WashU weekly Neuroscience publications

Cost-Effectiveness Analysis of Combined Dual Motor Nerve Transfers versus Alternative Surgical and Nonsurgical Management Strategies to Restore Shoulder Function Following Upper Brachial Plexus Injury
(2019) Neurosurgery, 84 (2), pp. 362-377. 

Khalifeh, J.M.^a , Dibble, C.F.^a , Dy, C.J.^b , Ray, W.Z.^{a c}

^a Department of Neurological Surgery, Washington University School of Medicine, Saint Louis, MO, United States
^b Department of Orthopedic Surgery, Washington University School of Medicine, Saint Louis, MO, United States
^c Department of Biomedical Engineering, Washington University School of Medicine, Saint Louis, MO, United States

Abstract
BACKGROUND: Restoration of shoulder function is an important treatment goal in upper brachial plexus injury (UBPI). Combined dual motor nerve transfer (CDNT) of spinal accessory to suprascapular and radial to axillary nerves demonstrates good functional recovery with minimal risk of perioperative complications. OBJECTIVE: To evaluate the cost-effectiveness of CDNT vs alternative operative and nonoperative treatments for UBPI. METHODS: A decision model was constructed to evaluate costs ($, third-party payer) and effectiveness (quality-adjusted life years [QALYs]) of CDNT compared to glenohumeral arthrodesis (GA), conservative management, and nontreatment strategies. Estimates for branch probabilities, costs, and QALYs were derived from published studies. Incremental cost-effectiveness ratios (ICER, $/QALY) were calculated to compare the competing strategies. One-way, 2-way, and probabilistic sensitivity analyses with 100 000 iterations were performed to account for effects of uncertainty in model inputs. RESULTS: Base case model demonstrated CDNT effectiveness, yielding an expected 21.04 lifetime QALYs, compared to 20.89 QALYs with GA, 19.68 QALYs with conservative management, and 19.15 QALYs with no treatment. The ICERs for CDNT, GA, and conservative management vs nontreatment were $5776.73/QALY, $10 483.52/QALY, and $882.47/QALY, respectively. Adjusting for potential income associated with increased likelihood of returning to work after clinical recovery demonstrated CDNT as the dominant strategy, with ICER = -$56 459.54/QALY relative to nontreatment. Probabilistic sensitivity analysis showed CDNT cost-effectiveness at a willingness-to-pay threshold of $50 000/QALY in 78.47% and 81.97% of trials with and without income adjustment, respectively. Conservative management dominated in <1% of iterations. CONCLUSION: CDNT and GA are cost-effective interventions to restore shoulder function in patients with UBPI.

Document Type: Article
Publication Stage: Final
Source: Scopus

Early increase of CSF sTREM2 in Alzheimer’s disease is associated with tau related-neurodegeneration but not with amyloid-β pathology
(2019) Molecular Neurodegeneration, 14 (1), art. no. 1, . 

Suárez-Calvet, M.^{a b p} , Morenas-Rodríguez, E.^{b c} , Kleinberger, G.^{a d} , Schlepckow, K.^b , Caballero, M.Á.A.^e , Franzmeier, N.^e , Capell, A.^a , Fellerer, K.^a , Nuscher, B.^a , Eren, E.^{a f g} , Levin, J.^{b h} , Deming, Y.ⁱ , Piccio, L.^{j k} , Karch, C.M.^{i k l} , Cruchaga, C.^{i k l} , Shaw, L.M.^{m n} , Trojanowski, J.Q.^{m n} , Weiner, M.^o , Ewers, M.^e , Haass, C.^{a b d}

^a Department of Metabolic Biochemistry, Biomedical Center (BMC), Faculty of Medicine, Ludwig-Maximilians-Universität München, Munich, Germany
^b German Center for Neurodegenerative Diseases (DZNE) Munich, Munich, Germany
^c Department of Neurology, Institut d’Investigacions Biomèdiques, Hospital de la Santa Creu i Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Catalonia, Spain
^d Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
^e Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-Universität München, Munich, Germany
^f Izmir International Biomedicine and Genome Institute, Dokuz Eylul University, Izmir, Turkey
^g Department of Neuroscience, Institute of Health Sciences, Dokuz Eylul University, Izmir, Turkey
^h Department of Neurology, Ludwig-Maximilians-Universität München, Munich, Germany
ⁱ Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, United States
^j Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
^k Hope Center for Neurological Disorders, Washington University in St. Louis, St. Louis, MO, United States
^l Knight Alzheimer’s Disease Research Center, Washington University in St. Louis, St. Louis, MO, United States
^m Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
ⁿ Center for Neurodegenerative Disease Research, Institute on Aging, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
^o University of California at San Francisco, San Francisco, CA, United States
^p Barcelonaeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Catalonia, Spain

Abstract
Background: TREM2 is a transmembrane receptor that is predominantly expressed by microglia in the central nervous system. Rare variants in the TREM2 gene increase the risk for late-onset Alzheimer’s disease (AD). Soluble TREM2 (sTREM2) resulting from shedding of the TREM2 ectodomain can be detected in the cerebrospinal fluid (CSF) and is a surrogate measure of TREM2-mediated microglia function. CSF sTREM2 has been previously reported to increase at different clinical stages of AD, however, alterations in relation to Amyloid β-peptide (Aβ) deposition or additional pathological processes in the amyloid cascade (such as tau pathology or neurodegeneration) remain unclear. In the current cross-sectional study, we employed the biomarker-based classification framework recently proposed by the NIA-AA consensus guidelines, in combination with clinical staging, in order to examine the CSF sTREM2 alterations at early asymptomatic and symptomatic stages of AD. Methods: A cross-sectional study of 1027 participants of the Alzheimer’s Disease Imaging Initiative (ADNI) cohort, including 43 subjects carrying TREM2 rare genetic variants, was conducted to measure CSF sTREM2 using a previously validated enzyme-linked immunosorbent assay (ELISA). ADNI participants were classified following the A/T/N framework, which we implemented based on the CSF levels of Aβ1-42 (A), phosphorylated tau (T) and total tau as a marker of neurodegeneration (N), at different clinical stages defined by the clinical dementia rating (CDR) score. Results: CSF sTREM2 differed between TREM2 variants, whereas the p.R47H variant had higher CSF sTREM2, p.L211P had lower CSF sTREM2 than non-carriers. We found that CSF sTREM2 increased in early symptomatic stages of late-onset AD but, unexpectedly, we observed decreased CSF sTREM2 levels at the earliest asymptomatic phase when only abnormal Aβ pathology (A+) but no tau pathology or neurodegeneration (TN-), is present. Conclusions: Aβ pathology (A) and tau pathology/neurodegeneration (TN) have differing associations with CSF sTREM2. While tau-related neurodegeneration is associated with an increase in CSF sTREM2, Aβ pathology in the absence of downstream tau-related neurodegeneration is associated with a decrease in CSF sTREM2. © 2019 The Author(s).

Author Keywords
Alzheimer’s disease;  Biomarkers;  Microglia;  Neurodegeneration;  Neuroinflammation;  Shedding;  TREM2

Document Type: Article
Publication Stage: Final
Source: Scopus

Genetically Determined Levels of Circulating Cytokines and Risk of Stroke
(2019) Circulation, 139 (2), pp. 256-268. 

