@article{sohn_kibbe_dioli_hector_bai_garrard_muddiman_2024, title={A statistical approach to system suitability testing for mass spectrometry imaging}, volume={38}, ISSN={["1097-0231"]}, DOI={10.1002/rcm.9725}, abstractNote={RATIONALE
Mass spectrometry imaging (MSI) elevates the power of conventional mass spectrometry (MS) to multidimensional space, elucidating both chemical composition and localization. However, the field lacks any robust quality control (QC) and/or system suitability testing (SST) protocols to monitor inconsistencies during data acquisition, both of which are integral to ensure the validity of experimental results. To satisfy this demand in the community, we propose an adaptable QC/SST approach with five analyte options amendable to various ionization MSI platforms (e.g., desorption electrospray ionization, matrix-assisted laser desorption/ionization [MALDI], MALDI-2, and infrared matrix-assisted laser desorption electrospray ionization [IR-MALDESI]).


METHODS
A novel QC mix was sprayed across glass slides to collect QC/SST regions-of-interest (ROIs). Data were collected under optimal conditions and on a compromised instrument to construct and refine the principal component analysis (PCA) model in R. Metrics, including mass measurement accuracy and spectral accuracy, were evaluated, yielding an individual suitability score for each compound. The average of these scores is utilized to inform if troubleshooting is necessary.


RESULTS
The PCA-based SST model was applied to data collected when the instrument was compromised. The resultant SST scores were used to determine a statistically significant threshold, which was defined as 0.93 for IR-MALDESI-MSI analyses. This minimizes the type-I error rate, where the QC/SST would report the platform to be in working condition when cleaning is actually necessary. Further, data scored after a partial cleaning demonstrate the importance of QC and frequent full instrument cleaning.


CONCLUSIONS
This study is the starting point for addressing an important issue and will undergo future development to improve the efficiency of the protocol. Ultimately, this work is the first of its kind and proposes this approach as a proof of concept to develop and implement universal QC/SST protocols for a variety of MSI platforms.}, number={9}, journal={RAPID COMMUNICATIONS IN MASS SPECTROMETRY}, author={Sohn, Alexandria L. and Kibbe, Russell R. and Dioli, Olivia E. and Hector, Emily C. and Bai, Hongxia and Garrard, Kenneth P. and Muddiman, David C.}, year={2024}, month={May} }
 @article{huang_hector_cape_mckennan_2023, title={A statistical framework for GWAS of high dimensional phenotypes using summary statistics, with application to metabolite GWAS}, volume={arXiv:2303.10221}, journal={arXiv}, author={Huang, Weiqiong and Hector, Emily C. and Cape, Joshua and McKennan, Chris}, year={2023} }
 @article{kim_ghosh_hector_2023, title={Bayesian estimation of clustered dependence structures in functional neuroconnectivity}, volume={arXiv:2305.18044}, journal={arXiv}, author={Kim, Hyoshin and Ghosh, Sujit and Hector, Emily C.}, year={2023} }
 @article{hector_reich_2023, title={Distributed Inference for Spatial Extremes Modeling in High Dimensions}, volume={4}, ISSN={["1537-274X"]}, url={https://doi.org/10.1080/01621459.2023.2186886}, DOI={10.1080/01621459.2023.2186886}, abstractNote={Extreme environmental events frequently exhibit spatial and temporal dependence. These data are often modeled using max stable processes (MSPs). MSPs are computationally prohibitive to fit for as few as a dozen observations, with supposed computationally-efficient approaches like the composite likelihood remaining computationally burdensome with a few hundred observations. In this paper, we propose a spatial partitioning approach based on local modeling of subsets of the spatial domain that delivers computationally and statistically efficient inference. Marginal and dependence parameters of the MSP are estimated locally on subsets of observations using censored pairwise composite likelihood, and combined using a modified generalized method of moments procedure. The proposed distributed approach is extended to estimate spatially varying coefficient models to deliver computationally efficient modeling of spatial variation in marginal parameters. We demonstrate consistency and asymptotic normality of estimators, and show empirically that our approach leads to a surprising reduction in bias of parameter estimates over a full data approach. We illustrate the flexibility and practicability of our approach through simulations and the analysis of streamflow data from the U.S. Geological Survey.