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1. Brain charts for the human lifespan

Author

Bethlehem, R. A. I., Seidlitz, J., White, S. R., Vogel, J. W., Anderson, K. M., Adamson, C., Adler, S., Alexopoulos, G. S., Anagnostou, E., Areces-Gonzalez, A., Astle, D. E., Auyeung, B., Ayub, M., Bae, J., Ball, G., Baron-Cohen, S., Beare, R., Bedford, S. A., Benegal, V., Beyer, F., Blangero, J., Blesa Cábez, M., Boardman, J. P., Borzage, M., Bosch-Bayard, J. F., Bourke, N., Calhoun, V. D., Chakravarty, M. M., Chen, C., Chertavian, C., Chetelat, G., Chong, Y. S., Cole, J. H., Corvin, A., Costantino, M., Courchesne, E., Crivello, F., Cropley, V. L., Crosbie, J., Crossley, N., Delarue, M., Delorme, R., Desrivieres, S., Devenyi, G. A., Di Biase, M. A., Dolan, R., Donald, K. A., Donohoe, G., Dunlop, K., Edwards, A. D., Elison, J. T., Ellis, C. T., Elman, J. A., Eyler, L., Fair, D. A., Feczko, E., Fletcher, P. C., Fonagy, P., Franz, C. E., Galan-Garcia, L., Gholipour, A., Giedd, J., Gilmore, J. H., Glahn, D. C., Goodyer, I. M., Grant, P. E., Groenewold, N. A., Gunning, F. M., Gur, R. E., Gur, R. C., Hammill, C. F., Hansson, O., Hedden, T., Heinz, A., Henson, R. N., Heuer, K., Hoare, J., Holla, B., Holmes, A. J., Holt, R., Huang, H., Im, K., Ipser, J., Jack, Jr, C. R., Jackowski, A. P., Jia, T., Johnson, K. A., Jones, P. B., Jones, D. T., Kahn, R. S., Karlsson, H., Karlsson, L., Kawashima, R., Kelley, E. A., Kern, S., Kim, K. W., Kitzbichler, M. G., Kremen, W. S., Lalonde, F., Landeau, B., Lee, S., Lerch, J., Lewis, J. D., Li, J., Liao, W., Liston, C., Lombardo, M. V., Lv, J., Lynch, C., Mallard, T. T., Marcelis, M., Markello, R. D., Mathias, S. R., Mazoyer, B., McGuire, P., Meaney, M. J., Mechelli, A., Medic, N., Misic, B., Morgan, S. E., Mothersill, D., Nigg, J., Ong, M. Q. W., Ortinau, C., Ossenkoppele, R., Ouyang, M., Palaniyappan, L., Paly, L., Pan, P. M., Pantelis, C., Park, M. M., Paus, T., Pausova, Z., Paz-Linares, D., Pichet Binette, A., Pierce, K., Qian, X., Qiu, J., Qiu, A., Raznahan, A., Rittman, T., Rodrigue, A., Rollins, C. K., Romero-Garcia, R., Ronan, L., Rosenberg, M. D., Rowitch, D. H., Salum, G. A., Satterthwaite, T. D., Schaare, H. L., Schachar, R. J., Schultz, A. P., Schumann, G., Schöll, M., Sharp, D., Shinohara, R. T., Skoog, I., Smyser, C. D., Sperling, R. A., Stein, D. J., Stolicyn, A., Suckling, J., Sullivan, G., Taki, Y., Thyreau, B., Toro, R., Traut, N., Tsvetanov, K. A., Turk-Browne, N. B., Tuulari, J. J., Tzourio, C., Vachon-Presseau, É., Valdes-Sosa, M. J., Valdes-Sosa, P. A., Valk, S. L., van Amelsvoort, T., Vandekar, S. N., Vasung, L., Victoria, L. W., Villeneuve, S., Villringer, A., Vértes, P. E., Wagstyl, K., Wang, Y. S., Warfield, S. K., Warrier, V., Westman, E., Westwater, M. L., Whalley, H. C., Witte, A. V., Yang, N., Yeo, B., Yun, H., Zalesky, A., Zar, H. J., Zettergren, A., Zhou, J. H., Ziauddeen, H., Zugman, A., Zuo, X. N., Bullmore, E. T., and Alexander-Bloch, A. F.

Published
2022
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2. Child, Maternal and Demographic Factors Influencing Caregiver-Reported Autistic Trait Symptomatology in Toddlers

Author

Goh, D. A., Gan, D., Kung, J., Baron-Cohen, S., Allison, C., Chen, H., Saw, S. M., Chong, Y. S., Rajadurai, V. S., Tan, K. H., Shek, P. C. L., Yap, F., Broekman, B. F. P., and Magiati, I.

Abstract

Current research on children's autistic traits in the general population relies predominantly on caregiver-report, yet the extent to which individual, caregiver or demographic characteristics are associated with informants' ratings has not been sufficiently explored. In this study, caregivers of 396 Singaporean two-year-olds from a birth cohort study completed the Quantitative Checklist for Autism in Toddlers. Children's gender, cognitive functioning and birth order, maternal age, and ethnic group membership were not significant predictors of caregiver-reported autistic traits. Poorer child language development and higher maternal depressive symptoms significantly predicted more social-communicative autistic traits, while lower maternal education predicted more behavioural autistic traits. Children's language and informants' educational level and depressive symptomatology may need to be considered in caregiver-reports of autistic traits.

Published
2018
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3. Publisher Correction: Brain charts for the human lifespan

Author

Bethlehem, R. A. I., Seidlitz, J., White, S. R., Vogel, J. W., Anderson, K. M., Adamson, C., Adler, S., Alexopoulos, G. S., Anagnostou, E., Areces-Gonzalez, A., Astle, D. E., Auyeung, B., Ayub, M., Bae, J., Ball, G., Baron-Cohen, S., Beare, R., Bedford, S. A., Benegal, V., Beyer, F., Blangero, J., Blesa Cábez, M., Boardman, J. P., Borzage, M., Bosch-Bayard, J. F., Bourke, N., Calhoun, V. D., Chakravarty, M. M., Chen, C., Chertavian, C., Chetelat, G., Chong, Y. S., Cole, J. H., Corvin, A., Costantino, M., Courchesne, E., Crivello, F., Cropley, V. L., Crosbie, J., Crossley, N., Delarue, M., Delorme, R., Desrivieres, S., Devenyi, G. A., Di Biase, M. A., Dolan, R., Donald, K. A., Donohoe, G., Dunlop, K., Edwards, A. D., Elison, J. T., Ellis, C. T., Elman, J. A., Eyler, L., Fair, D. A., Feczko, E., Fletcher, P. C., Fonagy, P., Franz, C. E., Galan-Garcia, L., Gholipour, A., Giedd, J., Gilmore, J. H., Glahn, D. C., Goodyer, I. M., Grant, P. E., Groenewold, N. A., Gunning, F. M., Gur, R. E., Gur, R. C., Hammill, C. F., Hansson, O., Hedden, T., Heinz, A., Henson, R. N., Heuer, K., Hoare, J., Holla, B., Holmes, A. J., Holt, R., Huang, H., Im, K., Ipser, J., Jack, Jr, C. R., Jackowski, A. P., Jia, T., Johnson, K. A., Jones, P. B., Jones, D. T., Kahn, R. S., Karlsson, H., Karlsson, L., Kawashima, R., Kelley, E. A., Kern, S., Kim, K. W., Kitzbichler, M. G., Kremen, W. S., Lalonde, F., Landeau, B., Lee, S., Lerch, J., Lewis, J. D., Li, J., Liao, W., Liston, C., Lombardo, M. V., Lv, J., Lynch, C., Mallard, T. T., Marcelis, M., Markello, R. D., Mathias, S. R., Mazoyer, B., McGuire, P., Meaney, M. J., Mechelli, A., Medic, N., Misic, B., Morgan, S. E., Mothersill, D., Nigg, J., Ong, M. Q. W., Ortinau, C., Ossenkoppele, R., Ouyang, M., Palaniyappan, L., Paly, L., Pan, P. M., Pantelis, C., Park, M. M., Paus, T., Pausova, Z., Paz-Linares, D., Pichet Binette, A., Pierce, K., Qian, X., Qiu, J., Qiu, A., Raznahan, A., Rittman, T., Rodrigue, A., Rollins, C. K., Romero-Garcia, R., Ronan, L., Rosenberg, M. D., Rowitch, D. H., Salum, G. A., Satterthwaite, T. D., Schaare, H. L., Schachar, R. J., Schultz, A. P., Schumann, G., Schöll, M., Sharp, D., Shinohara, R. T., Skoog, I., Smyser, C. D., Sperling, R. A., Stein, D. J., Stolicyn, A., Suckling, J., Sullivan, G., Taki, Y., Thyreau, B., Toro, R., Traut, N., Tsvetanov, K. A., Turk-Browne, N. B., Tuulari, J. J., Tzourio, C., Vachon-Presseau, É., Valdes-Sosa, M. J., Valdes-Sosa, P. A., Valk, S. L., van Amelsvoort, T., Vandekar, S. N., Vasung, L., Victoria, L. W., Villeneuve, S., Villringer, A., Vértes, P. E., Wagstyl, K., Wang, Y. S., Warfield, S. K., Warrier, V., Westman, E., Westwater, M. L., Whalley, H. C., Witte, A. V., Yang, N., Yeo, B., Yun, H., Zalesky, A., Zar, H. J., Zettergren, A., Zhou, J. H., Ziauddeen, H., Zugman, A., Zuo, X. N., Bullmore, E. T., and Alexander-Bloch, A. F.