Georgakis, M.K.^{a b} , Gill, D.^c , Rannikmäe, K.^d , Traylor, M.^e , Anderson, C.D.^{f g h} , Lee, J.-M.ⁱ , Kamatani, Y.^j , Hopewell, J.C.^k , Worrall, B.B.^l , Bernhagen, J.^{a m} , Sudlow, C.L.M.^{c n} , Malik, R.^a , Dichgans, M.^{a m o}

^a Institute for Stroke and Dementia Research, University Hospital of Ludwig-Maximilians-University, Germany (M.K.G., R.M., Munich, Germany
^b Graduate School for Systemic Neurosciences, Ludwig-Maximilians-University, Germany (M.K.G.), Munich, Germany
^c Department of Biostatistics and Epidemiology, School of Public Health, Imperial College London, United Kingdom
^d Centre for Clinical Brain Sciences, University of Edinburgh, United Arab Emirates
^e Stroke Research Group, Department of Clinical Neurosciences, University of Cambridge, Australia
^f Center for Genomic Medicine (C.D.A.), Massachusetts General Hospital, Boston, United Kingdom
^g Division of Neurocritical Care and Emergency Neurology, Department of Neurology (C.D.A.), Massachusetts General Hospital, Boston, United Kingdom
^h Program in Medical and Population Genetics, Broad Institute, Cambridge, Canada
ⁱ Department of Neurology, Radiology, Biomedical Engineering, Washington University School of Medicine, St Louis, United States
^j Laboratory for Statistical Analysis, Japan (Y.K.), RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
^k Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Argentina
^l Departments of Neurology and Public Health Sciences, University of Virginia School of Medicine, United States
^m Munich Cluster for Systems Neurology (SyNergy), Germany (J.B., Munich, Germany
ⁿ Institute for Genetics and Molecular Medicine, University of Edinburgh, United Arab Emirates
^o German Centre for Neurodegenerative Diseases

Abstract
BACKGROUND: Cytokines and growth factors have been implicated in the initiation and propagation of vascular disease. Observational studies have shown associations of their circulating levels with stroke. Our objective was to explore whether genetically determined circulating levels of cytokines and growth factors are associated with stroke and its etiologic subtypes by conducting a 2-sample Mendelian randomization (MR) study. METHODS: Genetic instruments for 41 cytokines and growth factors were obtained from a genome-wide association study of 8293 healthy adults. Their associations with stroke and stroke subtypes were evaluated in the MEGASTROKE genome-wide association study data set (67 162 cases; 454 450 controls) applying inverse variance-weighted meta-analysis, weighted-median analysis, Mendelian randomization-Egger regression, and multivariable Mendelian randomization. The UK Biobank cohort was used as an independent validation sample (4985 cases; 364 434 controls). Genetic instruments for monocyte chemoattractant protein-1 (MCP-1/CCL2) were further tested for association with etiologically related vascular traits by using publicly available genome-wide association study data. RESULTS: Genetic predisposition to higher MCP-1 levels was associated with higher risk of any stroke (odds ratio [OR] per 1 SD increase, 1.06; 95% CI, 1.02-1.09; P=0.0009), any ischemic stroke (OR, 1.06; 95% CI, 1.02-1.10; P=0.002), large-artery stroke (OR, 1.19; 95% CI, 1.09-1.30; P=0.0002), and cardioembolic stroke (OR, 1.14; 95% CI, 1.06-1.23; P=0.0004), but not with small-vessel stroke or intracerebral hemorrhage. The results were stable in sensitivity analyses and remained significant after adjustment for cardiovascular risk factors. Analyses in the UK Biobank showed similar associations for available phenotypes (any stroke: OR, 1.08; 95% CI, 0.99-1.17; P=0.09; any ischemic stroke: OR, 1.07; 95% CI, 0.97-1.18; P=0.17). Genetically determined higher MCP-1 levels were further associated with coronary artery disease (OR, 1.04; 95% CI, 1.00-1.08; P=0.04) and myocardial infarction (OR, 1.05; 95% CI, 1.01-1.09; P=0.02), but not with atrial fibrillation. A meta-analysis of observational studies showed higher circulating MCP-1 levels in patients with stroke in comparison with controls. CONCLUSIONS: Genetic predisposition to elevated circulating levels of MCP-1 is associated with higher risk of stroke, in particular with large-artery stroke and cardioembolic stroke. Whether targeting MCP-1 or its receptors can lower stroke incidence requires further study.

Author Keywords
atherosclerosis;  chemokine CCL2;  cytokines;  human genetics;  inflammation;  Mendelian randomization analysis;  stroke

Document Type: Article
Publication Stage: Final
Source: Scopus

Foundations of idiographic methods in psychology and applications for psychotherapy
(2019) Clinical Psychology Review, . Article in Press. 

Piccirillo, M.L., Rodebaugh, T.L.

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

Abstract
Researchers have long called for greater recognition and use of longitudinal, individual-level research in the study of psychopathology and psychotherapy. Much of our current research attempts to indirectly investigate individual-level, or idiographic, psychological processes via group-based, or nomothetic, designs. However, results from nomothetic research do not necessarily translate to the individual-level. In this review, we discuss how idiographic analyses can be integrated into psychotherapy and psychotherapy research. We examine and review key statistical methods for conducting idiographic analyses. These methods include factor-based and vector autoregressive approaches using longitudinal data. The theoretical framework behind each approach is reviewed and critically evaluated. Empirical examples of each approach are discussed, with the aim of helping interested readers consider how they may use idiographic methods to analyze longitudinal data and psychological processes. Finally, we conclude by citing key limitations of the idiographic approach, calling for greater development of these analyses to ease their successful integration into clinical settings. © 2019 Elsevier Ltd

Author Keywords
Idiographic;  Individual-level;  Methodology;  Psychopathology;  Psychotherapy

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

Melanotan-II reverses autistic features in a maternal immune activation mouse model of autism
(2019) PLoS ONE, 14 (1), art. no. e0210389, . 

Minakova, E.^{a f} , Lang, J.^b , Medel-Matus, J.-S.^c , Gould, G.G.^d , Reynolds, A.^c , Shin, D.^c , Mazarati, A.^{c e} , Sankar, R.^{c e}

^a Department of Pediatrics, Division of Neonatology, David Geffen School of Medicine at UCLA, Los Angeles, CA, United States
^b Department of Internal Medicine, Huntington Memorial Hospital, Pasadena, CA, United States
^c Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, United States
^d University of Texas Health Science Center at San Antonio, Department of Cellular and Integrative Physiology, San Antonio, TX, United States
^e Children’s Discovery and Innovation Institute, UCLA, Los Angeles, CA, United States
^f Department of Pediatrics, Division of Neonatology, Washington University School of Medicine, Saint Louis, MO, United States

Abstract
Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder characterized by impaired social interactions, difficulty with communication, and repetitive behavior patterns. In humans affected by ASD, there is a male pre-disposition towards the condition with a male to female ratio of 4:1. In part due to the complex etiology of ASD including genetic and environmental interplay, there are currently no available medical therapies to improve the social deficits of ASD. Studies in rodent models and humans have shown promising therapeutic effects of oxytocin in modulating social adaptation. One pharmacological approach to stimulating oxytocinergic activity is the melanocortin receptor 4 agonist Melano-tan-II (MT-II). Notably the effects of oxytocin on environmental rodent autism models has not been investigated to date. We used a maternal immune activation (MIA) mouse model of autism to assess the therapeutic potential of MT-II on autism-like features in adult male mice. The male MIA mice exhibited autism-like features including impaired social behavioral metrics, diminished vocal communication, and increased repetitive behaviors. Continuous administration of MT-II to male MIA mice over a seven-day course resulted in rescue of social behavioral metrics. Normal background C57 male mice treated with MT-II showed no significant alteration in social behavioral metrics. Additionally, there was no change in anxiety-like or repetitive behaviors following MT-II treatment of normal C57 mice, though there was significant weight loss following subacute treatment. These data demonstrate MT-II as an effective agent for improving autism-like behavioral deficits in the adult male MIA mouse model of autism. © 2019 Minakova et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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

How and when taking pictures undermines the enjoyment of experiences
(2019) Psychology and Marketing, . Article in Press. 

Nardini, G.^a , Lutz, R.J.^b , LeBoeuf, R.A.^c

^a Daniels College of Business, University of Denver, Denver, CO, United States
^b Warrington College of Business, University of Florida, Gainesville, FL, United States
^c Olin Business School, Washington University in St. Louis, St Louis, MO, United States

Abstract
The consumption of experiences (as opposed to products) has seen an increasing amount of research attention in recent years. A key aspect of the experiential consumption journey is how the experience is consumed. For instance, people almost invariably take pictures during highly enjoyable experiences such as vacations or important family events. Although past research has suggested that taking pictures may enhance the enjoyment of moderately enjoyable experiences, the effect of picture taking on the real-time enjoyment of highly enjoyable experiences is not clear. To address this matter, the authors investigate whether taking pictures affects consumers’ enjoyment of highly enjoyable hedonic experiences. A series of laboratory studies demonstrate that taking pictures (compared with not taking pictures) can decrease enjoyment of highly enjoyable experiences. This study suggests that, by constantly striving to document their experiences, consumers may unwittingly fail to enjoy those experiences to the fullest. These results have implications for how firms may best stage experiential offerings to enhance their customers’ experiences. © 2019 Wiley Periodicals, Inc.