}, journal={JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION}, publisher={Taylor & Francis}, author={Hector, Emily C. and Reich, Brian J.}, year={2023}, month={Apr} }
 @article{hector_eloyan_2023, title={Distributed model building and recursive integration for big spatial data modeling}, journal={arXiv:2305.15951}, author={Hector, Emily C. and Eloyan, Ani}, year={2023} }
 @article{hector_2023, title={Fused mean structure learning in data integration with dependence}, volume={10}, ISSN={["1708-945X"]}, DOI={10.1002/cjs.11797}, abstractNote={Motivated by image‐on‐scalar regression with data aggregated across multiple sites, we consider a setting in which multiple independent studies each collect multiple dependent vector outcomes, with potential mean model parameter homogeneity between studies and outcome vectors. To determine the validity of a joint analysis of these data sources, we must learn which of them share mean model parameters. We propose a new model fusion approach that delivers improved flexibility and statistical performance over existing methods. Our proposed approach specifies a quadratic inference function within each data source and fuses mean model parameter vectors in their entirety based on a new formulation of a pairwise fusion penalty. We establish theoretical properties of our estimator and propose an asymptotically equivalent weighted oracle meta‐estimator that is more computationally efficient. Simulations and an application to the ABIDE neuroimaging consortium highlight the flexibility of the proposed approach. An R package is provided for ease of implementation.}, journal={CANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE}, author={Hector, Emily C.}, year={2023}, month={Oct} }
 @article{hector_2023, title={Fused mean structure learning in data integration with dependence}, volume={arXiv:2210.02198}, journal={To appear in The Canadian Journal of Statistics}, author={Hector, Emily C.}, year={2023} }
 @article{hector_luo_song_2023, title={Parallel-and-stream accelerator for computationally fast supervised learning}, volume={177}, ISSN={["1872-7352"]}, url={https://doi.org/10.1016/j.csda.2022.107587}, DOI={10.1016/j.csda.2022.107587}, abstractNote={Two dominant distributed computing strategies have emerged to overcome the computational bottleneck of supervised learning with big data: parallel data processing in the MapReduce paradigm and serial data processing in the online streaming paradigm. Despite the two strategies' common divide-and-combine approach, they differ in how they aggregate information, leading to different trade-offs between statistical and computational performance. The authors propose a new hybrid paradigm, termed a Parallel-and-Stream Accelerator (PASA), that uses the strengths of both strategies for computationally fast and statistically efficient supervised learning. PASA's architecture nests online streaming processing into each distributed and parallelized data process in a MapReduce framework. PASA leverages the advantages and mitigates the disadvantages of both the MapReduce and online streaming approaches to deliver a more flexible paradigm satisfying practical computing needs. The authors study the analytic properties and computational complexity of PASA, and detail its implementation for two key statistical learning tasks. PASA's performance is illustrated through simulations and a large-scale data example building a prediction model for online purchases from advertising data.}, journal={COMPUTATIONAL STATISTICS & DATA ANALYSIS}, author={Hector, Emily C. and Luo, Lan and Song, Peter X. -K.}, year={2023}, month={Jan} }