Published
2022
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8. Brain charts for the human lifespan

Author

British Academy, Autism Research Trust, National Institute of Mental Health (US), UK Research and Innovation, Medical Research Council (UK), National Institute for Health and Care Research (US), Wellcome Trust, University of Cambridge, Cambridge Biomedical Research Centre, Bethlehem, Richard A. I., Seidlitz, J., White, S. R., Vogel, Jacob W., Anderson, K. M., Adamson, C., Adler, S., Alexopoulos, G. S., Anagnostou, E., Areces-González, A., Astle, D. E., Auyeung, Bonnie, Ayub, Muhammad, Bae, J., Ball, G, Baron-Cohen, Simon, Beare, R., Bedford, S. A., Benegal, V., Beyer, Frauke, Blangero, John, Blesa Cábez, M., Boardman, J. P., Borzage, M., Bosch-Bayard, J. F., Bourke, N., Calhoun, V. D., Chakravarty, M. M., Chen, C., Chertavian, C., Chetelat, G., Chong, Y. S., Cole, J. H., Corvin, Aiden, Costantino, M., Courchesne, E., Crivello, Fabrice, Cropley, V. L., Crosbie, J., Crossley, N., Delarue, M., Delorme, R., Desrivieres, Sylvane, Devenyi, G. A., Di Biase, M. A., Dolan, R., Donald, K. A., Donohoe, Gary, Dunlop, K., Edwards, A. D., Elison, J. T., Ellis, C. T., Elman, J. A., Eyler, L., Fair, D. A., Feczko, E., Fletcher, P. C., Fonagy, P., Franz, C. E., Galán-García, L., Gholipour, A., Giedd, J., Gilmore, J. H., Glahn, David C., Goodyer, I. M., Grant, P. E., Groenewold, Nynke A., Gunning, F. M., Gur, Rachel E., Gur, Ruben C., Hammill, C. F., Hansson, Oskar, Hedden, T., Heinz, Andreas, Henson, R. N., Heuer, K., Hoare, J., Holla, B., Holmes, Avram J., Holt, Rosemary, Huang, Hailiang, Im, K., Ipser, J., Jack, C. R., Jackowski, A. P., Jia, T., Johnson, K. A., Jones, P. B., Jones, David T. W., Kahn, René S., Karlsson, H., Karlsson, L., Kawashima, R., Kelley, E. A., Kern, Silke, Kim, K. W., Kitzbichler, M. G., Kremen, W. S., Lalonde, F., Landeau, B., Lee, S., Lerch, J., Lewis, J. D., Li, J., Liao, W., Liston, C., Lombardo, Michael, V., Lv, J., Lynch, Christopher D., Mallard, T. T., Marcelis, Machteld, Markello, R. D., Mathias, Samuel R., Mazoyer, Bernard, McGuire, Philip, Meaney, M. J., Mechelli, Andrea, Medic, N., Misic, B., Morgan, S. E., Mothersill, David O., Nigg, J., Ong, M. Q. W., Ortinau, C., Ossenkoppele, Rik, Ouyang, M., Palaniyappan, L., Paly, L., Pan, P. M., Pantelis, Christos, Park, M. M., Paus, Tomas, Pausova, Zdenka, Paz-Linares, D., Pichler, Alexander, Pierce, K., Qian, X., Qiu, Jing, Qiu, A., Raznahan, A., Rittman, T., Rodrigue, A., Rollins, C. K., Romero García, Rafael, Ronan, L., Rosenberg, M. D., Rowitch, D. H., Salum, G. A., Satterthwaite, Theodore D., Schaare, H. L., Schachar, R. J., Schultz, A. P., Schumann, Gunter, Schöll, Michael, Sharp, D., Shinohara, R. T., Skoog, Ingmar, Smyser, C. D., Sperling, R. A., Stein, Dan J., Stolicyn, A., Suckling, John, Sullivan, G., Taki, Y., Thyreau, B., Toro, Raquel del, Traut, N., Tsvetanov, K. A., Turk-Browne, N. B., Tuulari, J. J., Tzourio, C., Vachon-Presseau, É., Valdes-Sosa, M. J., Valdes-Sosa, P. A., Valk, Sofie L., van Amelsvoort, Therese, Vandekar, S. N., Vasung, L., Victoria, L. W., Villeneuve, S., Villringer, Arno, Vértes, P. E., Wagstyl, K., Wang, Y. S., Warfield, S. K., Warrier, V., Westman, Eric, Westwater, M. L., Whalley, Heather, Witte, A. Veronica, Yang, N., Yeo, B., Yun, H., Zalesky, A., Zar, H. J., Zettergren, Anna, Zhou, J. H., Ziauddeen, H., Zugman, A., Zuo, X. N., Bullmore, E. T., Alexander-Bloch, A. F., British Academy, Autism Research Trust, National Institute of Mental Health (US), UK Research and Innovation, Medical Research Council (UK), National Institute for Health and Care Research (US), Wellcome Trust, University of Cambridge, Cambridge Biomedical Research Centre, Bethlehem, Richard A. I., Seidlitz, J., White, S. R., Vogel, Jacob W., Anderson, K. M., Adamson, C., Adler, S., Alexopoulos, G. S., Anagnostou, E., Areces-González, A., Astle, D. E., Auyeung, Bonnie, Ayub, Muhammad, Bae, J., Ball, G, Baron-Cohen, Simon, Beare, R., Bedford, S. A., Benegal, V., Beyer, Frauke, Blangero, John, Blesa Cábez, M., Boardman, J. P., Borzage, M., Bosch-Bayard, J. F., Bourke, N., Calhoun, V. D., Chakravarty, M. M., Chen, C., Chertavian, C., Chetelat, G., Chong, Y. S., Cole, J. H., Corvin, Aiden, Costantino, M., Courchesne, E., Crivello, Fabrice, Cropley, V. L., Crosbie, J., Crossley, N., Delarue, M., Delorme, R., Desrivieres, Sylvane, Devenyi, G. A., Di Biase, M. A., Dolan, R., Donald, K. A., Donohoe, Gary, Dunlop, K., Edwards, A. D., Elison, J. T., Ellis, C. T., Elman, J. A., Eyler, L., Fair, D. A., Feczko, E., Fletcher, P. C., Fonagy, P., Franz, C. E., Galán-García, L., Gholipour, A., Giedd, J., Gilmore, J. H., Glahn, David C., Goodyer, I. M., Grant, P. E., Groenewold, Nynke A., Gunning, F. M., Gur, Rachel E., Gur, Ruben C., Hammill, C. F., Hansson, Oskar, Hedden, T., Heinz, Andreas, Henson, R. N., Heuer, K., Hoare, J., Holla, B., Holmes, Avram J., Holt, Rosemary, Huang, Hailiang, Im, K., Ipser, J., Jack, C. R., Jackowski, A. P., Jia, T., Johnson, K. A., Jones, P. B., Jones, David T. W., Kahn, René S., Karlsson, H., Karlsson, L., Kawashima, R., Kelley, E. A., Kern, Silke, Kim, K. W., Kitzbichler, M. G., Kremen, W. S., Lalonde, F., Landeau, B., Lee, S., Lerch, J., Lewis, J. D., Li, J., Liao, W., Liston, C., Lombardo, Michael, V., Lv, J., Lynch, Christopher D., Mallard, T. T., Marcelis, Machteld, Markello, R. D., Mathias, Samuel R., Mazoyer, Bernard, McGuire, Philip, Meaney, M. J., Mechelli, Andrea, Medic, N., Misic, B., Morgan, S. E., Mothersill, David O., Nigg, J., Ong, M. Q. W., Ortinau, C., Ossenkoppele, Rik, Ouyang, M., Palaniyappan, L., Paly, L., Pan, P. M., Pantelis, Christos, Park, M. M., Paus, Tomas, Pausova, Zdenka, Paz-Linares, D., Pichler, Alexander, Pierce, K., Qian, X., Qiu, Jing, Qiu, A., Raznahan, A., Rittman, T., Rodrigue, A., Rollins, C. K., Romero García, Rafael, Ronan, L., Rosenberg, M. D., Rowitch, D. H., Salum, G. A., Satterthwaite, Theodore D., Schaare, H. L., Schachar, R. J., Schultz, A. P., Schumann, Gunter, Schöll, Michael, Sharp, D., Shinohara, R. T., Skoog, Ingmar, Smyser, C. D., Sperling, R. A., Stein, Dan J., Stolicyn, A., Suckling, John, Sullivan, G., Taki, Y., Thyreau, B., Toro, Raquel del, Traut, N., Tsvetanov, K. A., Turk-Browne, N. B., Tuulari, J. J., Tzourio, C., Vachon-Presseau, É., Valdes-Sosa, M. J., Valdes-Sosa, P. A., Valk, Sofie L., van Amelsvoort, Therese, Vandekar, S. N., Vasung, L., Victoria, L. W., Villeneuve, S., Villringer, Arno, Vértes, P. E., Wagstyl, K., Wang, Y. S., Warfield, S. K., Warrier, V., Westman, Eric, Westwater, M. L., Whalley, Heather, Witte, A. Veronica, Yang, N., Yeo, B., Yun, H., Zalesky, A., Zar, H. J., Zettergren, Anna, Zhou, J. H., Ziauddeen, H., Zugman, A., Zuo, X. N., Bullmore, E. T., and Alexander-Bloch, A. F.

