Arts & Sciences Brown School McKelvey School Medicine Weekly Publications

WashU weekly Neuroscience publications

“Neuroimaging of individual differences: A latent variable modeling perspective” (2019) Neuroscience and Biobehavioral Reviews

Neuroimaging of individual differences: A latent variable modeling perspective
(2019) Neuroscience and Biobehavioral Reviews, 98, pp. 29-46. 

Cooper, S.R., Jackson, J.J., Barch, D.M., Braver, T.S.

Washington University in St. Louis, Psychological and Brain Sciences, St. Louis, MO, United States

Abstract
Neuroimaging data is being increasingly utilized to address questions of individual difference. When examined with task-related fMRI (t-fMRI), individual differences are typically investigated via correlations between the BOLD activation signal at every voxel and a particular behavioral measure. This can be problematic because: 1) correlational designs require evaluation of t-fMRI psychometric properties, yet these are not well understood; and 2) bivariate correlations are severely limited in modeling the complexities of brain-behavior relationships. Analytic tools from psychometric theory such as latent variable modeling (e.g., structural equation modeling) can help simultaneously address both concerns. This review explores the advantages gained from integrating psychometric theory and methods with cognitive neuroscience for the assessment and interpretation of individual differences. The first section provides background on classic and modern psychometric theories and analytics. The second section details current approaches to t-fMRI individual difference analyses and their psychometric limitations. The last section uses data from the Human Connectome Project to provide illustrative examples of how t-fMRI individual differences research can benefit by utilizing latent variable models. © 2019 Elsevier Ltd

Author Keywords
Human connectome project;  Latent variable;  Psychometrics;  Structural equation modeling

Document Type: Review
Publication Stage: Final
Source: Scopus

“Aldehyde adducts inhibit 3,4-dihydroxyphenylacetaldehyde-induced α-synuclein aggregation and toxicity: Implication for Parkinson neuroprotective therapy” (2019) European Journal of Pharmacology

Aldehyde adducts inhibit 3,4-dihydroxyphenylacetaldehyde-induced α-synuclein aggregation and toxicity: Implication for Parkinson neuroprotective therapy
(2019) European Journal of Pharmacology, 845, pp. 65-73. 

Kumar, V.B.a c f , Hsu, F.-F.d , Lakshmi, V.M.a c , Gillespie, K.N.e , Burke, W.J.b c

a Saint Louis Veterans Affairs Medical Center, Saint Louis, MO 63125, United States
b Departments of Neurology, School of Medicine, Saint Louis University Medical Center, 3635 Vista at Grand, Saint Louis, MO 63110, United States
c Departments of Medicine, School of Medicine, Saint Louis University Medical Center, 3635 Vista at Grand, Saint Louis, MO 63110, United States
d Center for Biomedical and Bioorganic Mass Spectrometry, Washington University School of Medicine, 660S. Euclid, Saint Louis, MO 63110, United States
e Department of Health Management & Policy, College for Public Health and Social Justice, Saint Louis University Health Sciences Center, 3635 Vista at Grand, Saint Louis, MO 63110, United States
f Department of Geriatric Research, 3635 Vista at Grand, Saint Louis, MI 63110, United States

Abstract
3,4-Dihydroxyphenylacetaldehyde (DOPAL), the monoamine oxidase (MAO) metabolite of dopamine, plays a role in pathogenesis of Parkinson disease, inducing α-synuclein aggregation. DOPAL generates discrete α-synuclein aggregates. Inhibiting this aggregation could provide therapy for slowing Parkinson disease progression. Primary and secondary amines form adducts with aldehydes. Rasagiline and aminoindan contain these amine groups. DOPAL-induced α-synuclein aggregates were resolved in the presence and absence of rasagiline or aminoindan using quantitative Western blotting. DOPAL levels in incubation mixtures, containing increased rasagiline or aminoindan concentrations, were determined by high pressure liquid chromatography (HPLC). Schiff base adducts between DOPAL and rasagiline or aminoindan were determined using mass spectrometry. A neuroprotective effect of rasagiline and aminoindan against DOPAL-induced toxicity was demonstrated using PC-12 cells. Rasagiline and aminoindan significantly reduced aggregation of α-synuclein of all sizes in test tube and PC-12 cells experiments. Dimethylaminoindan did not reduce aggregation. DOPAL levels in incubation mixtures were reduced with increasing rasagiline or aminoindan concentrations but not with dimethylaminoindan. Schiff base adducts between DOPAL and either rasagiline or aminoindan were demonstrated by mass spectrometry. A neuroprotective effect against DOPAL-induced toxicity in PC-12 cells was demonstrated for both rasagiline and aminoindan. Inhibiting DOPAL-induced α-synuclein aggregation through amine adducts provides a therapeutic approach for slowing Parkinson disease progression. © 2018 Elsevier B.V.

Author Keywords
3,4-Dihydroxyphenylacetaldehyde;  Schiff base adducts;  α-Synuclein aggregation

Document Type: Article
Publication Stage: Final
Source: Scopus

“Cell-Type-Specific Profiling of Alternative Translation Identifies Regulated Protein Isoform Variation in the Mouse Brain” (2019) Cell Reports

Cell-Type-Specific Profiling of Alternative Translation Identifies Regulated Protein Isoform Variation in the Mouse Brain
(2019) Cell Reports, 26 (3), pp. 594-607.e7. 

Sapkota, D.a b , Lake, A.M.a b , Yang, W.a , Yang, C.a b , Wesseling, H.c , Guise, A.c , Uncu, C.c , Dalal, J.S.c , Kraft, A.W.d , Lee, J.-M.d , Sands, M.S.a e , Steen, J.A.c , Dougherty, J.D.a b

a Department of Genetics, Washington University School of Medicine, St. Louis, MO 63110, United States
b Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, United States
c Boston Children’s Hospital, F.M. Kirby Center for Neurobiology, Harvard Medical School, Boston, MA 02115, United States
d Departments of Neurology, Radiology, and Biomedical Engineering, Washington University School of Medicine, St. Louis, MO 63110, United States
e Deparment of Medicine, Washington University School of Medicine, St. Louis, MO 63112, United States

Abstract
By deep sequencing ribosome-bound mRNA fragments, Sapkota et al. demonstrate non-canonical translation initiation and stop codon readthrough that diversify proteins in the mouse brain. They also show that neuronal stimulation regulates the choice of initiation sites, whereas gliotic diseases differentially regulate the levels of normal and readthrough AQP4 isoforms. © 2018 The Author(s)

