., Browne J. L., Allen R. E., Costa F. D. S., Klipstein-Grobusch Browne K., Crowther C. A., Jorgensen J. S., Forest J. -C., Rumbold A. R., Mol B. W., Giguere Y., Kenny L. C., Ganzevoort W., Odibo A. O., Myers J., Yeo S. A., Goffinet F., McCowan L., Pajkrt E., Teede H. J., Haddad B. G., Dekker G., Kleinrouweler E. C., LeCarpentier E., Roberts C. T., Groen H., Skrastad R. B., Heinonen S., Eero K., Anggraini D., Souka A., Cecatti J. G., Monterio I., Pillalis A., Souza R., Hawkins L. A., Gabbay-Benziv R., Crovetto F., Figuera F., Jorgensen L., Dodds J., Patel M., Aviram A., Papageorghiou A., and Khan K.

Abstract

Objective: Stillbirth is a potentially preventable complication of pregnancy. Identifying women at high risk of stillbirth can guide decisions on the need for closer surveillance and timing of delivery in order to prevent fetal death. Prognostic models have been developed to predict the risk of stillbirth, but none has yet been validated externally. In this study, we externally validated published prediction models for stillbirth using individual participant data (IPD) meta-analysis to assess their predictive performance. Methods: MEDLINE, EMBASE, DH-DATA and AMED databases were searched from inception to December 2020 to identify studies reporting stillbirth prediction models. Studies that developed or updated prediction models for stillbirth for use at any time during pregnancy were included. IPD from cohorts within the International Prediction of Pregnancy Complications (IPPIC) Network were used to validate externally the identified prediction models whose individual variables were available in the IPD. The risk of bias of the models and cohorts was assessed using the Prediction study Risk Of Bias ASsessment Tool (PROBAST). The discriminative performance of the models was evaluated using the C-statistic, and calibration was assessed using calibration plots, calibration slope and calibration-in-the-large. Performance measures were estimated separately in each cohort, as well as summarized across cohorts using random-effects meta-analysis. Clinical utility was assessed using net benefit. Results: Seventeen studies reporting the development of 40 prognostic models for stillbirth were identified. None of the models had been previously validated externally, and the full model equation was reported for only one-fifth (20%, 8/40) of the models. External validation was possible for three of these models, using IPD from 19 cohorts (491 201 pregnant women) within the IPPIC Network database. Based on evaluation of the model development studies, all three models had an overa

Published
2022

6. External validation of prognostic models to predict stillbirth using International Prediction of Pregnancy Complications (IPPIC) Network database: individual participant data meta-analysis

Author

Allotey, J., Whittle, R., Snell, K. I. E., Smuk, M., Townsend, R., von Dadelszen, P., Heazell, A. E. P., Magee, L., Smith, G. C. S., Sandall, J., Thilaganathan, B., Zamora, J., Riley, R. D., Khalil, A., Thangaratinam, S., Coomarasamy, A., Kwong, A., Savitri, A. I., Salvesen, K. A., Bhattacharya, S., Uiterwaal, C. S. P. M., Staff, A. C., Andersen, L. B., Olive, E. L., Redman, C., Sletner, L., Daskalakis, G., Macleod, M., Abdollahain, M., Ramirez, J. A., Masse, J., Audibert, F., Magnus, P. M., Jenum, A. K., Baschat, A., Ohkuchi, A., Mcauliffe, F. M., West, J., Askie, L. M., Mone, F., Farrar, D., Zimmerman, P. A., Smits, L. J. M., Riddell, C., Kingdom, J. C., van de Post, J., Illanes, S. E., Holzman, C., van Kuijk, S. M. J., Carbillon, L., Villa, P. M., Eskild, A., Chappell, L., Prefumo, F., Velauthar, L., Seed, P., van Oostwaard, M., Verlohren, S., Poston, L., Ferrazzi, E., Vinter, C. A., Nagata, C., Brown, M., Vollebregt, K. C., Takeda, S., Langenveld, J., Widmer, M., Saito, S., Haavaldsen, C., Carroli, G., Olsen, J., Wolf, H., Zavaleta, N., Eisensee, I., Vergani, P., Lumbiganon, P., Makrides, M., Facchinetti, F., Sequeira, E., Gibson, R., Ferrazzani, S., Frusca, T., Norman, J. E., Figueiro, E. A., Lapaire, O., Laivuori, H., Lykke, J. A., Conde-Agudelo, A., Galindo, A., Mbah, A., Betran, A. P., Herraiz, I., Trogstad, L., Smith, G. G. S., Steegers, E. A. P., Salim, R., Huang, T., Adank, A., Zhang, J., Meschino, W. S., Browne, J. L., Allen, R. E., Costa, F. D. S., Klipstein-Grobusch Browne, K., Crowther, C. A., Jorgensen, J. S., Forest, J. -C., Rumbold, A. R., Mol, B. W., Giguere, Y., Kenny, L. C., Ganzevoort, W., Odibo, A. O., Myers, J., Yeo, S. A., Goffinet, F., Mccowan, L., Pajkrt, E., Teede, H. J., Haddad, B. G., Dekker, G., Kleinrouweler, E. C., Lecarpentier, E., Roberts, C. T., Groen, H., Skrastad, R. B., Heinonen, S., Eero, K., Anggraini, D., Souka, A., Cecatti, J. G., Monterio, I., Pillalis, A., Souza, R., Hawkins, L. A., Gabbay-Benziv, R., Crovetto, F., Figuera, F., Jorgensen, L., Dodds, J., Patel, M., Aviram, A., Papageorghiou, A., Khan, K., Clinicum, HUS Gynecology and Obstetrics, Department of Obstetrics and Gynecology, HUS Children and Adolescents, Lastentautien yksikkö, Children's Hospital, Allotey, J, Whittle, R, Snell, K, Smuk, M, Townsend, R, von Dadelszen, P, Heazell, A, Magee, L, Smith, G, Sandall, J, Thilaganathan, B, Zamora, J, Riley, R, Khalil, A, Thangaratinam, S, Coomarasamy, A, Kwong, A, Savitri, A, Salvesen, K, Bhattacharya, S, Uiterwaal, C, Staff, A, Andersen, L, Olive, E, Redman, C, Sletner, L, Daskalakis, G, Macleod, M, Abdollahain, M, Ramirez, J, Masse, J, Audibert, F, Magnus, P, Jenum, A, Baschat, A, Ohkuchi, A, Mcauliffe, F, West, J, Askie, L, Mone, F, Farrar, D, Zimmerman, P, Smits, L, Riddell, C, Kingdom, J, van de Post, J, Illanes, S, Holzman, C, van Kuijk, S, Carbillon, L, Villa, P, Eskild, A, Chappell, L, Prefumo, F, Velauthar, L, Seed, P, van Oostwaard, M, Verlohren, S, Poston, L, Ferrazzi, E, Vinter, C, Nagata, C, Brown, M, Vollebregt, K, Takeda, S, Langenveld, J, Widmer, M, Saito, S, Haavaldsen, C, Carroli, G, Olsen, J, Wolf, H, Zavaleta, N, Eisensee, I, Vergani, P, Lumbiganon, P, Makrides, M, Facchinetti, F, Sequeira, E, Gibson, R, Ferrazzani, S, Frusca, T, Norman, J, Figueiro, E, Lapaire, O, Laivuori, H, Lykke, J, Conde-Agudelo, A, Galindo, A, Mbah, A, Betran, A, Herraiz, I, Trogstad, L, Steegers, E, Salim, R, Huang, T, Adank, A, Zhang, J, Meschino, W, Browne, J, Allen, R, Costa, F, Klipstein-Grobusch Browne, K, Crowther, C, Jorgensen, J, Forest, J, Rumbold, A, Mol, B, Giguere, Y, Kenny, L, Ganzevoort, W, Odibo, A, Myers, J, Yeo, S, Goffinet, F, Mccowan, L, Pajkrt, E, Teede, H, Haddad, B, Dekker, G, Kleinrouweler, E, Lecarpentier, E, Roberts, C, Groen, H, Skrastad, R, Heinonen, S, Eero, K, Anggraini, D, Souka, A, Cecatti, J, Monterio, I, Pillalis, A, Souza, R, Hawkins, L, Gabbay-Benziv, R, Crovetto, F, Figuera, F, Jorgensen, L, Dodds, J, Patel, M, Aviram, A, Papageorghiou, A, Khan, K, Tampere University, Obstetrics and Gynaecology, APH - Quality of Care, Amsterdam Reproduction & Development (AR&D), APH - Personalized Medicine, APH - Digital Health, and Obstetrics and gynaecology