Abstract

Over the past few decades, neuroimaging has become a ubiquitous tool in basic research and clinical studies of the human brain. However, no reference standards currently exist to quantify individual differences in neuroimaging metrics over time, in contrast to growth charts for anthropometric traits such as height and weight1. Here we assemble an interactive open resource to benchmark brain morphology derived from any current or future sample of MRI data ( http://www.brainchart.io/ ). With the goal of basing these reference charts on the largest and most inclusive dataset available, acknowledging limitations due to known biases of MRI studies relative to the diversity of the global population, we aggregated 123,984 MRI scans, across more than 100 primary studies, from 101,457 human participants between 115 days post-conception to 100 years of age. MRI metrics were quantified by centile scores, relative to non-linear trajectories2 of brain structural changes, and rates of change, over the lifespan. Brain charts identified previously unreported neurodevelopmental milestones3, showed high stability of individuals across longitudinal assessments, and demonstrated robustness to technical and methodological differences between primary studies. Centile scores showed increased heritability compared with non-centiled MRI phenotypes, and provided a standardized measure of atypical brain structure that revealed patterns of neuroanatomical variation across neurological and psychiatric disorders. In summary, brain charts are an essential step towards robust quantification of individual variation benchmarked to normative trajectories in multiple, commonly used neuroimaging phenotypes.

Published
2022

11. Dietary patterns of five-year-old children and their correlates: findings from a multi-ethnic Asian cohort

Author

Sugianto, Ray, Toh, Jia Ying, Wong, Shu Fang, Tint, M.T., Colega, Marjorelee T., Lee, Y.S., Yap, Fabian, Shek, Lynette P., Tan, K.H., Godfrey, Keith, Chong, Y-S, Tai, B.C., and Chong, Mary Foong-Fong

Abstract

There is limited data on the dietary patterns of 5-year-old children in Asia. The study examined childhood dietary patterns and their maternal and child correlates in a multi-ethnic Asian cohort. Based on caregiver-reported one-month quantitative food frequency questionnaires of 777 children from the Growing Up in Singapore Towards healthy Outcomes (GUSTO) cohort, cluster analysis identified two mutually exclusive clusters. Children in the “Unhealthy” cluster (43.9%) consumed more fries, processed meat, biscuits and ice cream, and less fish, fruits and vegetables compared to those in the “Healthy” cluster (56.1%). Children with mothers of lower educational attainment had twice the odds of being assigned to the “Unhealthy” cluster (adjusted OR (95% CI) = 2.19 (1.49-3.24)). Children of Malay and Indian ethnicities had higher odds of being assigned to the “Unhealthy” cluster (adjusted OR = 25.46 (15.40-42.10) and 4.03 (2.68-6.06)) respectively, relative to Chinese ethnicity. In conclusion, this study identified two dietary patterns in children, labelled as the “Unhealthy” and “Healthy” clusters. Mothers’ educational attainment and ethnicity were two correlates that were associated with the children’s assignments to the clusters. These findings can assist in informing health promotion programmes targeted at Asian children.

Published
2021

13. Income disparity in school readiness and the mediating role of perinatal maternal mental health: a longitudinal birth cohort study

Author

Law, E. C., primary, Aishworiya, R., additional, Cai, S., additional, Bouvette-Turcot, A.-A., additional, Broekman, B. F. P., additional, Chen, H., additional, Daniel, L. M., additional, Gluckman, P. D., additional, Shek, L. P. C., additional, Tay, S. K. H., additional, Chong, Y. S., additional, Koh, G. C.-H., additional, and Meaney, M. J., additional

Published
2021
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17. A 2.5 μ W KWS Engine With Pruned LSTM and Embedded MFCC for IoT Applications.

Author

Chong, Y. S., Goh, W. L., Nambiar, V. P., and Do, A. T.

Abstract

Always-on keyword spotting (KWS) hardware is gaining popularity in ultra-low power IoT applications where specific words are used to wake up and activate the power hungry downstream system. This work proposes a low power KWS engine with a power-optimized Mel-frequency cepstral coefficients (MFCC) feature extraction module and a memory-optimized long short term memory (LSTM) accelerator. Our LSTM model is pruned and compressed to reduce 89% of model size and 76% of computation. The LSTM accelerator adopts the weight stationary dataflow to reduce energy. Our simulation using a 40nm CMOS process achieves a power consumption of 2.51 $\mu \text{W}$ , which is at least $2\times $ lower than the state-of-the-art. [ABSTRACT FROM AUTHOR]

Published
2022
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18. Is breastfeeding associated with later child eating behaviours?

Author

Pang, WW, McCrickerd, K, Quah, PL, Fogel, A, Aris, IM, Yuan, WL, Fok, D, Chua, MC, Lim, SB, Shek, LP, Chan, S-Y, Tan, KH, Yap, F, Godfrey, KM, Meaney, MJ, Wlodek, ME, Eriksson, JG, Kramer, MS, Forde, CG, Chong, MFF, Chong, Y-S, Pang, WW, McCrickerd, K, Quah, PL, Fogel, A, Aris, IM, Yuan, WL, Fok, D, Chua, MC, Lim, SB, Shek, LP, Chan, S-Y, Tan, KH, Yap, F, Godfrey, KM, Meaney, MJ, Wlodek, ME, Eriksson, JG, Kramer, MS, Forde, CG, Chong, MFF, and Chong, Y-S

Abstract

Individual differences in children's eating behaviours emerge early. We examined the relationship between breastfeeding exposure and subsequent eating behaviours among children from the Growing Up in Singapore Towards healthy Outcomes (GUSTO) cohort. Children (n = 970) were grouped according to their breastfeeding exposure: high (full breastfeeding ≥ 4 months with continued breastfeeding ≥ 6 months), low (any breastfeeding < 3 months or no breastfeeding) and intermediate (between low and high breastfeeding categories). Aspects of eating behaviour from ages 15 months to 6 years were captured using a combination of maternal reports (Child Eating Behaviour Questionnaire; Infant Feeding Questionnaire; Preschooler Feeding Questionnaire) and laboratory-based measures of meal size, oral processing behaviours (e.g. average eating speed and bite size) and tendency to eat in the absence of hunger. Most children had low (44%) or intermediate (44%) breastfeeding exposure; only 12% had high exposure. After adjusting for confounders, multivariable linear regression analyses indicated the high (but not intermediate) breastfeeding group was associated with significantly lower reported food fussiness at 3 years compared to low breastfeeding group (-0.38 [-0.70, -0.06]), with similar but non-significant trends observed at 6 years (-0.27 [-0.66, 0.11]). At 3 years, mothers in the high breastfeeding group also reported the least difficulty in child feeding compared to low breastfeeding group (-0.22 [-0.43, -0.01]). However, high breastfeeding was not associated with any other maternal-reports of child feeding or eating behaviours, and no significant associations were observed between breastfeeding exposure and any of the laboratory measures of eating behaviour at any of the time points. These results do not strongly support the view that increased breastfeeding exposure alone has lasting and consistent associations with eating behaviours in early childhood.

Published
2020

20. Associations of physical activity levels and screen time with oral glucose tolerance test profiles in Singaporean women of reproductive age actively trying to conceive: the S‐PRESTO study

Author

Bernard, J. Y., primary, Ng, S., additional, Natarajan, P., additional, Loy, S. L., additional, Aris, I. M., additional, Tint, M. T., additional, Chong, Y.‐S., additional, Shek, L., additional, Chan, J., additional, Godfrey, K. M., additional, Khoo, C. M., additional, Leow, M. K.‐S., additional, Müller‐Riemenschneider, F., additional, and Chan, S.‐Y., additional

Published
2019
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22. Association between antenatal blood pressure and 5-year postpartum retinal arteriolar structural and functional changes.

