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590 results on '"Lowe JR"'

3. Negative association of acetate with visceral adipose tissue and insulin levels

Author

Layden BT, Yalamanchi SK, Wolever TMS, Dunaif A, and Lowe Jr WL

Subjects
Specialties of internal medicine, RC581-951
Abstract

Brian T Layden1, Sudha K Yalamanchi1, Thomas MS Wolever2, Andrea Dunaif1, William L Lowe Jr11Division of Endocrinology, Metabolism and Molecular Medicine (BTL, SKY, AD, WLL), Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA; 2Department of Nutritional Sciences (TMSW), University of Toronto, Toronto, CanadaBackground: The composition of gut flora has been proposed as a cause of obesity, a major risk factor for type 2 diabetes. The objective of this study was to assess whether serum short chain fatty acids, a major by-product of fermentation in gut flora, are associated with obesity and/or diabetes-related traits (insulin sensitivity and secretion).Methods: The association of serum short chain fatty acids levels with measures of obesity was assessed using body mass index, computerized tomography scan, and dual photon X-ray absorptiometry scan. Insulin sensitivity and insulin secretion were both determined from an oral glucose tolerance test and insulin sensitivity was also determined from a hyperinsulinemic euglycemic clamp.Results: In this population of young, obese women, acetate was negatively associated with visceral adipose tissue determined by computerized tomography scan and dual photon X-ray absorptiometry scan, but not body mass index. The level of the short chain fatty acids acetate, but not propionate or butyrate, was also negatively associated with fasting serum insulin and 2 hour insulin levels in the oral glucose tolerance test.Conclusions: In this population, serum acetate was negatively associated with visceral adipose tissue and insulin levels. Future studies need to verify these findings and expand on these observations in larger cohorts of subjects.Keywords: obesity, insulin, gut flora, short chain fatty acids

Published
2012

4. Estimating Gaussian graphical models of multi-study data with Multi-Study Factor Analysis

Author

Shutta, Katherine H., Scholtens, Denise M., Lowe Jr., William L., Balasubramanian, Raji, and De Vito, Roberta

Subjects
Statistics - Methodology
Abstract

Network models are powerful tools for gaining new insights from complex biological data. Most lines of investigation in biology involve comparing datasets in the setting where the same predictors are measured across multiple studies or conditions (multi-study data). Consequently, the development of statistical tools for network modeling of multi-study data is a highly active area of research. Multi-study factor analysis (MSFA) is a method for estimation of latent variables (factors) in multi-study data. In this work, we generalize MSFA by adding the capacity to estimate Gaussian graphical models (GGMs). Our new tool, MSFA-X, is a framework for latent variable-based graphical modeling of shared and study-specific signals in multi-study data. We demonstrate through simulation that MSFA-X can recover shared and study-specific GGMs and outperforms a graphical lasso benchmark. We apply MSFA-X to analyze maternal response to an oral glucose tolerance test in targeted metabolomic profiles from the Hyperglycemia and Adverse Pregnancy Outcomes (HAPO) Study, identifying network-level differences in glucose metabolism between women with and without gestational diabetes mellitus.

Published
2022

6. Second international consensus report on gaps and opportunities for the clinical translation of precision diabetes medicine