Subjects
Calibration (statistics), Perinatal Death, Overfitting, Cohort Studies, Fetal Development, 0302 clinical medicine, Discriminative model, 3123 Gynaecology and paediatrics, Models, Pregnancy, GROWTH RESTRICTION, Statistics, Medicine, Prenatal, 030212 general & internal medicine, Ultrasonography, RISK, 030219 obstetrics & reproductive medicine, PRETERM, Radiological and Ultrasound Technology, LOW-DOSE ASPIRIN, DIAGNOSIS TRIPOD, Obstetrics and Gynecology, General Medicine, Statistical, Stillbirth, Prognosis, Pregnancy Complication, external validation, individual participant data, intrauterine death, prediction model, stillbirth, Female, Humans, Infant, Newborn, Models, Statistical, Pregnancy Complications, Regression Analysis, Risk Assessment, Ultrasonography, Prenatal, 3. Good health, PREECLAMPSIA, Meta-analysis, Human, Cohort study, Prognosi, MEDLINE, Regression Analysi, WEEKS GESTATION, 03 medical and health sciences, VELOCIMETRY, Radiology, Nuclear Medicine and imaging, RECURRENCE, business.industry, Infant, Newborn, R1, HYPERTENSIVE DISORDERS, Reproductive Medicine, Sample size determination, Cohort Studie, RG, business, RA, Predictive modelling
Abstract

Objective Stillbirth is a potentially preventable complication of pregnancy. Identifying women at high risk of stillbirth can guide decisions on the need for closer surveillance and timing of delivery in order to prevent fetal death. Prognostic models have been developed to predict the risk of stillbirth, but none has yet been validated externally. In this study, we externally validated published prediction models for stillbirth using individual participant data (IPD) meta-analysis to assess their predictive performance. Methods MEDLINE, EMBASE, DH-DATA and AMED databases were searched from inception to December 2020 to identify studies reporting stillbirth prediction models. Studies that developed or updated prediction models for stillbirth for use at any time during pregnancy were included. IPD from cohorts within the International Prediction of Pregnancy Complications (IPPIC) Network were used to validate externally the identified prediction models whose individual variables were available in the IPD. The risk of bias of the models and cohorts was assessed using the Prediction study Risk Of Bias ASsessment Tool (PROBAST). The discriminative performance of the models was evaluated using the C-statistic, and calibration was assessed using calibration plots, calibration slope and calibration-in-the-large. Performance measures were estimated separately in each cohort, as well as summarized across cohorts using random-effects meta-analysis. Clinical utility was assessed using net benefit. Results Seventeen studies reporting the development of 40 prognostic models for stillbirth were identified. None of the models had been previously validated externally, and the full model equation was reported for only one-fifth (20%, 8/40) of the models. External validation was possible for three of these models, using IPD from 19 cohorts (491 201 pregnant women) within the IPPIC Network database. Based on evaluation of the model development studies, all three models had an overall high risk of bias, according to PROBAST. In the IPD meta-analysis, the models had summary C-statistics ranging from 0.53 to 0.65 and summary calibration slopes ranging from 0.40 to 0.88, with risk predictions that were generally too extreme compared with the observed risks. The models had little to no clinical utility, as assessed by net benefit. However, there remained uncertainty in the performance of some models due to small available sample sizes. Conclusions The three validated stillbirth prediction models showed generally poor and uncertain predictive performance in new data, with limited evidence to support their clinical application. The findings suggest methodological shortcomings in their development, including overfitting. Further research is needed to further validate these and other models, identify stronger prognostic factors and develop more robust prediction models. (c) 2021 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Published
2022

7. External validation of prognostic models predicting pre-eclampsia: individual participant data meta-analysis

Author

Snell, K, Allotey, J, Smuk, M, Hooper, R, Chan, C, Ahmed, A, Chappell, L, Von Dadelszen, P, Green, M, Kenny, L, Khalil, A, Khan, K, Mol, B, Myers, J, Poston, L, Thilaganathan, B, Staff, A, Smith, G, Ganzevoort, W, Laivuori, H, Odibo, A, Arenas Ramirez, J, Kingdom, J, Daskalakis, G, Farrar, D, Baschat, A, Seed, P, Prefumo, F, da Silva Costa, F, Groen, H, Audibert, F, Masse, J, Skrastad, R, Salvesen, K, Haavaldsen, C, Nagata, C, Rumbold, A, Heinonen, S, Askie, L, Smits, L, Vinter, C, Magnus, P, Eero, K, Villa, P, Jenum, A, Andersen, L, Norman, J, Ohkuchi, A, Eskild, A, Bhattacharya, S, Mcauliffe, F, Galindo, A, Herraiz, I, Carbillon, L, Klipstein-Grobusch, K, Yeo, S, Browne, J, Moons, K, Riley, R, Thangaratinam, S, Vergani, P, Snell K. I. E., Allotey J., Smuk M., Hooper R., Chan C., Ahmed A., Chappell L. C., Von Dadelszen P., Green M., Kenny L., Khalil A., Khan K. S., Mol B. W., Myers J., Poston L., Thilaganathan B., Staff A. C., Smith G. C. S., Ganzevoort W., Laivuori H., Odibo A. O., Arenas Ramirez J., Kingdom J., Daskalakis G., Farrar D., Baschat A. A., Seed P. T., Prefumo F., da Silva Costa F., Groen H., Audibert F., Masse J., Skrastad R. B., Salvesen K. A., Haavaldsen C., Nagata C., Rumbold A. R., Heinonen S., Askie L. M., Smits L. J. M., Vinter C. A., Magnus P., Eero K., Villa P. M., Jenum A. K., Andersen L. B., Norman J. E., Ohkuchi A., Eskild A., Bhattacharya S., McAuliffe F. M., Galindo A., Herraiz I., Carbillon L., Klipstein-Grobusch K., Yeo S. A., Browne J. L., Moons K. G. M., Riley R. D., Thangaratinam S., Vergani P., Snell, K, Allotey, J, Smuk, M, Hooper, R, Chan, C, Ahmed, A, Chappell, L, Von Dadelszen, P, Green, M, Kenny, L, Khalil, A, Khan, K, Mol, B, Myers, J, Poston, L, Thilaganathan, B, Staff, A, Smith, G, Ganzevoort, W, Laivuori, H, Odibo, A, Arenas Ramirez, J, Kingdom, J, Daskalakis, G, Farrar, D, Baschat, A, Seed, P, Prefumo, F, da Silva Costa, F, Groen, H, Audibert, F, Masse, J, Skrastad, R, Salvesen, K, Haavaldsen, C, Nagata, C, Rumbold, A, Heinonen, S, Askie, L, Smits, L, Vinter, C, Magnus, P, Eero, K, Villa, P, Jenum, A, Andersen, L, Norman, J, Ohkuchi, A, Eskild, A, Bhattacharya, S, Mcauliffe, F, Galindo, A, Herraiz, I, Carbillon, L, Klipstein-Grobusch, K, Yeo, S, Browne, J, Moons, K, Riley, R, Thangaratinam, S, Vergani, P, Snell K. I. E., Allotey J., Smuk M., Hooper R., Chan C., Ahmed A., Chappell L. C., Von Dadelszen P., Green M., Kenny L., Khalil A., Khan K. S., Mol B. W., Myers J., Poston L., Thilaganathan B., Staff A. C., Smith G. C. S., Ganzevoort W., Laivuori H., Odibo A. O., Arenas Ramirez J., Kingdom J., Daskalakis G., Farrar D., Baschat A. A., Seed P. T., Prefumo F., da Silva Costa F., Groen H., Audibert F., Masse J., Skrastad R. B., Salvesen K. A., Haavaldsen C., Nagata C., Rumbold A. R., Heinonen S., Askie L. M., Smits L. J. M., Vinter C. A., Magnus P., Eero K., Villa P. M., Jenum A. K., Andersen L. B., Norman J. E., Ohkuchi A., Eskild A., Bhattacharya S., McAuliffe F. M., Galindo A., Herraiz I., Carbillon L., Klipstein-Grobusch K., Yeo S. A., Browne J. L., Moons K. G. M., Riley R. D., Thangaratinam S., and Vergani P.