 @article{twiddy_hector_dubljević_2023, title={Perceived Invasiveness and Therapeutic Acceptability of Transcranial Magnetic Stimulation}, url={https://doi.org/10.1080/21507740.2022.2150710}, DOI={10.1080/21507740.2022.2150710}, abstractNote={rier opening in amyotrophic lateral sclerosis using MRguided focused ultrasound. Nature Communications 10(1): 4373. doi:10.1038/s41467-019-12426-9. Bluhm, R., M. Cortright, E. D. Achtyes, and L. Y. Cabrera. 2023. “They are invasive in different ways.”: Stakeholders’ perceptions of the invasiveness of psychiatric electroceutical interventions. AJOB Neuroscience 14(1): 1–12. doi:10. 1080/21507740.2021.1958098. Coates McCall, I., N. Minielly, A. Bethune, N. Lipsman, P. J. McDonald, and J. Illes. 2020. Readiness for first-inhuman neuromodulatory interventions. The Canadian Journal of Neurological Sciences 47(6): 785–92. doi:10. 1017/cjn.2020.113. Cole, J., M. N. Sohn, A. D. Harris, S. L. Bray, S. B. Patten, and A. McGirr. 2022. Efficacy of adjunctive D-cycloserine to intermittent theta-burst stimulation for major depressive disorder: A randomized clinical trial. JAMA Psychiatry 2022: e223255. doi:10.1001/jamapsychiatry.2022.3255. Elias, W. J., N. Lipsman, W. G. Ondo, P. Ghanouni, Y. G. Kim, W. Lee, M. Schwartz, K. Hynynen, A. M. Lozano, and B. B. Shah. 2016. A randomized trial of focused ultrasound thalamotomy for essential tremor. The New England Journal of Medicine 375(8): 730–9. doi:10.1056/ NEJMoa1600159. Kondziolka, D., J. G. Ong, J. Y. Lee, R. Y. Moore, J. C. Flickinger, and L. D. Lunsford. 2008. Gamma Knife thalamotomy for essential tremor. Journal of Neurosurgery 108(1): 111–7. doi:10.3171/JNS/2008/108/01/0111. Lipsman, N., Y. Meng, A. J. Bethune, Y. Huang, B. Lam, M. Masellis, N. Herrmann, C. Heyn, I. Aubert, and A. Boutet. 2018. Blood-brain barrier opening in Alzheimer’s disease using MR-guided focused ultrasound. Nature Communications 9(1): 2336. doi:10.1038/s41467-018-04529-6. Mureb, M., D. Golub, C. Benjamin, J. Gurewitz, B. A. Strickland, G. Zada, and D. Kondziolka. 2020. Earlier radiosurgery leads to better pain relief and less medication usage for trigeminal neuralgia patients: An international multicenter study. Journal of Neurosurgery 2020: 1–8. doi:10.3171/2020.4.JNS192780. Silk, E. M., Diwan, T. Rabelo, H. Katzman, A. C. P. Campos, F. V. Gouveia, P. Giacobbe, N. Lipsman, and C. Hamani. 2022. Serotonin 5-HT1B receptors mediate the antidepressantand anxiolytic-like effects of ventromedial prefrontal cortex deep brain stimulation in a mouse model of social defeat. Psychopharmacology 239(12): 3875–92. doi:10.1007/s00213-022-06259-6.}, journal={AJOB Neuroscience}, author={Twiddy, Jack and Hector, Emily C. and Dubljević, Veljko}, year={2023}, month={Jan} }
 @article{luo_wang_hector_2023, title={Rejoinder: 'Statistical inference for streamed longitudinal data'}, volume={110}, ISSN={["1464-3510"]}, DOI={10.1093/biomet/asad051}, abstractNote={Journal Article Rejoinder: ‘Statistical inference for streamed longitudinal data’ Get access Lan Luo, Lan Luo Department of Biostatistics and Epidemiology, Rutgers School of Public Health, 683 Hoes Lane West, Piscataway, New Jersey 08854, U.S.A.l.luo@rutgers.edu https://orcid.org/0000-0002-7901-2148 Search for other works by this author on: Oxford Academic Google Scholar Jingshen Wang, Jingshen Wang Division of Biostatistics, University of California, Berkeley, 2121 Berkeley Way, Berkeley, California 94720, U.S.A.jingshenwang@berkeley.edu https://orcid.org/0000-0002-1432-3834 Search for other works by this author on: Oxford Academic Google Scholar Emily C Hector Emily C Hector Department of Statistics, North Carolina State University, 2311 Stinson Drive, Raleigh, North Carolina 27695, U.S.A Email: ehector@ncsu.edu https://orcid.org/0000-0003-1488-3150 Search for other works by this author on: Oxford Academic Google Scholar Biometrika, Volume 110, Issue 4, December 2023, Pages 871–874, https://doi.org/10.1093/biomet/asad051 Published: 15 November 2023 Article history Received: 31 August 2023 Editorial decision: 04 September 2023 Published: 15 November 2023}, number={4}, journal={BIOMETRIKA}, author={Luo, Lan and Wang, Jingshen and Hector, Emily C.}, year={2023}, month={Nov}, pages={871–874} }
