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Nguyen TQ, Kerley CI, Key AP, Maxwell-Horn AC, Wells QS, Neul JL, Cutting LE, Landman BA. Phenotyping Down syndrome: discovery and predictive modelling with electronic medical records. J Intellect Disabil Res 2024; 68:491-511. [PMID: 38303157 PMCID: PMC11023778 DOI: 10.1111/jir.13124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 11/20/2023] [Accepted: 12/27/2023] [Indexed: 02/03/2024]
Abstract
BACKGROUND Individuals with Down syndrome (DS) have a heightened risk for various co-occurring health conditions, including congenital heart disease (CHD). In this two-part study, electronic medical records (EMRs) were leveraged to examine co-occurring health conditions among individuals with DS (Study 1) and to investigate health conditions linked to surgical intervention among DS cases with CHD (Study 2). METHODS De-identified EMRs were acquired from Vanderbilt University Medical Center and facilitated creating a cohort of N = 2282 DS cases (55% females), along with comparison groups for each study. In Study 1, DS cases were one-by-two sex and age matched with samples of case-controls and of individuals with other intellectual and developmental difficulties (IDDs). The phenome-disease association study (PheDAS) strategy was employed to reveal co-occurring health conditions in DS versus comparison groups, which were then ranked for how often they are discussed in relation to DS using the PubMed database and Novelty Finding Index. In Study 2, a subset of DS individuals with CHD [N = 1098 (48%)] were identified to create longitudinal data for N = 204 cases with surgical intervention (19%) versus 204 case-controls. Data were included in predictive models and assessed which model-based health conditions, when more prevalent, would increase the likelihood of surgical intervention. RESULTS In Study 1, relative to case-controls and those with other IDDs, co-occurring health conditions among individuals with DS were confirmed to include heart failure, pulmonary heart disease, atrioventricular block, heart transplant/surgery and primary pulmonary hypertension (circulatory); hypothyroidism (endocrine/metabolic); and speech and language disorder and Alzheimer's disease (neurological/mental). Findings also revealed more versus less prevalent co-occurring health conditions in individuals with DS when comparing with those with other IDDs. Findings with high Novelty Finding Index were abnormal electrocardiogram, non-rheumatic aortic valve disorders and heart failure (circulatory); acid-base balance disorder (endocrine/metabolism); and abnormal blood chemistry (symptoms). In Study 2, the predictive models revealed that among individuals with DS and CHD, presence of health conditions such as congestive heart failure (circulatory), valvular heart disease and cardiac shunt (congenital), and pleural effusion and pulmonary collapse (respiratory) were associated with increased likelihood of surgical intervention. CONCLUSIONS Research efforts using EMRs and rigorous statistical methods could shed light on the complexity in health profile among individuals with DS and other IDDs and motivate precision-care development.
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Affiliation(s)
- T Q Nguyen
- Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN, USA
- Peabody College of Education and Human Development, Vanderbilt University, Nashville, TN, USA
| | - C I Kerley
- School of Engineering, Vanderbilt University, Nashville, TN, USA
| | - A P Key
- Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN, USA
- Vanderbilt Kennedy Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Speech and Hearing Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - A C Maxwell-Horn
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Q S Wells
- Division of Cardiovascular Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - J L Neul
- Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN, USA
- Vanderbilt Kennedy Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - L E Cutting
- Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN, USA
- Peabody College of Education and Human Development, Vanderbilt University, Nashville, TN, USA
- Vanderbilt Kennedy Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - B A Landman
- Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN, USA
- School of Engineering, Vanderbilt University, Nashville, TN, USA
- Vanderbilt Kennedy Center, Vanderbilt University Medical Center, Nashville, TN, USA
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Cai LY, Tanase C, Anderson AW, Patel NJ, Lee CA, Jones RS, LeStourgeon LM, Mahon A, Taki I, Juvera J, Pruthi S, Gwal K, Ozturk A, Kang H, Rewers A, Rewers MJ, Alonso GT, Glaser N, Ghetti S, Jaser SS, Landman BA, Jordan LC. Exploratory Multisite MR Spectroscopic Imaging Shows White Matter Neuroaxonal Loss Associated with Complications of Type 1 Diabetes in Children. AJNR Am J Neuroradiol 2023; 44:820-827. [PMID: 37263786 PMCID: PMC10337627 DOI: 10.3174/ajnr.a7895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2023] [Accepted: 05/03/2023] [Indexed: 06/03/2023]
Abstract
BACKGROUND AND PURPOSE Type 1 diabetes affects over 200,000 children in the United States and is associated with an increased risk of cognitive dysfunction. Prior single-site, single-voxel MRS case reports and studies have identified associations between reduced NAA/Cr, a marker of neuroaxonal loss, and type 1 diabetes. However, NAA/Cr differences among children with various disease complications or across different brain tissues remain unclear. To better understand this phenomenon and the role of MRS in characterizing it, we conducted a multisite pilot study. MATERIALS AND METHODS In 25 children, 6-14 years of age, with type 1 diabetes across 3 sites, we acquired T1WI and axial 2D MRSI along with phantom studies to calibrate scanner effects. We quantified tissue-weighted NAA/Cr in WM and deep GM and modeled them against study covariates. RESULTS We found that MRSI differentiated WM and deep GM by NAA/Cr on the individual level. On the population level, we found significant negative associations of WM NAA/Cr with chronic hyperglycemia quantified by hemoglobin A1c (P < .005) and a history of diabetic ketoacidosis at disease onset (P < .05). We found a statistical interaction (P < .05) between A1c and ketoacidosis, suggesting that neuroaxonal loss from ketoacidosis may outweigh that from poor glucose control. These associations were not present in deep GM. CONCLUSIONS Our pilot study suggests that MRSI differentiates GM and WM by NAA/Cr in this population, disease complications may lead to neuroaxonal loss in WM in children, and deeper investigation is warranted to further untangle how diabetic ketoacidosis and chronic hyperglycemia affect brain health and cognition in type 1 diabetes.