Abstract

BACKGROUND: Pre-eclampsia is a leading cause of maternal and perinatal mortality and morbidity. Early identification of women at risk during pregnancy is required to plan management. Although there are many published prediction models for pre-eclampsia, few have been validated in external data. Our objective was to externally validate published prediction models for pre-eclampsia using individual participant data (IPD) from UK studies, to evaluate whether any of the models can accurately predict the condition when used within the UK healthcare setting. METHODS: IPD from 11 UK cohort studies (217,415 pregnant women) within the International Prediction of Pregnancy Complications (IPPIC) pre-eclampsia network contributed to external validation of published prediction models, identified by systematic review. Cohorts that measured all predictor variables in at least one of the identified models and reported pre-eclampsia as an outcome were included for validation. We reported the model predictive performance as discrimination (C-statistic), calibration (calibration plots, calibration slope, calibration-in-the-large), and net benefit. Performance measures were estimated separately in each available study and then, where possible, combined across studies in a random-effects meta-analysis. RESULTS: Of 131 published models, 67 provided the full model equation and 24 could be validated in 11 UK cohorts. Most of the models showed modest discrimination with summary C-statistics between 0.6 and 0.7. The calibration of the predicted compared to observed risk was generally poor for most models with observed calibration slopes less than 1, indicating that predictions were generally too extreme, although confidence intervals were wide. There was large between-study heterogeneity in each model's calibration-in-the-large, suggesting poor calibration of the predicted overall risk across populations. In a subset of models, the net benefit of using the models to inform clinical decisions

Published
2020

8. Planned delivery for pre-eclampsia between 34 and 37 weeks of gestation: the PHOENIX RCT.

Author

Chappell, LC, Brocklehurst, P, Green, M, Hardy, P, Hunter, R, Beardmore-Gray, A, Bowler, U, Brockbank, A, Chiocchia, V, Cox, A, Duhig, K, Fleminger, J, Gill, C, Greenland, M, Hendy, E, Kennedy, A, Leeson, P, Linsell, L, McCarthy, FP, O'Driscoll, J, Placzek, A, Poston, L, Robson, S, Rushby, P, Sandall, J, Scholtz, L, Seed, PT, Sparkes, J, Stanbury, K, Tohill, S, Thilaganathan, B, Townend, J, Juszczak, E, Marlow, N, Shennan, A, Chappell, LC, Brocklehurst, P, Green, M, Hardy, P, Hunter, R, Beardmore-Gray, A, Bowler, U, Brockbank, A, Chiocchia, V, Cox, A, Duhig, K, Fleminger, J, Gill, C, Greenland, M, Hendy, E, Kennedy, A, Leeson, P, Linsell, L, McCarthy, FP, O'Driscoll, J, Placzek, A, Poston, L, Robson, S, Rushby, P, Sandall, J, Scholtz, L, Seed, PT, Sparkes, J, Stanbury, K, Tohill, S, Thilaganathan, B, Townend, J, Juszczak, E, Marlow, N, and Shennan, A

Abstract

BACKGROUND: In women with late preterm pre-eclampsia (i.e. at 34+0 to 36+6 weeks' gestation), the optimal delivery time is unclear because limitation of maternal-fetal disease progression needs to be balanced against infant complications. The aim of this trial was to determine whether or not planned earlier initiation of delivery reduces maternal adverse outcomes without substantial worsening of perinatal or infant outcomes, compared with expectant management, in women with late preterm pre-eclampsia. METHODS: We undertook an individually randomised, triple non-masked controlled trial in 46 maternity units across England and Wales, with an embedded health economic evaluation, comparing planned delivery and expectant management (usual care) in women with late preterm pre-eclampsia. The co-primary maternal outcome was a maternal morbidity composite or recorded systolic blood pressure of ≥ 160 mmHg (superiority hypothesis). The co-primary short-term perinatal outcome was a composite of perinatal deaths or neonatal unit admission (non-inferiority hypothesis). Analyses were by intention to treat, with an additional per-protocol analysis for the perinatal outcome. The primary 2-year infant neurodevelopmental outcome was measured using the PARCA-R (Parent Report of Children's Abilities-Revised) composite score. The planned sample size of the trial was 900 women; the trial is now completed. We undertook two linked substudies. RESULTS: Between 29 September 2014 and 10 December 2018, 901 women were recruited; 450 women [448 women (two withdrew consent) and 471 infants] were allocated to planned delivery and 451 women (451 women and 475 infants) were allocated to expectant management. The incidence of the co-primary maternal outcome was significantly lower in the planned delivery group [289 (65%) women] than in the expectant management group [338 (75%) women] (adjusted relative risk 0.86, 95% confidence interval 0.79 to 0.94; p = 0.0005). The incidence of the co-primary perina

Published
2022

9. Sexual dimorphism in COVID-19: potential clinical and public health implications

Author

Bechmann, N., Barthel, A., Schedl, A., Herzig, S., Varga, Z., Gebhard, C., Mayr, M., Hantel, C., Beuschlein, F., Wolfrum, C., Perakakis, N., Poston, L., Andoniadou, C. L., Siow, R., Gainetdinov, R. R., Dotan, A., Shoenfeld, Y., Mingrone, Geltrude, Bornstein, S. R., Mingrone G. (ORCID:0000-0003-2021-528X), Bechmann, N., Barthel, A., Schedl, A., Herzig, S., Varga, Z., Gebhard, C., Mayr, M., Hantel, C., Beuschlein, F., Wolfrum, C., Perakakis, N., Poston, L., Andoniadou, C. L., Siow, R., Gainetdinov, R. R., Dotan, A., Shoenfeld, Y., Mingrone, Geltrude, Bornstein, S. R., and Mingrone G. (ORCID:0000-0003-2021-528X)

Abstract

Current evidence suggests that severity and mortality of COVID-19 is higher in men than in women, whereas women might be at increased risk of COVID-19 reinfection and development of long COVID. Differences between sexes have been observed in other infectious diseases and in the response to vaccines. Sex-specific expression patterns of proteins mediating virus binding and entry, and divergent reactions of the immune and endocrine system, in particular the hypothalamic–pituitary–adrenal axis, in response to acute stress might explain the higher severity of COVID-19 in men. In this Personal View, we discuss how sex hormones, comorbidities, and the sex chromosome complement influence these mechanisms in the context of COVID-19. Due to its role in the severity and progression of SARS-CoV-2 infections, we argue that sexual dimorphism has potential implications for disease treatment, public health measures, and follow-up of patients predisposed to the development of long COVID. We suggest that sex differences could be considered in future pandemic surveillance and treatment of patients with COVID-19 to help to achieve better disease stratification and improved outcomes.

Published
2022

13. Development of composite outcomes for individual patient data (IPD) meta-analysis on the effects of diet and lifestyle in pregnancy: a Delphi survey

Author

Rogozinska, E, DʼAmico, M I, Khan, K S, Cecatti, J G, Teede, H, Yeo, S, Vinter, C A, Rayanagoudar, G, Barakat, R, Perales, M, Dodd, J M, Devlieger, R, Bogaerts, A, van Poppel, M NM, Haakstad, L, Shen, G X, Shub, A, Luoto, R, Kinnunen, T I, Phelan, S, Poston, L, Scudeller, T T, El Beltagy, N, Stafne, S N, Tonstad, S, Geiker, N RW, Ruifrok, A E, Mol, B W, Coomarasamy, A, and Thangaratinam, S

Published
2016
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15. External validation of prognostic models to predict stillbirth using International Prediction of Pregnancy Complications ( <scp>IPPIC</scp> ) Network database: individual participant data meta‐analysis

Author

Allotey, J, Whittle, R, Snell, KIE, Smuk, M, Townsend, R, Dadelszen, P, Heazell, AEP, Magee, L, Smith, GCS, Sandall, J, Thilaganathan, B, Zamora, J, Riley, RD, Khalil, A, Thangaratinam, S, Coomarasamy, A, Kwong, A, Savitri, AI, Salvesen, KÅ, Bhattacharya, S, Uiterwaal, CSPM, Staff, AC, Andersen, LB, Olive, EL, Redman, C, Sletner, L, Daskalakis, G, Macleod, M, Abdollahain, M, Ramírez, JA, Massé, J, Audibert, F, Magnus, PM, Jenum, AK, Baschat, A, Ohkuchi, A, McAuliffe, FM, West, J, Askie, LM, Mone, F, Farrar, D, Zimmerman, PA, Smits, LJM, Riddell, C, Kingdom, JC, Post, J, Illanes, SE, Holzman, C, Kuijk, SMJ, Carbillon, L, Villa, PM, Eskild, A, Chappell, L, Prefumo, F, Velauthar, L, Seed, P, Oostwaard, M, Verlohren, S, Poston, L, Ferrazzi, E, Vinter, CA, Nagata, C, Brown, M, Vollebregt, KC, Takeda, S, Langenveld, J, Widmer, M, Saito, S, Haavaldsen, C, Carroli, G, Olsen, J, Wolf, H, Zavaleta, N, Eisensee, I, Vergani, P, Lumbiganon, P, Makrides, M, Facchinetti, F, Sequeira, E, Gibson, R, Ferrazzani, S, Frusca, T, Norman, JE, Figueiró‐Filho, EA, Lapaire, O, Laivuori, H, Lykke, JA, Conde‐Agudelo, A, Galindo, A, Mbah, A, Betran, AP, Herraiz, I, Trogstad, L, Smith, GGS, Steegers, EAP, Salim, R, Huang, T, Adank, A, Zhang, J, Meschino, WS, Browne, JL, Allen, RE, Costa, F Da Silva, Klipstein‐Grobusch, K, Crowther, CA, Jørgensen, JS, Forest, J‐C, Rumbold, AR, Mol, BW, Giguère, Y, Kenny, LC, Ganzevoort, W, Odibo, AO, Myers, J, Yeo, SA, Goffinet, F, McCowan, L, Pajkrt, E, Teede, HJ, Haddad, BG, Dekker, G, Kleinrouweler, EC, LeCarpentier, É, Roberts, CT, Groen, H, Skråstad, RB, Heinonen, S, Eero, K, Anggraini, D, Souka, A, Cecatti, JG, Monterio, I, Pillalis, A, Souza, R, Hawkins, LA, Gabbay‐Benziv, R, Crovetto, F, Figuera, F, Jorgensen, L, Dodds, J, Patel, M, Aviram, A, Papageorghiou, A, and Khan, K