 @article{shi_wank_chen_wang_liu_hector_song_2023, title={Sleep Classification With Artificial Synthetic Imaging Data Using Convolutional Neural Networks}, volume={27}, ISSN={["2168-2208"]}, url={https://doi.org/10.1109/JBHI.2022.3210485}, DOI={10.1109/JBHI.2022.3210485}, abstractNote={Objective: We propose a new analytic framework, “Artificial Synthetic Imaging Data (ASID) Workflow,” for sleep classification from a wearable device comprising: 1) the creation of ASID from data collected by a non-invasive wearable device that permits real-time multi-modal physiological monitoring on heart rate (HR), 3-axis accelerometer, electrodermal activity, and skin temperature, denoted as “Temporal E4 Data” (TED) and 2) the use of an image classification supervised learning algorithm, convolutional neural network (CNN), to classify periods of sleep. Methods: We investigate ASID Workflow under 6 settings (3 data resolutions × 2 HR scenarios). Competing machine/deep learning classification algorithms, including logistic regression, support vector machine, random forest, k-nearest neighbors, and Long Short-Term Memory, are applied to TED as comparisons, termed “Competing Workflow.” Results: The ASID Workflow achieves excellent performance with mean weighted accuracy across settings of 94.7%, and is superior to the Competing Workflow with high and low resolution data regardless of the inclusion of HR modality. This superiority is maximized for low resolution data without HR. Additionally, CNN has a relatively low subject-wise test computational cost compared with competing algorithms. Conclusion: We demonstrate the utility of creating ASID from multi-modal physiological data and applying a preexisting image classification algorithm to achieve better classification accuracy. We shed light on the influence of data resolution and HR modality on the Workflow's performance. Significance: Applying CNN to ASID allows us to capture both temporal and spatial dependency among physiological variables and modalities by using 2D images' topological structure that competing algorithms fail to utilize.}, number={1}, journal={IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS}, author={Shi, Lan and Wank, Marianthie and Chen, Yan and Wang, Yibo and Liu, Yachuan and Hector, Emily C. and Song, Peter X. K.}, year={2023}, month={Jan}, pages={421–432} }
 @article{luo_wang_hector_2023, title={Statistical inference for streamed longitudinal data}, volume={2}, ISSN={["1464-3510"]}, url={https://doi.org/10.1093/biomet/asad010}, DOI={10.1093/biomet/asad010}, abstractNote={Summary Modern longitudinal data, for example from wearable devices, may consist of measurements of biological signals on a fixed set of participants at a diverging number of time-points. Traditional statistical methods are not equipped to handle the computational burden of repeatedly analysing the cumulatively growing dataset each time new data are collected. We propose a new estimation and inference framework for dynamic updating of point estimates and their standard errors along sequentially collected datasets with dependence, both within and between the datasets. The key technique is a decomposition of the extended inference function vector of the quadratic inference function constructed over the cumulative longitudinal data into a sum of summary statistics over data batches. We show how this sum can be recursively updated without the need to access the whole dataset, resulting in a computationally efficient streaming procedure with minimal loss of statistical efficiency. We prove consistency and asymptotic normality of our streaming estimator as the number of data batches diverges, even as the number of independent participants remains fixed. Simulations demonstrate the advantages of our approach over traditional statistical methods that assume independence between data batches. Finally, we investigate the relationship between physical activity and several diseases through analysis of accelerometry data from the National Health and Nutrition Examination Survey.}, journal={BIOMETRIKA}, author={Luo, Lan and Wang, Jingshen and Hector, Emily C.}, year={2023}, month={Feb} }