Author

Tobias, Deirdre K., Merino, Jordi, Ahmad, Abrar, Aiken, Catherine, Benham, Jamie L., Bodhini, Dhanasekaran, Clark, Amy L., Colclough, Kevin, Corcoy, Rosa, Cromer, Sara J., Duan, Daisy, Felton, Jamie L., Francis, Ellen C., Gillard, Pieter, Gingras, Véronique, Gaillard, Romy, Haider, Eram, Hughes, Alice, Ikle, Jennifer M., Jacobsen, Laura M., Kahkoska, Anna R., Kettunen, Jarno L. T., Kreienkamp, Raymond J., Lim, Lee-Ling, Männistö, Jonna M. E., Massey, Robert, Mclennan, Niamh-Maire, Miller, Rachel G., Morieri, Mario Luca, Most, Jasper, Naylor, Rochelle N., Ozkan, Bige, Patel, Kashyap Amratlal, Pilla, Scott J., Prystupa, Katsiaryna, Raghavan, Sridharan, Rooney, Mary R., Schön, Martin, Semnani-Azad, Zhila, Sevilla-Gonzalez, Magdalena, Svalastoga, Pernille, Takele, Wubet Worku, Tam, Claudia Ha-ting, Thuesen, Anne Cathrine B., Tosur, Mustafa, Wallace, Amelia S., Wang, Caroline C., Wong, Jessie J., Yamamoto, Jennifer M., Young, Katherine, Amouyal, Chloé, Andersen, Mette K., Bonham, Maxine P., Chen, Mingling, Cheng, Feifei, Chikowore, Tinashe, Chivers, Sian C., Clemmensen, Christoffer, Dabelea, Dana, Dawed, Adem Y., Deutsch, Aaron J., Dickens, Laura T., DiMeglio, Linda A., Dudenhöffer-Pfeifer, Monika, Evans-Molina, Carmella, Fernández-Balsells, María Mercè, Fitipaldi, Hugo, Fitzpatrick, Stephanie L., Gitelman, Stephen E., Goodarzi, Mark O., Grieger, Jessica A., Guasch-Ferré, Marta, Habibi, Nahal, Hansen, Torben, Huang, Chuiguo, Harris-Kawano, Arianna, Ismail, Heba M., Hoag, Benjamin, Johnson, Randi K., Jones, Angus G., Koivula, Robert W., Leong, Aaron, Leung, Gloria K. W., Libman, Ingrid M., Liu, Kai, Long, S. Alice, Lowe, Jr, William L., Morton, Robert W., Motala, Ayesha A., Onengut-Gumuscu, Suna, Pankow, James S., Pathirana, Maleesa, Pazmino, Sofia, Perez, Dianna, Petrie, John R., Powe, Camille E., Quinteros, Alejandra, Jain, Rashmi, Ray, Debashree, Ried-Larsen, Mathias, Saeed, Zeb, Santhakumar, Vanessa, Kanbour, Sarah, Sarkar, Sudipa, Monaco, Gabriela S. F., Scholtens, Denise M., Selvin, Elizabeth, Sheu, Wayne Huey-Herng, Speake, Cate, Stanislawski, Maggie A., Steenackers, Nele, Steck, Andrea K., Stefan, Norbert, Støy, Julie, Taylor, Rachael, Tye, Sok Cin, Ukke, Gebresilasea Gendisha, Urazbayeva, Marzhan, Van der Schueren, Bart, Vatier, Camille, Wentworth, John M., Hannah, Wesley, White, Sara L., Yu, Gechang, Zhang, Yingchai, Zhou, Shao J., Beltrand, Jacques, Polak, Michel, Aukrust, Ingvild, de Franco, Elisa, Flanagan, Sarah E., Maloney, Kristin A., McGovern, Andrew, Molnes, Janne, Nakabuye, Mariam, Njølstad, Pål Rasmus, Pomares-Millan, Hugo, Provenzano, Michele, Saint-Martin, Cécile, Zhang, Cuilin, Zhu, Yeyi, Auh, Sungyoung, de Souza, Russell, Fawcett, Andrea J., Gruber, Chandra, Mekonnen, Eskedar Getie, Mixter, Emily, Sherifali, Diana, Eckel, Robert H., Nolan, John J., Philipson, Louis H., Brown, Rebecca J., Billings, Liana K., Boyle, Kristen, Costacou, Tina, Dennis, John M., Florez, Jose C., Gloyn, Anna L., Gomez, Maria F., Gottlieb, Peter A., Greeley, Siri Atma W., Griffin, Kurt, Hattersley, Andrew T., Hirsch, Irl B., Hivert, Marie-France, Hood, Korey K., Josefson, Jami L., Kwak, Soo Heon, Laffel, Lori M., Lim, Siew S., Loos, Ruth J. F., Ma, Ronald C. W., Mathieu, Chantal, Mathioudakis, Nestoras, Meigs, James B., Misra, Shivani, Mohan, Viswanathan, Murphy, Rinki, Oram, Richard, Owen, Katharine R., Ozanne, Susan E., Pearson, Ewan R., Perng, Wei, Pollin, Toni I., Pop-Busui, Rodica, Pratley, Richard E., Redman, Leanne M., Redondo, Maria J., Reynolds, Rebecca M., Semple, Robert K., Sherr, Jennifer L., Sims, Emily K., Sweeting, Arianne, Tuomi, Tiinamaija, Udler, Miriam S., Vesco, Kimberly K., Vilsbøll, Tina, Wagner, Robert, Rich, Stephen S., and Franks, Paul W.

Published
2023
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8. Genetic effects on the timing of parturition and links to fetal birth weight

Author

Solé-Navais, Pol, Flatley, Christopher, Steinthorsdottir, Valgerdur, Vaudel, Marc, Juodakis, Julius, Chen, Jing, Laisk, Triin, LaBella, Abigail L., Westergaard, David, Bacelis, Jonas, Brumpton, Ben, Skotte, Line, Borges, Maria C., Helgeland, Øyvind, Mahajan, Anubha, Wielscher, Matthias, Lin, Frederick, Briggs, Catherine, Wang, Carol A., Moen, Gunn-Helen, Beaumont, Robin N., Bradfield, Jonathan P., Abraham, Abin, Thorleifsson, Gudmar, Gabrielsen, Maiken E., Ostrowski, Sisse R., Modzelewska, Dominika, Nohr, Ellen A., Hypponen, Elina, Srivastava, Amit, Talbot, Octavious, Allard, Catherine, Williams, Scott M., Menon, Ramkumar, Shields, Beverley M., Sveinbjornsson, Gardar, Xu, Huan, Melbye, Mads, Lowe, Jr, William, Bouchard, Luigi, Oken, Emily, Pedersen, Ole B., Gudbjartsson, Daniel F., Erikstrup, Christian, Sørensen, Erik, Lie, Rolv T., Teramo, Kari, Hallman, Mikko, Juliusdottir, Thorhildur, Hakonarson, Hakon, Ullum, Henrik, Hattersley, Andrew T., Sletner, Line, Merialdi, Mario, Rifas-Shiman, Sheryl L., Steingrimsdottir, Thora, Scholtens, Denise, Power, Christine, West, Jane, Nyegaard, Mette, Capra, John A., Skogholt, Anne H., Magnus, Per, Andreassen, Ole A., Thorsteinsdottir, Unnur, Grant, Struan F. A., Qvigstad, Elisabeth, Pennell, Craig E., Hivert, Marie-France, Hayes, Geoffrey M., Jarvelin, Marjo-Riitta, McCarthy, Mark I., Lawlor, Deborah A., Nielsen, Henriette S., Mägi, Reedik, Rokas, Antonis, Hveem, Kristian, Stefansson, Kari, Feenstra, Bjarke, Njolstad, Pål, Muglia, Louis J., Freathy, Rachel M., Johansson, Stefan, Zhang, Ge, and Jacobsson, Bo