Alternative translation initiation and stop codon readthrough in a few well-studied cases have been shown to allow the same transcript to generate multiple protein variants. Because the brain shows a particularly abundant use of alternative splicing, we sought to study alternative translation in CNS cells. We show that alternative translation is widespread and regulated across brain transcripts. In neural cultures, we identify alternative initiation on hundreds of transcripts, confirm several N-terminal protein variants, and show the modulation of the phenomenon by KCl stimulation. We also detect readthrough in cultures and show differential levels of normal and readthrough versions of AQP4 in gliotic diseases. Finally, we couple translating ribosome affinity purification to ribosome footprinting (TRAP-RF) for cell-type-specific analysis of neuronal and astrocytic translational readthrough in the mouse brain. We demonstrate that this unappreciated mechanism generates numerous and diverse protein isoforms in a cell-type-specific manner in the brain. © 2018 The Author(s)

Author Keywords
Aqp4;  brain;  cell-type specific;  gliosis;  initiation;  neuronal activity;  protein variants;  readthrough;  translation

Document Type: Article
Publication Stage: Final
Source: Scopus
Access Type: Open Access

“Apo-Opsin Exists in Equilibrium Between a Predominant Inactive and a Rare Highly Active State” (2019) The Journal of neuroscience : the official journal of the Society for Neuroscience

Apo-Opsin Exists in Equilibrium Between a Predominant Inactive and a Rare Highly Active State
(2019) The Journal of neuroscience : the official journal of the Society for Neuroscience, 39 (2), pp. 212-223. 

Sato, S.a , Jastrzebska, B.b , Engel, A.b , Palczewski, K.b , Kefalov, V.J.a

a Department of Ophthalmology and Visual Sciences, Washington University in St. Louis, St. Louis
b Department of Pharmacology, Case Western Reserve University School of Medicine, Cleveland, OH 44106, United States

Abstract
Bleaching adaptation in rod photoreceptors is mediated by apo-opsin, which activates phototransduction with effective activity 105- to 106-fold lower than that of photoactivated rhodopsin (meta II). However, the mechanism that produces such low opsin activity is unknown. To address this question, we sought to record single opsin responses in mouse rods. We used mutant mice lacking efficient calcium feedback to boosts rod responses and generated a small fraction of opsin by photobleaching 1% of rhodopsin. The bleach produced a dramatic increase in the frequency of discrete photoresponse-like events. This activity persisted for hours, was quenched by 11-cis-retinal, and was blocked by uncoupling opsin from phototransduction, all indicating opsin as its source. Opsin-driven discrete activity was also observed in rods containing non-activatable rhodopsin, ruling out transactivation of rhodopsin by opsin. We conclude that bleaching adaptation is mediated by opsin that exists in equilibrium between a predominant inactive and a rare meta II-like state.SIGNIFICANCE STATEMENT Electrophysiological analysis is used to show that the G-protein-coupled receptor opsin exists in equilibrium between a predominant inactive and a rare highly active state that mediates bleaching adaptation in photoreceptors. Copyright © 2019 the authors 0270-6474/19/390212-12$15.00/0.

Author Keywords
bleaching adaptation;  G-protein-coupled receptor;  GCAP;  opsin;  rhodopsin;  thermal activation

Document Type: Article
Publication Stage: Final
Source: Scopus

“Reduced non–rapid eye movement sleep is associated with tau pathology in early Alzheimer’s disease” (2019) Science Translational Medicine

Reduced non–rapid eye movement sleep is associated with tau pathology in early Alzheimer’s disease
(2019) Science Translational Medicine, 11 (474), art. no. eaau6550, . 

Lucey, B.P.a b , McCullough, A.c , Landsness, E.C.a , Toedebusch, C.D.a , McLeland, J.S.a , Zaza, A.M.c , Fagan, A.M.a b d , McCue, L.e , Xiong, C.e , Morris, J.C.a b d , Benzinger, T.L.S.c d , Holtzman, D.M.a b d

a Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States
b Hope Center for Neurological Disorders, Washington University School of Medicine, St Louis, MO 63110, United States
c Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, United States
d Knight Alzheimer’s Disease Research Center, Washington University School of Medicine, St. Louis, MO 63110, United States
e Division of Biostatistics, Washington University School of Medicine, St. Louis, MO 63110, United States

Abstract
In Alzheimer’s disease (AD), deposition of insoluble amyloid- (A) is followed by intracellular aggregation of tau in the neocortex and subsequent neuronal cell loss, synaptic loss, brain atrophy, and cognitive impairment. By the time even the earliest clinical symptoms are detectable, A accumulation is close to reaching its peak and neocortical tau pathology is frequently already present. The period in which AD pathology is accumulating in the absence of cognitive symptoms represents a clinically relevant time window for therapeutic intervention. Sleep is increasingly recognized as a potential marker for AD pathology and future risk of cognitive impairment. Previous studies in animal models and humans have associated decreased non–rapid eye movement (NREM) sleep slow wave activity (SWA) with A deposition. In this study, we analyzed cognitive performance, brain imaging, and cerebrospinal fluid (CSF) AD biomarkers in participants enrolled in longitudinal studies of aging. In addition, we monitored their sleep using a single-channel electroencephalography (EEG) device worn on the forehead. After adjusting for multiple covariates such as age and sex, we found that NREM SWA showed an inverse relationship with AD pathology, particularly tauopathy, and that this association was most evident at the lowest frequencies of NREM SWA. Given that our study participants were predominantly cognitively normal, this suggested that changes in NREM SWA, especially at 1 to 2 Hz, might be able to discriminate tau pathology and cognitive impairment either before or at the earliest stages of symptomatic AD. Copyright © 2019 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works

Document Type: Article
Publication Stage: Final
Source: Scopus

“Novelty, Salience, and Surprise Timing Are Signaled by Neurons in the Basal Forebrain” (2019) Current Biology

Novelty, Salience, and Surprise Timing Are Signaled by Neurons in the Basal Forebrain
(2019) Current Biology, 29 (1), pp. 134-142.e3. 

Zhang, K.a b , Chen, C.D.a , Monosov, I.E.a b

a Department of Neuroscience, Washington University in St. Louis, St. Louis, MO 63110, United States
b Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO 63110, United States

Abstract
Zhang et al. show that cells in the primate basal forebrain (BF), an area that mediates activity of the neocortex, predict the timing of events that capture attention, such as surprising reinforcements and novel objects. After a salient event, other BF cells’ burst activations rapidly signal higher-order statistical information about motivational salience, novelty, and surprise. © 2018 Elsevier Ltd