Author Keywords
consumption experiences;  consumption journey;  customer experience;  enjoyment;  picture taking

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

Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use
(2019) Nature Genetics, . Article in Press. 

Liu, M.^a , Jiang, Y.^{b c} , Wedow, R.^{d e f} , Li, Y.^{g h} , Brazel, D.M.^{d i j} , Chen, F.^{b c} , Datta, G.^a , Davila-Velderrain, J.^{g h} , McGuire, D.^{b c} , Tian, C.^k , Zhan, X.^{l m} , Agee, M.^k , Alipanahi, B.^k , Auton, A.^k , Bell, R.K.^k , Bryc, K.^k , Elson, S.L.^k , Fontanillas, P.^k , Furlotte, N.A.^k , Hinds, D.A.^k , Hromatka, B.S.^k , Huber, K.E.^k , Kleinman, A.^k , Litterman, N.K.^k , McIntyre, M.H.^k , Mountain, J.L.^k , Northover, C.A.M.^k , Sathirapongsasuti, J.F.^k , Sazonova, O.V.^k , Shelton, J.F.^k , Shringarpure, S.^k , Tian, C.^k , Tung, J.Y.^k , Vacic, V.^k , Wilson, C.H.^k , Pitts, S.J.^k , Mitchell, A.^bl , Skogholt, A.H.^s , Winsvold, B.S.^{bl bw} , Sivertsen, B.^{bx by bz} , Stordal, E.^{by ca} , Morken, G.^{by cb} , Kallestad, H.^{by cb} , Heuch, I.^bz , Zwart, J.-A.^{bl bw cc} , Fjukstad, K.K.^{cd ce} , Pedersen, L.M.^bl , Gabrielsen, M.E.^s , Johnsen, M.B.^{bl cc} , Skrove, M.^cf , Indredavik, M.S.^{by cf} , Drange, O.K.^{by cb} , Bjerkeset, O.^{by cg} , Børte, S.^{bl cc} , Stensland, S.Ø.^{bl ch} , Choquet, H.ⁿ , Docherty, A.R.^{o p} , Faul, J.D.^q , Foerster, J.R.^r , Fritsche, L.G.^r , Gabrielsen, M.E.^s , Gordon, S.D.^t , Haessler, J.^u , Hottenga, J.-J.^v , Huang, H.^{w x} , Jang, S.-K.^a , Jansen, P.R.^{y z} , Ling, Y.^{b i} , Mägi, R.^aa , Matoba, N.^ab , McMahon, G.^ac , Mulas, A.^ad , Orrù, V.^ad , Palviainen, T.^ae , Pandit, A.^r , Reginsson, G.W.^af , Skogholt, A.H.^s , Smith, J.A.^{q ag} , Taylor, A.E.^ac , Turman, C.^{w x} , Willemsen, G.^v , Young, H.^a , Young, K.A.^ah , Zajac, G.J.M.^r , Zhao, W.^ag , Zhou, W.^ai , Bjornsdottir, G.^af , Boardman, J.D.^{d e f} , Boehnke, M.^r , Boomsma, D.I.^v , Chen, C.^u , Cucca, F.^ad , Davies, G.E.^aj , Eaton, C.B.^ak , Ehringer, M.A.^{d al} , Esko, T.^{h aa} , Fiorillo, E.^ad , Gillespie, N.A.^{o t} , Gudbjartsson, D.F.^{af am} , Haller, T.^aa , Harris, K.M.^{an ao} , Heath, A.C.^ap , Hewitt, J.K.^{d aq} , Hickie, I.B.^ar , Hokanson, J.E.^ah , Hopfer, C.J.^{d as} , Hunter, D.J.^{w x at} , Iacono, W.G.^a , Johnson, E.O.^au , Kamatani, Y.^ab , Kardia, S.L.R.^ag , Keller, M.C.^{d aq} , Kellis, M.^{g h} , Kooperberg, C.^u , Kraft, P.^{w x av} , Krauter, K.S.^{d i} , Laakso, M.^{aw ax} , Lind, P.A.^ay , Loukola, A.^ae , Lutz, S.M.^az , Madden, P.A.F.^ap , Martin, N.G.^t , McGue, M.^a , McQueen, M.B.^{d al} , Medland, S.E.^ay , Metspalu, A.^aa , Mohlke, K.L.^ba , Nielsen, J.B.^bb , Okada, Y.^{ab bc} , Peters, U.^{u bd} , Polderman, T.J.C.^y , Posthuma, D.^{y be} , Reiner, A.P.^{u bd} , Rice, J.P.^bf , Rimm, E.^{x bg} , Rose, R.J.^bh , Runarsdottir, V.^bi , Stallings, M.C.^{d aq} , Stančáková, A.^aw , Stefansson, H.^af , Thai, K.K.ⁿ , Tindle, H.A.^bj , Tyrfingsson, T.^bi , Wall, T.L.^bk , Weir, D.R.^q , Weisner, C.ⁿ , Whitfield, J.B.^t , Winsvold, B.S.^bl , Yin, J.ⁿ , Zuccolo, L.^{ac bm} , Bierut, L.J.^bf , Hveem, K.^{s bn bo} , Lee, J.J.^a , Munafò, M.R.^{bm bp} , Saccone, N.L.^bq , Willer, C.J.^{ai bb br} , Cornelis, M.C.^bs , David, S.P.^bt , Hinds, D.A.^k , Jorgenson, E.ⁿ , Kaprio, J.^{ae bu} , Stitzel, J.A.^{d al} , Stefansson, K.^{af bv} , Thorgeirsson, T.E.^af , Abecasis, G.^r , Liu, D.J.^{b c} , Vrieze, S.^a , 23andMe Research Team^ci , HUNT All-In Psychiatry^ci