Published
2023
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10. Early emergence of sexual dimorphism in offspring leukocyte telomere length was associated with maternal and children’s glucose metabolism—a longitudinal study

Author

Wong, Kwun Kiu, Cheng, Feifei, Lim, Cadmon K. P., Tam, Claudia H. T., Tutino, Greg, Yuen, Lai Yuk, Wang, Chi Chiu, Hou, Yong, Chan, Michael H. M., Ho, Chung Shun, Joglekar, Mugdha V., Hardikar, Anandwardhan A., Jenkins, Alicia J., Metzger, Boyd E., Lowe, Jr., William L., Tam, Wing Hung, and Ma, Ronald C. W.

Published
2022
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12. Diet2Vec: Multi-scale analysis of massive dietary data

Author

Tansey, Wesley, Lowe Jr., Edward W., and Scott, James G.

Subjects
Statistics - Machine Learning, Computer Science - Learning, Statistics - Applications
Abstract

Smart phone apps that enable users to easily track their diets have become widespread in the last decade. This has created an opportunity to discover new insights into obesity and weight loss by analyzing the eating habits of the users of such apps. In this paper, we present diet2vec: an approach to modeling latent structure in a massive database of electronic diet journals. Through an iterative contract-and-expand process, our model learns real-valued embeddings of users' diets, as well as embeddings for individual foods and meals. We demonstrate the effectiveness of our approach on a real dataset of 55K users of the popular diet-tracking app LoseIt\footnote{http://www.loseit.com/}. To the best of our knowledge, this is the largest fine-grained diet tracking study in the history of nutrition and obesity research. Our results suggest that diet2vec finds interpretable results at all levels, discovering intuitive representations of foods, meals, and diets., Comment: Accepted to the NIPS 2016 Workshop on Machine Learning for Health

Published
2016

13. Author Correction: Genetic effects on the timing of parturition and links to fetal birth weight

Author

Solé-Navais, Pol, Flatley, Christopher, Steinthorsdottir, Valgerdur, Vaudel, Marc, Juodakis, Julius, Chen, Jing, Laisk, Triin, LaBella, Abigail L., Westergaard, David, Bacelis, Jonas, Brumpton, Ben, Skotte, Line, Borges, Maria C., Helgeland, Øyvind, Mahajan, Anubha, Wielscher, Matthias, Lin, Frederick, Briggs, Catherine, Wang, Carol A., Moen, Gunn-Helen, Beaumont, Robin N., Bradfield, Jonathan P., Abraham, Abin, Thorleifsson, Gudmar, Gabrielsen, Maiken E., Ostrowski, Sisse R., Modzelewska, Dominika, Nohr, Ellen A., Hypponen, Elina, Srivastava, Amit, Talbot, Octavious, Allard, Catherine, Williams, Scott M., Menon, Ramkumar, Shields, Beverley M., Sveinbjornsson, Gardar, Xu, Huan, Melbye, Mads, Lowe, Jr, William, Bouchard, Luigi, Oken, Emily, Pedersen, Ole B., Gudbjartsson, Daniel F., Erikstrup, Christian, Sørensen, Erik, Lie, Rolv T., Teramo, Kari, Hallman, Mikko, Juliusdottir, Thorhildur, Hakonarson, Hakon, Ullum, Henrik, Hattersley, Andrew T., Sletner, Line, Merialdi, Mario, Rifas-Shiman, Sheryl L., Steingrimsdottir, Thora, Scholtens, Denise, Power, Christine, West, Jane, Nyegaard, Mette, Capra, John A., Skogholt, Anne H., Magnus, Per, Andreassen, Ole A., Thorsteinsdottir, Unnur, Grant, Struan F. A., Qvigstad, Elisabeth, Pennell, Craig E., Hivert, Marie-France, Hayes, Geoffrey M., Jarvelin, Marjo-Riitta, McCarthy, Mark I., Lawlor, Deborah A., Nielsen, Henriette S., Mägi, Reedik, Rokas, Antonis, Hveem, Kristian, Stefansson, Kari, Feenstra, Bjarke, Njolstad, Pål, Muglia, Louis J., Freathy, Rachel M., Johansson, Stefan, Zhang, Ge, and Jacobsson, Bo

Published
2023
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15. Higher maternal adiposity reduces offspring birthweight if associated with a metabolically favourable profile

Author

Thompson, William D., Beaumont, Robin N., Kuang, Alan, Warrington, Nicole M., Ji, Yingjie, Tyrrell, Jessica, Wood, Andrew R., Scholtens, Denise M., Knight, Bridget A., Evans, David M., Lowe, Jr, William L., Santorelli, Gillian, Azad, Rafaq, Mason, Dan, Hattersley, Andrew T., Frayling, Timothy M., Yaghootkar, Hanieh, Borges, Maria Carolina, Lawlor, Deborah A., and Freathy, Rachel M.