The basal forebrain (BF) is a principal source of modulation of the neocortex [1–6] and is thought to regulate cognitive functions such as attention, motivation, and learning by broadcasting information about salience [2, 3, 5, 7–19]. However, events can be salient for multiple reasons—such as novelty, surprise, or reward prediction errors [20–24]—and to date, precisely which salience-related information the BF broadcasts is unclear. Here, we report that the primate BF contains at least two types of neurons that often process salient events in distinct manners: one with phasic burst responses to cues predicting salient events and one with ramping activity anticipating such events. Bursting neurons respond to cues that convey predictions about the magnitude, probability, and timing of primary reinforcements. They also burst to the reinforcement itself, particularly when it is unexpected. However, they do not have a selective response to reinforcement omission (the unexpected absence of an event). Thus, bursting neurons do not convey value-prediction errors but do signal surprise associated with external events. Indeed, they are not limited to processing primary reinforcement: they discriminate fully expected novel visual objects from familiar objects and respond to object-sequence violations. In contrast, ramping neurons predict the timing of many salient, novel, and surprising events. Their ramping activity is highly sensitive to the subjects’ confidence in event timing and on average encodes the subjects’ surprise after unexpected events occur. These data suggest that the primate BF contains mechanisms to anticipate the timing of a diverse set of important external events (via ramping activity) and to rapidly deploy cognitive resources when these events occur (via short latency bursting). © 2018 Elsevier Ltd

Author Keywords
attention;  basal forebrain;  motivation;  neurophysiology;  novelty;  salience

Document Type: Article
Publication Stage: Final
Source: Scopus

“Loss of TREM2 function increases amyloid seeding but reduces plaque-associated ApoE” (2019) Nature Neuroscience

Loss of TREM2 function increases amyloid seeding but reduces plaque-associated ApoE
(2019) Nature Neuroscience, . Article in Press. 

Parhizkar, S.a , Arzberger, T.b c d e , Brendel, M.f , Kleinberger, G.a b , Deussing, M.f , Focke, C.f , Nuscher, B.a , Xiong, M.g , Ghasemigharagoz, A.h , Katzmarski, N.i , Krasemann, S.j k , Lichtenthaler, S.F.b c l m , Müller, S.A.c l , Colombo, A.c , Monasor, L.S.c , Tahirovic, S.c , Herms, J.b c d , Willem, M.a , Pettkus, N.a , Butovsky, O.j n , Bartenstein, P.b f , Edbauer, D.b c , Rominger, A.b f q , Ertürk, A.h , Grathwohl, S.A.o , Neher, J.J.o p , Holtzman, D.M.g , Meyer-Luehmann, M.i , Haass, C.a b c

a Chair of Metabolic Biochemistry, Biomedical Center (BMC), Faculty of Medicine, Ludwig-Maximilians-Universität München, Munich, Germany
b Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
c German Center for Neurodegenerative Diseases (DZNE) Munich, Munich, Germany
d Center for Neuropathology and Prion Research, Ludwig-Maximilians-Universität München, Munich, Germany
e Department of Psychiatry and Psychotherapy, Ludwig-Maximilians-Universität München, Munich, Germany
f Department of Nuclear Medicine, University Hospital, Ludwig-Maximilians-Universität München, Munich, Germany
g Department of Neurology, Hope Center for Neurological Disorders, and Charles F. and Joanne Knight Alzheimer’s Disease Research Center, Washington University School of Medicine, St. Louis, MO, United States
h Institute for Stroke and Dementia Research, Klinikum der Universität München, Munich, Germany
i Department of Neurology, Medical Center University of Freiburg, and Faculty of Medicine, University of Freiburg, Freiburg, Germany
j Ann Romney Center for Neurologic Diseases, Department of Neurology, Brigham and Women´s Hospital, Harvard Medical School, Boston, MA, United States
k Institute of Neuropathology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
l Neuroproteomics, School of Medicine, Klinikum Rechts der Isar, Technische Universität München, Munich, Germany
m Institute for Advanced Study, Technische Universität München, Garching, Germany
n Evergrande Center for Immunologic Diseases, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
o Department of Cellular Neurology, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
p German Center for Neurodegenerative Diseases (DZNE) Tübingen, Tübingen, Germany
q Department of Nuclear Medicine, Inselspital, University Hospital Bern, Bern, Switzerland

Abstract
Coding variants in the triggering receptor expressed on myeloid cells 2 (TREM2) are associated with late-onset Alzheimer’s disease (AD). We demonstrate that amyloid plaque seeding is increased in the absence of functional Trem2. Increased seeding is accompanied by decreased microglial clustering around newly seeded plaques and reduced plaque-associated apolipoprotein E (ApoE). Reduced ApoE deposition in plaques is also observed in brains of AD patients carrying TREM2 coding variants. Proteomic analyses and microglia depletion experiments revealed microglia as one origin of plaque-associated ApoE. Longitudinal amyloid small animal positron emission tomography demonstrates accelerated amyloidogenesis in Trem2 loss-of-function mutants at early stages, which progressed at a lower rate with aging. These findings suggest that in the absence of functional Trem2, early amyloidogenesis is accelerated due to reduced phagocytic clearance of amyloid seeds despite reduced plaque-associated ApoE. © 2019, The Author(s), under exclusive licence to Springer Nature America, Inc.

Document Type: Article in Press
Publication Stage: Article in Press
Source: Scopus

“White matter integrity in schizophrenia and bipolar disorder: Tract- and voxel-based analyses of diffusion data from the Connectom scanner” (2019) NeuroImage: Clinical

White matter integrity in schizophrenia and bipolar disorder: Tract- and voxel-based analyses of diffusion data from the Connectom scanner
(2019) NeuroImage: Clinical, art. no. 101649, . Article in Press. 

Mamah, D.a , Ji, A.a , Rutlin, J.b , Shimony, J.S.b

a Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, United States
b Department Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, United States

Abstract
Background: Diffusion imaging abnormalities have been associated with schizophrenia (SZ) and bipolar disorder (BD), indicating impaired structural connectivity. Newer methods permit the automated reconstruction of major white matter tracts from diffusion-weighted MR images in each individual’s native space. Using high-definition diffusion data from SZ and BP subjects, we investigated brain white matter integrity using both an automated tract-based and voxel-based methods. Methods: Using a protocol matched to the NIH (Young-Adult) Human Connectome Project (and collected on the same customized ‘Connectom’ scanner), diffusion scans were acquired from 87 total participants (aged 18–30), grouped as SZ (n = 24), BD (n = 33) and healthy controls (n = 30). Fractional anisotropy (FA) of eighteen white matter tracks were analyzed using the TRACULA software. Voxel-wise statistical analyses of diffusion data was carried out using the tract-based spatial statistics (TBSS) software. TRACULA group effects and clinical correlations were investigated using analyses of variance and multiple regression. Results: TRACULA analysis identified a trend towards lower tract FA in SZ patients, most significantly in the left anterior thalamic radiation (ATR; p =.04). TBSS results showed significantly lower FA voxels bilaterally within the cerebellum and unilaterally within the left ATR, posterior thalamic radiation, corticospinal tract, and superior longitudinal fasciculus in SZ patients compared to controls (FDR corrected p <.05). FA in BD patients did not significantly differ from controls using either TRACULA or TBSS. Multiple regression showed FA of the ATR as predicting chronic mania (p =.0005) and the cingulum-angular bundle as predicting recent mania (p =.02) in patients. TBSS showed chronic mania correlating with FA voxels within the left ATR and corpus callosum. Conclusions: White matter abnormality in SZ varies in severity across different white matter tract regions. Our results indicate that voxel-based analysis of diffusion data is more sensitive than tract-based analysis in identifying such abnormalities. Absence of white matter abnormality in BD may be related to medication effects and age. © 2019 The Authors

Author Keywords
Bipolar Disorder;  DTI;  Schizophrenia;  TBSS;  TRACULA

Document Type: Article in Press
Publication Stage: Article in Press
Source: Scopus
Access Type: Open Access

“EEG-based age-prediction models as stable and heritable indicators of brain maturational level in children and adolescents” (2019) Human Brain Mapping

EEG-based age-prediction models as stable and heritable indicators of brain maturational level in children and adolescents
(2019) Human Brain Mapping, . Article in Press. 