^a Department of Psychology, University of Minnesota Twin Cities, Minneapolis, MN, United States
^b Department of Public Health Sciences, College of Medicine, Pennsylvania State University, Hershey, PA, United States
^c Institute of Personalized Medicine, College of Medicine, Pennsylvania State University, Hershey, PA, United States
^d Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, United States
^e Department of Sociology, University of Colorado Boulder, Boulder, CO, United States
^f Institute of Behavioral Science, University of Colorado Boulder, Boulder, CO, United States
^g Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, MA, United States
^h The Broad Institute of MIT and Harvard, Cambridge, MA, United States
ⁱ Department of Molecular, Cellular, and Developmental Biology, University of Colorado Boulder, Boulder, CO, United States
^j Interdisciplinary Quantitative Biology Graduate Group, University of Colorado Boulder, Boulder, CO, United States
^k 23andMe, Inc., Mountain View, CA, United States
^l Quantitative Biomedical Research Center, Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX, United States
^m Center for the Genetics of Host Defense, Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX, United States
ⁿ Division of Research, Kaiser Permanente Northern California, Oakland, CA, United States
^o Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, United States
^p Department of Psychiatry and Human Genetics, University of Utah, Salt Lake City, UT, United States
^q Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, United States
^r Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, United States
^s K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology, Trondheim, Norway
^t Genetic Epidemiology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
^u Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
^v Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
^w Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA, United States
^x Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States
^y Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
^z Department of Child and Adolescent Psychiatry, Erasmus MC Rotterdam, Rotterdam, Netherlands
^aa Estonian Genome Center, University of Tartu, Tartu, Estonia
^ab Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama City, Japan
^ac Department of Population Health Science, Bristol Medical School, Oakfield Grove, Bristol, United Kingdom
^ad Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche, Monserrato, Italy
^ae Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
^af deCODE Genetics/Amgen, Inc., Reykjavik, Iceland
^ag Department of Epidemiology, University of Michigan, Ann Arbor, MI, United States
^ah Department of Epidemiology, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
^ai Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, United States
^aj Avera Institute for Human Genetics, Sioux Falls, SD, United States
^ak Department of Family Medicine and Community Health, Alpert Medical School, Brown University, Providence, RI, United States
^al Department of Integrative Physiology, University of Colorado Boulder, Boulder, CO, United States
^am School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland
^an Department of Sociology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
^ao Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
^ap Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, United States
^aq Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, United States
^ar Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia
^as Department of Psychiatry, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
^at Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
^au Fellows Program, RTI International, Research Triangle Park, NC, United States
^av Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, United States
^aw Department of Internal Medicine, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland
^ax Department of Medicine, Kuopio University Hospital, Kuopio, Finland
^ay Psychiatric Genetics, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
^az Department of Biostatistics and Bioinformatics, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
^ba Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
^bb Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan, Ann Arbor, MI, United States
^bc Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
^bd Department of Epidemiology, University of Washington, Seattle, WA, United States
^be Department of Clinical Genetics, VU Medical Centre Amsterdam, Amsterdam, Netherlands
^bf Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, United States
^bg Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, United States
^bh Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, United States
^bi SAA—National Center of Addiction Medicine, Vogur Hospital, Reykjavik, Iceland
^bj Department of Medicine, Vanderbilt University, Nashville, TN, United States
^bk Department of Psychiatry, University of California, San Diego, San Diego, CA, United States
^bl FORMI and Department of Neurology, Oslo University Hospital, Oslo, Norway
^bm MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
^bn HUNT Research Centre, Department of Public Health and Nursing, Norwegian University of Science and Technology, Levanger, Norway
^bo Department of Medicine, Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway
^bp UK Centre for Tobacco and Alcohol Studies, School of Psychological Science, University of Bristol, Bristol, United Kingdom
^bq Department of Genetics, Washington University School of Medicine, St. Louis, MO, United States
^br Department of Human Genetics, University of Michigan, Ann Arbor, MI, United States
^bs Department of Preventative Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
^bt Department of Medicine, Stanford University School of Medicine, Stanford, CA, United States
^bu Department of Public Health, University of Helsinki, Helsinki, Finland
^bv Faculty of Medicine, University of Iceland, Reykjavik, Iceland
^bw Department of Neurology, Oslo University Hospital, Oslo, Norway
^bx Department of Health Promotion, Norwegian Institute of Public Health, Bergen, Norway
^by Department of Mental Health, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
^bz Department of Research and Innovation, Helse-Fonna HF, Haugesund, Norway
^ca Department of Psychiatry, Hospital Namsos, Nord-Trøndelag Health Trust, Namsos, Norway
^cb Division of Mental Health Care, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
^cc Institute of Clinical Medicine, University of Oslo, Oslo, Norway
^cd Department of Psychiatry, Nord-Trøndelag Hospital Trust, Levanger Hospital, Levanger, Norway
^ce Department of Laboratory Medicine, Children’s and Women’s Health, Norwegian University of Science and Technology, Trondheim, Norway
^cf Regional Centre for Child and Youth Mental Health and Child Welfare, Department of Mental Health, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
^cg Faculty of Nursing and Health Sciences, Nord University, Levanger, Norway
^ch Norwegian Centre for Violence and Traumatic Stress Studies, Oslo, Norway

Abstract
Tobacco and alcohol use are leading causes of mortality that influence risk for many complex diseases and disorders1. They are heritable2,3 and etiologically related4,5 behaviors that have been resistant to gene discovery efforts6–11. In sample sizes up to 1.2 million individuals, we discovered 566 genetic variants in 406 loci associated with multiple stages of tobacco use (initiation, cessation, and heaviness) as well as alcohol use, with 150 loci evidencing pleiotropic association. Smoking phenotypes were positively genetically correlated with many health conditions, whereas alcohol use was negatively correlated with these conditions, such that increased genetic risk for alcohol use is associated with lower disease risk. We report evidence for the involvement of many systems in tobacco and alcohol use, including genes involved in nicotinic, dopaminergic, and glutamatergic neurotransmission. The results provide a solid starting point to evaluate the effects of these loci in model organisms and more precise substance use measures. © 2019, The Author(s), under exclusive licence to Springer Nature America, Inc.

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

Change in eating disorder symptoms following pediatric obesity treatment
(2019) International Journal of Eating Disorders, . Article in Press. 

Eichen, D.M.^a , Strong, D.R.^b , Rhee, K.E.^a , Rock, C.L.^b , Crow, S.J.^c , Epstein, L.H.^d , Wilfley, D.E.^e , Boutelle, K.N.^{a b f}

^a Department of Pediatrics, University of California, San Diego, La Jolla, CA, United States
^b Department of Family Medicine and Public Health, University of California, San Diego, La Jolla, CA, United States
^c Department of Psychiatry, University of Minnesota, Minneapolis, MN, United States
^d Department of Pediatrics, University at Buffalo, Buffalo, NY, United States
^e Department of Psychiatry, Washington University St Louis, St. Louis, MO, United States
^f Department of Psychiatry, University of California, San Diego, La Jolla, CA, United States

Abstract
Objective: The purpose of this study is to evaluate whether children with overweight or obesity participating in an evidence-based treatment, family-based behavioral treatment (FBT) for obesity, or a parent-only variant of FBT (PBT), experience an increase of eating disorder (ED) symptoms during and following treatment. Method: Children (N = 150) participating in a randomized controlled trial of FBT or PBT completed measures of EDs attitudes and behaviors at baseline, following 6-months of treatment, 6 months, and 18 months after treatment. Results: Linear-mixed effects models suggest that ED attitudes did not significantly increase. Rather, significant decreases of shape, weight, and eating concerns were shown following treatment. Loss of control over eating significantly decreased over treatment and follow-up. No participant endorsed purging at any time point. Discussion: Results confirm the hypothesis that ED symptoms do not increase after participating in FBT or a FBT-based treatment. These findings should help assuage fears of parents that enrolling their child will exacerbate ED symptoms and aid children to access evidence-based treatments that may help reduce significant physical and psychosocial complications of childhood obesity. © 2019 Wiley Periodicals, Inc.

Author Keywords
childhood obesity;  family based behavioral treatment;  feeding and eating disorders;  loss of control eating;  purging

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

Genome-wide association analyses of risk tolerance and risky behaviors in over 1 million individuals identify hundreds of loci and shared genetic influences
(2019) Nature Genetics, . Article in Press. 