Author

Sim, R, Aris, I, Chong, Y-S, Wong, TY, Li, L-J, Sim, R, Aris, I, Chong, Y-S, Wong, TY, and Li, L-J

Abstract

OBJECTIVE: Studies have shown that hypertensive disorders of pregnancy (HDP) are associated with both postpartum retinal microvascular changes and cardiovascular (CV) risks. However, the underlying mechanism of HDP transitioning to microvascular and macrovascular changes remains unknown, due to the interaction between microvasculature and CV risks. In this study, we examined whether associations between antenatal systolic blood pressure (SBP) and postpartum retinal arteriolar changes are independent of postpartum CV risks. METHODS: We included 276 Singaporean mothers attending both baseline index pregnancy (2009-2010) and 5-year postpartum follow-up visits (2014-2015). We measured SBP at baseline. At follow-up, we assessed retinal microvascular structure and function with retinal photography and dynamic vessel analyser, together with CV risks using a validated 2008 Framingham Risk Score (FRS). We performed a traditional four-step mediation analysis using linear regression by adjusting for a series of baseline characteristics: age, ethnicity, college degree, prepregnancy body mass index and gestational diabetes mellitus diagnosis at baseline. RESULTS: We found that each 10 mm Hg increase in baseline SBP was associated with reduced arteriolar calibre (-1.3 µm; 95% CI -3.0 to 0.2) and fractal dimension (-0.4 degrees of freedom (df); -1.0 to 0.2), and significantly with increased arteriolar constriction (0.5%; 0.001 to 1.0) at 5-year postpartum. Even though baseline SBP was associated with postpartum FRS, the latter was not associated with any retinal arteriolar measures. Therefore, no further mediation analysis was required. CONCLUSION: Our study suggested that elevated SBP during pregnancy was associated with suboptimal retinal arteriolar structure and function independent of postpartum CV risks.

Published
2019

23. Female adiposity and time-to-pregnancy: a multiethnic prospective cohort

Author

Loy, S L, primary, Cheung, Y B, additional, Soh, S E, additional, Ng, S, additional, Tint, M T, additional, Aris, I M, additional, Bernard, J Y, additional, Chong, Y S, additional, Godfrey, K M, additional, Shek, L P, additional, Tan, K H, additional, Lee, Y S, additional, Tan, H H, additional, Chern, B S M, additional, Lek, N, additional, Yap, F, additional, Chan, S Y, additional, Chi, C, additional, and Chan, J K Y, additional

Published
2018
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24. Associations of physical activity and sedentary behavior during pregnancy with gestational diabetes mellitus among Asian women in Singapore

Author

Padmapriya, N, Bernard, J Y, Liang, S, Loy, S L, Cai, S, Zhe, I S, Kwek, K, Godfrey, K M, Gluckman, P D, Saw, S M, Chong, Y.-S., Chan, J K Y, Müller-Riemenschneider, F, Sheppard, A, Chinnadurai, A, Neo Goh, A E, Rifkin-Graboi, A, Qiu, A, Biswas, A, Lee, B W, Broekman, B F P, Quah, B L, Shuter, B, Chng, C K, Ngo, C, Bong, C L, Henry, C J, Ing Chee, C Y, Daniel Goh, Y T, Fok, D, Yap, F, Heong Yeo, G S, Chen, H, van Bever, H P S, Magiati, I, Yun Wong, I B, Lau, I.Y.-M., Kapur, J, Richmond, J L, Holbrook, J D, Gooley, J J, Tan, K H, Niduvaje, K, Singh, L, Su, L L, Daniel, L M, Shek, L.P.-C., Fortier, M V, Hanson, M, Chong, M.F.-F., Rauff, M, Chua, M C, Meaney, M, Tint, M T, Karnani, N, Lek, N, Teoh, O H, Wong, P C, Agarwal, P, van Dam, R M, Rebello, S A, Chong, S C, Soh, S.-E., Lim, S B, Hsu, C.-Y.S., Rajadurai, V S, Stunkel, W, Han, W M, Pang, W W, Cheung, Y B, Chan, Y H, Lee, Y S, and APH - Mental Health

Subjects
Blood Glucose, endocrine system diseases, Overweight, Logistic regression, 600 Technik, Medizin, angewandte Wissenschaften::610 Medizin und Gesundheit, Gestational diabetes mellitus, Cohort Studies, 0302 clinical medicine, Risk Factors, Pregnancy, 030212 general & internal medicine, Singapore, Obstetrics, Obstetrics and Gynecology, Fasting, Postprandial Period, 3. Good health, Gestational diabetes, Postprandial, Pregnancy Trimester, Second, Gestation, Female, medicine.symptom, Research Article, Cohort study, Adult, medicine.medical_specialty, 030209 endocrinology & metabolism, Sitting, lcsh:Gynecology and obstetrics, 03 medical and health sciences, Asian People, medicine, Humans, Exercise, lcsh:RG1-991, Gynecology, business.industry, Physical activity, nutritional and metabolic diseases, Glucose Tolerance Test, medicine.disease, Diabetes, Gestational, Sedentary behavior, Logistic Models, Linear Models, business, Maternal glucose levels
Abstract

Background: Few studies have investigated physical activity (PA) and sedentary behavior (SB) in relation to fasting (FG) and 2-h postprandial plasma glucose (2hPG) levels and gestational diabetes mellitus (GDM); we investigated these associations among Asian pregnant women.Methods: As part of the Growing Up in Singapore Towards healthy Outcomes cohort study, PA and SB (sitting and television times) were assessed by interviewer-administered questionnaire. During 75 g oral glucose tolerance tests at 26–28 weeks' gestation we measured FG, 2hPG levels and GDM (FG ≥ 7.0 mmol/L and/or 2hPG ≥ 7.8 mmol/L). Associations were analysed by multiple linear and logistic regression.Results: Among the 1083 women studied, 18.6% had GDM. SB was not associated with FG, 2hPG and GDM. Higher categories of PA were associated with lower 2hPG and a lower likelihood of GDM (p-trend p = 0.020) and were less likely to have GDM [OR: 0.56 (0.32–0.98), p = 0.040]. Stratified analysis revealed no associations among under/normal-weight women, but significant associations among overweight/obese women; in those with BMI ≥23 kg/m2, sufficiently active and highly active women were less likely to have GDM [OR: 0.52, (0.29–0.93), p = 0.028, and OR: 0.34, (0.15–0.77), p = 0.010, respectively].Conclusion: Higher PA was associated with lower 2hPG levels and a lower prevalence of GDM, particularly in overweight/obese women. Further studies are warranted to confirm these findings, and to examine the effectiveness of PA promotion strategies for the prevention of gestational hyperglycemia.

Published
2017

25. Does the maternal vaginal microbiota play a role in seeding the microbiota of neonatal gut and nose?

Author

Sakwinska, O., primary, Foata, F., additional, Berger, B., additional, Brüssow, H., additional, Combremont, S., additional, Mercenier, A., additional, Dogra, S., additional, Soh, S.-E., additional, Yen, J.C.K., additional, Heong, G.Y.S., additional, Lee, Y.S., additional, Yap, F., additional, Meaney, M.J., additional, Chong, Y.-S., additional, Godfrey, K.M., additional, and Holbrook, J.D., additional

Published
2017
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27. Body composition measurement in young children using quantitative magnetic resonance: a comparison with air displacement plethysmography

Author

Chen, L.-W., primary, Tint, M.-T., additional, Fortier, M. V., additional, Aris, I. M., additional, Shek, L. P.-C., additional, Tan, K. H., additional, Rajadurai, V. S., additional, Gluckman, P. D., additional, Chong, Y.-S., additional, Godfrey, K. M., additional, Kramer, M. S., additional, Henry, C. J., additional, Yap, F., additional, and Lee, Y. S., additional

Published
2017
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28. Which anthropometric measures best reflect neonatal adiposity?