 @article{luo_wang_hector_2023, title={Statistical inference for streamed longitudinal data}, volume={doi: 10.1093/biomet/asad010}, journal={Biometrika}, author={Luo, Lan and Wang, Jingshen and Hector, Emily C.}, year={2023} }
 @article{hector_martin_2022, title={An information-sharing dial for efficient inference in data integration}, volume={arXiv:2207.08886}, journal={arXiv}, author={Hector, Emily C. and Martin, Ryan}, year={2022} }
 @article{manschot_hector_2022, title={Functional Regression with Intensively Measured Longitudinal Outcomes: A New Lens through Data Partitioning}, volume={arXiv:2207.13014}, journal={arXiv}, author={Manschot, Cole and Hector, Emily C.}, year={2022} }
 @article{yin_chan_bose_jackson_vandehaar_locke_fuchsberger_stringham_welch_yu_et al._2022, title={Genome-wide association studies of metabolites in Finnish men identify disease-relevant loci}, volume={13}, ISSN={["2041-1723"]}, DOI={10.1038/s41467-022-29143-5}, abstractNote={Few studies have explored the impact of rare variants (minor allele frequency < 1%) on highly heritable plasma metabolites identified in metabolomic screens. The Finnish population provides an ideal opportunity for such explorations, given the multiple bottlenecks and expansions that have shaped its history, and the enrichment for many otherwise rare alleles that has resulted. Here, we report genetic associations for 1391 plasma metabolites in 6136 men from the late-settlement region of Finland. We identify 303 novel association signals, more than one third at variants rare or enriched in Finns. Many of these signals identify genes not previously implicated in metabolite genome-wide association studies and suggest mechanisms for diseases and disease-related traits.}, number={1}, journal={NATURE COMMUNICATIONS}, author={Yin, Xianyong and Chan, Lap Sum and Bose, Debraj and Jackson, Anne U. and VandeHaar, Peter and Locke, Adam E. and Fuchsberger, Christian and Stringham, Heather M. and Welch, Ryan and Yu, Ketian and et al.}, year={2022}, month={Mar} }
 @article{sohn_ping_glass_seyfried_hector_muddiman_2022, title={Interrogating the Metabolomic Profile of Amyotrophic Lateral Sclerosis in the Post-Mortem Human Brain by Infrared Matrix-Assisted Laser Desorption Electrospray Ionization (IR-MALDESI) Mass Spectrometry Imaging (MSI)}, volume={12}, ISSN={["2218-1989"]}, DOI={10.3390/metabo12111096}, abstractNote={Amyotrophic lateral sclerosis (ALS) is an idiopathic, fatal neurodegenerative disease characterized by progressive loss of motor function with an average survival time of 2–5 years after diagnosis. Due to the lack of signature biomarkers and heterogenous disease phenotypes, a definitive diagnosis of ALS can be challenging. Comprehensive investigation of this disease is imperative to discovering unique features to expedite the diagnostic process and improve diagnostic accuracy. Here, we present untargeted metabolomics by mass spectrometry imaging (MSI) for comparing sporadic ALS (sALS) and C9orf72 positive (C9Pos) post-mortem frontal cortex human brain tissues against a control cohort. The spatial distribution and relative abundance of metabolites were measured by infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) MSI for association to biological pathways. Proteomic studies on the same patients were completed via LC-MS/MS in a previous study, and results were integrated with imaging metabolomics results to enhance the breadth of molecular coverage. Utilizing METASPACE annotation platform and MSiPeakfinder, nearly 300 metabolites were identified across the sixteen samples, where 25 were identified as dysregulated between disease cohorts. The dysregulated metabolites were further examined for their relevance to alanine, aspartate, and glutamate metabolism, glutathione metabolism, and arginine and proline metabolism. The dysregulated pathways discussed are consistent with reports from other ALS studies. To our knowledge, this work is the first of its kind, reporting on the investigation of ALS post-mortem human brain tissue analyzed by multiomic MSI.}, number={11}, journal={METABOLITES}, author={Sohn, Alexandria L. and Ping, Lingyan and Glass, Jonathan D. and Seyfried, Nicholas T. and Hector, Emily C. and Muddiman, David C.}, year={2022}, month={Nov} }