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Affiliation(s)
- L Y Cai
- From the Department of Biomedical Engineering (L.Y.C., A.W.A., B.A.L.)
| | - C Tanase
- Departments of Psychiatry and Behavioral Sciences (C.T.)
| | - A W Anderson
- From the Department of Biomedical Engineering (L.Y.C., A.W.A., B.A.L.)
- Vanderbilt University Institute of Imaging Science (A.W.A., B.A.L.)
- Departments of Radiology and Radiological Sciences (A.W.A., S.P., B.A.L.)
| | - N J Patel
- Pediatrics (N.J.P., R.S.J., S.S.J., L.C.J.)
| | | | - R S Jones
- Pediatrics (N.J.P., R.S.J., S.S.J., L.C.J.)
| | | | - A Mahon
- Psychology (A.M., S.G.), University of California, Davis, Davis, California
| | - I Taki
- Department of Pediatrics (I.T., A.R., M.J.R.)
| | - J Juvera
- Department of Psychiatry (J.J.), University of Colorado Anschutz Medical Campus, Aurora, Colorado
| | - S Pruthi
- Departments of Radiology and Radiological Sciences (A.W.A., S.P., B.A.L.)
| | - K Gwal
- Departments of Radiology (K.G., A.O.)
| | - A Ozturk
- Departments of Radiology (K.G., A.O.)
| | - H Kang
- Biostatistics (H.K.), Vanderbilt University Medical Center, Nashville, Tennessee
| | - A Rewers
- Department of Pediatrics (I.T., A.R., M.J.R.)
| | - M J Rewers
- Department of Pediatrics (I.T., A.R., M.J.R.)
| | | | - N Glaser
- Pediatrics (N.G.), University of California Davis Health, University of California Davis School of Medicine, Sacramento, California
| | - S Ghetti
- Psychology (A.M., S.G.), University of California, Davis, Davis, California
| | - S S Jaser
- Pediatrics (N.J.P., R.S.J., S.S.J., L.C.J.)
| | - B A Landman
- From the Department of Biomedical Engineering (L.Y.C., A.W.A., B.A.L.)
- Vanderbilt University Institute of Imaging Science (A.W.A., B.A.L.)
- Department of Electrical and Computer Engineering (B.A.L.), Vanderbilt University, Nashville, Tennessee
- Departments of Radiology and Radiological Sciences (A.W.A., S.P., B.A.L.)
| | - L C Jordan
- Pediatrics (N.J.P., R.S.J., S.S.J., L.C.J.)
- Neurology (C.A.L., L.C.J.)
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Kochunov P, Charlesworth J, Winkler A, Hong LE, Nichols TE, Curran JE, Sprooten E, Jahanshad N, Thompson PM, Johnson MP, Kent JW, Landman BA, Mitchell B, Cole SA, Dyer TD, Moses EK, Goring HHH, Almasy L, Duggirala R, Olvera RL, Glahn DC, Blangero J. Transcriptomics of cortical gray matter thickness decline during normal aging. Neuroimage 2013; 82:273-83. [PMID: 23707588 DOI: 10.1016/j.neuroimage.2013.05.066] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2013] [Revised: 04/23/2013] [Accepted: 05/14/2013] [Indexed: 01/27/2023] Open
Abstract
INTRODUCTION We performed a whole-transcriptome correlation analysis, followed by the pathway enrichment and testing of innate immune response pathway analyses to evaluate the hypothesis that transcriptional activity can predict cortical gray matter thickness (GMT) variability during normal cerebral aging. METHODS Transcriptome and GMT data were available for 379 individuals (age range=28-85) community-dwelling members of large extended Mexican American families. Collection of transcriptome data preceded that of neuroimaging data by 17 years. Genome-wide gene transcriptome data consisted of 20,413 heritable lymphocytes-based transcripts. GMT measurements were performed from high-resolution (isotropic 800 μm) T1-weighted MRI. Transcriptome-wide and pathway enrichment analysis was used to classify genes correlated with GMT. Transcripts for sixty genes from seven innate immune pathways were tested as specific predictors of GMT variability. RESULTS Transcripts for eight genes (IGFBP3, LRRN3, CRIP2, SCD, IDS, TCF4, GATA3, and HN1) passed the transcriptome-wide significance threshold. Four orthogonal factors extracted from this set predicted 31.9% of the variability in the whole-brain and between 23.4 and 35% of regional GMT measurements. Pathway enrichment analysis identified six functional categories including cellular proliferation, aggregation, differentiation, viral infection, and metabolism. The integrin signaling pathway was significantly (p<10(-6)) enriched with GMT. Finally, three innate immune pathways (complement signaling, toll-receptors and scavenger and immunoglobulins) were significantly associated with GMT. CONCLUSION Expression activity for the genes that regulate cellular proliferation, adhesion, differentiation and inflammation can explain a significant proportion of individual variability in cortical GMT. Our findings suggest that normal cerebral aging is the product of a progressive decline in regenerative capacity and increased neuroinflammation.