Karlsson Linnér, R.^{a b c} , Biroli, P.^d , Kong, E.^e , Meddens, S.F.W.^{a b c} , Wedow, R.^{f g h i} , Fontana, M.A.^{j k} , Lebreton, M.^{l m} , Tino, S.P.ⁿ , Abdellaoui, A.^o , Hammerschlag, A.R.^a , Nivard, M.G.^o , Okbay, A.^{a c} , Rietveld, C.A.^{b p q} , Timshel, P.N.^{r s} , Trzaskowski, M.^t , Vlaming, R.^{a b c} , Zünd, C.L.^d , Bao, Y.^u , Buzdugan, L.^{v w} , Caplin, A.H.^x , Chen, C.-Y.^{f h y} , Eibich, P.^{z aa ab} , Fontanillas, P.^ac , Gonzalez, J.R.^{ad ae af} , Joshi, P.K.^ag , Karhunen, V.^ah , Kleinman, A.^ac , Levin, R.Z.^ai , Lill, C.M.^aj , Meddens, G.A.^ak , Muntané, G.^{al am an} , Sanchez-Roige, S.^ao , Rooij, F.J.^q , Taskesen, E.^a , Wu, Y.^t , Zhang, F.^t , Agee, M.^ac , Alipanahi, B.^ac , Bell, R.K.^ac , Bryc, K.^ac , Elson, S.L.^ac , Furlotte, N.A.^ac , Huber, K.E.^ac , Litterman, N.K.^ac , McCreight, J.C.^ac , McIntyre, M.H.^ac , Mountain, J.L.^ac , Northover, C.A.M.^ac , Pitts, S.J.^ac , Sathirapongsasuti, J.F.^ac , Sazonova, O.V.^ac , Shelton, J.F.^ac , Shringarpure, S.^ac , Tian, C.^ac , Tung, J.Y.^ac , Vacic, V.^ac , Wilson, C.H.^ac , Agbessi, M.^cj , Ahsan, H.^ck , Alves, I.^cj , Andiappan, A.^cl , Awadalla, P.^cj , Battle, A.^cm , Beutner, F.^cn , Jan Bonder, M.^co , Boomsma, D.I.^o , Christiansen, M.^cp , Claringbould, A.^co , Deelen, P.^co , Esko, T.^br , Favé, M.-J.^cj , Franke, L.^co , Frayling, T.^cq , Gharib, S.A.^{cr cs} , Gibson, G.^ct , Heijmans, B.^cu , Hemani, G.^cv , Jansen, R.^cw , Kähönen, M.^cx , Kalnapenkis, A.^br , Kasela, S.^br , Kettunen, J.^cy , Kim, Y.^{cm cz} , Kirsten, H.^da , Kovacs, P.^db , Krohn, K.^dc , Kronberg-Guzman, J.^br , Kukushkina, V.^br , Kutalik, Z.^dd , Lee, B.^cl , Lehtimäki, T.^de , Loeffler, M.^da , Marigorta, U.M.^ct , Metspalu, A.^br , Milani, L.^br , Montgomery, G.W.^t , Müller-Nurasyid, M.^df , Nauck, M.^dg , Nivard, M.G.^cw , Penninx, B.^cw , Perola, M.^dh , Pervjakova, N.^br , Pierce, B.^ck , Powell, J.^di , Prokisch, H.^dj , Psaty, B.M.^cp , Raitakari, O.^dk , Ring, S.^{cv dl} , Ripatti, S.^cy , Rotzchke, O.^cl , Rüeger, S.^dd , Saha, A.^cm , Scholz, M.^da , Schramm, K.^df , Seppälä, I.^de , Stumvoll, M.^dm , Sullivan, P.^ba , Hoen, P.-B.^dn , Teumer, A.^do , Thiery, J.^dp , Tong, L.^ck , Tönjes, A.^dm , Dongen, J.^cw , Meurs, J.^bl , Verlouw, J.^bl , Visscher, P.M.^t , Völker, U.^dq , Võsa, U.^co , Westra, H.-J.^co , Yaghootkar, H.^cq , Yang, J.^{t bo} , Zeng, B.^ct , Zhang, F.^t , Okbay, A.^{a c} , Beauchamp, J.P.ⁿ , Fontana, M.A.^{j k} , Lee, J.J.^ce , Pers, T.H.^{r s} , Rietveld, C.A.^{b p q} , Turley, P.^{f h bz} , Chen, G.-B.^bo , Emilsson, V.^{dr ds} , Meddens, S.F.W.^{a b c} , Oskarsson, S.^dt , Pickrell, J.K.^du , Thom, K.^cf , Timshel, P.^{r s} , de Vlaming, R.^{a b c} , Abdellaoui, A.^o , Ahluwalia, T.S.^{r dv dw} , Bacelis, J.^dx , Baumbach, C.^{dy dz} , Bjornsdottir, G.^ea , Brandsma, J.H.^eb , Concas, M.P.^ec , Derringer, J.^ed , Furlotte, N.A.^ac , Galesloot, T.E.^dn , Girotto, G.^ee , Gupta, R.^ef , Hall, L.M.^{eg eh} , Harris, S.E.^{ei ej} , Hofer, E.^{ek el} , Horikoshi, M.^{em en} , Huffman, J.E.^cc , Kaasik, K.^eo , Kalafati, I.P.^ep , Karlsson, R.^ba , Kong, A.^ea , Lahti, J.^{eo eq} , Lee, S.J.^q , de Leeuw, C.^{a er} , Lind, P.A.^es , Lindgren, K.-O.^dt , Liu, T.^cb , Mangino, M.^{et eu} , Marten, J.^cc , Mihailov, E.^br , Miller, M.B.^ce , Most, P.J.^ev , Oldmeadow, C.^{ew ex} , Payton, A.^{ey ez} , Pervjakova, N.^br , Peyrot, W.J.^{fa fb} , Qian, Y.^fc , Raitakari, O.^dk , Rueedi, R.^{fd fe} , Salvi, E.^ff , Schmidt, B.^fg , Schraut, K.E.^ag , Shi, J.^fh , Smith, A.V.^{dr fi} , Poot, R.A.^eb , Pourcain, B.S.^{cv fj} , Teumer, A.^do , Thorleifsson, G.^ea , Verweij, N.^fk , Vuckovic, D.^ee , Wellmann, J.^fl , Westra, H.-J.^co , Yang, J.^{fm fn} , Zhao, W.^fo , Zhu, Z.^bo , Alizadeh, B.Z.^{ev fp} , Amin, N.^q , Bakshi, A.^bo , Baumeister, S.E.^{do fq} , Biino, G.^fr , Bønnelykke, K.^dv , Boyle, P.A.^{fm fs} , Campbell, H.^ag , Cappuccio, F.P.^ft , Davies, G.^{ei fu} , De Neve, J.-E.^fv , Deloukas, P.^{fw fx} , Demuth, I.^{fy fz} , Ding, J.^fc , Eibich, P.^{z aa ab} , Eisele, L.^fg , Eklund, N.^dh , Evans, D.M.^{cv ga} , Faul, J.D.^gb , Feitosa, M.F.^gc , Forstner, A.J.^{gd ge} , Gandin, I.^ee , Gunnarsson, B.^ec , Halldórsson, B.V.^{ea gf} , Harris, T.B.^gg , Heath, A.C.^gh , Hocking, L.J.^gi , Holliday, E.G.^{ew ex} , Homuth, G.^dq , Horan, M.A.^gj , Hottenga, J.