Author

Chen, L-W, primary, Tint, M-T, additional, Fortier, M V, additional, Aris, I M, additional, Shek, L P-C, additional, Tan, K H, additional, Chan, S-Y, additional, Gluckman, P D, additional, Chong, Y-S, additional, Godfrey, K M, additional, Rajadurai, V S, additional, Yap, F, additional, Kramer, M S, additional, and Lee, Y S, additional

Published
2017
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29. Modifiable risk factors in the first 1000 days for subsequent risk of childhood overweight in an Asian cohort: significance of parental overweight status

Author

Aris, I M, primary, Bernard, J Y, additional, Chen, L-W, additional, Tint, M T, additional, Pang, W W, additional, Soh, S E, additional, Saw, S-M, additional, Shek, L P-C, additional, Godfrey, K M, additional, Gluckman, P D, additional, Chong, Y-S, additional, Yap, F, additional, Kramer, M S, additional, and Lee, Y S, additional

Published
2017
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32. Use of Fetal Electrocardiogram for Intrapartum Monitoring

Author

Su, L. -L, Chong, Y. -S, and ARIJIT BISWAS

Subjects
Electrocardiography, Singapore, Pregnancy, Humans, Female, General Medicine, Fetal Hypoxia, Fetal Monitoring
Abstract

Introduction: Intrapartum fetal monitoring is essential for the identification of fetal hypoxia to reduce perinatal morbidity and mortality. Cardiotocography is associated with low specificity for fetal acidosis and poor perinatal outcome leading to unnecessary operative deliveries. ST waveform analysis of the fetal electrocardiogram has been shown to be a promising adjunctive intrapartum assessment tool. We aim to present the pathophysiology, the role of intrapartum monitoring and the practical usage of this relatively new technology in our review. Methods: An electronic search of Medline and OVID was carried out, followed by a manual search of the references identified by the electronic search. Results: The incorporation of ST waveform analysis to cardiotocography has been shown to reduce the rates of neonatal metabolic acidosis, moderate and severe neonatal encephalopathy, thus improving perinatal outcome. The reduction in operative delivery rates due to fetal distress is also significant. The pathophysiology and practical usage of this technology were discussed. Conclusions: With more accurate identification of fetal hypoxia and reduction of unnecessary intervention rates, incorporation of ST waveform analysis of fetal electrocardiography into cardiotocography can improve the standard of intrapartum fetal monitoring. Key words: Cardiotocography, Fetal electocardiography, Fetal ST waveform analysis, Intrapartum, Perinatal outcomes

Published
2007
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33. Associations of Maternal Vitamin B12 Concentration in Pregnancy with the Risks of Preterm Birth and Low Birth Weight: A Systematic Review and Meta-Analysis of Individual Participant Data

Author

Rogne, T. (Tormod), Tielemans, M.J. (Myrte), Chong, M.F.-F. (Mary Foong-Fong), Yajnik, C.S. (Chittaranjan S.), Krishnaveni, G.V. (Ghattu V.), Poston, L. (Lucilla), Jaddoe, V.W.V. (Vincent), Steegers, E.A.P. (Eric), Joshi, S. (Suyog), Chong, Y.-S. (Yap-Seng), Godfrey, N., Yap, F. (Fabian), Yahyaoui, R. (Raquel), Thomas, T. (Tinku), Hay, G. (Gry), Hogeveen, M. (Marije), Demir, A. (Ahmet), Saravanan, P. (Ponnusamy), Skovlund, E. (Eva), Martinussen, M.P. (Marit P.), Jacobsen, G.W. (Geir W.), Franco, O.H. (Oscar), Bracken, M.B. (Michael B.), Risnes, K.R. (Kari R.), Rogne, T. (Tormod), Tielemans, M.J. (Myrte), Chong, M.F.-F. (Mary Foong-Fong), Yajnik, C.S. (Chittaranjan S.), Krishnaveni, G.V. (Ghattu V.), Poston, L. (Lucilla), Jaddoe, V.W.V. (Vincent), Steegers, E.A.P. (Eric), Joshi, S. (Suyog), Chong, Y.-S. (Yap-Seng), Godfrey, N., Yap, F. (Fabian), Yahyaoui, R. (Raquel), Thomas, T. (Tinku), Hay, G. (Gry), Hogeveen, M. (Marije), Demir, A. (Ahmet), Saravanan, P. (Ponnusamy), Skovlund, E. (Eva), Martinussen, M.P. (Marit P.), Jacobsen, G.W. (Geir W.), Franco, O.H. (Oscar), Bracken, M.B. (Michael B.), and Risnes, K.R. (Kari R.)

Abstract

Vitamin B12 (hereafter referred to as B12) deficiency in pregnancy is prevalent and has been associated with both lower birth weight (birth weight <2,500 g) and preterm birth (length of gestation <37 weeks). Nevertheless, current evidence is contradictory. We performed a systematic review and a meta-analysis of individual participant data to evaluate the associations of maternal serum or plasma B12 concentrations in pregnancy with offspring birth weight and length of gestation. Twenty-two eligible studies were identified (11,993 observations). Eighteen studies were included in the meta-analysis (11,216 observations). No linear association was observed between maternal B12 levels in pregnancy and birth weight, but B12 deficiency (<148 pmol/L) was associated with a higher risk of low birth weight in newborns (adjusted risk ratio = 1.15, 95% confidence interval (CI): 1.01, 1.31). There was a linear association between maternal levels of B12 and preterm birth (per each 1-standarddeviation increase in B12, adjusted risk ratio = 0.89, 95% CI: 0.82, 0.97). Accordingly, B12 deficiency was associated with a higher risk of preterm birth (adjusted risk ratio = 1.21, 95% CI: 0.99, 1.49). This finding supports the need for randomized controlled trials of vitamin B12 supplementation in pregnancy.

Published
2017
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34. The association of maternal Vitamin D status with infant birth outcomes, postnatal growth and adiposity in the first 2 years of life in a multi-ethnic Asian population: The Growing Up in Singapore Towards healthy Outcomes (GUSTO) cohort study

Author

Ong, Y.L. (Yi Lin), Quah, P.L. (Phaik Ling), Tint, M.T. (Mya Thway), Aris, I.M. (Izzuddin M.), Chen, L.W. (Ling Wei), Dam, R.M. (Rob) van, Heppe, D.H.M. (Denise), Saw, S-M. (Seang-Mei), Godfrey, N., Gluckman, E. (Eliane), Chong, Y.-S. (Yap-Seng), Yap, F. (Fabian), Lee, Y.S. (Yung Seng), Foong-Fong Chong, M. (Mary), Ong, Y.L. (Yi Lin), Quah, P.L. (Phaik Ling), Tint, M.T. (Mya Thway), Aris, I.M. (Izzuddin M.), Chen, L.W. (Ling Wei), Dam, R.M. (Rob) van, Heppe, D.H.M. (Denise), Saw, S-M. (Seang-Mei), Godfrey, N., Gluckman, E. (Eliane), Chong, Y.-S. (Yap-Seng), Yap, F. (Fabian), Lee, Y.S. (Yung Seng), and Foong-Fong Chong, M. (Mary)

Abstract

Maternal vitamin D status during pregnancy has been associated with infant birth and postnatal growth outcomes, but reported findings have been inconsistent, especially in relation to postnatal growth and adiposity outcomes. In a mother-offspring cohort in Singapore, maternal plasma vitamin D was measured between 26 and 28 weeks of gestation, and anthropometric measurements were obtained from singleton offspring during the first 2 years of life with 3-month follow-up intervals to examine birth, growth and adiposity outcomes. Associations were analysed using multivariable linear regression. Of a total of 910 mothers, 13·2

Published
2016
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36. Body composition measurement in young children using quantitative magnetic resonance: a comparison with air displacement plethysmography.

Author

Chen, L.‐W., Tint, M.‐T., Fortier, M. V., Aris, I. M., Shek, L. P.‐C., Tan, K. H., Rajadurai, V. S., Gluckman, P. D., Chong, Y.‐S., Godfrey, K. M., Kramer, M. S., Henry, C. J., Yap, F., and Lee, Y. S.

Subjects
BODY composition, ETHNIC groups, MAGNETIC resonance imaging, MULTIPLE birth, PLETHYSMOGRAPHY, REGRESSION analysis, SEX distribution, BODY mass index, LEAN body mass, DESCRIPTIVE statistics, INTRACLASS correlation, CHILDREN
Abstract

Summary: Background: Quantitative magnetic resonance (QMR) has been increasingly used to measure human body composition, but its use and validation in children is limited. Objective: We compared body composition measurement by QMR and air displacement plethysmography (ADP) in preschool children from Singapore's multi‐ethnic Asian population (n = 152; mean ± SD age: 5.0 ± 0.1 years). Methods: Agreements between QMR‐based and ADP‐based fat mass and fat mass index (FMI) were assessed using intraclass correlation coefficient (ICC), reduced major axis regression and Bland–Altman plot analyses. Analyses were stratified for the child's sex. Results: Substantial agreement was observed between QMR‐based and ADP‐based fat mass (ICC: 0.85) and FMI (ICC: 0.82). Reduced major axis regression analysis suggested that QMR measurements were generally lower than ADP measurements. Bland–Altman analysis similarly revealed that QMR‐based fat mass were (mean difference [95% limits of agreement]) −0.5 (−2.1 to +1.1) kg lower than ADP‐based fat mass and QMR‐based FMI were −0.4 (−1.8 to +0.9) kg/m2 lower than ADP‐based FMI. Stratification by offspring sex revealed better agreement of QMR and ADP measurements in girls than in boys. Conclusions: QMR‐based fat mass and FMI showed substantial agreement with, but was generally lower than, ADP‐based measures in young Asian children. [ABSTRACT FROM AUTHOR]