Published
2021
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18. All thresholds of maternal hyperglycaemia from the WHO 2013 criteria for gestational diabetes identify women with a higher genetic risk for type 2 diabetes [version 3; peer review: 1 approved, 2 approved with reservations]

Author

Alice E. Hughes, M. Geoffrey Hayes, Aoife M. Egan, Kashyap A. Patel, Denise M. Scholtens, Lynn P. Lowe, William L. Lowe Jr, Fidelma P. Dunne, Andrew T. Hattersley, and Rachel M. Freathy

Subjects
Medicine, Science
Abstract

Background: Using genetic scores for fasting plasma glucose (FPG GS) and type 2 diabetes (T2D GS), we investigated whether the fasting, 1-hour and 2-hour glucose thresholds from the WHO 2013 criteria for gestational diabetes (GDM) have different implications for genetic susceptibility to raised fasting glucose and type 2 diabetes in women from the Hyperglycemia and Adverse Pregnancy Outcome (HAPO) and Atlantic Diabetes in Pregnancy (DIP) studies. Methods: Cases were divided into three subgroups: (i) FPG ≥5.1 mmol/L only, n=222; (ii) 1-hour glucose post 75 g oral glucose load ≥10 mmol/L only, n=154 (iii) 2-hour glucose ≥8.5 mmol/L only, n=73; and (iv) both FPG ≥5.1 mmol/L and either of a 1-hour glucose ≥10 mmol/L or 2-hour glucose ≥8.5 mmol/L, n=172. We compared the FPG and T2D GS of these groups with controls (n=3,091) in HAPO and DIP separately. Results: In HAPO and DIP, the mean FPG GS in women with a FPG ≥5.1 mmol/L, either on its own or with 1-hour glucose ≥10 mmol/L or 2-hour glucose ≥8.5 mmol/L, was higher than controls (all P

Published
2021
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21. Maternal glucose levels during pregnancy and childhood adiposity in the Hyperglycemia and Adverse Pregnancy Outcome Follow-up Study

Author

Lowe, Jr, William L., Lowe, Lynn P., Kuang, Alan, Catalano, Patrick M., Nodzenski, Michael, Talbot, Octavious, Tam, Wing-Hung, Sacks, David A., McCance, David, Linder, Barbara, Lebenthal, Yael, Lawrence, Jean M., Lashley, Michele, Josefson, Jami L., Hamilton, Jill, Deerochanawong, Chaicharn, Clayton, Peter, Brickman, Wendy J., Dyer, Alan R., Scholtens, Denise M., Metzger, Boyd E., and on behalf of the HAPO Follow-up Study Cooperative Research Group

Published
2019
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25. Heritability of obesity-related traits among Nigerians, Jamaicans and US black people

Author

Luke, A, Guo, X, Adeyemo, AA, Wilks, R, Forrester, T, Lowe Jr, W, Comuzzie, AG, Martin, LJ, Zhu, X, Rotimi, CN, and Cooper, RS

Subjects
Biomedical and Clinical Sciences, Nutrition and Dietetics, Health Sciences, Nutrition, Obesity, Cardiovascular, Stroke, Metabolic and endocrine, Cancer, Adult, Anthropometry, Black People, Body Constitution, Body Mass Index, Cross-Cultural Comparison, Electric Impedance, Environment, Female, Humans, Jamaica, Leptin, Male, Nigeria, United States, Epidemiology, Genetics, Genetic Testing, Adiposity, Gene Expression, Gene-Environment Interaction, Genetic Association Studies, Genotype, Inheritance Patterns, Models, Genetic, Phenotype, Receptors, Leptin, Sex Factors, Siblings, Young Adult, Medical and Health Sciences, Education, Endocrinology & Metabolism, Biomedical and clinical sciences, Health sciences
Abstract

"Quantile-dependent expressivity" occurs when the effect size of a genetic variant depends upon whether the phenotype (e.g., leptin) is high or low relative to its distribution. Leptin concentrations are strongly related to adiposity, whose heritability is quantile dependent. Whether inheritance of leptin concentrations is quantile dependent, and whether this explains the greater heritability in women than men in accordance with their greater adiposity, and explains other gene-environment interactions, remains to be determined. Therefore, leptin and leptin receptor concentrations from 3068 siblings in 1133 sibships from the Framingham Heart Study Third Generation Cohort were analyzed. Free leptin index (FLI) was calculated as the ratio of leptin to soluble leptin receptor concentrations. Full-sib (βFS) regression slopes were robustly estimated by quantile regression with nonparametric significance assigned from 1000 bootstrap samples. The analyses showed βFS increased significantly with increasing percentiles of the offspring's age- and sex-adjusted leptin distribution (Plinear = 0.0001), which was accelerated at the higher concentrations (Pquadratic = 0.0003). βFS at the 90th percentile (0.418 ± 0.066) was 4.7-fold greater than at the 10th percentile (0.089 ± 0.032, Pdifference = 3.6 × 10-6). Consistent with quantile-dependent expressivity, the βFS was greater in female sibs, which was attributable to their higher leptin concentrations. Reported gene-environment interactions involving adiposity and LEP, LEPR, MnSOD, PPARγ, PPARγ2, and IRS-1 polymorphisms were consistent with quantile-dependent expressivity of leptin concentrations. βFS for leptin receptor concentrations and free leptin index also increased significantly with increasing percentiles of their distributions (Plinear = 0.04 and Plinear = 8.5 × 10-6, respectively). In conclusion, inherited genetic and shared environmental effects on leptin concentrations were quantile dependent, which likely explains male-female differences in heritability and some gene-environment interactions.