-J.^o , de Jager, P.L.^{gk gl gm} , Joshi, P.K.^ag , Jugessur, A.^gn , Kaakinen, M.A.^go , Kähönen, M.^cx , Kanoni, S.^fw , Keltigangas-Järvinen, L.^eo , Kiemeney, L.A.L.M.^dn , Kolcic, I.^gp , Koskinen, S.^dh , Kraja, A.T.^gc , Kroh, M.^aa , Kutalik, Z.^dd , Latvala, A.^ef , Launer, L.J.^gq , Lebreton, M.P.^{l m} , Levinson, D.F.^gr , Lichtenstein, P.^ba , Lichtner, P.^dj , Liewald, D.C.M.^{ei fu} , Loukola, A.^ef , Madden, P.A.^gh , Mägi, R.^br , Mäki-Opas, T.^dh , Marioni, R.E.^{bo ei gs} , Marques-Vidal, P.^gt , Meddens, G.A.^ak , McMahon, G.^cv , Meisinger, C.^dz , Meitinger, T.^dj , Milaneschi, Y.^{fa fb} , Milani, L.^br , Montgomery, G.W.^t , Myhre, R.^gn , Nelson, C.P.^{eg eh} , Nyholt, D.R.^{gu gv} , Ollier, W.E.R.^ey , Palotie, A.^{f y gk gw gx gy} , Paternoster, L.^cv , Pedersen, N.L.^ba , Petrovic, K.E.^ek , Porteous, D.J.^ej , Räikkönen, K.^{eo eq} , Ring, S.M.^cv , Robino, A.^gz , Rostapshova, O.^{e ha} , Rudan, I.^ag , Rustichini, A.^hb , Salomaa, V.^dh , Sanders, A.R.^{bn hc} , Sarin, A.-P.^{gx hd} , Schmidt, H.^{ek he} , Scott, R.J.^{ex hf} , Smith, B.H.^hg , Smith, J.A.^fo , Staessen, J.A.^{hh hi} , Steinhagen-Thiessen, E.^fy , Strauch, K.^{df hj} , Terracciano, A.^hk , Tobin, M.D.^hl , Ulivi, S.^gz , Vaccargiu, S.^ec , Quaye, L.^et , Rooij, F.J.^q , Venturini, C.^{et eu} , Vinkhuyzen, A.A.E.^bo , Völker, U.^dq , Völzke, H.^do , Vonk, J.M.^ev , Vozzi, D.^ha , Waage, J.^{dv dw} , Ware, E.B.^{fo hm} , Willemsen, G.^o , Attia, J.R.^{ew ex} , Bennett, D.A.^{fm fn} , Berger, K.^fk , Bertram, L.^{aj bp bq} , Bisgaard, H.^dv , Boomsma, D.I.^o , Borecki, I.B.^gc , Bültmann, U.^hn , Chabris, C.F.^ho , Cucca, F.^hp , Cusi, D.^{ff hq} , Deary, I.J.^{ei fu} , Dedoussis, G.V.^ep , Duijn, C.M.^q , Eriksson, J.G.^{eq hr} , Franke, B.^hs , Franke, L.^co , Gasparini, P.^{ee gz ht} , Gejman, P.V.^{bn hc} , Gieger, C.^dy , Grabe, H.-J.^{hu hv} , Gratten, J.^{t cd} , Groenen, P.J.F.^{b av} , Gudnason, V.^{dr fi} , Harst, P.^{co fk hw} , Hayward, C.^{cc hx} , Hinds, D.A.^ac , Hoffmann, W.^do , Hyppönen, E.^{hy hz ia} , Iacono, W.G.^ce , Jacobsson, B.^{dx gn} , Järvelin, M.-R.^{ib ic id ie} , Jöckel, K.-H.^fg , Kaprio, J.^{dh ef gx} , Kardia, S.L.R.^fo , Lehtimäki, T.^de , Lehrer, S.F.^{if ig} , Magnusson, P.K.E.^ba , Martin, N.G.^ih , McGue, M.^ce , Metspalu, A.^br , Pendleton, N.^{ii ij} , Penninx, B.^cw , Perola, M.^dh , Pirastu, N.^ee , Pirastu, M.^ec , Polasek, O.^{ag ik} , Posthuma, D.^{a bx} , Power, C.^ia , Province, M.A.^gc , Samani, N.J.^{eg eh} , Schlessinger, D.^fc , Schmidt, R.^ek , Sørensen, T.I.A.^{r cv il} , Spector, T.D.^et , Stefansson, K.^{ea fi} , Thorsteinsdottir, U.^{ea fi} , Thurik, A.R.^{b bk} , Timpson, N.J.^cv , Tiemeier, H.^q , Tung, J.Y.^ac , Uitterlinden, A.G.^bl , Vitart, V.^cc , Vollenweider, P.^gt , Weir, D.R.^gb , Wilson, J.F.^{ag cc} , Wright, A.F.^cc , Conley, D.C.^{im in} , Krueger, R.F.^ce , Smith, G.D.^cv , Hofman, A.^{g q} , Laibson, D.I.^e , Medland, S.E.^es , Meyer, M.N.^bt , Yang, J.^{t bo} , Johannesson, M.^bs , Visscher, P.M.^t , Esko, T.^br , Koellinger, P.D.^{a c ci} , Cesarini, D.^cf , Benjamin, D.J.^{bz cg ch} , Auton, A.^ac , Boardman, J.D.^{ap aq ar} , Clark, D.W.^ag , Conlin, A.^as , Dolan, C.C.^o , Fischbacher, U.^{at au} , Groenen, P.J.F.^{b av} , Harris, K.M.^{aw ax} , Hasler, G.^ay , Hofman, A.^{g q} , Ikram, M.A.^q , Jain, S.^az , Karlsson, R.^ba , Kessler, R.C.^bb , Kooyman, M.^bc , MacKillop, J.^{bd be} , Männikkö, M.^ah , Morcillo-Suarez, C.^al , McQueen, M.B.^bf , Schmidt, K.M.^bg , Smart, M.C.^u , Sutter, M.^{bh bi bj} , Thurik, A.R.^{b bk} , Uitterlinden, A.G.^bl , White, J.^bm , Wit, H.^bn , Yang, J.^{t bo} , Bertram, L.^{aj bp bq} , Boomsma, D.I.^o , Esko, T.^br , Fehr, E.^w , Hinds, D.A.^ac , Johannesson, M.^bs , Kumari, M.^u , Laibson, D.^e , Magnusson, P.K.E.^ba , Meyer, M.N.^bt , Navarro, A.^{al bu bv} , Palmer, A.A.^{ao bw} , Pers, T.H.^{r s} , Posthuma, D.^{a bx} , Schunk, D.^by , Stein, M.B.^{ao az} , Svento, R.^as , Tiemeier, H.^q , Timmers, P.R.H.J.^ag , Turley, P.^{f h bz} , Ursano, R.J.^ca , Wagner, G.G.^{aa cb} , Wilson, J.F.^{ag cc} , Gratten, J.^{t cd} , Lee, J.J.^ce , Cesarini, D.^cf , Benjamin, D.J.^{bz cg ch} , Koellinger, P.D.^{a c ci} , Beauchamp, J.P.ⁿ , 23and Me Research Team^io , eQTLgen Consortium^io , International Cannabis Consortium^ip , Social Science Genetic Association Consortium^ip