Published
2018
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38. Trans-ancestry genome-wide association study identifies 12 genetic loci influencing blood pressure and implicates a role for DNA methylation

Author

Kato, N. (Norihiro), Loh, M. (Marie), Takeuchi, F. (Fumihiko), Verweij, N. (Niek), Wang, X. (Xu), Zhang, W. (Weihua), NKelly, T. (Tanika), Saleheen, D., Lehne, B. (Benjamin), Leach, I.M. (Irene Mateo), Drong, A. (Alexander), Abbott, J. (James), Wahl, S. (Simone), Tan, S.-T. (Sian-Tsung), Scott, W.R. (William R.), Campanella, G. (Gianluca), Chadeau-Hyam, M. (Marc), Afzal, U. (Uzma), Ahluwalia, T.S. (Tarunveer Singh), Bonder, M.J. (Marc), Chen, P. (Ping), Dehghan, A. (Abbas), Edwards, T.L. (Todd L.), Esko, T. (Tõnu), Go, M.J. (Min Jin), Harris, S.E. (Sarah), Hartiala, J. (Jaana), Kasela, S. (Silva), Kasturiratne, A. (Anuradhani), Khor, C.C., Kleber, M.E. (Marcus), Li, H. (Huaixing), Mok, Z.Y. (Zuan Yu), Nakatochi, M. (Masahiro), Sapari, N.S. (Nur Sabrina), Saxena, R. (Richa), Stewart, A.F. (Alexandre F.), Stolk, L. (Lisette), Tabara, Y. (Yasuharu), Teh, A.L. (Ai Ling), Wu, Y. (Ying), Wu, J.-Y. (Jer-Yuarn), Zhang, Y. (Yi), Aits, I. (Imke), Da Silva Couto Alves, A. (Alexessander), Das, S. (Shikta), Dorajoo, R. (Rajkumar), CHopewell, J. (Jemma), Kim, Y.K. (Yun Kyoung), WKoivula, R. (Robert), Luan, J. (Jian'An), Lyytikäinen, L.-P. (Leo-Pekka), NNguyen, Q. (Quang), Pereira, M.A. (Mark A), Postmus, D. (Douwe), TRaitakari, O. (Olli), Scannell Bryan, M. (Molly), Scott, R.A. (Robert), Sorice, R., Tragante, V. (Vinicius), Traglia, M. (Michela), White, J. (Jon), Yamamoto, K. (Ken), Zhang, Y. (Yonghong), Adair, L.S. (Linda), Ahmed, A. (Alauddin), Akiyama, K. (Koichi), Asif, R. (Rasheed), Aung, T. (Tin), Barroso, I.E. (Inês), Bjonnes, A. (Andrew), Braun, T.R. (Timothy R.), Cai, H. (Hui), Chang, L.-C. (Li-Ching), Chen, C.-H., Cheng, C-Y. (Ching-Yu), Chong, Y.-S. (Yap-Seng), Collins, F.S. (Francis), Courtney, R. (Regina), Davies, G. (Gail), Delgado, G., Do, L.D. (Loi D.), Doevendans, P.A. (Pieter), Gansevoort, R.T. (Ron), Gao, Y. (Ying), Grammer, T.B. (Tanja B), Grarup, N. (Niels), Grewal, J. (Jagvir), Gu, D. (D.), SWander, G. (Gurpreet), Hartikainen, A.L., Hazen, S.L. (Stanley), He, J. (Jing), Heng, C.K. (Chew-Kiat), Hixso, E.J.A. (E. James Ames), Hofman, A. (Albert), Hsu, C. (Chris), Huang, W. (Wei), Husemoen, L.L.N. (Lise Lotte), Hwang, J.-Y. (Joo-Yeon), Ichihara, S. (Sahoko), Igase, M. (Michiya), Isono, M. (Masato), Justesen, J.M. (Johanne M.), Katsuya, T. (Tomohiro), GKibriya, M. (Muhammad), Kim, Y.J., Kishimoto, M. (Miyako), Koh, W.-P. (Woon-Puay), Kohara, K. (Katsuhiko), Kumari, M. (Meena), Kwek, K. (Kenneth), Lee, N.R. (Nanette), Lee, J. (Jeannette), Liao, J. (Jie), Lieb, W. (Wolfgang), Liewald, D.C.M. (David), Matsubara, T. (Tatsuaki), Matsushita, Y. (Yumi), Meitinger, T. (Thomas), Mihailov, E. (Evelin), Milani, L. (Lili), Mills, R. (Rebecca), Mononen, K. (Kari), Müller-Nurasyid, M. (Martina), Nabika, T. (Toru), Nakashima, E. (Eitaro), Ng, H.K. (Hong Kiat), Nikus, K. (Kjell), Nutile, T., Ohkubo, T. (Takayoshi), Ohnaka, K. (Keizo), Parish, S. (Sarah), Paternoster, L. (Lavinia), Peng, H. (Hao), Peters, A. (Annette), TPham, S. (Son), Pinidiyapathirage, M.J. (Mohitha J.), Rahman, M. (Mahfuzar), Rakugi, H. (Hiromi), Rolandsson, O. (Olov), Rozario, M.A. (Michelle Ann), Ruggiero, D., Sala, C. (Cinzia), Sarju, R. (Ralhan), Shimokawa, K. (Kazuro), Snieder, H. (Harold), Sparsø, T. (Thomas), Spiering, W. (Wilko), Starr, J.M. (John), Stott, D.J. (David J.), OStram, D. (Daniel), Sugiyama, T. (Takao), Szymczak, S. (Silke), Tang, W.H.W. (W.H. Wilson), Tong, L. (Lin), Trompet, S. (Stella), Turjanmaa, V. (Väinö), Ueshima, H. (Hirotsugu), Uitterlinden, A.G. (André), Umemura, S. (Satoshi), Vaarasmaki, M. (Marja), Dam, R.M. (Rob Mvan), Gilst, W.H. (Wiek) van, Veldhuisen, D.J. (Dirk) van, Viikari, J. (Jorma), Waldenberger, M. (Melanie), Wang, Y. (Yiqin), Wang, A. (Aili), Wilson, R. (Rory), Wong, T.Y. (Tien Yin), Xiang, Y.-B. (Yong-Bing), Yamaguchi, S. (Shuhei), Ye, X. (Xingwang), Young, R. (Robin), Young, T.L. (Terri), Yuan, J.-M. (Jian-Min), Zhou, X. (Xueya), Asselbergs, F.W. (Folkert), Ciullo, M., Clarke, R. (Robert), Deloukas, P. (Panagiotis), Franke, A. (Andre), Paul, W.F. (W. Frank), Franks, S. (Steve), Friedlander, Y. (Yechiel), Gross, M.D. (Myron D.), Guo, Z. (Zhirong), Hansen, T. (T.), Jarvelin, M.-R. (Marjo-Riitta), Jørgensen, T. (Torben), Jukema, J.W. (Jan Wouter), Kähönen, M. (Mika), Kajio, H. (Hiroshi), Kivimaki, M. (Mika), Lee, J.-Y. (Jong-Young), Lehtimäki, T. (Terho), Linneberg, A. (Allan), Miki, T. (Tetsuro), Pedersen, O. (Oluf), Samani, N.J. (Nilesh), Sørensen, T.I.A. (Thorkild), Takayanagi, R. (Ryoichi), Toniolo, D. (Daniela), Ahsan, H. (Habibul), Allayee, H. (Hooman), Chen, Y.-T. (Yuan-Tsong), Danesh, J. (John), Deary, I.J. (Ian), Franco, O.H. (Oscar), Franke, L. (Lude), THeijman, B. (Bastiaan), Holbrook, J.D. (Joanna D.), Isaacs, A.J. (Aaron), Kim, B.-J. (Bong-Jo), Lin, X. (Xu), Liu, J. (Jianjun), März, W. (Winfried), Metspalu, A. (Andres), Mohlke, K.L. (Karen), Sangher, K., Harambir, D. (Dharambir), Shu, X.-O. (Xiao-Ou), Meurs, J.B.J. (Joyce) van, Vithana, E.N. (Eranga), Wickremasinghe, A.R. (Ananda), Wijmenga, C. (Cisca), Wolffenbuttel, B.H.W. (Bruce H.W.), Yokota, M. (Mitsuhiro), Zheng, W. (Wei), Zhu, D. (Dingliang), Vineis, P. (Paolo), Kyrtopoulos, S.A. (Soterios A.), Kleinjans, J.C.S. (Jos C.S.), McCarthy, M.I. (Mark), Soong, R. (Richie), Gieger, C. (Christian), Scott, J. (James), Teo, Y.Y. (Yik Ying), He, J. (Jiang), Elliott, P. (Paul), Tai, E.S. (Shyong), Harst, P. (Pim) van der, Kooner, J.S. (Jaspal S.), Chambers, J.C. (John), Kato, N. (Norihiro), Loh, M. (Marie), Takeuchi, F. (Fumihiko), Verweij, N. (Niek), Wang, X. (Xu), Zhang, W. (Weihua), NKelly, T. (Tanika), Saleheen, D., Lehne, B. (Benjamin), Leach, I.M. (Irene Mateo), Drong, A. (Alexander), Abbott, J. (James), Wahl, S. (Simone), Tan, S.