Abstract

Objective: Stillbirth is a potentially preventable complication of pregnancy. Identifying women at risk can guide decisions on closer surveillance or timing of birth to prevent fetal death.Prognostic models have been developed to predict the risk of stillbirth, but none have yet been externally validated. We externally validated published prediction models for stillbirth using individual participant data (IPD) meta-analysis to assess their predictive performance. Methods: We searched Medline, EMBASE, DH-DATA and AMED databases from inception to December 2020 to identify stillbirth prediction models. We included studies that developed or updated prediction models for stillbirth for use at any time during pregnancy. IPD from cohorts within the International Prediction of Pregnancy Complication (IPPIC) Network were used to externally validate the identified prediction models whose individual variables were available in the IPD. We assessed the risk of bias of the models and IPD using PROBAST, and reported discriminative performance using the C-statistic, and calibration performance using calibration plots, calibration slopeand calibration-in-the-large. We estimated performance measures separately in each study, and then summarised across studies using random-effects meta-analysis. Clinical utility was assessed using net benefit. Results: We identified 17 studies reporting the development of 40 prognostic models for stillbirth. None of the models were previously externally validated, and only a fifth (20%, 8/40) reported the full model equation. We were able to validate three of these models using the IPD from 19 cohort studies (491,201 pregnant women) within the IPPIC Network database. Based on evaluating their development studies, all three models had an overall high risk of bias according to PROBAST. In our IPD meta-analysis, the models had summary C-statistics ranging from 0.53 to 0.65; summary calibration slopes of 0.40to 0.88, and generally with observed risks predictions that were too extreme compared to observed risks; and little to no clinical utility as assessed by net benefit. However, there remained uncertainty in performance for some models due to small available sample sizes. Conclusion: The three validated models generally showed poor and uncertain predictive performancein new data, with limited evidence to support their clinical application. Findings suggest methodological shortcomings in their development including overfitting of models. Further research is needed to further validate these and other models, identify stronger prognostic factors, and to develop more robust prediction models

Published
2021

16. Nutrition During Pregnancy, Lactation and Early Childhood and its Implications for Maternal and Long-Term Child Health

Author

Koletzko, B, Godfrey, KM, Poston, L, Szajewska, H, van Goudoever, JB, de Waard, M, Brands, B, Grivell, RM, Deussen, AR, Dodd, JM, Patro-Golab, B, Zalewski, BM, Alberdi, G, Buonocore, G, Campoy, C, Demmelmair, H, Desoye, G, Gomez, MD, Escribano, J, Geraghty, A, Gil, A, Hanson, M, Inskip, HM, Larque, E, Lassel, T, Luque, V, Mader, S, Manios, Y, Mearin, LM, Oddy, WH, Reynolds, RM, Rueda, R, Sherry, C, Socha, P, Taylor, P, Van der Beek, EM, Weber, M, Crespo-Escobar, P, Gutser, M, Kouwenhoven, SMP, Calvo-Lerma, J, Veldhorst, M, and EarlyNutr Project Systematic Revie

Subjects
0301 basic medicine, Gerontology, Infancy, PROTEIN-INTAKE, Maternal Health, Breastfeeding, Medicine (miscellaneous), Disease, Overweight, Recommendations, Nutrition Policy, 0302 clinical medicine, Pregnancy, FORMULA-FED INFANTS, AGE FOLLOW-UP, Medicine, Early childhood, Micronutrients, Infant Nutritional Physiological Phenomena, Prenatal Nutritional Physiological Phenomena, media_common, 2. Zero hunger, Child health, GESTATIONAL WEIGHT-GAIN, Nutrition and Dietetics, Preconception, 3. Good health, Systematic review, Breast Feeding, Child, Preschool, Female, LIFE-STYLE, medicine.symptom, VITAMIN-D DEFICIENCY, YOUNG-CHILDREN, 030209 endocrinology & metabolism, Early nutrition, Developmental programming, 03 medical and health sciences, Metabolic programming, media_common.cataloged_instance, Humans, Lactation, Obesity, European union, Life Style, 030109 nutrition & dietetics, business.industry, Infant, medicine.disease, Lifestyle, PERICONCEPTIONAL FOLIC-ACID, BODY-MASS INDEX, BREAST-FED INFANTS, business, Systematic Reviews as Topic
Abstract

Background: A considerable body of evidence accumulated especially during the last decade, demonstrating that early nutrition and lifestyle have long-term effects on later health and disease (“developmental or metabolic programming”). Methods: Researchers involved in the European Union funded international EarlyNutrition research project consolidated the scientific evidence base and existing recommendations to formulate consensus recommendations on nutrition and lifestyle before and during pregnancy, during infancy and early childhood that take long-term health impact into account. Systematic reviews were performed on published dietary guidelines, standards and recommendations, with special attention to long-term health consequences. In addition, systematic reviews of published systematic reviews on nutritional interventions or exposures in pregnancy and in infants and young children aged up to 3 years that describe effects on subsequent overweight, obesity and body composition were performed. Experts developed consensus recommendations incorporating the wide-ranging expertise from additional 33 stakeholders. Findings: Most current recommendations for pregnant women, particularly obese women, and for young children do not take long-term health consequences of early nutrition into account, although the available evidence for relevant consequences of lifestyle, diet and growth patterns in early life on later health and disease risk is strong. Interpretation: We present updated recommendations for optimized nutrition before and during pregnancy, during lactation, infancy and toddlerhood, with special reference to later health outcomes. These recommendations are developed for affluent populations, such as women and children in Europe, and should contribute to the primary prevention of obesity and associated non-communicable diseases.

Published
2019

21. The transCampus metabolic training programme explores the link of SARS-CoV-2 virus to metabolic disease

Author

Bornstein, S.R., Guan, K., Brunßen, C., Mueller, G., Kamvissi-Lorenz, V., Lechler, R., Trembath, R., Mayr, M., Poston, L., Sancho, R., Ahmed, S., Alfar, E., Aljani, B., Alves, T.C., Amiel, S., Andoniadou, C.L., Bandral, M., Belavgeni, A., Berger, I., Birkenfeld, A.L., Bonifacio, E., Chavakis, T., Chawla, P., Choudhary, P., Cujba, A.M., Delgadillo Silva, L.F., Demcollari, T., Drotar, D.M., Duin, S., El-Agroudy, N.N., El-Armouche, A., Eugster, A., Gado, M., Gavalas, A., Gelinsky, M., Guirgus, M., Hansen, S., Hanton, E., Hasse, M., Henneicke, H., Heller, C., Hempel, H., Hogstrand, C., Hopkins, D., Jarc, L., Jones, P.M., Kamel, M., Kämmerer, S., King, A.J.F., Kurzbach, A., Lambert, C., Latunde-Dada, Y., Lieberam, I., Liers, J., Li, J.W., Linkermann, A., Locke, S., Ludwig, B., Manea, T., Maremonti, F., Marinicova, Z., McGowan, B.M., Mickunas, M., Mingrone, G., Mohanraj, K., Morawietz, H., Ninov, N., Peakman, M., Persaud, S.J., Pietzsch, J., Cachorro, E., Pullen, T.J., Pyrina, I., Rubino, F., Santambrogio, A., Schepp, F., Schlinkert, P., Scriba, L.D., Siow, R., Solimena, M., Spagnoli, F.M., Speier, S., Stavridou, A., Steenblock, C., Strano, A., Taylor, P., Tiepner, A., Tonnus, W., Tree, T., Watt, F.E., Werdermann, M., Wilson, M., Yusuf, N., and Ziegler, C.G.