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Affiliation(s)
- P Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, USA.
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Gray WR, Bogovic JA, Vogelstein JT, Landman BA, Prince JL, Vogelstein RJ. Magnetic Resonance Connectome Automated Pipeline: An Overview. IEEE Pulse 2012; 3:42-8. [DOI: 10.1109/mpul.2011.2181023] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Landman BA, Hoyt JC, Sax EJ, Smith KP. NeuroWeb Database: A Laboratory Management System for Neuroimaging Coordination, Collaboration and Analysis. Neuroimage 2009. [DOI: 10.1016/s1053-8119(09)70586-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
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Landman BA, Farrell JAD, Smith SA, Calabresi PA, van Zijl PCM, Prince JL. ROBUST MAXIMUM LIKELIHOOD ESTIMATION IN Q-SPACE MRI. Proc IEEE Int Symp Biomed Imaging 2008; 2008:867-870. [PMID: 20490362 PMCID: PMC2872926 DOI: 10.1109/isbi.2008.4541134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2023]
Abstract
Q-space imaging is an emerging diffusion weighted MR imaging technique to estimate molecular diffusion probability density functions (PDF's) without the need to assume a Gaussian distribution. We present a robust M-estimator, Q-space Estimation by Maximizing Rician Likelihood (QEMRL), for diffusion PDF's based on maximum likelihood. PDF's are modeled by constrained Gaussian mixtures. In QEMRL, robust likelihood measures mitigate the impacts of imaging artifacts. In simulation and in vivo human spinal cord, the method improves reliability of estimated PDF's and increases tissue contrast. QEMRL enables more detailed exploration of the PDF properties than prior approaches and may allow acquisitions at higher spatial resolution.
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Affiliation(s)
- B A Landman
- Johns Hopkins University School of Medicine and Kennedy Krieger Institute Biomedical Engineering, Biophysics, Neurology, Radiology, and the F.M. Kirby Center Baltimore, Maryland, USA
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Ozturk A, Sasson AD, Farrell JAD, Landman BA, da Motta ACBS, Aralasmak A, Yousem DM. Regional differences in diffusion tensor imaging measurements: assessment of intrarater and interrater variability. AJNR Am J Neuroradiol 2008; 29:1124-7. [PMID: 18356471 DOI: 10.3174/ajnr.a0998] [Citation(s) in RCA: 36] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
BACKGROUND AND PURPOSE Diffusion tensor imaging (DTI) has become a valuable tool in both the research and clinical evaluation of subjects. We sought to quantify interobserver and intraobserver variability of diffusivity and diffusion anisotropy measurements with regard to specific regions of interest (ROIs). MATERIALS AND METHODS The subject group consisted of 5 healthy control subjects and 7 study subjects (all males; 16-19 years old; mean age = 17.5 years), as part of a protocol for closed head injury. Two whole-brain DTI scans were acquired on a 3T scanner for each subject. Analysis was performed using a ROI approach. Two independent observers analyzed the apparent diffusion coefficient (ADC) and fractional anisotropy (FA) indices in the corpus callosum, cortical spinal tract, internal capsules (ICs), basal ganglia, and centrum semiovale (CSO). Intraobserver and interobserver variability were calculated for the mean ADC, FA, and ordered eigenvalues of the diffusion tensor (lambda(1), lambda(2), and lambda(3)). RESULTS The overall kappa statistic for intraobserver variability for both observers showed slight-to-substantial agreement (kappa = 0.02-0.69), however FA values in the CSO showed only slight agreement. Interobserver agreement was also slight to substantial for these DTI measurements with high variability in FA values in the IC and CSO. CONCLUSIONS When one is comparing 2 DTI measurements, it is important to assess intraobserver and interobserver variability. We recommend caution in the analysis of DTI contrasts in the IC and CSO, because we have found the widest range of variability in measurements within these structures.
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Affiliation(s)
- A Ozturk
- Russell H. Morgan Department of Radiology and Radiological Science, F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD 21287, USA
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