-T. (Sian-Tsung), Scott, W.R. (William R.), Campanella, G. (Gianluca), Chadeau-Hyam, M. (Marc), Afzal, U. (Uzma), Ahluwalia, T.S. (Tarunveer Singh), Bonder, M.J. (Marc), Chen, P. (Ping), Dehghan, A. (Abbas), Edwards, T.L. (Todd L.), Esko, T. (Tõnu), Go, M.J. (Min Jin), Harris, S.E. (Sarah), Hartiala, J. (Jaana), Kasela, S. (Silva), Kasturiratne, A. (Anuradhani), Khor, C.C., Kleber, M.E. (Marcus), Li, H. (Huaixing), Mok, Z.Y. (Zuan Yu), Nakatochi, M. (Masahiro), Sapari, N.S. (Nur Sabrina), Saxena, R. (Richa), Stewart, A.F. (Alexandre F.), Stolk, L. (Lisette), Tabara, Y. (Yasuharu), Teh, A.L. (Ai Ling), Wu, Y. (Ying), Wu, J.-Y. (Jer-Yuarn), Zhang, Y. (Yi), Aits, I. (Imke), Da Silva Couto Alves, A. (Alexessander), Das, S. (Shikta), Dorajoo, R. (Rajkumar), CHopewell, J. (Jemma), Kim, Y.K. (Yun Kyoung), WKoivula, R. (Robert), Luan, J. (Jian'An), Lyytikäinen, L.-P. (Leo-Pekka), NNguyen, Q. (Quang), Pereira, M.A. (Mark A), Postmus, D. (Douwe), TRaitakari, O. (Olli), Scannell Bryan, M. (Molly), Scott, R.A. (Robert), Sorice, R., Tragante, V. (Vinicius), Traglia, M. (Michela), White, J. (Jon), Yamamoto, K. (Ken), Zhang, Y. (Yonghong), Adair, L.S. (Linda), Ahmed, A. (Alauddin), Akiyama, K. (Koichi), Asif, R. (Rasheed), Aung, T. (Tin), Barroso, I.E. (Inês), Bjonnes, A. (Andrew), Braun, T.R. (Timothy R.), Cai, H. (Hui), Chang, L.-C. (Li-Ching), Chen, C.-H., Cheng, C-Y. (Ching-Yu), Chong, Y.-S. (Yap-Seng), Collins, F.S. (Francis), Courtney, R. (Regina), Davies, G. (Gail), Delgado, G., Do, L.D. (Loi D.), Doevendans, P.A. (Pieter), Gansevoort, R.T. (Ron), Gao, Y. (Ying), Grammer, T.B. (Tanja B), Grarup, N. (Niels), Grewal, J. (Jagvir), Gu, D. (D.), SWander, G. (Gurpreet), Hartikainen, A.L., Hazen, S.L. (Stanley), He, J. (Jing), Heng, C.K. (Chew-Kiat), Hixso, E.J.A. (E. James Ames), Hofman, A. (Albert), Hsu, C. (Chris), Huang, W. (Wei), Husemoen, L.L.N. (Lise Lotte), Hwang, J.-Y. (Joo-Yeon), Ichihara, S. (Sahoko), Igase, M. (Michiya), Isono, M. (Masato), Justesen, J.M. (Johanne M.), Katsuya, T. (Tomohiro), GKibriya, M. (Muhammad), Kim, Y.J., Kishimoto, M. (Miyako), Koh, W.-P. (Woon-Puay), Kohara, K. (Katsuhiko), Kumari, M. (Meena), Kwek, K. (Kenneth), Lee, N.R. (Nanette), Lee, J. (Jeannette), Liao, J. (Jie), Lieb, W. (Wolfgang), Liewald, D.C.M. (David), Matsubara, T. (Tatsuaki), Matsushita, Y. (Yumi), Meitinger, T. (Thomas), Mihailov, E. (Evelin), Milani, L. (Lili), Mills, R. (Rebecca), Mononen, K. (Kari), Müller-Nurasyid, M. (Martina), Nabika, T. (Toru), Nakashima, E. (Eitaro), Ng, H.K. (Hong Kiat), Nikus, K. (Kjell), Nutile, T., Ohkubo, T. (Takayoshi), Ohnaka, K. (Keizo), Parish, S. (Sarah), Paternoster, L. (Lavinia), Peng, H. (Hao), Peters, A. (Annette), TPham, S. (Son), Pinidiyapathirage, M.J. (Mohitha J.), Rahman, M. (Mahfuzar), Rakugi, H. (Hiromi), Rolandsson, O. (Olov), Rozario, M.A. (Michelle Ann), Ruggiero, D., Sala, C. (Cinzia), Sarju, R. (Ralhan), Shimokawa, K. (Kazuro), Snieder, H. (Harold), Sparsø, T. (Thomas), Spiering, W. (Wilko), Starr, J.M. (John), Stott, D.J. (David J.), OStram, D. (Daniel), Sugiyama, T. (Takao), Szymczak, S. (Silke), Tang, W.H.W. (W.H. Wilson), Tong, L. (Lin), Trompet, S. (Stella), Turjanmaa, V. (Väinö), Ueshima, H. (Hirotsugu), Uitterlinden, A.G. (André), Umemura, S. (Satoshi), Vaarasmaki, M. (Marja), Dam, R.M. (Rob Mvan), Gilst, W.H. (Wiek) van, Veldhuisen, D.J. (Dirk) van, Viikari, J. (Jorma), Waldenberger, M. (Melanie), Wang, Y. (Yiqin), Wang, A. (Aili), Wilson, R. (Rory), Wong, T.Y. (Tien Yin), Xiang, Y.-B. (Yong-Bing), Yamaguchi, S. (Shuhei), Ye, X. (Xingwang), Young, R. (Robin), Young, T.L. (Terri), Yuan, J.-M. (Jian-Min), Zhou, X. (Xueya), Asselbergs, F.W. (Folkert), Ciullo, M., Clarke, R. (Robert), Deloukas, P. (Panagiotis), Franke, A. (Andre), Paul, W.F. (W. Frank), Franks, S. (Steve), Friedlander, Y. (Yechiel), Gross, M.D. (Myron D.), Guo, Z. (Zhirong), Hansen, T. (T.), Jarvelin, M.-R. (Marjo-Riitta), Jørgensen, T. (Torben), Jukema, J.W. (Jan Wouter), Kähönen, M. (Mika), Kajio, H. (Hiroshi), Kivimaki, M. (Mika), Lee, J.-Y. (Jong-Young), Lehtimäki, T. (Terho), Linneberg, A. (Allan), Miki, T. (Tetsuro), Pedersen, O. (Oluf), Samani, N.J. (Nilesh), Sørensen, T.I.A. (Thorkild), Takayanagi, R. (Ryoichi), Toniolo, D. (Daniela), Ahsan, H. (Habibul), Allayee, H. (Hooman), Chen, Y.-T. (Yuan-Tsong), Danesh, J. (John), Deary, I.J. (Ian), Franco, O.H. (Oscar), Franke, L. (Lude), THeijman, B. (Bastiaan), Holbrook, J.D. (Joanna D.), Isaacs, A.J. (Aaron), Kim, B.-J. (Bong-Jo), Lin, X. (Xu), Liu, J. (Jianjun), März, W. (Winfried), Metspalu, A. (Andres), Mohlke, K.L. (Karen), Sangher, K., Harambir, D. (Dharambir), Shu, X.-O. (Xiao-Ou), Meurs, J.B.J. (Joyce) van, Vithana, E.N. (Eranga), Wickremasinghe, A.R. (Ananda), Wijmenga, C. (Cisca), Wolffenbuttel, B.H.W. (Bruce H.W.), Yokota, M. (Mitsuhiro), Zheng, W. (Wei), Zhu, D. (Dingliang), Vineis, P. (Paolo), Kyrtopoulos, S.A. (Soterios A.), Kleinjans, J.C.S. (Jos C.S.), McCarthy, M.I. (Mark), Soong, R. (Richie), Gieger, C. (Christian), Scott, J. (James), Teo, Y.Y. (Yik Ying), He, J. (Jiang), Elliott, P. (Paul), Tai, E.S. (Shyong), Harst, P. (Pim) van der, Kooner, J.S. (Jaspal S.), and Chambers, J.C. (John)

Abstract

We carried out a trans-ancestry genome-wide association and replication study of blood pressure phenotypes among up to 320,251 individuals of East Asian, European and South Asian ancestry. We find genetic variants at 12 new loci to be associated with blood pressure (P = 3.9 × 10 -11 to 5.0 × 10 -21). The sentinel blood pressure SNPs are enriched for association with DNA methylation at multiple nearby CpG sites, suggesting that, at some of the loci identified, DNA methylation may lie on the regulatory pathway linking sequence variation to blood pressure. The sentinel SNPs at the 12 new loci point to genes involved in vascular smooth muscle (IGFBP3, KCNK3, PDE3A and PRDM6) and renal (ARHGAP24, OSR1, SLC22A7 and TBX2) function. The new and known genetic variants predict increased left ventricular mass, circulating levels of NT-proBNP, and cardiovascular and all-cause mortality (P = 0.04 to 8.6 × 10 -6). Our results provide new evidence for the role of DNA methylation in blood pressure regulation.