Published
2001

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

Author

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

Published
2019
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29. Fetal alleles predisposing to metabolically favorable adiposity are associated with higher birth weight

Author

William D Thompson, Robin N Beaumont, Alan Kuang, Nicole M Warrington, Yingjie Ji, Jessica Tyrrell, Andrew R Wood, Denise M Scholtens, Bridget A Knight, David M Evans, William L Lowe Jr, Gillian Santorelli, Raq Azad, Dan Mason, Andrew T Hattersley, Timothy M Frayling, Hanieh Yaghootkar, Maria Carolina Borges, Deborah A Lawlor, and Rachel M Freathy

Subjects
Adult, General Medicine, Polymorphism, Single Nucleotide, Body Mass Index, Genetics, Birth Weight, Humans, Genetic Predisposition to Disease, Obesity, Molecular Biology, Alleles, Genetics (clinical), Adiposity, Genome-Wide Association Study
Abstract

Background Higher birthweight is associated with higher adult body mass index (BMI). Alleles that predispose to greater adult adiposity might act in fetal life to increase fetal growth and birthweight. Whether there are fetal effects of recently identified adult metabolically favorable adiposity alleles on birthweight is unknown. Aim We aimed to test the effect on birthweight of fetal genetic predisposition to higher metabolically favorable adult adiposity and compare that with the effect of fetal genetic predisposition to higher adult BMI. Methods We used published genome wide association study data (n = upto 406 063) to estimate fetal effects on birthweight (adjusting for maternal genotype) of alleles known to raise metabolically favorable adult adiposity or BMI. We combined summary data across single nucleotide polymorphisms (SNPs) with random effects meta-analyses. We performed weighted linear regression of SNP-birthweight effects against SNP-adult adiposity effects to test for a dose-dependent association. Results Fetal genetic predisposition to higher metabolically favorable adult adiposity and higher adult BMI were both associated with higher birthweight (3 g per effect allele (95% CI: 1–5) averaged over 14 SNPs; P = 0.002; 0.5 g per effect allele (95% CI: 0–1) averaged over 76 SNPs; P = 0.042, respectively). SNPs with greater effects on metabolically favorable adiposity tended to have greater effects on birthweight (R2 = 0.2912, P = 0.027). There was no dose-dependent association for BMI (R2 = −0.0019, P = 0.602). Conclusions Fetal genetic predisposition to both higher adult metabolically favorable adiposity and BMI is associated with birthweight. Fetal effects of metabolically favorable adiposity-raising alleles on birthweight are modestly proportional to their effects on future adiposity, but those of BMI-raising alleles are not.

Published
2021

30. Maternal and Cord Blood Serum Metabolite Associations with Childhood Adiposity and Body Composition Outcomes.

Author

Bianco, Monica E., Vu, My H., Bain, James R., Muehlbauer, Michael J., Ilkayeva, Olga R., Scholtens, Denise M., Josefson, Jami, and Lowe Jr., William L.

Subjects
BODY composition, CORD blood, HYPERGLYCEMIA, OBESITY, PREGNANCY outcomes, BODY mass index
Abstract

Maternal metabolites influence the size of newborns independently of maternal body mass index (BMI) and glycemia, highlighting the importance of maternal metabolism on offspring outcomes. This study examined associations of maternal metabolites during pregnancy with childhood adiposity, and cord blood metabolites with childhood adiposity using phenotype and metabolomic data from the Hyperglycemia and Adverse Pregnancy Outcome (HAPO) Study and the HAPO Follow-Up Study. The maternal metabolites analyses included 2324 mother–offspring pairs, while the cord blood metabolites analyses included 937 offspring. Multiple logistic and linear regression were used to examine associations between primary predictors, maternal or cord blood metabolites, and childhood adiposity outcomes. Multiple maternal fasting and 1 hr metabolites were significantly associated with childhood adiposity outcomes in Model 1 but were no longer significant after adjusting for maternal BMI and/or maternal glycemia. In the fully adjusted model, fasting lactose levels were negatively associated with child BMI z-scores and waist circumference, while fasting urea levels were positively associated with waist circumference. One-hour methionine was positively associated with fat-free mass. There were no significant associations between cord blood metabolites and childhood adiposity outcomes. Few metabolites were associated with childhood adiposity outcomes after adjusting for maternal BMI and glucose, suggesting that maternal BMI accounts for the association between maternal metabolites and childhood adiposity. [ABSTRACT FROM AUTHOR]

Published
2023
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32. Introduction to the BioChemical Library (BCL): An Application-Based Open-Source Toolkit for Integrated Cheminformatics and Machine Learning in Computer-Aided Drug Discovery