Subjects
0301 basic medicine, medicine.medical_specialty, Endocrinology, Diabetes and Metabolism, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Clinical Biochemistry, education, MEDLINE, Disease, Settore MED/17 - MALATTIE INFETTIVE, Biochemistry, 03 medical and health sciences, 0302 clinical medicine, Endocrinology, Internal medicine, Pandemic, Diabetes Mellitus, medicine, Humans, Obesity, Metabolic disease, Pandemics, metabolic training programme, Medical education, Communicable disease, Scope (project management), SARS-CoV-2, Biochemistry (medical), COVID-19, General Medicine, 030104 developmental biology, Infectious disease (medical specialty), transCampus, Education, Medical, Continuing, Covid-19, Metabolic Training Programme, Transcampus, 030217 neurology & neurosurgery
Abstract

Currently, we are experiencing a true pandemic of a communicable disease by the virus SARS-CoV-2 holding the whole world firmly in its grasp. Amazingly and unfortunately, this virus uses a metabolic and endocrine pathway via ACE2 to enter our cells causing damage and disease. Our international research training programme funded by the German Research Foundation has a clear mission to train the best students wherever they may come from to learn to tackle the enormous challenges of diabetes and its complications for our society. A modern training programme in diabetes and metabolism does not only involve a thorough understanding of classical physiology, biology and clinical diabetology but has to bring together an interdisciplinary team. With the arrival of the coronavirus pandemic, this prestigious and unique metabolic training programme is facing new challenges but also new opportunities. The consortium of the training programme has recognized early on the need for a guidance and for practical recommendations to cope with the COVID-19 pandemic for the community of patients with metabolic disease, obesity and diabetes. This involves the optimal management from surgical obesity programmes to medications and insulin replacement. We also established a global registry analyzing the dimension and role of metabolic disease including new onset diabetes potentially triggered by the virus. We have involved experts of infectious disease and virology to our faculty with this metabolic training programme to offer the full breadth and scope of expertise needed to meet these scientific challenges. We have all learned that this pandemic does not respect or heed any national borders and that we have to work together as a global community. We believe that this transCampus metabolic training programme provides a prime example how an international team of established experts in the field of metabolism can work together with students from all over the world to address a new pandemic.Currently, we are experiencing a true pandemic of a communicable disease by the virus SARS-CoV-2 holding the whole world firmly in its grasp. Amazingly and unfortunately, this virus uses a metabolic and endocrine pathway via ACE2 to enter our cells causing damage and disease. Our international research training programme funded by the German Research Foundation has a clear mission to train the best students wherever they may come from to learn to tackle the enormous challenges of diabetes and its complications for our society. A modern training programme in diabetes and metabolism does not only involve a thorough understanding of classical physiology, biology and clinical diabetology but has to bring together an interdisciplinary team. With the arrival of the coronavirus pandemic, this prestigious and unique metabolic training programme is facing new challenges but also new opportunities. The consortium of the training programme has recognized early on the need for a guidance and for practical recommendations to cope with the COVID-19 pandemic for the community of patients with metabolic disease, obesity and diabetes. This involves the optimal management from surgical obesity programmes to medications and insulin replacement. We also established a global registry analyzing the dimension and role of metabolic disease including new onset diabetes potentially triggered by the virus. We have involved experts of infectious disease and virology to our faculty with this metabolic training programme to offer the full breadth and scope of expertise needed to meet these scientific challenges. We have all learned that this pandemic does not respect or heed any national borders and that we have to work together as a global community. We believe that this transCampus metabolic training programme provides a prime example how an international team of established experts in the field of metabolism can work together with students from all over the world to address a new pandemic.

Published
2021

22. External validation of prognostic models predicting pre-eclampsia: individual participant data meta-analysis.

Author

Kingdom J., Poston L., Thilaganathan B., Staff A.C., Smith G.C.S., Ganzevoort W., Laivuori H., Odibo A.O., Arenas Ramirez J., Daskalakis G., Farrar D., Baschat A.A., Seed P.T., Prefumo F., da Silva Costa F., Groen H., Audibert F., Masse J., Skrastad R.B., Salvesen K.A., Haavaldsen C., Nagata C., Rumbold A.R., Heinonen S., Askie L.M., Smits L.J.M., Vinter C.A., Magnus P., Eero K., Villa P.M., Jenum A.K., Andersen L.B., Norman J.E., Ohkuchi A., Eskild A., Bhattacharya S., McAuliffe F.M., Galindo A., Herraiz I., Carbillon L., Klipstein-Grobusch K., Yeo S.A., Browne J.L., Moons K.G.M., Riley R.D., Thangaratinam S., Snell K.I.E., Allotey J., Smuk M., Hooper R., Chan C., Ahmed A., Chappell L.C., Von Dadelszen P., Green M., Kenny L., Khalil A., Khan K.S., Mol B.W., Myers J., Kingdom J., Poston L., Thilaganathan B., Staff A.C., Smith G.C.S., Ganzevoort W., Laivuori H., Odibo A.O., Arenas Ramirez J., Daskalakis G., Farrar D., Baschat A.A., Seed P.T., Prefumo F., da Silva Costa F., Groen H., Audibert F., Masse J., Skrastad R.B., Salvesen K.A., Haavaldsen C., Nagata C., Rumbold A.R., Heinonen S., Askie L.M., Smits L.J.M., Vinter C.A., Magnus P., Eero K., Villa P.M., Jenum A.K., Andersen L.B., Norman J.E., Ohkuchi A., Eskild A., Bhattacharya S., McAuliffe F.M., Galindo A., Herraiz I., Carbillon L., Klipstein-Grobusch K., Yeo S.A., Browne J.L., Moons K.G.M., Riley R.D., Thangaratinam S., Snell K.I.E., Allotey J., Smuk M., Hooper R., Chan C., Ahmed A., Chappell L.C., Von Dadelszen P., Green M., Kenny L., Khalil A., Khan K.S., Mol B.W., and Myers J.

Abstract

BACKGROUND: Pre-eclampsia is a leading cause of maternal and perinatal mortality and morbidity. Early identification of women at risk during pregnancy is required to plan management. Although there are many published prediction models for pre-eclampsia, few have been validated in external data. Our objective was to externally validate published prediction models for pre-eclampsia using individual participant data (IPD) from UK studies, to evaluate whether any of the models can accurately predict the condition when used within the UK healthcare setting. METHOD(S): IPD from 11 UK cohort studies (217,415 pregnant women) within the International Prediction of Pregnancy Complications (IPPIC) pre-eclampsia network contributed to external validation of published prediction models, identified by systematic review. Cohorts that measured all predictor variables in at least one of the identified models and reported pre-eclampsia as an outcome were included for validation. We reported the model predictive performance as discrimination (C-statistic), calibration (calibration plots, calibration slope, calibration-in-the-large), and net benefit. Performance measures were estimated separately in each available study and then, where possible, combined across studies in a random-effects meta-analysis. RESULT(S): Of 131 published models, 67 provided the full model equation and 24 could be validated in 11 UK cohorts. Most of the models showed modest discrimination with summary C-statistics between 0.6 and 0.7. The calibration of the predicted compared to observed risk was generally poor for most models with observed calibration slopes less than 1, indicating that predictions were generally too extreme, although confidence intervals were wide. There was large between-study heterogeneity in each model's calibration-in-the-large, suggesting poor calibration of the predicted overall risk across populations. In a subset of models, the net benefit of using the models to inform clinical decis

Published
2021

23. Validation and development of models using clinical, biochemical and ultrasound markers for predicting pre-eclampsia: An individual participant data meta-analysis.

Author

Allotey J., Smuk M., Hooper R., Chan C.L., Ahmed A., Chappell L.C., von Dadelszen P., Dodds J., Green M., Kenny L., Khalil A., Khan K.S., Mol B.W., Myers J., Poston L., Thilaganathan B., Eskild A., Bhattacharya S., McAuliffe F.M., Galindo A., Herraiz I., Carbillon L., Klipstein-Grobusch K., Yeo S., Teede H.J., Browne J.L., Moons K.G.M., Riley R.D., Thangaratinam S., Snell K.I.E., Staff A.C., Smith G.C.S., Ganzevoort W., Laivuori H., Odibo A.O., Ramirez J.A., Kingdom J., Daskalakis G., Farrar D., Baschat A.A., Seed P.T., Prefumo F., da Silva Costa F., Groen H., Audibert F., Masse J., Skrastad R.B., Salvesen K.A., Haavaldsen C., Nagata C., Rumbold A.R., Heinonen S., Askie L.M., Smits L.J.M., Vinter C.A., Magnus P.M., Eero K., Villa P.M., Jenum A.K., Andersen L.B., Norman J.E., Ohkuchi A., Allotey J., Smuk M., Hooper R., Chan C.L., Ahmed A., Chappell L.C., von Dadelszen P., Dodds J., Green M., Kenny L., Khalil A., Khan K.S., Mol B.W., Myers J., Poston L., Thilaganathan B., Eskild A., Bhattacharya S., McAuliffe F.M., Galindo A., Herraiz I., Carbillon L., Klipstein-Grobusch K., Yeo S., Teede H.J., Browne J.L., Moons K.G.M., Riley R.D., Thangaratinam S., Snell K.I.E., Staff A.C., Smith G.C.S., Ganzevoort W., Laivuori H., Odibo A.O., Ramirez J.A., Kingdom J., Daskalakis G., Farrar D., Baschat A.A., Seed P.T., Prefumo F., da Silva Costa F., Groen H., Audibert F., Masse J., Skrastad R.B., Salvesen K.A., Haavaldsen C., Nagata C., Rumbold A.R., Heinonen S., Askie L.M., Smits L.J.M., Vinter C.A., Magnus P.M., Eero K., Villa P.M., Jenum A.K., Andersen L.B., Norman J.E., and Ohkuchi A.