Published
2015
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39. MC3R gene polymorphisms are associated with early childhood adiposity gain and infant appetite in an Asian population

Author

Aris, I. M., primary, Tint, M. T., additional, Teh, A. L., additional, Holbrook, J. D., additional, Quah, P. L., additional, Chong, M. F.‐F., additional, Lin, X., additional, Soh, S. E., additional, Saw, S.‐M., additional, Kwek, K., additional, Godfrey, K. M., additional, Gluckman, P. D., additional, Chong, Y. S., additional, Lek, N., additional, Yap, F., additional, and Lee, Y. S., additional

Published
2015
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41. Maternal sensitivity, infant limbic structure volume and functional connectivity: a preliminary study

Author

Rifkin-Graboi, A, primary, Kong, L, additional, Sim, L W, additional, Sanmugam, S, additional, Broekman, B F P, additional, Chen, H, additional, Wong, E, additional, Kwek, K, additional, Saw, S-M, additional, Chong, Y-S, additional, Gluckman, P D, additional, Fortier, M V, additional, Pederson, D, additional, Meaney, M J, additional, and Qiu, A, additional

Published
2015
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43. Maternal hyperglycemia in singleton pregnancies conceived by IVF may be modified by first-trimester BMI.

Author

Cai, S., Natarajan, P., Chan, J. K. Y., Wong, P. C., Tan, K. H., Godfrey, K. M., Gluckman, P. D., Shek, L. P. C., Yap, F., Kramer, M. S., Chan, S. Y., and Chong, Y. S.

Subjects
HYPERGLYCEMIA prevention, HYPERGLYCEMIA treatment, FERTILIZATION in vitro, FIRST trimester of pregnancy, BODY mass index, PREVENTION of pregnancy complications, GESTATIONAL diabetes, PREVENTION, GLUCOSE tolerance tests, RESEARCH funding, CROSS-sectional method
Abstract

Study Question: Does IVF independently increase the risk of gestational diabetes mellitus (GDM) and is this increase in risk modified by maternal body mass index?Summary Answer: IVF appears to be an independent risk factor for GDM and elevated blood glucose levels in overweight women (BMI > 25 kg/m2).What Is Known Already: IVF has been associated with increased risk of GDM, but most previous studies did not adequately assess confounding or effect modification by other risk factors.Study Design, Size, Duration: Cross-sectional study using data from 1089 women with singleton pregnancies who participated in a Singaporean birth cohort study (GUSTO) and received a 75 g oral glucose tolerance test (OGTT) at 26-28 weeks gestation.Participants/materials, Setting, Methods: A total of 1089 women (n = 1013 conceived spontaneously, n = 76 conceived through IVF) with singleton pregnancies received a 75 g OGTT at 26-28 weeks gestation. Fasting and 2 h postprandial blood glucose levels were assayed. World Health Organization criteria (1999) standard criteria were used to classify GDM: ≥7.0 mmol/L for fasting and/or ≥7.8 mmol/L for 2-h postprandial plasma glucose levels, which was the clinical guideline in use during the study.Main Results and the Role Of Chance: IVF pregnancies had nearly double the odds of GDM (OR = 1.83, 95% CI: 1.03-3.26) and elevated fasting (mean difference = 0.12 mmol/L, 95% CI: 0.00-0.24) and OGTT 2-h blood glucose levels (mean difference = 0.64 mmol/L, 95% CI: 0.27-1.01), after adjusting for commonly recognized risk factors for GDM. After stratification by first-trimester BMI, these increased risks of GDM (OR = 3.54, 95% CI: 1.44-8.72) and elevated fasting (mean difference = 0.39 mmol/L, 95% CI: 0.13-0.65) and 2-h blood (mean difference = 1.24 mmol/L, 95% CI: 0.56-1.91) glucose levels were significant only in the IVF group who is also overweight or obese (BMI > 25 kg/m2).Limitations Reasons For Caution: One limitation of our study is the absence of a 1 h post-OGTT plasma glucose sample, as we were using the 1999 WHO diagnostic criteria (the clinical guideline in Singapore) at the time of our study, instead of the revised 2013 WHO diagnostic criteria. Our cohort may not be representative of the general Singapore obstetric population, although participants were recruited from the two largest maternity hospitals in the country and include both private and subsidized patients.Wider Implications Of the Findings: IVF appears to be an independent risk factor for GDM and elevated blood glucose levels in overweight women. Our findings reinforce the need to advise overweight or obese women contemplating IVF to lose weight before the procedure to reduce their risk of GDM and hyperglycemia-related adverse outcomes arising therefrom. In settings where universal GDM screening is not routine, overweight or obese women who conceive by IVF should be screened.Study Funding/competing Interest(s): This research was supported by the Singapore National Research Foundation under its Translational and Clinical Research (TCR) Flagship Program and administered by the Singapore Ministry of Health's National Medical Research Council (NMRC), Singapore (NMRC/TCR/004-NUS/2008; NMRC/TCR/012-NUHS/2014). Additional funding was provided by the Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research (A*STAR). K.M.G. and Y.S.C. have received lecture fees from Nestle Nutrition Institute and Danone, respectively. K.M.G., Y.S.C. and S.Y.C. are part of an academic consortium that has received research funding from Abbott Nutrition, Nestec and Danone. The other authors have nothing to disclose. The other authors have nothing to disclose.Trial Registration Number: N/A. [ABSTRACT FROM AUTHOR]

Published
2017
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47. Which anthropometric measures best reflect neonatal adiposity?

Author

Chen, L-W, Tint, M-T, Fortier, M V, Aris, I M, Shek, L P-C, Tan, K H, Chan, S-Y, Gluckman, P D, Chong, Y-S, Godfrey, K M, Rajadurai, V S, Yap, F, Kramer, M S, and Lee, Y S

Abstract

Background:Studying the determinants and the long-term consequences of fetal adipose accretion requires accurate assessment of neonatal body composition. In large epidemiological studies, in-depth body composition measurement methods are usually not feasible for cost and logistical reasons, and there is a need to identify anthropometric measures that adequately reflect neonatal adiposity.Methods:In a multiethnic Asian mother–offspring cohort in Singapore, anthropometric measures (weight, length, abdominal circumference, skinfold thicknesses) were measured using standardized protocols in newborn infants, and anthropometric indices (weight/length, weight/length2(body mass index, BMI), weight/length3(ponderal index, PI)) derived. Neonatal total adiposity was measured using air displacement plethysmography (ADP) and abdominal adiposity using magnetic resonance imaging (MRI). Correlations of the anthropometric measures with ADP- and MRI-based adiposity were assessed using Pearson’s correlation coefficients (rp), including in subsamples stratified by sex and ethnicity.Results:Study neonates (n=251) had a mean (s.d.) age of 10.2 (2.5) days. Correlations between ADP-based fat mass (ADPFM) and anthropometric measures were moderate (rprange: 0.44–0.67), with the strongest being with weight/length, weight, BMI and sum of skinfolds (rp=0.67, 0.66, 0.62, 0.62, respectively, all P<0.01). All anthropometric measures except skinfold thicknesses correlated more strongly with ADP-based fat-free mass than ADPFM, indicating that skinfold measures may have more discriminative power in terms of neonatal total body adiposity. For MRI-based measures, weight and weight/length consistently showed strong positive correlations (rp?0.7) with abdominal adipose tissue compartments. These correlations were consistent in boys and girls, across different ethnic groups, and when conventional determinants of neonatal adiposity were adjusted for potential confounding. Abdominal circumference was not strongly associated with ADPFMor abdominal fat mass.Conclusions:Simple anthropometric measures (weight and weight/length) correlated strongly with neonatal adiposity, with some evidence for greater discriminative power for skinfold measures. These simple measures could be of value in large epidemiological studies.

Published
2018
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