 @article{hector_song_2022, title={JOINT INTEGRATIVE ANALYSIS OF MULTIPLE DATA SOURCES WITH CORRELATED VECTOR OUTCOMES}, volume={16}, ISSN={["1941-7330"]}, DOI={10.1214/21-AOAS1563}, abstractNote={We propose a distributed quadratic inference function framework to jointly estimate regression parameters from multiple potentially heterogeneous data sources with correlated vector outcomes. The primary goal of this joint integrative analysis is to estimate covariate effects on all outcomes through a marginal regression model in a statistically and computationally efficient way. We develop a data integration procedure for statistical estimation and inference of regression parameters that is implemented in a fully distributed and parallelized computational scheme. To overcome computational and modeling challenges arising from the high-dimensional likelihood of the correlated vector outcomes, we propose to analyze each data source using Qu, Lindsay and Li (2000)'s quadratic inference functions, and then to jointly reestimate parameters from each data source by accounting for correlation between data sources using a combined meta-estimator in a similar spirit to Hansen (1982)'s generalised method of moments. We show both theoretically and numerically that the proposed method yields efficiency improvements and is computationally fast. We illustrate the proposed methodology with the joint integrative analysis of the association between smoking and metabolites in a large multi-cohort study and provide an R package for ease of implementation.}, number={3}, journal={ANNALS OF APPLIED STATISTICS}, author={Hector, Emily C. and Song, Peter X-K}, year={2022}, month={Sep}, pages={1700–1717} }
 @article{hickey_williams_hector_2022, title={Transfer learning with uncertainty quantification: Random Effect Calibration of Source to Target (RECaST)}, volume={arXiv:2211.16557}, journal={arXiv}, author={Hickey, Jimmy and Williams, Jonathan P. and Hector, Emily C.}, year={2022} }
 @article{hector_song_2021, title={A Distributed and Integrated Method of Moments for High-Dimensional Correlated Data Analysis}, volume={116}, url={https://doi.org/10.1080/01621459.2020.1736082}, DOI={10.1080/01621459.2020.1736082}, abstractNote={Abstract This article is motivated by a regression analysis of electroencephalography (EEG) neuroimaging data with high-dimensional correlated responses with multilevel nested correlations. We develop a divide-and-conquer procedure implemented in a fully distributed and parallelized computational scheme for statistical estimation and inference of regression parameters. Despite significant efforts in the literature, the computational bottleneck associated with high-dimensional likelihoods prevents the scalability of existing methods. The proposed method addresses this challenge by dividing responses into subvectors to be analyzed separately and in parallel on a distributed platform using pairwise composite likelihood. Theoretical challenges related to combining results from dependent data are overcome in a statistically efficient way using a meta-estimator derived from Hansen’s generalized method of moments. We provide a rigorous theoretical framework for efficient estimation, inference, and goodness-of-fit tests. We develop an R package for ease of implementation. We illustrate our method’s performance with simulations and the analysis of the EEG data, and find that iron deficiency is significantly associated with two auditory recognition memory related potentials in the left parietal-occipital region of the brain. Supplementary materials for this article are available online.}, number={534}, journal={Journal of the American Statistical Association}, publisher={Informa UK Limited}, author={Hector, Emily C. and Song, Peter X.-K.}, year={2021}, month={Apr}, pages={805–818} }