Abstract

Background: Pre-eclampsia is a leading cause of maternal and perinatal mortality and morbidity. Early identification of women at risk is needed to plan management. Objective(s): To assess the performance of existing pre-eclampsia prediction models and to develop and validate models for pre-eclampsia using individual participant data meta-analysis. We also estimated the prognostic value of individual markers. Design(s): This was an individual participant data meta-analysis of cohort studies. Setting(s): Source data from secondary and tertiary care. Predictors: We identified predictors from systematic reviews, and prioritised for importance in an international survey. Primary outcomes: Early-onset (delivery at < 34 weeks' gestation), late-onset (delivery at >= 34 weeks' gestation) and any-onset pre-eclampsia. Analysis: We externally validated existing prediction models in UK cohorts and reported their performance in terms of discrimination and calibration.We developed and validated 12 new models based on clinical characteristics, clinical characteristics and biochemical markers, and clinical characteristics and ultrasound markers in the first and second trimesters. We summarised the data set-specific performance of each model using a random-effects meta-analysis. Discrimination was considered promising for C-statistics of >= 0.7, and calibration was considered good if the slope was near 1 and calibration-in-the-large was near 0. Heterogeneity was quantified using I2 and 2. A decision curve analysis was undertaken to determine the clinical utility (net benefit) of the models. We reported the unadjusted prognostic value of individual predictors for pre-eclampsia as odds ratios with 95% confidence and prediction intervals. Result(s): The International Prediction of Pregnancy Complications network comprised 78 studies (3,570,993 singleton pregnancies) identified from systematic reviews of tests to predict pre-eclampsia. Twenty-four of the 131 published prediction models c

Published
2021

24. The transCampus Metabolic Training Programme Explores the Link of SARS-CoV-2 Virus to Metabolic Disease

Author

Bornstein, S. R., Guan, K., Brunbetaen, C., Mueller, G., Kamvissi-Lorenz, V., Lechler, R., Trembath, R., Mayr, M., Poston, L., Sancho, R., Ahmed, Sadek, Alfar, E., Aljani, B., Alves, T. C., Amiel, S., Andoniadou, C. L., Bandral, M., Belavgeni, A., Berger, I., Birkenfeld, A., Bonifacio, E., Chavakis, T., Chawla, P., Choudhary, P., Cujba, A. M., Delgadillo Silva, L. F., Demcollari, T., Drotar, D. M., Duin, S., El-Agroudy, N. N., El-Armouche, A., Eugster, A., Gado, M., Gavalas, A., Gelinsky, M., Guirgus, M., Hansen, S., Hanton, E., Hasse, M., Henneicke, H., Heller, C., Hempel, H., Hogstrand, C., Hopkins, D., Jarc, L., Jones, P. M., Kamel, M., Kammerer, S., King, A. J. F., Kurzbach, A., Lambert, C., Latunde-Dada, Y., Lieberam, I., Liers, J., Li, J. W., Linkermann, A., Locke, S., Ludwig, B., Manea, T., Maremonti, F., Marinicova, Z., Mcgowan, B. M., Mickunas, M., Mingrone, Geltrude, Mohanraj, K., Morawietz, H., Ninov, N., Peakman, M., Persaud, S. J., Pietzsch, J., Cachorro, E., Pullen, T. J., Pyrina, I., Rubino, F., Santambrogio, Alberto, Schepp, F., Schlinkert, P., Scriba, L. D., Siow, R., Solimena, M., Spagnoli, F. M., Speier, S., Stavridou, A., Steenblock, C., Strano, A., Taylor, P., Tiepner, A., Tonnus, W., Tree, T., Watt, F., Werdermann, M., Wilson, M., Yusuf, N., Ziegler, C. G., Ahmed S., Mingrone G. (ORCID:0000-0003-2021-528X), Santambrogio A., Bornstein, S. R., Guan, K., Brunbetaen, C., Mueller, G., Kamvissi-Lorenz, V., Lechler, R., Trembath, R., Mayr, M., Poston, L., Sancho, R., Ahmed, Sadek, Alfar, E., Aljani, B., Alves, T. C., Amiel, S., Andoniadou, C. L., Bandral, M., Belavgeni, A., Berger, I., Birkenfeld, A., Bonifacio, E., Chavakis, T., Chawla, P., Choudhary, P., Cujba, A. M., Delgadillo Silva, L. F., Demcollari, T., Drotar, D. M., Duin, S., El-Agroudy, N. N., El-Armouche, A., Eugster, A., Gado, M., Gavalas, A., Gelinsky, M., Guirgus, M., Hansen, S., Hanton, E., Hasse, M., Henneicke, H., Heller, C., Hempel, H., Hogstrand, C., Hopkins, D., Jarc, L., Jones, P. M., Kamel, M., Kammerer, S., King, A. J. F., Kurzbach, A., Lambert, C., Latunde-Dada, Y., Lieberam, I., Liers, J., Li, J. W., Linkermann, A., Locke, S., Ludwig, B., Manea, T., Maremonti, F., Marinicova, Z., Mcgowan, B. M., Mickunas, M., Mingrone, Geltrude, Mohanraj, K., Morawietz, H., Ninov, N., Peakman, M., Persaud, S. J., Pietzsch, J., Cachorro, E., Pullen, T. J., Pyrina, I., Rubino, F., Santambrogio, Alberto, Schepp, F., Schlinkert, P., Scriba, L. D., Siow, R., Solimena, M., Spagnoli, F. M., Speier, S., Stavridou, A., Steenblock, C., Strano, A., Taylor, P., Tiepner, A., Tonnus, W., Tree, T., Watt, F., Werdermann, M., Wilson, M., Yusuf, N., Ziegler, C. G., Ahmed S., Mingrone G. (ORCID:0000-0003-2021-528X), and Santambrogio A.

Abstract

Currently, we are experiencing a true pandemic of a communicable disease by the virus SARS-CoV-2 holding the whole world firmly in its grasp. Amazingly and unfortunately, this virus uses a metabolic and endocrine pathway via ACE2 to enter our cells causing damage and disease. Our international research training programme funded by the German Research Foundation has a clear mission to train the best students wherever they may come from to learn to tackle the enormous challenges of diabetes and its complications for our society. A modern training programme in diabetes and metabolism does not only involve a thorough understanding of classical physiology, biology and clinical diabetology but has to bring together an interdisciplinary team. With the arrival of the coronavirus pandemic, this prestigious and unique metabolic training programme is facing new challenges but also new opportunities. The consortium of the training programme has recognized early on the need for a guidance and for practical recommendations to cope with the COVID-19 pandemic for the community of patients with metabolic disease, obesity and diabetes. This involves the optimal management from surgical obesity programmes to medications and insulin replacement. We also established a global registry analyzing the dimension and role of metabolic disease including new onset diabetes potentially triggered by the virus. We have involved experts of infectious disease and virology to our faculty with this metabolic training programme to offer the full breadth and scope of expertise needed to meet these scientific challenges. We have all learned that this pandemic does not respect or heed any national borders and that we have to work together as a global community. We believe that this transCampus metabolic training programme provides a prime example how an international team of established experts in the field of metabolism can work together with students from all over the world to address a new pandemic.

Published
2021

39. Genetic predisposition to hypertension is associated with preeclampsia in European and Central Asian women

Author

Steinthorsdottir, V. (Valgerdur), McGinnis, R. (Ralph), Williams, N. O. (Nicholas O.), Stefansdottir, L. (Lilja), Thorleifsson, G. (Gudmar), Shooter, S. (Scott), Fadista, J. (Joao), Sigurdsson, J. K. (Jon K.), Auro, K. M. (Kirsi M.), Berezina, G. (Galina), Borges, M.-C. (Maria-Carolina), Bumpstead, S. (Suzannah), Bybjerg-Grauholm, J. (Jonas), Colgiu, I. (Irina), Dolby, V. A. (Vivien A.), Dudbridge, F. (Frank), Engel, S. M. (Stephanie M.), Franklin, C. S. (Christopher S.), Frigge, M. L. (Michael L.), Frisbaek, Y. (Yr), Geirsson, R. T. (Reynir T.), Geller, F. (Frank), Gretarsdottir, S. (Solveig), Gudbjartsson, D. F. (Daniel F.), Harmon, Q. (Quaker), Hougaard, D. M. (David Michael), Hegay, T. (Tatyana), Helgadottir, A. (Anna), Hjartardottir, S. (Sigrun), Jaeaeskelaeinen, T. (Tiina), Johannsdottir, H. (Hrefna), Jonsdottir, I. (Ingileif), Juliusdottir, T. (Thorhildur), Kalsheker, N. (Noor), Kasimov, A. (Abdumadjit), Kemp, J. P. (John P.), Kivinen, K. (Katja), Klungsoyr, K. (Kari), Lee, W. K. (Wai K.), Melbye, M. (Mads), Miedzybrodska, Z. (Zosia), Moffett, A. (Ashley), Najmutdinova, D. (Dilbar), Nishanova, F. (Firuza), Olafsdottir, T. (Thorunn), Perola, M. (Markus), Pipkin, F. B. (Fiona Broughton), Poston, L. (Lucilla), Prescott, G. (Gordon), Saevarsdottir, S. (Saedis), Salimbayeva, D. (Damilya), Scaife, P. J. (Paula Juliet), Skotte, L. (Line), Staines-Urias, E. (Eleonora), Stefansson, O. A. (Olafur A.), Sorensen, K. M. (Karina Meden), Thomsen, L. C. (Liv Cecilie Vestrheim), Tragante, V. (Vinicius), Trogstad, L. (Lill), Simpson, N. A. (Nigel A. B.), Laivuori, H. (Hannele), Morgan, L. (Linda), Aripova, T. (Tamara), Casas, J. P. (Juan P.), Dominiczak, A. F. (Anna F.), Walker, J. J. (James J.), Thorsteinsdottir, U. (Unnur), Iversen, A.-C. (Ann-Charlotte), Feenstra, B. (Bjarke), Lawlor, D. A. (Deborah A.), Boyd, H. A. (Heather Allison), Magnus, P. (Per), Zakhidova, N. (Nodira), Svyatova, G. (Gulnara), Stefansson, K. (Kari), Heinonen, S. (Seppo), Kajantie, E. (Eero), Kere, J. (Juha), Pouta, A. (Anneli), Macphail, S. (Sheila), Kilby, M. (Mark), Habiba, M. (Marwan), Williamson, C. (Catherine), O'Shaughnessy, K. (Kevin), O'Brien, S. (Shaughn), Cameron, A. (Alan), Redman, C. W. (Christopher W. G.), Farrall, M. (Martin), and Caulfield, M. (Mark)