Vandenbosch, M.M.L.J.Z.a , Ent, D.V.a , Boomsma, D.I.a , Anokhin, A.P.b , Smit, D.J.A.c

a Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
b Washington University School of Medicine, Department of Psychiatry, St. Louis, MO, United States
c Department of Psychiatry, Amsterdam Universitair Medisch Centrum, Amsterdam Neuroscience, Amsterdam, Netherlands

Abstract
The human brain shows remarkable development of functional brain activity from childhood to adolescence. Here, we investigated whether electroencephalogram (EEG) recordings are suitable for predicting the age of children and adolescents. Moreover, we investigated whether overestimation or underestimation of age was stable over longer time periods, as stable prediction error can be interpreted as reflecting individual brain maturational level. Finally, we established whether the age-prediction error was genetically determined. Then, 3 min eyes-closed resting-state EEG data from the longitudinal EEG studies of Netherlands Twin Register (NTR; n = 836) and Washington University in St. Louis (n = 702) were used at ages 5, 7, 12, 14, 16, and 18. Longitudinal data were available within childhood (5–7 years) and adolescence (16–18 years). We calculated power in 1 Hz wide bins (1–24 Hz). Random forest (RF) regression and relevance vector machine with sixfold cross-validation were applied. The best mean absolute prediction error was obtained with RF (1.22 years). Classification of childhood versus puberty/adolescence reached over 94% accuracy. Prediction errors were moderately to highly stable over periods of 1.5–2.1 years (0.53 < r < 0.74) and signifcantly affected by genetic factors (heritability between 42 and 79%). Our results show that age prediction from low-cost EEG recordings is comparable in accuracy to those obtained with magnetic resonance imaging. Children and adolescents showed stable overestimation or underestimation of their age, which means that some participants have stable brain activity patterns that reflect those of an older or younger age, and could therefore reflect individual brain maturational level. This prediction error is heritable, suggesting that genes underlie maturational level of functional brain activity. We propose that age prediction based on EEG recordings can be used for tracking neurodevelopment in typically developing children, in preterm children, and in children with neurodevelopmental disorders. © 2019 Wiley Periodicals, Inc.

Author Keywords
age prediction;  brain age;  brain maturation;  development;  electroencephalography (EEG);  machine learning

Document Type: Article in Press
Publication Stage: Article in Press
Source: Scopus

“Meta-analysis of up to 622,409 individuals identifies 40 novel smoking behaviour associated genetic loci” (2019) Molecular Psychiatry

Meta-analysis of up to 622,409 individuals identifies 40 novel smoking behaviour associated genetic loci
(2019) Molecular Psychiatry, . Article in Press. 

Erzurumluoglu, A.M.a , Liu, M.b , Jackson, V.E.a c d , Barnes, D.R.e , Datta, G.b f , Melbourne, C.A.a , Young, R.e , Batini, C.a , Surendran, P.e , Jiang, T.e , Adnan, S.D.g , Afaq, S.h , Agrawal, A.i , Altmaier, E.j , Antoniou, A.C.k , Asselbergs, F.W.l m n o , Baumbach, C.j , Bierut, L.p , Bertelsen, S.q , Boehnke, M.r , Bots, M.L.s t , Brazel, D.M.f u , Chambers, J.C.h v w x , Chang-Claude, J.y z , Chen, C.aa ab , Corley, J.ac ad , Chou, Y.-L.i , David, S.P.ae , de Boer, R.A.af , de Leeuw, C.A.ag , Dennis, J.G.k , Dominiczak, A.F.ah , Dunning, A.M.ai , Easton, D.F.k ai , Eaton, C.ab , Elliott, P.aj ak al am , Evangelou, E.h an , Faul, J.D.cb , Foroud, T.ao , Goate, A.ap , Gong, J.aq , Grabe, H.J.ar , Haessler, J.aq , Haiman, C.as , Hallmans, G.at , Hammerschlag, A.R.ag , Harris, S.E.ac au , Hattersley, A.av , Heath, A.i , Hsu, C.aw , Iacono, W.G.b , Kanoni, S.ax ay , Kapoor, M.q , Kaprio, J.az ba , Kardia, S.L.bb , Karpe, F.bc bd , Kontto, J.be , Kooner, J.S.w x ak bf , Kooperberg, C.aq bg , Kuulasmaa, K.be , Laakso, M.bh , Lai, D.ao , Langenberg, C.bi , Le, N.bj , Lettre, G.bk bl , Loukola, A.az ba , Luan, J.bi , Madden, P.A.F.i , Mangino, M.bm dy , Marioni, R.E.ac au , Marouli, E.ax ay , Marten, J.bn , Martin, N.G.bo , McGue, M.b , Michailidou, K.k bp , Mihailov, E.bq , Moayyeri, A.br , Moitry, M.bs , Müller-Nurasyid, M.bt bu bv , Naheed, A.bw , Nauck, M.bx by , Neville, M.J.bc bd , Nielsen, S.F.bz , North, K.ca , Perola, M.az be , Pharoah, P.D.P.k ai , Pistis, G.cc , Polderman, T.J.ag , Posthuma, D.ag cd , Poulter, N.ce , Qaiser, B.az ba , Rasheed, A.cf , Reiner, A.ab aq , Renström, F.cg ch , Rice, J.ci , Rohde, R.cj , Rolandsson, O.ck , Samani, N.J.cl , Samuel, M.cf , Schlessinger, D.cm , Scholte, S.H.cn , Scott, R.A.bi , Sever, P.bf ce , Shao, Y.cj , Shrine, N.a , Smith, J.A.bb , Starr, J.M.ac co , Stirrups, K.ax cp , Stram, D.cq , Stringham, H.M.r , Tachmazidou, I.cr , Tardif, J.-C.bk bl , Thompson, D.J.k , Tindle, H.A.cs , Tragante, V.ct , Trompet, S.cu cv , Turcot, V.bk , Tyrrell, J.av , Vaartjes, I.s t , van der Leij, A.R.cn , van der Meer, P.af , Varga, T.V.cg , Verweij, N.af cw , Völzke, H.by cx , Wareham, N.J.bi , Warren, H.R.cy cz , Weir, D.R.cb , Weiss, S.by da , Wetherill, L.ao , Yaghootkar, H.av , Yavas, E.db dc , Jiang, Y.dd , Chen, F.dd , Zhan, X.de , Zhang, W.h df , Zhao, W.dg , Zhao, W.bb , Zhou, K.dh , Amouyel, P.di , Blankenberg, S.dj dk , Caulfield, M.J.cy cz , Chowdhury, R.e , Cucca, F.cc , Deary, I.J.ac ad , Deloukas, P.cr dl dm , Di Angelantonio, E.e dn , Ferrario, M.do , Ferrières, J.dp , Franks, P.W.cg dq , Frayling, T.M.av , Frossard, P.cf , Hall, I.P.dr , Hayward, C.bn , Jansson, J.-H.ds , Jukema, J.W.dt du , Kee, F.dv , Männistö, S.be , Metspalu, A.bq , Munroe, P.B.cy cz , Nordestgaard, B.G.bz , Palmer, C.N.A.dw , Salomaa, V.be , Sattar, N.dx , Spector, T.dy , Strachan, D.P.dz , van der Harst, P.af ea , Zeggini, E.cr , Saleheen, D.e eb ec , Butterworth, A.S.e dn , Wain, L.V.a ed , Abecasis, G.R.r , Danesh, J.e cr dn , Tobin, M.D.a ed , Vrieze, S.b , Liu, D.J.dd , Howson, J.M.M.e , Understanding Society Scientific Group, EPIC-CVD, GSCAN, Consortium for Genetics of Smoking Behaviour, CHD Exome+ consortiumee