 @article{hector_song_2020, title={Doubly distributed supervised learning and inference with high-dimensional correlated outcomes}, volume={21}, journal={Journal of Machine Learning Research}, author={Hector, Emily C. and Song, Peter X.-K.}, year={2020}, pages={1–35} }
 @article{goodrich_hector_tang_labarre_dolinoy_mercado-garcia_cantoral_song_téllez-rojo_peterson_2020, title={Integrative Analysis of Gene-Specific DNA Methylation and Untargeted Metabolomics Data from the ELEMENT Cohort}, url={https://doi.org/10.1177/2516865720977888}, DOI={10.1177/2516865720977888}, abstractNote={Epigenetic modifications, such as DNA methylation, influence gene expression and cardiometabolic phenotypes that are manifest in developmental periods in later life, including adolescence. Untargeted metabolomics analysis provide a comprehensive snapshot of physiological processes and metabolism and have been related to DNA methylation in adults, offering insights into the regulatory networks that influence cellular processes. We analyzed the cross-sectional correlation of blood leukocyte DNA methylation with 3758 serum metabolite features (574 of which are identifiable) in 238 children (ages 8-14 years) from the Early Life Exposures in Mexico to Environmental Toxicants (ELEMENT) study. Associations between these features and percent DNA methylation in adolescent blood leukocytes at LINE-1 repetitive elements and genes that regulate early life growth (IGF2, H19, HSD11B2) were assessed by mixed effects models, adjusting for sex, age, and puberty status. After false discovery rate correction (FDR q < 0.05), 76 metabolites were significantly associated with LINE-1 DNA methylation, 27 with HSD11B2, 103 with H19, and 4 with IGF2. The ten identifiable metabolites included dicarboxylic fatty acids (five associated with LINE-1 or H19 methylation at q < 0.05) and 1-octadecanoyl-rac-glycerol (q < 0.0001 for association with H19 and q = 0.04 for association with LINE-1). We then assessed the association between these ten known metabolites and adiposity 3 years later. Two metabolites, dicarboxylic fatty acid 17:3 and 5-oxo-7-octenoic acid, were inversely associated with measures of adiposity (P < .05) assessed approximately 3 years later in adolescence. In stratified analyses, sex-specific and puberty-stage specific (Tanner stage = 2 to 5 vs Tanner stage = 1) associations were observed. Most notably, hundreds of statistically significant associations were observed between H19 and LINE-1 DNA methylation and metabolites among children who had initiated puberty. Understanding relationships between subclinical molecular biomarkers (DNA methylation and metabolites) may increase our understanding of genes and biological pathways contributing to metabolic changes that underlie the development of adiposity during adolescence.}, journal={Epigenetics Insights}, author={Goodrich, Jaclyn M and Hector, Emily C and Tang, Lu and LaBarre, Jennifer L and Dolinoy, Dana C and Mercado-Garcia, Adriana and Cantoral, Alejandra and Song, Peter XK and Téllez-Rojo, Martha Maria and Peterson, Karen E}, year={2020}, month={Jan} }
 @article{jansen_hector_goodrich_cantoral_rojo_basu_song_olascoaga_peterson_2020, title={Mercury exposure in relation to sleep duration, timing, and fragmentation among adolescents in Mexico City}, volume={191}, journal={Environmental Research}, author={Jansen, E.C. and Hector, Emily C. and Goodrich, J.M. and Cantoral, A. and Rojo, M.M. Téllez and Basu, N. and Song, P.X.-K. and Olascoaga, L. Torres and Peterson, K.E.}, year={2020}, pages={110216} }
 @article{perng_hector_song_rojo_raskind_kachman_cantoral_burant_peterson_2017, title={Metabolomic determinants of metabolic risk in Mexican adolescents}, volume={25}, number={9}, journal={Obesity (Silver Spring)}, author={Perng, W. and Hector, Emily C. and Song, P.X.-K. and Rojo, M.M. Téllez and Raskind, S. and Kachman, M. and Cantoral, A. and Burant, B.F. and Peterson, K.E.}, year={2017}, pages={1594–1602} }