^a Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, VU University Amsterdam, Amsterdam, Netherlands
^b Erasmus University Rotterdam Institute for Behavior and Biology, Erasmus School of Economics, Erasmus University Rotterdam, Rotterdam, Netherlands
^c Department of Economics, VU University Amsterdam, Amsterdam, Netherlands
^d Department of Economics, University of Zurich, Zurich, Switzerland
^e Department of Economics, Harvard University, Cambridge, MA, United States
^f Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, United States
^g Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, United States
^h Analytic Translational Genetics Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
ⁱ Department of Sociology, Harvard University, Cambridge, MA, United States
^j Center for the Advancement of Value in Musculoskeletal Care, Hospital for Special Surgery, New York, NY, United States
^k Department of Healthcare Policy and Research, Weill Cornell Medical College, Cornell University, New York, NY, United States
^l Amsterdam School of Economics, University of Amsterdam, Amsterdam, Netherlands
^m Amsterdam Brain and Cognition, University of Amsterdam, Amsterdam, Netherlands
ⁿ Department of Economics, University of Toronto, Toronto, ON, Canada
^o Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
^p Department of Applied Economics, Erasmus School of Economics, Erasmus University Rotterdam, Rotterdam, Netherlands
^q Department of Epidemiology, Erasmus Medical Center, Rotterdam, Netherlands
^r Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
^s Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark
^t Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
^u Institute for Social and Economic Research, University of Essex, Colchester, United Kingdom
^v Seminar for Statistics, Department of Mathematics, ETH Zurich, Zurich, Switzerland
^w Department of Economics, University of Zurich, Zurich, Switzerland
^x Stuyvesant High School, New York, NY, United States
^y Psychiatric & Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston, MA, United States
^z Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
^aa Socio-Economic Panel Study, DIW Berlin, Berlin, Germany
^ab Max Planck Institute for Demographic Research, Rostock, Germany
^ac Research, 23andMe, Inc., Mountain View, CA, United States
^ad Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
^ae Universitat Pompeu Fabra (UPF), Barcelona, Spain
^af CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
^ag Centre for Global Health Research, Usher Institute for Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, United Kingdom
^ah Center for Life Course Health Research, University of Oulu, Oulu, Finland
^ai Department of Economics, University of California San Diego, La Jolla, CA, United States
^aj Genetic and Molecular Epidemiology Group, Lübeck Interdisciplinary Platform for Genome Analytics, Institutes of Neurogenetics & Cardiogenetics, University of Lübeck, Lübeck, Germany
^ak Team Loyalty BV, Hoofddorp, Netherlands
^al Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra, Barcelona, Spain
^am Research Department, Hospital Universitari Institut Pere Mata, Institut d’Investigació Sanitària Pere Virgili (IISPV), Reus, Spain
^an Centro de Investigación Biomédica en Red en Salud Mental (CIBERSAM), Reus, Spain
^ao Department of Psychiatry, University of California San Diego, La Jolla, CA, United States
^ap Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, United States
^aq Institute of Behavioral Science, University of Colorado Boulder, Boulder, CO, United States
^ar Department of Sociology, University of Colorado Boulder, Boulder, CO, United States
^as Department of Economics and Finance, Oulu Business School, University of Oulu, Oulu, Finland
^at Department of Economics, University of Konstanz, Konstanz, Germany
^au Thurgau Institute of Economics, Kreuzlingen, Switzerland
^av Department of Econometrics, Erasmus University Rotterdam, Rotterdam, Rotterdam, Netherlands
^aw Department of Sociology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
^ax Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
^ay Unit of Psychiatry Research, University of Fribourg, Fribourg, Switzerland
^az Family Medicine and Public Health, University of California San Diego, La Jolla, CA, United States
^ba Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
^bb Department of Health Care Policy, Harvard Medical School, Boston, MA, United States
^bc SURFsara, Amsterdam, Netherlands
^bd Peter Boris Centre for Addictions Research, McMaster University/St. Joseph’s Healthcare Hamilton, Hamilton, ON, Canada
^be Homewood Research Institute, Guelph, ON, Canada
^bf Department of Integrative Physiology, University of Colorado Boulder, Boulder, CO, United States
^bg Department of Economics, University of Munich, Munich, Germany
^bh Department of Economics, University of Cologne, Cologne, Germany
^bi Experimental Economics Group, Max Planck Institute for Research into Collective Goods, Bonn, Germany
^bj Department of Public Finance, University of Innsbruck, Innsbruck, Austria
^bk Montpellier Business School, Montpellier, France
^bl Department of Internal Medicine, Erasmus Medical Centre, Rotterdam, Netherlands
^bm UCL Genetics Institute, Department of Genetics, Evolution and Environment, University College London, London, United Kingdom
^bn Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL, United States
^bo Queensland Brain Institute, The University of Queensland, Brisbane, Australia
^bp Dept of Psychology, University of Olso, Oslo, Norway
^bq School of Public Health, Imperial College London, London, United Kingdom
^br Estonian Genome Center, University of Tartu, Tartu, Estonia
^bs Department of Economics, Stockholm School of Economics, Stockholm, Sweden
^bt Center for Translational Bioethics and Health Care Policy, Geisinger Health System, Danville, PA, United States
^bu Centre for Genomic Regulation, Barcelona Institute of Science and Technology, Barcelona, Spain
^bv Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Catalonia, Spain
^bw Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, United States
^bx Department of Clinical Genetics, VU Medical Centre, Amsterdam, Netherlands
^by Department of Economics, Johannes Gutenberg University, Mainz, Germany
^bz Behavioral and Health Genomics Center, Center for Economic and Social Research, University of Southern California, Los Angeles, CA, United States
^ca Department of Psychiatry, Center for the Study of Traumatic Stress, Uniformed Services University of the Health Sciences, Bethesda, MD, United States
^cb Max Planck Institute for Human Development, Berlin, Germany
^cc MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, United Kingdom
^cd Mater Medical Research Institute, Translational Research Institute, Brisbane, Australia
^ce Department of Psychology, University of Minnesota Twin Cities, Minneapolis, MN, United States
^cf Department of Economics, New York University, New York, NY, United States
^cg Department of Economics, University of Southern California, Los Angeles, CA, United States
^ch National Bureau of Economic Research, Cambridge, MA, United States
^ci German Institute for Economic Research, DIW Berlin, Berlin, Germany
^cj Computational Biology, Ontario Institute for Cancer Research, Toronto, Canada
^ck Department of Public Health Sciences, University of Chicago, Chicago, IL, United States
^cl Singapore Immunology Network, Agency for Science, Technology and Research, Singapore, Singapore
^cm Department of Computer Science, Johns Hopkins University, Baltimore, MD, United States
^cn Heart Center Leipzig, Universität Leipzig, Leipzig, Germany
^co Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
^cp Cardiovascular Health Research Unit, Departments of Epidemiology, Medicine, and Health Services, University of Washington, Seattle, WA, United States
^cq Medical School, University of Exeter, Exeter, United Kingdom
^cr Department of Medicine, University of Washington, Seattle, WA, United States
^cs Kaiser Permanente Washington Health Research Institute, Seattle, WA, United States
^ct School of Biological Sciences, Georgia Tech, Atlanta, GA, United States
^cu Leiden University Medical Centre, Leiden, Netherlands
^cv MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
^cw Vrije Universiteit Amsterdam, Amsterdam, Netherlands
^cx Department of Clinical Physiology, Tampere University Hospital and Faculty of Medicine and Life Sciences, University of Tampere, Tampere, Finland
^cy University of Helsinki, Helsinki, Finland
^cz Genetics and Genomic Science Department, Icahn School of Medicine at Mount Sinai, New York, NY, United States
^da Institut für Medizinische Informatik, Statistik und Epidemiologie, Leipzig Research Centre for Civilization Diseases (LIFE), Universität Leipzig, Leipzig, Germany
^db IFB Adiposity Diseases, Universität Leipzig, Leipzig, Germany
^dc Interdisciplinary Center for Clinical Research, Faculty of Medicine, Universität Leipzig, Leipzig, Germany
^dd Lausanne University Hospital, Lausanne, Switzerland
^de Department of Clinical Chemistry, Fimlab Laboratories and Faculty of Medicine and Life Sciences, University of Tampere, Tampere, Finland
^df Institute of Genetic Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
^dg Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany
^dh National Institute for Health and Welfare, University of Helsinki, Helsinki, Finland
^di Garvan Institute of Medical Research, Garvan-Weizmann Centre for Cellular Genomics, Sydney, NSW, Australia
^dj Institute of Human Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
^dk Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital and University of Turku, Turku, Finland
^dl Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
^dm Department of Medicine, Universität Leipzig, Leipzig, Germany
^dn Radboud University Medical Center, Radboud University, Nijmegen, Netherlands
^do Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
^dp Institute for Laboratory Medicine, Leipzig Research Center for Civilization Diseases (LIFE), Universität Leipzig, Leipzig, Germany
^dq Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany
^dr Icelandic Heart Association, Kopavogur, Iceland
^ds Faculty of Pharmaceutical Sciences, University of Iceland, Reykjavík, Iceland
^dt Department of Government, Uppsala University, Uppsala, Sweden
^du New York Genome Center, New York, NY, United States
^dv Copenhagen Prospective Studies on Asthma in Childhood (COPSAC), Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark
^dw Steno Diabetes Center, Gentofte, Denmark
^dx Department of Obstetrics and Gynecology, Institute of Clinical Sciences, Sahlgrenska Academy, Gothenburg, Sweden
^dy Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
^dz Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
^ea deCODE Genetics/Amgen Inc., Reykjavik, Iceland
^eb Department of Cell Biology, Erasmus Medical Center, Rotterdam, Netherlands
^ec Istituto di Ricerca Genetica e Biomedica UOS di Sassari, National Research Council of Italy, Sassari, Italy