a Department of Health Sciences, University of Leicester, Leicester, United Kingdom
b Department of Psychology, University of Minnesota, Minneapolis, MN, United States
c Population Health and Immunity Division, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Pde, Parkville, 3052, Australia
d Department of Medical Biology, University of Melbourne, Melbourne, Parkville, 3010, Australia
e MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, United Kingdom
f Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, United States
g National Institute of Cardiovascular Diseases, Sher-e-Bangla Nagar, Dhaka, Bangladesh
h Department of Epidemiology and Biostatistics, Imperial College London, London, W2 1PG, United Kingdom
i Department of Psychiatry, Washington University, St. Louis, MO, United States
j Research Unit of Molecular Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
k Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, United Kingdom
l Department of Cardiology, Division Heart & Lungs, University Medical Center Utrecht, University of Utrecht, Utrecht, Netherlands
m Durrer Center for Cardiovascular Research, Netherlands Heart Institute, Utrecht, Netherlands
n Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, United Kingdom
o Farr Institute of Health Informatics Research and Institute of Health Informatics, University College London, London, United Kingdom
p Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, United States
q Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, United States
r Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, United States
s Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, 3508GA, Netherlands
t Center for Circulatory Health, University Medical Center Utrecht, Utrecht, 3508GA, Netherlands
u Department of Molecular, Cellular, and Developmental Biology, University of Colorado Boulder, Boulder, CO, United States
v Lee Kong Chian School of Medicine, Nanyang Technological University308232, Singapore
w Department of Cardiology, Ealing Hospital, Middlesex, UB1 3HW, United Kingdom
x Imperial College Healthcare NHS Trust, London, W12 0HS, United Kingdom
y Division of Cancer Epidemiology, German Cancer Research Centre (DKFZ), Heidelberg, Germany
z Cancer Epidemiology Group, University Medical Centre Hamburg-Eppendorf, University Cancer Centre Hamburg (UCCH), Hamburg, Germany
aa Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
ab Department of Epidemiology, University of Washington, Seattle, WA, United States
ac Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, EH8 9JZ, United Kingdom
ad Psychology, University of Edinburgh, Edinburgh, EH8 9JZ, United Kingdom
ae Department of Medicine, Stanford University, Stanford, CA, United States
af Department of Cardiology, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
ag Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, VU University Amsterdam, Amsterdam, Netherlands
ah Institute of Cardiovascular and Medical Sciences, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, United Kingdom
ai Centre for Cancer Genetic Epidemiology, Department of Oncology, Cambridge Centre, University of Cambridge, Cambridge, CB1 8RN, United Kingdom
aj Department of Epidemiology and Biostatistics, Imperial College London, London, United Kingdom
ak MRC-PHE Centre for Environment and Health, Imperial College London, London, W2 1PG, United Kingdom
al National Institute for Health Research Imperial Biomedical Research Centre, Imperial College Healthcare NHS Trust and Imperial College London, London, United Kingdom
am UK Dementia Research Institute (UK DRI) at Imperial College London, London, United Kingdom
an Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece
ao Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, United States
ap Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, United States
aq Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
ar Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, 17475, Germany
as Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
at Department of Public Health and Clinical Medicine, Nutritional research, Umeå University, Umeå, Sweden
au Centre for Genomic and Experimental Medicine, University of Edinburgh, Edinburgh, EH4 2XU, United Kingdom
av Genetics of Complex Traits, University of Exeter Medical School, Exeter, United Kingdom
aw University of Southern CaliforniaCA, United States
ax William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, EC1M 6BQ, United Kingdom
ay Centre for Genomic Health, Queen Mary University of London, London, EC1M 6BQ, United Kingdom
az Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
ba Department of Public Health, University of Helsinki, Helsinki, Finland
bb Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, United States
bc Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, United Kingdom
bd Oxford National Institute for Health Research, Biomedical Research Centre, Churchill Hospital, Oxford, United Kingdom
be Department of Public Health Solutions, National Institute for Health and Welfare, Helsinki, FI-00271, Finland
bf National Heart and Lung Institute, Imperial College London, London, W12 0NN, United Kingdom
bg Department of Biostatistics, University of Washington School of Medicine, Seattle, WA, United States
bh University of Eastern Finland, Finland
bi MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, CB2 0QQ, United Kingdom
bj Department of Medical Microbiology, Immunology and Cell Biology, Southern Illinois University School of Medicine, Springfield, IL, United States
bk Montreal Heart Institute, Montreal, QC H1T 1C8, Canada
bl Department of Medicine, Faculty of Medicine, Universite de Montreal, Montreal, QC H3T 1J4, Canada
bm NIHR Biomedical Research Centre at Guy’s and St Thomas’ Foundation Trust, London, SE1 9RT, United Kingdom
bn MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, United Kingdom
bo Queensland Institute for Medical Research, Brisbane, Australia
bp Department of Electron Microscopy/Molecular Pathology, The Cyprus Institute of Neurology and Genetics, Nicosia, 1683, Cyprus
bq Estonian Genome Center, University of Tartu, Tartu, Estonia
br Institute of Health Informatics, University College London, London, United Kingdom
bs Department of Epidemiology and Public health, University Hospital of Strasbourg, Strasbourg, France
bt Institute of Genetic Epidemiology, Helmholtz Zentrum München – German Research Center for Environmental Health, Neuherberg, Germany
bu Department of Medicine I, Ludwig-Maximilians-University Munich, Munich, Germany
bv DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany
bw Initiative for Noncommunicable Diseases, Health Systems and Population Studies Division, International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b) International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh
bx Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, 17475, Germany
by DZHK (German Centre for Cardiovascular Research), Partner Site Greifswald, University Medicine, Greifswald, Germany
bz Department of Clinical Biochemistry Herlev Hospital, Copenhagen University Hospital, Herlev Ringvej 74, Herlev, DK-2730, Denmark
ca Department of Epidemiology, University of North Carolina, Chapel Hill, NC, United States
cb Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, United States
cc Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), Monserrato, Cagliari, Italy
cd Department of Clinical Genetics, VU University Medical Centre Amsterdam, Amsterdam Neuroscience, Amsterdam, Netherlands
ce International Centre for Circulatory Health, Imperial College London, London, United Kingdom
cf Centre for Non-Communicable Diseases, Karachi, Pakistan
cg Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Sciences, Skåne University Hospital, Lund University, SE-214 28, Malmö, Sweden
ch Department of Biobank Research, Umeå University, SE-901 87, Umeå, Sweden
ci Departments of Psychiatry and Mathematics, Washington University St. Louis, St. Louis, MO, United States
cj University of North Carolina, Chapel Hill, NC, United States
ck Department of Public Health & Clinical Medicine, Section for Family Medicine, Umeå universitet, Umeå, SE 90185, Sweden
cl Department of Cardiovascular Sciences, University of Leicester, Cardiovascular Research Centre, Glenfield Hospital, Leicester, LE3 9QP, United Kingdom
cm National Institute on Aging, National Institutes of Health, Bethesda, MD, United States
cn Department of Psychology, University of Amsterdam & Amsterdam Brain and Cognition, University of Amsterdam, Amsterdam, Netherlands
co Alzheimer Scotland Research Centre, University of Edinburgh, Edinburgh, EH8 9JZ, United Kingdom
cp Department of Haematology, University of Cambridge, Cambridge, CB2 0PT, United Kingdom
cq Department of Preventative Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
cr Wellcome Trust Sanger Institute, Hinxton, Cambridge, CB10 1SA, United Kingdom
cs Department of Medicine, Vanderbilt University, Nashville, TN, United States
ct Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, 3508GA, Netherlands
cu Department of gerontology and geriatrics, Leiden University Medical Center, Leiden, Netherlands
cv Department of cardiology, Leiden University Medical Center, Leiden, Netherlands
cw Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, 301 Binney Street, Cambridge, MA 02142, United States
cx Institute for Community Medicine, University Medicine Greifswald, Greifswald, 17475, Germany
cy Clinical Pharmacology, William Harvey Research Institute, Queen Mary University of London, London, EC1M 6BQ, United Kingdom
cz NIHR Barts Cardiovascular Biomedical Research Centre, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, EC1M 6BQ, United Kingdom
da Interfaculty Institute for Genetics and Functional Genomics, University Medicine and Ernst-Moritz-Arndt-University Greifswald, Greifswald, 17475, Germany
db Department of Neuroscience, Psychology and Behaviour, University of Leicester, Leicester, United Kingdom
dc Department of Biomedical Engineering, The Pennsylvania State University, University ParkPA 16802, United States
dd Institute of Personalized Medicine, Penn State College of Medicine, Hershey, PA, United States
de Department of Clinical Science, Center for Genetics of Host Defense, University of Texas Southwestern, Dallas, TX, United States
df Department of Cardiology, Ealing Hospital, London North West Healthcare NHS Trust, Middlesex, UB1 3HW, United Kingdom
dg Department of Biostatistics and Epidemiology, University of PennsylvaniaPA, United States
dh School of Medicine, University of Dundee, Dundee, United Kingdom
di Department of Epidemiology and Public Health, Institut Pasteur de Lille, Lille, France
dj Department of General and Interventional Cardiology, University Heart Center Hamburg, Hamburg, Germany
dk University Medical Center Hamburg Eppendorf, Hamburg, Germany
dl William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, EC1M 6BQ, United Kingdom
dm Princess Al-Jawhara Al-Brahim Centre of Excellence in Research of Hereditary Disorders (PACER-HD), King Abdulaziz University, Jeddah, 21589, Saudi Arabia
dn National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, United Kingdom
do EPIMED Research Centre, Department of Medicine and Surgery, University of Insubria at Varese, Varese, Italy
dp Department of Epidemiology, UMR 1027- INSERM, Toulouse University-CHU Toulouse, Toulouse, France
dq Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA 02115, United States
dr Division of Respiratory Medicine and NIHR Nottingham Biomedical Research Centre, University of Nottingham, Nottingham, United Kingdom
ds Department of Public Health and Clinical Medicine, Skellefteå Research Unit, Umeå University, Umeå, Sweden
dt Department of Cardiology, Leiden University Medical Center, Leiden, Netherlands
du The Interuniversity Cardiology Institute of the Netherlands, Utrecht, Netherlands
dv UKCRC Centre of Excellence for Public Health, Queens, University, Belfast, Belfast, United Kingdom
dw Medical Research Institute, University of Dundee, Ninewells Hospital and Medical School, Dundee, United Kingdom
dx Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, United Kingdom
dy Department of Twin Research and Genetic Epidemiology, Kings College London, London, SE1 7EH, United Kingdom
dz Population Health Research Institute, St George!s, University of London, London, SW17 0RE, United Kingdom
ea Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
eb Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of PennsylvaniaPA, United States
ec Center for Non-Communicable Diseases, Karachi, Pakistan
ed National Institute for Health Research Leicester Respiratory Biomedical Research Centre, Glenfield Hospital, Leicester, United Kingdom