Subjects
embryonic structures, reproductive and urinary physiology, female genital diseases and pregnancy complications
Abstract

Preeclampsia is a serious complication of pregnancy, affecting both maternal and fetal health. In genome-wide association meta-analysis of European and Central Asian mothers, we identify sequence variants that associate with preeclampsia in the maternal genome at ZNF831/20q13 and FTO/16q12. These are previously established variants for blood pressure (BP) and the FTO variant has also been associated with body mass index (BMI). Further analysis of BP variants establishes that variants at MECOM/3q26, FGF5/4q21 and SH2B3/12q24 also associate with preeclampsia through the maternal genome. We further show that a polygenic risk score for hypertension associates with preeclampsia. However, comparison with gestational hypertension indicates that additional factors modify the risk of preeclampsia. Studies to identify maternal variants associated with preeclampsia have been limited by sample size. Here, the authors meta-analyze eight GWAS of 9,515 preeclamptic women, identifying five variants associated with preeclampsia and showing that genetic predisposition to hypertension is a major risk factor for preeclampsia.

Published
2020

40. Validation and development of models using clinical, biochemical and ultrasound markers for predicting pre-eclampsia: An individual participant data meta-analysis

Author

Allotey, J. Snell, K.I.E. Smuk, M. Hooper, R. Chan, C.L. Ahmed, A. Chappell, L.C. von Dadelszen, P. Dodds, J. Green, M. Kenny, L. Khalil, A. Khan, K.S. Mol, B.W. Myers, J. Poston, L. Thilaganathan, B. Staff, A.C. Smith, G.C.S. Ganzevoort, W. Laivuori, H. Odibo, A.O. Ramírez, J.A. Kingdom, J. Daskalakis, G. Farrar, D. Baschat, A.A. Seed, P.T. Prefumo, F. da Silva Costa, F. Groen, H. Audibert, F. Massé, J. Skråstad, R.B. Salvesen, K.A. Haavaldsen, C. Nagata, C. Rumbold, A.R. Heinonen, S. Askie, L.M. Smits, L.J.M. Vinter, C.A. Magnus, P.M. Eero, K. Villa, P.M. Jenum, A.K. Andersen, L.B. Norman, J.E. Ohkuchi, A. Eskild, A. Bhattacharya, S. McAuliffe, F.M. Galindo, A. Herraiz, I. Carbillon, L. Klipstein-Grobusch, K. Yeo, S. Teede, H.J. Browne, J.L. Moons, K.G.M. Riley, R.D. Thangaratinam, S. The IPPIC Collaborative Network

Abstract

Background: Pre-eclampsia is a leading cause of maternal and perinatal mortality and morbidity. Early identification of women at risk is needed to plan management. Objectives: To assess the performance of existing pre-eclampsia prediction models and to develop and validate models for pre-eclampsia using individual participant data meta-analysis. We also estimated the prognostic value of individual markers. Design: This was an individual participant data meta-analysis of cohort studies. Setting: Source data from secondary and tertiary care. Predictors: We identified predictors from systematic reviews, and prioritised for importance in an international survey. Primary outcomes: Early-onset (delivery at < 34 weeks’ gestation), late-onset (delivery at ≥ 34 weeks’ gestation) and any-onset pre-eclampsia. Analysis: We externally validated existing prediction models in UK cohorts and reported their performance in terms of discrimination and calibration.We developed and validated 12 new models based on clinical characteristics, clinical characteristics and biochemical markers, and clinical characteristics and ultrasound markers in the first and second trimesters. We summarised the data set-specific performance of each model using a random-effects meta-analysis. Discrimination was considered promising for C-statistics of ≥ 0.7, and calibration was considered good if the slope was near 1 and calibration-in-the-large was near 0. Heterogeneity was quantified using I2 and 2. A decision curve analysis was undertaken to determine the clinical utility (net benefit) of the models. We reported the unadjusted prognostic value of individual predictors for pre-eclampsia as odds ratios with 95% confidence and prediction intervals. Results: The International Prediction of Pregnancy Complications network comprised 78 studies (3,570,993 singleton pregnancies) identified from systematic reviews of tests to predict pre-eclampsia. Twenty-four of the 131 published prediction models could be validated in 11 UK cohorts. Summary C-statistics were between 0.6 and 0.7 for most models, and calibration was generally poor owing to large between-study heterogeneity, suggesting model overfitting. The clinical utility of the models varied between showing net harm to showing minimal or no net benefit. The average discrimination for IPPIC models ranged between 0.68 and 0.83. This was highest for the second-trimester clinical characteristics and biochemical markers model to predict early-onset pre-eclampsia, and lowest for the first-trimester clinical characteristics models to predict any pre-eclampsia. Calibration performance was heterogeneous across studies. Net benefit was observed for International Prediction of Pregnancy Complications first and second-trimester clinical characteristics and clinical characteristics and biochemical markers models predicting any pre-eclampsia, when validated in singleton nulliparous women managed in the UK NHS. History of hypertension, parity, smoking, mode of conception, placental growth factor and uterine artery pulsatility index had the strongest unadjusted associations with pre-eclampsia. Limitations: Variations in study population characteristics, type of predictors reported, too few events in some validation cohorts and the type of measurements contributed to heterogeneity in performance of the International Prediction of Pregnancy Complications models. Some published models were not validated because model predictors were unavailable in the individual participant data. Conclusion: For models that could be validated, predictive performance was generally poor across data sets. Although the International Prediction of Pregnancy Complications models show good predictive performance on average, and in the singleton nulliparous population, heterogeneity in calibration performance is likely across settings. © 2020, NIHR Journals Library. All rights reserved.

Published
2020

41. External validation of prognostic models predicting pre-eclampsia: individual participant data meta-analysis.

Author

Snell, KIE, Allotey, J, Smuk, M, Hooper, R, Chan, C, Ahmed, A, Chappell, LC, Von Dadelszen, P, Green, M, Kenny, L, Khalil, A, Khan, KS, Mol, BW, Myers, J, Poston, L, Thilaganathan, B, Staff, AC, Smith, GCS, Ganzevoort, W, Laivuori, H, Odibo, AO, Arenas Ramírez, J, Kingdom, J, Daskalakis, G, Farrar, D, Baschat, AA, Seed, PT, Prefumo, F, da Silva Costa, F, Groen, H, Audibert, F, Masse, J, Skråstad, RB, Salvesen, KÅ, Haavaldsen, C, Nagata, C, Rumbold, AR, Heinonen, S, Askie, LM, Smits, LJM, Vinter, CA, Magnus, P, Eero, K, Villa, PM, Jenum, AK, Andersen, LB, Norman, JE, Ohkuchi, A, Eskild, A, Bhattacharya, S, McAuliffe, FM, Galindo, A, Herraiz, I, Carbillon, L, Klipstein-Grobusch, K, Yeo, SA, Browne, JL, Moons, KGM, Riley, RD, Thangaratinam, S, IPPIC Collaborative Network, Snell, KIE, Allotey, J, Smuk, M, Hooper, R, Chan, C, Ahmed, A, Chappell, LC, Von Dadelszen, P, Green, M, Kenny, L, Khalil, A, Khan, KS, Mol, BW, Myers, J, Poston, L, Thilaganathan, B, Staff, AC, Smith, GCS, Ganzevoort, W, Laivuori, H, Odibo, AO, Arenas Ramírez, J, Kingdom, J, Daskalakis, G, Farrar, D, Baschat, AA, Seed, PT, Prefumo, F, da Silva Costa, F, Groen, H, Audibert, F, Masse, J, Skråstad, RB, Salvesen, KÅ, Haavaldsen, C, Nagata, C, Rumbold, AR, Heinonen, S, Askie, LM, Smits, LJM, Vinter, CA, Magnus, P, Eero, K, Villa, PM, Jenum, AK, Andersen, LB, Norman, JE, Ohkuchi, A, Eskild, A, Bhattacharya, S, McAuliffe, FM, Galindo, A, Herraiz, I, Carbillon, L, Klipstein-Grobusch, K, Yeo, SA, Browne, JL, Moons, KGM, Riley, RD, Thangaratinam, S, and IPPIC Collaborative Network