Author

Brown, Benjamin P., Vu, Oanh, Geanes, Alexander R., Kothiwale, Sandeepkumar, Butkiewicz, Mariusz, Lowe Jr., Edward W., Mueller, Ralf, Pape, Richard, Mendenhall, Jeffrey, Meiler, Jens, Brown, Benjamin P., Vu, Oanh, Geanes, Alexander R., Kothiwale, Sandeepkumar, Butkiewicz, Mariusz, Lowe Jr., Edward W., Mueller, Ralf, Pape, Richard, Mendenhall, Jeffrey, and Meiler, Jens

Abstract

The BioChemical Library (BCL) cheminformatics toolkit is an application-based academic open-source software package designed to integrate traditional small molecule cheminformatics tools with machine learning-based quantitative structure-activity/ property relationship (QSAR/QSPR) modeling. In this pedagogical article we provide a detailed introduction to core BCL cheminformatics functionality, showing how traditional tasks (e.g., computing chemical properties, estimating druglikeness) can be readily combined with machine learning. In addition, we have included multiple examples covering areas of advanced use, such as reaction-based library design. We anticipate that this manuscript will be a valuable resource for researchers in computer-aided drug discovery looking to integrate modular cheminformatics and machine learning tools into their pipelines.

Published
2022

33. Genetics and Epigenetics: Implications for the Life Course of Gestational Diabetes.

Author

Lowe Jr., William L.

Subjects
*GESTATIONAL diabetes, *GENETICS, *TYPE 2 diabetes, *EPIGENOMICS, *EPIGENETICS, *LINCRNA, *PREGNANCY complications, *GENOME-wide association studies
Abstract

Gestational diabetes (GDM) is one of the most common complications of pregnancy, affecting as many as one in six pregnancies. It is associated with both short- and long-term adverse outcomes for the mother and fetus and has important implications for the life course of affected women. Advances in genetics and epigenetics have not only provided new insight into the pathophysiology of GDM but have also provided new approaches to identify women at high risk for progression to postpartum cardiometabolic disease. GDM and type 2 diabetes share similarities in their pathophysiology, suggesting that they also share similarities in their genetic architecture. Candidate gene and genome-wide association studies have identified susceptibility genes that are shared between GDM and type 2 diabetes. Despite these similarities, a much greater effect size for MTNR1B in GDM compared to type 2 diabetes and association of HKDC1, which encodes a hexokinase, with GDM but not type 2 diabetes suggest some differences in the genetic architecture of GDM. Genetic risk scores have shown some efficacy in identifying women with a history of GDM who will progress to type 2 diabetes. The association of epigenetic changes, including DNA methylation and circulating microRNAs, with GDM has also been examined. Targeted and epigenome-wide approaches have been used to identify DNA methylation in circulating blood cells collected during early, mid-, and late pregnancy that is associated with GDM. DNA methylation in early pregnancy had some ability to identify women who progressed to GDM, while DNA methylation in blood collected at 26–30 weeks gestation improved upon the ability of clinical factors alone to identify women at risk for progression to abnormal glucose tolerance post-partum. Finally, circulating microRNAs and long non-coding RNAs that are present in early or mid-pregnancy and associated with GDM have been identified. MicroRNAs have also proven efficacious in predicting both the development of GDM as well as its long-term cardiometabolic complications. Studies performed to date have demonstrated the potential for genetic and epigenetic technologies to impact clinical care, although much remains to be done. [ABSTRACT FROM AUTHOR]

Published
2023
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34. Association of Maternal Metabolites and Metabolite Networks with Newborn Outcomes in a Multi-Ancestry Cohort.

Author

Gleason, Brooke, Kuang, Alan, Bain, James R., Muehlbauer, Michael J., Ilkayeva, Olga R., Scholtens, Denise M., and Lowe Jr., William L.

Subjects
NEWBORN infants, BIRTH size, GESTATIONAL diabetes, METABOLITES, PREGNANCY outcomes, GLUCOSE tolerance tests
Abstract

The in utero environment is important for newborn size at birth, which is associated with childhood adiposity. We examined associations between maternal metabolite levels and newborn birthweight, sum of skinfolds (SSF), and cord C-peptide in a multinational and multi-ancestry cohort of 2337 mother–newborn dyads. Targeted and untargeted metabolomic assays were performed on fasting and 1 h maternal serum samples collected during an oral glucose tolerance test performed at 24–32 week gestation in women participating in the Hyperglycemia and Adverse Pregnancy Outcome (HAPO) Study. Anthropometric measurements were obtained on newborns at birth. Following adjustment for maternal BMI and glucose, per-metabolite analyses demonstrated significant associations between maternal metabolite levels and birthweight, SSF, and cord C-peptide. In the fasting state, triglycerides were positively associated and several long-chain acylcarnitines were inversely associated with birthweight and SSF. At 1 h, additional metabolites including branched-chain amino acids, proline, and alanine were positively associated with newborn outcomes. Network analyses demonstrated distinct clusters of inter-connected metabolites significantly associated with newborn phenotypes. In conclusion, numerous maternal metabolites during pregnancy are significantly associated with newborn birthweight, SSF, and cord C-peptide independent of maternal BMI and glucose, suggesting that metabolites in addition to glucose contribute to newborn size at birth and adiposity. [ABSTRACT FROM AUTHOR]

Published
2023
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37. Fetal alleles predisposing to metabolically favorable adiposity are associated with higher birth weight