Abstract
Smoking is a major heritable and modifiable risk factor for many diseases, including cancer, common respiratory disorders and cardiovascular diseases. Fourteen genetic loci have previously been associated with smoking behaviour-related traits. We tested up to 235,116 single nucleotide variants (SNVs) on the exome-array for association with smoking initiation, cigarettes per day, pack-years, and smoking cessation in a fixed effects meta-analysis of up to 61 studies (up to 346,813 participants). In a subset of 112,811 participants, a further one million SNVs were also genotyped and tested for association with the four smoking behaviour traits. SNV-trait associations with P &lt; 5 × 10−8 in either analysis were taken forward for replication in up to 275,596 independent participants from UK Biobank. Lastly, a meta-analysis of the discovery and replication studies was performed. Sixteen SNVs were associated with at least one of the smoking behaviour traits (P &lt; 5 × 10−8) in the discovery samples. Ten novel SNVs, including rs12616219 near TMEM182, were followed-up and five of them (rs462779 in REV3L, rs12780116 in CNNM2, rs1190736 in GPR101, rs11539157 in PJA1, and rs12616219 near TMEM182) replicated at a Bonferroni significance threshold (P &lt; 4.5 × 10−3) with consistent direction of effect. A further 35 SNVs were associated with smoking behaviour traits in the discovery plus replication meta-analysis (up to 622,409 participants) including a rare SNV, rs150493199, in CCDC141 and two low-frequency SNVs in CEP350 and HDGFRP2. Functional follow-up implied that decreased expression of REV3L may lower the probability of smoking initiation. The novel loci will facilitate understanding the genetic aetiology of smoking behaviour and may lead to the identification of potential drug targets for smoking prevention and/or cessation. © 2019, The Author(s).