Abstract

BACKGROUND: Pre-eclampsia is a leading cause of maternal and perinatal mortality and morbidity. Early identification of women at risk during pregnancy is required to plan management. Although there are many published prediction models for pre-eclampsia, few have been validated in external data. Our objective was to externally validate published prediction models for pre-eclampsia using individual participant data (IPD) from UK studies, to evaluate whether any of the models can accurately predict the condition when used within the UK healthcare setting. METHODS: IPD from 11 UK cohort studies (217,415 pregnant women) within the International Prediction of Pregnancy Complications (IPPIC) pre-eclampsia network contributed to external validation of published prediction models, identified by systematic review. Cohorts that measured all predictor variables in at least one of the identified models and reported pre-eclampsia as an outcome were included for validation. We reported the model predictive performance as discrimination (C-statistic), calibration (calibration plots, calibration slope, calibration-in-the-large), and net benefit. Performance measures were estimated separately in each available study and then, where possible, combined across studies in a random-effects meta-analysis. RESULTS: Of 131 published models, 67 provided the full model equation and 24 could be validated in 11 UK cohorts. Most of the models showed modest discrimination with summary C-statistics between 0.6 and 0.7. The calibration of the predicted compared to observed risk was generally poor for most models with observed calibration slopes less than 1, indicating that predictions were generally too extreme, although confidence intervals were wide. There was large between-study heterogeneity in each model's calibration-in-the-large, suggesting poor calibration of the predicted overall risk across populations. In a subset of models, the net benefit of using the models to inform clinical decisions

Published
2020

42. The PRECISE (PREgnancy Care Integrating translational Science, Everywhere) Network's first protocol: deep phenotyping in three sub-Saharan African countries.

Author

von Dadelszen, P, Flint-O'Kane, M, Poston, L, Craik, R, Russell, D, Tribe, RM, d'Alessandro, U, Roca, A, Jah, H, Temmerman, M, Koech Etyang, A, Sevene, E, Chin, P, Lawn, JE, Blencowe, H, Sandall, J, Salisbury, TT, Barratt, B, Shennan, AH, Makanga, PT, Magee, LA, PRECISE Network, von Dadelszen, P, Flint-O'Kane, M, Poston, L, Craik, R, Russell, D, Tribe, RM, d'Alessandro, U, Roca, A, Jah, H, Temmerman, M, Koech Etyang, A, Sevene, E, Chin, P, Lawn, JE, Blencowe, H, Sandall, J, Salisbury, TT, Barratt, B, Shennan, AH, Makanga, PT, Magee, LA, and PRECISE Network

Abstract

BACKGROUND:The PRECISE (PREgnancy Care Integrating translational Science, Everywhere) Network is a new and broadly-based group of research scientists and health advocates based in the UK, Africa and North America. METHODS:This paper describes the protocol that underpins the clinical research activity of the Network, so that the investigators, and broader global health community, can have access to 'deep phenotyping' (social determinants of health, demographic and clinical parameters, placental biology and agnostic discovery biology) of women as they advance through pregnancy to the end of the puerperium, whether those pregnancies have normal outcomes or are complicated by one/more of the placental disorders of pregnancy (pregnancy hypertension, fetal growth restriction and stillbirth). Our clinical sites are in The Gambia (Farafenni), Kenya (Kilifi County), and Mozambique (Maputo Province). In each country, 50 non-pregnant women of reproductive age will be recruited each month for 1 year, to provide a final national sample size of 600; these women will provide culturally-, ethnically-, seasonally- and spatially-relevant control data with which to compare women with normal and complicated pregnancies. Between the three countries we will recruit ≈10,000 unselected pregnant women over 2 years. An estimated 1500 women will experience one/more placental complications over the same epoch. Importantly, as we will have accurate gestational age dating using the TraCer device, we will be able to discriminate between fetal growth restriction and preterm birth. Recruitment and follow-up will be primarily facility-based and will include women booking for antenatal care, subsequent visits in the third trimester, at time-of-disease, when relevant, during/immediately after birth and 6 weeks after birth. CONCLUSIONS:To accelerate progress towards the women's and children's health-relevant Sustainable Development Goals, we need to understand how a variety of social, chronic disease

Published
2020

44. A high-fat diet during rat pregnancy or suckling induces cardiovascular dysfunction in adult offspring

Author

Khan, I.Y., Dekou, V., Douglas, G., Jensen, R., Hanson, M.A., Poston, L., and Taylor, P.D.

Subjects
Endothelium -- Research, Blood pressure -- Risk factors, Heart -- Abnormalities, Heart -- Research, Biological sciences
Abstract

Epidemiological and animal studies suggest that diet-induced epigenetic modifications in early life can contribute to development of the metabolic syndrome in adulthood. We previously reported features of the metabolic syndrome in adult offspring of rats fed a diet rich in animal fat during pregnancy and suckling. We now report a study to compare the relative effects of high-fat feeding during 1) pregnancy and 2) the suckling period in the development of these disorders. As observed previously, 6-mo-old female offspring of fat-fed dams suckled by the same fat-fed dams (OHF) demonstrated raised blood pressure, despite being fed a balanced diet from weaning. Female offspring of fat-fed dams 'cross fostered' to dams consuming a control diet during suckling (OHF/C) demonstrated raised blood pressure compared with controls (OC) ]systolic blood pressure (SBP; mmHg) means [+ or -] SE: OHF/C, 132.5 [+ or -] 3.0, n = 6 vs. OC, 119.0 [+ or -] 3.8, n = 7, P < 0.05]. Female offspring of controls cross fostered to dams consuming the fat diet (OC/HF) were also hypertensive [SBP (mmHg) 131.0 [+ or -] 2.5 mmHg, n = 6 vs. OC, P < 0.05]. Endothelium-dependent relaxation (EDR) of male and female OHF and OHF/C mesenteric small arteries was similar and blunted compared with OC (P < 0.001). OC/HF arteries showed profoundly impaired EDR (OC/HF vs. OHF, P < 0.001). OHF/C and OC/HF demonstrated hyperinsulinemia and increased adiposity. Features of the metabolic syndrome in adult offspring of fat-fed rats can be acquired both antenatally and during suckling. However, exposure during pregnancy confers adaptive protection against endothelial dysfunction induced by maternal fat feeding during suckling. blood pressure; endothelium; developmental programming

Published
2005

50. DNA methylation signatures in cord blood associated with birthweight are enriched for dmCpGs previously associated with maternal hypertension or pre-eclampsia, smoking and folic acid intake.

Author

Antoun, E, Titcombe, P, Dalrymple, K, Kitaba, NT, Barton, SJ, Flynn, Ac, Murray, R, Garratt, ES, Seed, PT, White, SL, Cooper, Cyrus, Inskip, H M, Hanson, M, Poston, L, Godfrey, KM, and Lillycrop, KA

Subjects
DNA methylation, FOLIC acid, CORD blood, BIRTH weight, PREECLAMPSIA, PREGNANCY complications
Abstract

Many epidemiological studies have linked low birthweight to an increased risk of non-communicable diseases (NCDs) in later life, with epigenetic proceseses suggested as an underlying mechanism. Here, we sought to identify neonatal methylation changes associated with birthweight, at the individual CpG and genomic regional level, and whether the birthweight-associated methylation signatures were associated with specific maternal factors. Using the Illumina Human Methylation EPIC array, we assessed DNA methylation in the cord blood of 557 and 483 infants from the UK Pregnancies Better Eating and Activity Trial and Southampton Women's Survey, respectively. Adjusting for gestational age and other covariates, an epigenome-wide association study identified 2911 (FDR≤0.05) and 236 (Bonferroni corrected p ≤ 6.45×10−8) differentially methylated CpGs (dmCpGs), and 1230 differentially methylated regions (DMRs) (Stouffer ≤0.05) associated with birthweight. The top birthweight-associated dmCpG was located within the Homeobox Telomere-Binding Protein 1 (HMBOX1) gene with a 195 g (95%CI: −241, −149 g) decrease in birthweight per 10% increase in methylation, while the top DMR was located within the promoter of corticotropin-releasing hormone-binding protein (CRHBP). Furthermore, the birthweight-related dmCpGs were enriched for dmCpGs previously associated with gestational hypertension/pre-eclampsia (14.51%, p = 1.37×10−255), maternal smoking (7.71%, p = 1.50 x 10−57) and maternal plasma folate levels during pregnancy (0.33%, p = 0.029). The identification of birthweight-associated methylation markers, particularly those connected to specific pregnancy complications and exposures, may provide insights into the developmental pathways that affect birthweight and suggest surrogate markers to identify adverse prenatal exposures for stratifying for individuals at risk of later NCDs. [ABSTRACT FROM AUTHOR]

Published
2022
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