Author

Thompson, William D, primary, Beaumont, Robin N, additional, Kuang, Alan, additional, Warrington, Nicole M, additional, Ji, Yingjie, additional, Tyrrell, Jessica, additional, Wood, Andrew R, additional, Scholtens, Denise M, additional, Knight, Bridget A, additional, Evans, David M, additional, Lowe Jr, William L, additional, Santorelli, Gillian, additional, Azad, Raq, additional, Mason, Dan, additional, Hattersley, Andrew T, additional, Frayling, Timothy M, additional, Yaghootkar, Hanieh, additional, Borges, Maria Carolina, additional, Lawlor, Deborah A, additional, and Freathy, Rachel M, additional

Published
2021
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39. Newborn Adiposity and Cord Blood C-Peptide as Mediators of the Maternal Metabolic Environment and Childhood Adiposity

Author

the HAPO Follow-up Study Cooperative Research Group, Boyd E. Metzger, William L. Lowe, Jr., Lucia C. Petito, Alan R. Dyer, Lynn P. Lowe, Patrick M. Catalano, Alan Kuang, Denise M. Scholtens, and Jami L. Josefson

Abstract

OBJECTIVE Excessive childhood adiposity is a risk factor for adverse metabolic health. The objective was to investigate associations of newborn body composition and cord C-peptide with childhood anthropometrics and explore whether these newborn measures mediate associations of maternal mid-pregnancy glucose and BMI with childhood adiposity. RESEARCH DESIGN AND METHODS Data on mother/offspring pairs (N=4832) from the epidemiological Hyperglycemia and Adverse Pregnancy Outcome (HAPO) Study and HAPO Follow Up Study (HAPO FUS) were analyzed. Linear regression was used to study associations between newborn and childhood anthropometrics. Structural equation modeling was used to explore newborn anthropometric measures as potential mediators of the associations of maternal BMI and glucose during pregnancy with childhood anthropometric outcomes. RESULTS In models including maternal glucose and BMI adjustments, newborn adiposity as measured by sum of skinfolds was associated with child outcomes (adjusted mean difference, 95% CI, p-value) BMI(0.26,0.12-0.39, CONCLUSIONS Newborn adiposity is independently associated with childhood adiposity and, along with fetal hyperinsulinemia, mediates, in part, associations of maternal glucose and BMI with childhood adiposity.

Published
2021

41. Network Approaches to Integrate Analyses of Genetics and Metabolomics Data with Applications to Fetal Programming Studies.

Author

Kuang, Alan, Hayes, M. Geoffrey, Hivert, Marie-France, Balasubramanian, Raji, Lowe Jr., William L., and Scholtens, Denise M.

Subjects
GENOME-wide association studies, BAYESIAN analysis, GENETICS, PREGNANCY outcomes, METABOLOMICS, GENE regulatory networks, PHENOTYPES
Abstract

The integration of genetics and metabolomics data demands careful accounting of complex dependencies, particularly when modelling familial omics data, e.g., to study fetal programming of related maternal–offspring phenotypes. Efforts to identify genetically determined metabotypes using classic genome wide association approaches have proven useful for characterizing complex disease, but conclusions are often limited to a series of variant–metabolite associations. We adapt Bayesian network models to integrate metabotypes with maternal–offspring genetic dependencies and metabolic profile correlations in order to investigate mechanisms underlying maternal–offspring phenotypic associations. Using data from the multiethnic Hyperglycemia and Adverse Pregnancy Outcome (HAPO) study, we demonstrate that the strategic specification of ordered dependencies, pre-filtering of candidate metabotypes, incorporation of metabolite dependencies, and penalized network estimation methods clarify potential mechanisms for fetal programming of newborn adiposity and metabolic outcomes. The exploration of Bayesian network growth over a range of penalty parameters, coupled with interactive plotting, facilitate the interpretation of network edges. These methods are broadly applicable to integration of diverse omics data for related individuals. [ABSTRACT FROM AUTHOR]

Published
2022
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42. All thresholds of maternal hyperglycaemia from the WHO 2013 criteria for gestational diabetes identify women with a higher genetic risk for type 2 diabetes

Author

Hughes, Alice E., primary, Hayes, M. Geoffrey, additional, Egan, Aoife M., additional, Patel, Kashyap A., additional, Scholtens, Denise M., additional, Lowe, Lynn P., additional, Lowe Jr, William L., additional, Dunne, Fidelma P., additional, Hattersley, Andrew T., additional, and Freathy, Rachel M., additional

Published
2021
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43. Genetic Loci and Physiologic Pathways Involved in Gestational Diabetes Implicated Through Clustering

Author

Marie-France Hivert, Jose C. Florez, Denise Scholtens, William L. Lowe Jr, Luigi Bouchard, Patrice Perron, Alisa K. Manning, Alan Kuang, Catherine Allard, Sarah Hsu, Miriam S. Udler, Camille E. Powe, and Ada Admin

Subjects
endocrine system diseases, nutritional and metabolic diseases, female genital diseases and pregnancy complications
Abstract

Hundreds of common genetic variants acting through distinguishable physiologic pathways influence the risk of type 2 diabetes (T2D). It is unknown to what extent the physiology underlying gestational diabetes (GDM) is distinct from that underlying T2D. In this study of over 5,000 pregnant women from three cohorts, we aimed to identify physiologically related groups of maternal variants associated with GDM using two complementary approaches based on Bayesian non-negative matrix factorization (bNMF) clustering. First, we tested five bNMF clusters of maternal T2D-associated variants grouped based on physiology outside of pregnancy for association with GDM. We found that cluster polygenic scores representing genetic determinants of reduced beta-cell function and abnormal hepatic lipid metabolism were associated with GDM; these clusters were not associated with infant birthweight. Second, we derived bNMF clusters of maternal variants based on pregnancy physiology and tested these clusters for association with GDM. We identified a cluster which was strongly associated with GDM and also associated with higher infant birthweight. The effect size for this cluster’s association with GDM appeared greater than that for T2D. Our findings imply that the genetic and physiologic pathways that lead to GDM differ, at least in part, from those that lead to T2D.