Document Type: Article in Press
Publication Stage: Article in Press
Source: Scopus

“Gender Differences in the Relationship Between Depression, Antisocial Behavior, Alcohol Use, and Gambling during Emerging Adulthood” (2019) International Journal of Mental Health and Addiction

Gender Differences in the Relationship Between Depression, Antisocial Behavior, Alcohol Use, and Gambling during Emerging Adulthood
(2019) International Journal of Mental Health and Addiction, . Article in Press. 

Jun, H.-J.a , Sacco, P.a , Bright, C.a , Cunningham-Williams, R.M.b

a School of Social Work, University of Maryland, 525 West Redwood Street, Baltimore, MD 21201, United States
b Brown School, Washington University in St. Louis, St. Louis, MO, United States

Abstract
Emerging adults show higher prevalence of harmful risk behaviors, such as alcohol use and gambling, compared to other age groups. In existing research, it appears that patterns of risk behaviors vary by gender during emerging adulthood. However, scarce research has examined gender differences in prospective relations among risk behaviors in emerging adults. This study explores gender differences in the developmental risks of depression, antisocial behavior, and alcohol use (Wave III) on gambling (Waves III and IV) in emerging adulthood in a sample of emerging adults (N = 8282) from the National Longitudinal Study of Adolescent to Adult Health. Results showed that antisocial behavior was associated with increased risk of alcohol use. Heavy drinking in early emerging adulthood was associated with increased risk of gambling later, but depression was marginally protective of gambling. Among men, contemporaneous associations between alcohol use and heavy drinking were stronger than among women. Among women, earlier binge drinking conferred increased risk of later gambling problems, but in men negative relationships between the two were found. The results highlight the importance of ongoing efforts in early prevention and intervention for the co-occurrence of risk behaviors in emerging adulthood. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.

Author Keywords
Alcohol use;  Antisocial behavior;  Depression;  Emerging adults;  Gambling;  Gender differences

Document Type: Article in Press
Publication Stage: Article in Press
Source: Scopus

“Early childhood depression, emotion regulation, episodic memory, and hippocampal development” (2019) Journal of Abnormal Psychology

Early childhood depression, emotion regulation, episodic memory, and hippocampal development
(2019) Journal of Abnormal Psychology, 128 (1), pp. 81-95. 

Barch, D.M.a , Harms, M.P.b , Tillman, R.b , Hawkey, E.b , Luby, J.L.b

a Department of Psychological and Brain Sciences, Washington University in St. Louis, One Brookings Drive, St. Louis, MO 63130, United States
b Department of Psychiatry, Washington University, St. Louis, United States

Abstract
Depression in adults is associated with deficits in a number of cognitive domains, however it remains less clear how early in development theses deficits can be detected in early onset depression. There are several different hypotheses about the links between cognitive function and depression. For example, it has been argued that executive function deficits contribute to emotion regulation difficulties, which in turn increase risk for depression. Further, it has been suggested that some cognitive deficits, such as episodic memory, may reflect hippocampal abnormalities linked to both depression and episodic memory. We examined these questions in adolescents participating in a longitudinal study of preschool onset depression. We measured cognitive function at adolescence using the National Institutes of Health toolbox (vocabulary, processing speed, executive function, working memory and episodic memory), and examined relationships of cognitive deficits to depression, emotion regulation, life stress and adversity, as well as hippocampal volume trajectories over three imaging assessments starting at school age. Depression related deficits in episodic memory were found. Youths with either current and past depression showed episodic memory deficits even after controlling for other psychopathology and family income. Depression severity, emotion dysregulation, and life stress/ adversity all predicted episodic memory impairment, as did smaller intercepts and slopes of hippocampal growth over time. Modest relationships of depression to hippocampal volume and strong relationships between emotion regulation and both episodic memory and hippocampal volume were found. These data are consistent with prior work in adults linking depression, episodic memory, emotion regulation, life stress/ adversity, and hippocampal volume in adults and suggest similar relations are evident as early as adolescence when memory systems are under development. © 2018 American Psychological Association.

Author Keywords
depression;  emotion regulation;  episodic memory;  hippocampus

Document Type: Article
Publication Stage: Final
Source: Scopus

“Effect of routing paradigm on patient centered outcomes in acute ischemic stroke” (2018) Journal of NeuroInterventional Surgery

Effect of routing paradigm on patient centered outcomes in acute ischemic stroke
(2018) Journal of NeuroInterventional Surgery, . Article in Press. 

Zhou, M.H.a , Kansagra, A.P.b c d

a Washington University, School of Medicine, St Louis, MO, United States
b Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO 63110, United States
c Department of Neurological Surgery, Washington University School of Medicine, St Louis, MO, United States
d Department of Neurology, Washington University School of Medicine, St Louis, MO, United States

Abstract
Background: To compare performance of routing paradigms for patients with acute ischemic stroke using clinical outcomes. Methods: We simulated different routing paradigms in a system comprising one primary stroke center (PSC) and one comprehensive stroke center (CSC), separated by distances representative of urban, suburban, and rural environments. In the nearest center paradigm, patients are initially sent to the nearest center, while in CSC first, patients are sent to the CSC. In the Rhode Island and distributive paradigms, patients with a FAST-ED (Facial palsy, Arm weakness, Speech changes, Time, Eye deviation, and Denial/neglect) score ≥4 are sent to the CSC, while others are sent to the nearest center or PSC, respectively. Performance and efficiency were compared using rates of good clinical outcome, determined by type and timing of treatment using clinical trial data, and number needed to bypass (NNB). Results: Good clinical outcome was achieved in 43.76% of patients in nearest center, 44.48% in CSC first, and 44.44% in Rhode Island and distributive in an urban setting; 43.38% in nearest center, 44.19% in CSC first, and 44.17% in Rhode Island in a suburban setting; and 41.10% in nearest center, 43.20% in CSC first, and 42.73% in Rhode Island in a rural setting. In all settings, NNB was generally higher for CSC first compared with Rhode Island or distributive. Conclusion: Routing paradigms that allow bypass of nearer hospitals for thrombectomy capable centers improve population level patient outcomes. Differences are more pronounced with increasing distance between hospitals; therefore, paradigm choice may be most impactful in rural settings. Selective bypass, as implemented in the Rhode Island and distributive paradigms, improves system efficiency with minimal impact on outcomes. © Author(s) (or their employer(s)) 2019. No commercial re-use. See rights and permissions. Published by BMJ.

Author Keywords
stroke;  thrombectomy

Document Type: Article in Press
Publication Stage: Article in Press
Source: Scopus

“Dying on Hospice in the Midst of an Opioid Crisis: What Should We Do Now?” (2018) American Journal of Hospice and Palliative Medicine

Dying on Hospice in the Midst of an Opioid Crisis: What Should We Do Now?
(2018) American Journal of Hospice and Palliative Medicine, . Article in Press. 

Gabbard, J.a , Jordan, A.b , Mitchell, J.c , Corbett, M.d , White, P.e , Childers, J.f

a Department of Internal Medicine, Internal Medicine Section of Gerontology and Geriatrics, Wake Forest School of Medicine, Winston-Salem, NC, United States
b Department of Palliative Medicine, Christian and Alton Memorial Hospitals, BJC Hospice, Washington University School of Medicine, St. Louis, MO, United States
c Department of Internal Medicine, Division of Hospital Medicine, Emory Palliative Care Center, Emory University School of Medicine, Atlanta, GA, United States
d Trellis Supportive Care, Winston-Salem, NC, United States
e Department of Internal Medicine, BJC Home Care, Washington University School of Medicine, St. Louis, MO, United States
f Section of Palliative Care and Medical Ethics and Section of Treatment, Research and Education in Addiction Medicine, Division of General Internal Medicine, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States

Abstract
The current opioid crisis in the United States is a major problem facing health-care providers, even at the end of life. Opioids continue to be the mainstay treatment for pain at the end of life, with the prevalence of pain reported in up to 80% of patients and tends to increase as one gets closer toward the end of life. In the past year, 20.2 million Americans had a substance use disorder (SUD) and SUDs are disabling disorders that largely go untreated. In addition, the coexistence of both a mental health and SUD is very common with the use of opioids often as a means of chemical coping. Most hospice programs do not have standardized SUD policies/guidelines in place despite the increasing concerns about substance abuse within the United States. The goal of this article is to review the literature on this topic and offer strategies on how to manage pain in patients who have active SUD or who are at risk for developing SUD in those dying on hospice. © The Author(s) 2018.

Author Keywords
addiction;  end-of-life care;  hospice care;  opioid-related disorders;  opioids;  pain management;  palliative care;  substance-related disorders

Document Type: Article in Press
Publication Stage: Article in Press
Source: Scopus

“Assessment of Racial Disparities in Biomarkers for Alzheimer Disease” (2018) JAMA Neurology

Assessment of Racial Disparities in Biomarkers for Alzheimer Disease
(2018) JAMA Neurology, . Article in Press. Cited 1 time.

Morris, J.C.a b , Schindler, S.E.a b , McCue, L.M.b c , Moulder, K.L.a b , Benzinger, T.L.S.b d , Cruchaga, C.b e , Fagan, A.M.a b , Grant, E.b c , Gordon, B.A.b d , Holtzman, D.M.a b , Xiong, C.b c

a Department of Neurology, Washington University, School of Medicine, St Louis, MO, United States
b Knight Alzheimer Disease Research Center, Washington University, School of Medicine, St Louis, MO, United States
c Division of Biostatistics, Washington University, School of Medicine, St Louis, MO, United States
d Department of Radiology, Washington University, School of Medicine, St Louis, MO, United States
e Department of Psychiatry, Washington University, School of Medicine, St Louis, MO, United States

Abstract
Importance: Racial differences in molecular biomarkers for Alzheimer disease may suggest race-dependent biological mechanisms. Objective: To ascertain whether there are racial disparities in molecular biomarkers for Alzheimer disease. Design, Setting, and Participants: A total of 1255 participants (173 African Americans) were enrolled from January 1, 2004, through December 31, 2015, in longitudinal studies at the Knight Alzheimer Disease Research Center at Washington University and completed a magnetic resonance imaging study of the brain and/or positron emission tomography of the brain with Pittsburgh compound B (radioligand for aggregated amyloid-β) and/or cerebrospinal fluid (CSF) assays for the concentrations of amyloid-β42, total tau, and phosphorylated tau181. Independent cross-sectional analyses were conducted from April 22, 2016, to August 27, 2018, for each biomarker modality with an analysis of variance or analysis of covariance including age, sex, educational level, race, apolipoprotein E (APOE) ϵ4 allele status, and clinical status (normal cognition or dementia). All biomarker assessments were conducted without knowledge of the clinical status of the participants. Main Outcomes and Measures: The primary outcomes were hippocampal volumes adjusted for differences in intracranial volumes, global cerebral amyloid burden as transformed into standardized uptake value ratios (partial volume corrected), and CSF concentrations of amyloid-β42, total tau, and phosphorylated tau181. Results: Of the 1255 participants (707 women and 548 men; mean [SD] age, 70.8 [9.9] years), 116 of 173 African American participants (67.1%) and 724 of 1082 non-Hispanic white participants (66.9%) had normal cognition. There were no racial differences in the frequency of cerebral ischemic lesions noted on results of brain magnetic resonance imaging, mean cortical standardized uptake value ratios for Pittsburgh compound B, or for amyloid-β42 concentrations in CSF. However, in individuals with a reported family history of dementia, mean (SE) total hippocampal volumes were lower for African American participants than for white participants (6418.26 [138.97] vs 6990.50 [44.10] mm3). Mean (SE) CSF concentrations of total tau were lower in African American participants than in white participants (293.65 [34.61] vs 443.28 [18.20] pg/mL; P &lt;.001), as were mean (SE) concentrations of phosphorylated tau181 (53.18 [4.91] vs 70.73 [2.46] pg/mL; P &lt;.001). There was a significant race by APOE ϵ4 interaction for both CSF total tau and phosphorylated tau181 such that only APOE ϵ4-positive participants showed the racial differences. Conclusions and Relevance: The results of this study suggest that analyses of molecular biomarkers of Alzheimer disease should adjust for race. The lower CSF concentrations of total tau and phosphorylated tau181 in African American individuals appear to reflect a significant race by APOE ϵ4 interaction, suggesting a differential effect of this Alzheimer risk variant in African American individuals compared with white individuals.. © 2019 American Medical Association. All rights reserved.

Document Type: Article in Press
Publication Stage: Article in Press
Source: Scopus