Published
2020

45. Identification of HKDC1 and BACE2 as genes influencing glycemic traits during pregnancy through genome-wide association studies

Author

Hayes, M. Geoffrey, Urbanek, Margrit, Hivert, Marie-France, Armstrong, Loren L., Morrison, Jean, Guo, Cong, Lowe, Lynn P., Scheftner, Douglas A., Pluzhnikov, Anna, Levine, David M., McHugh, Caitlin P., Ackerman, Christine M., Bouchard, Luigi, Brisson, Diane, Layden, Brian T., Mirel, Daniel, Doheny, Kimberly F., Leya, Marysa V., Lown-Hecht, Rachel N., Dyer, Alan R., Metzger, Boyd E., Reddy, Timothy E., Cox, Nancy J., and Lowe, Jr., William L.

Subjects
Diabetes in pregnancy -- Risk factors -- Genetic aspects -- Research, Genetic susceptibility -- Research, Glucose metabolism -- Physiological aspects -- Genetic aspects -- Research, Health
Abstract

Maternal metabolism during pregnancy impacts the developing fetus, affecting offspring birth weight and adiposity. This has important implications for metabolic health later in life (e.g., offspring of mothers with pre-existing or gestational diabetes mellitus have an increased risk of metabolic disorders in childhood). To identify genetic loci associated with measures of maternal metabolism obtained during an oral glucose tolerance test at ~28 weeks' gestation, we performed a genome-wide association study of 4,437 pregnant mothers of European (n = 1,367), Thai (n = 1,178), Afro-Caribbean (n = 1,075), and Hispanic (n = 817) ancestry, along with replication of top signals in three additional European ancestry cohorts. In addition to identifying associations with genes previously implicated with measures of glucose metabolism in nonpregnant populations, we identified two novel genome-wide significant associations: 2-h plasma glucose and HKDC1, and fasting C-peptide and BACE2. These results suggest that the genetic architecture underlying glucose metabolism may differ, in part, in pregnancy., The intrauterine milieu of the developing fetus, as determined largely by maternal metabolism, impacts both fetal and later health outcomes. Offspring of mothers with pre-existing or gestational diabetes mellitus (GDM) [...]

Published
2013
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46. All thresholds of maternal hyperglycaemia from the WHO 2013 criteria for gestational diabetes identify women with a higher genetic risk for type 2 diabetes

Author

Hughes, Alice E., primary, Hayes, M. Geoffrey, additional, Egan, Aoife M., additional, Patel, Kashyap A., additional, Scholtens, Denise M., additional, Lowe, Lynn P., additional, Lowe Jr, William L., additional, Dunne, Fidelma P., additional, Hattersley, Andrew T., additional, and Freathy, Rachel M., additional

Published
2020
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48. Women with isolated fasting hyperglycemia in pregnancy are at a higher genetic risk for type 2 diabetes

Author

Hughes, Alice E, Hayes, M. Geoffrey, Egan, Aoife M, Patel, Kashyap A, Scholtens, Denise M, Lowe, Lynn P, Lowe Jr, William L, Dunne, Fidelma P, Hattersley, Andrew T, and Freathy, Rachel M

Subjects
endocrine system diseases, nutritional and metabolic diseases
Abstract

Using genetic scores for fasting plasma glucose (FPG GS) and Type 2 diabetes (T2D GS), we investigated whether different diagnostic criteria for gestational diabetes (GDM) have different implications for genetic susceptibility to later T2D in women from the the Hyperglycemia and Adverse Pregnancy Outcome (HAPO) and Atlantic Diabetes in Pregnancy (DIP) studies. To investigate different features of WHO (1999) and IADPSG (2010) criteria for diagnosing GDM, cases were divided into three subgroups: (i) FPG ≥5.1 mmol/L only, n=261; (ii) 2-hour glucose post 75 g oral glucose load ≥7.8 mmol/L only, n=305); and (iii) both FPG ≥5.1 mmol/L and 2-hour glucose ≥7.8 mmol/L, n=135. We compared the FPG and T2D genetic scores of these groups with controls (n=3,083). GDM as defined by fasting hyperglycemia was associated with a higher genetic score for both FPG and T2D, whether or not 2-hour glucose was also raised. GDM defined only by 2-hour hyperglycemia, was associated with a higher T2D GS, but not a higher FPG GS. The T2D GS was similar whether the fasting glucose alone, 2-hour glucose alone, or both fasting and 2-hour glucose were raised. Thus, the newest criteria identify women with a genetic predisposition to T2D who may not have been identified by previous criteria.

Published
2019
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