^ed Psychology, University of Illinois, Champaign, IL, United States
^ee Department of Medical, Surgical and Health Sciences, University of Trieste, Trieste, Italy
^ef Department of Public Health, University of Helsinki, Helsinki, Finland
^eg Department of Cardiovascular Sciences, University of Leicester, Leicester, United Kingdom
^eh NIHR Leicester Cardiovascular Biomedical Research Unit, Glenfield Hospital, Leicester, United Kingdom
^ei Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom
^ej Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, United Kingdom
^ek Department of Neurology, General Hospital and Medical University Graz, Graz, Austria
^el Institute for Medical Informatics, Statistics and Documentation, General Hospital and Medical University Graz, Graz, Austria
^em Oxford Centre for Diabetes, Endocrinology & Metabolism, University of Oxford, Oxford, United Kingdom
^en Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
^eo Institute of Behavioural Sciences, University of Helsinki, Helsinki, Finland
^ep Nutrition and Dietetics, Health Science and Education, Harokopio University, Athens, Greece
^eq Folkhälsan Research Centre, Helsingfors, Finland
^er Institute for Computing and Information Sciences, Radboud University Nijmegen, Nijmegen, Netherlands
^es Quantitative Genetics, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
^et Department of Twin Research and Genetic Epidemiology, King’s College London, London, United Kingdom
^eu NIHR Biomedical Research Centre, Guy’s and St. Thomas’ Foundation Trust, London, United Kingdom
^ev Department of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
^ew Public Health Stream, Hunter Medical Research Institute, New Lambton, NSW, Australia
^ex Faculty of Health and Medicine, University of Newcastle, Newcastle, NSW, Australia
^ey Centre for Integrated Genomic Medical Research, Institute of Population Health, University of Manchester, Manchester, United Kingdom
^ez Human Communication and Deafness, School of Psychological Sciences, University of Manchester, Manchester, United Kingdom
^fa Department of Psychiatry, VU University Medical Center, Amsterdam, Netherlands
^fb GGZ inGeest, Amsterdam, Netherlands
^fc Laboratory of Genetics, National Institute on Aging, Baltimore, MD, United States
^fd Department of Medical Genetics, University of Lausanne, Lausanne, Switzerland
^fe Swiss Institute of Bioinformatics, Lausanne, Switzerland
^ff Department Of Health Sciences, University of Milan, Milano, Italy
^fg Institute for Medical Informatics, Biometry and Epidemiology, University Hospital of Essen, Essen, Germany
^fh Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, United States
^fi Faculty of Medicine, University of Iceland, Reykjavik, Iceland
^fj School of Oral and Dental Sciences, University of Bristol, Bristol, United Kingdom
^fk Department of Cardiology, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
^fl Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany
^fm Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL, United States
^fn Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, United States
^fo Department of Epidemiology, University of Michigan, Ann Arbor, MI, United States
^fp Department of Gastroenterology and Hepatology, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
^fq Institute of Epidemiology and Preventive Medicine, University of Regensburg, Regensburg, Germany
^fr Institute of Molecular Genetics, National Research Council of Italy, Pavia, Italy
^fs Department of Behavioral Sciences, Rush University Medical Center, Chicago, IL, United States
^ft Warwick Medical School, University of Warwick, Coventry, United Kingdom
^fu Department of Psychology, University of Edinburgh, Edinburgh, United Kingdom
^fv Saïd Business School, University of Oxford, Oxford, United Kingdom
^fw William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
^fx Princess Al-Jawhara Al-Brahim Centre of Excellence in Research of Hereditary Disorders (PACER-HD), King Abdulaziz University, Jeddah, Saudi Arabia
^fy The Berlin Aging Study II Research Group on Geriatrics, Charité–Universitätsmedizin Berlin, Berlin, Germany
^fz Institute of Medical and Human Genetics, Charité–Universitätsmedizin Berlin, Berlin, Germany
^ga University of Queensland Diamantina Institute, Translational Research Institute, University of Queensland, Brisbane, QLD, Australia
^gb Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, United States
^gc Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO, United States
^gd Institute of Human Genetics, University of Bonn, Bonn, Germany
^ge Department of Genomics, Life and Brain Center, University of Bonn, Bonn, Germany
^gf Institute of Biomedical and Neural Engineering, School of Science and Engineering, Reykjavik University, Reykjavik, Iceland
^gg Laboratory of Epidemiology & Population Science, National Institute on Aging, National Institutes of Health, Bethesda, MD, United States
^gh Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, United States
^gi Division of Applied Health Sciences, University of Aberdeen, Aberdeen, United Kingdom
^gj Manchester Medical School, University of Manchester, Manchester, United Kingdom
^gk Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, United States
^gl Program in Translational NeuroPsychiatric Genomics, Departments of Neurology and Psychiatry, Brigham and Women’s Hospital, Boston, MA, United States
^gm Harvard Medical School, Boston, MA, United States
^gn Department of Genes and Environment, Norwegian Institute of Public Health, Oslo, Norway
^go Department of Genomics of Common Disease, Imperial College London, London, United Kingdom
^gp Public Health, Medical School, University of Split, Split, Croatia
^gq Neuroepidemiology Section, National Institute on Aging, National Institutes of Health, Bethesda, MD, United States
^gr Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, United States
^gs Medical Genetics Section, Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, United Kingdom
^gt Department of Internal Medicine, Lausanne University Hospital (CHUV), Lausanne, Switzerland
^gu Molecular Epidemiology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
^gv Institute of Health and Biomedical Innovation, Queensland Institute of Technology, Brisbane, QLD, Australia
^gw Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, United States
^gx Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
^gy Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
^gz Medical Genetics, Institute for Maternal and Child Health IRCCS ‘Burlo Garofolo’, Trieste, Italy
^ha Social Impact, Arlington, VA, United States
^hb Department of Economics, University of Minnesota Twin Cities, Minneapolis, MN, United States
^hc Department of Psychiatry and Behavioral Sciences, NorthShore University HealthSystem, Evanston, IL, United States
^hd Public Health Genomics Unit, National Institute for Health and Welfare, Helsinki, Finland
^he Research Unit for Genetic Epidemiology, Institute of Molecular Biology and Biochemistry, Center of Molecular Medicine, General Hospital and Medical University, Graz, Austria
^hf Information Based Medicine Stream, Hunter Medical Research Institute, New Lambton, NSW, Australia
^hg Medical Research Institute, University of Dundee, Dundee, United Kingdom
^hh Research Unit Hypertension and Cardiovascular Epidemiology, Department of Cardiovascular Science, University of Leuven, Leuven, Belgium
^hi R&D VitaK Group, Maastricht University, Maastricht, Netherlands
^hj Institute of Medical Informatics, Biometry and Epidemiology, Chair of Genetic Epidemiology, Ludwig Maximilians-Universität, Munich, Germany
^hk Department of Geriatrics, Florida State University College of Medicine, Tallahassee, FL, United States
^hl Department of Health Sciences and Genetics, University of Leicester, Leicester, United Kingdom
^hm Research Center for Group Dynamics, Institute for Social Research, University of Michigan, Ann Arbor, MI, United States
^hn Department of Health Sciences, Community and Occupational Medicine, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
^ho Department of Psychology, Union College, Schenectady, NY, United States
^hp Istituto di Ricerca Genetica e Biomedica (IRGB), Consiglio Nazionale delle Ricerche, Cittadella Universitaria di Monserrato, Cagliari, Italy
^hq Institute of Biomedical Technologies, Italian National Research Council, Milan, Italy
^hr Department of General Practice and Primary Health Care, University of Helsinki, Helsinki, Finland
^hs Departments of Human Genetics and Psychiatry, Donders Centre for Neuroscience, Nijmegen, Netherlands
^ht Experimental Genetics Division, Sidra, Doha, Qatar
^hu Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
^hv Department of Psychiatry and Psychotherapy, HELIOS-Hospital Stralsund, Stralsund, Germany
^hw Durrer Center for Cardiogenetic Research, ICIN-Netherlands Heart Institute, Utrecht, Netherlands
^hx Generation Scotland, Centre for Genomics and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, United Kingdom
^hy Centre for Population Health Research, School of Health Sciences and Sansom Institute, University of South Australia, Adelaide, SA, Australia
^hz South Australian Health and Medical Research Institute, Adelaide, SA, Australia
^ia Population, Policy and Practice, UCL Institute of Child Health, London, United Kingdom
^ib Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, United Kingdom
^ic Center for Life Course Epidemiology, Faculty of Medicine, University of Oulu, Oulu, Finland
^id Unit of Primary Care, Oulu University Hospital, Oulu, Finland
^ie Biocenter Oulu, University of Oulu, Oulu, Finland
^if Economics, NYU Shanghai, Pudong, China
^ig Policy Studies, Queen’s University, Kingston, ON, Canada
^ih Genetic Epidemiology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
ⁱⁱ Centre for Clinical and Cognitive Neuroscience, Institute of Brain Behaviour and Mental Health, Salford Royal Hospital, Manchester, United Kingdom
^ij Manchester Institute for Collaborative Research in Ageing, University of Manchester, Manchester, United Kingdom
^ik Faculty of Medicine, University of Split, Split, Croatia
^il Institute of Preventive Medicine, Bispebjerg and Frederiksberg Hospitals, Frederiksberg, Denmark
^im Department of Sociology, New York University, New York, NY, United States
ⁱⁿ School of Medicine, New York University, New York, NY, United States

Abstract
Humans vary substantially in their willingness to take risks. In a combined sample of over 1 million individuals, we conducted genome-wide association studies (GWAS) of general risk tolerance, adventurousness, and risky behaviors in the driving, drinking, smoking, and sexual domains. Across all GWAS, we identified hundreds of associated loci, including 99 loci associated with general risk tolerance. We report evidence of substantial shared genetic influences across risk tolerance and the risky behaviors: 46 of the 99 general risk tolerance loci contain a lead SNP for at least one of our other GWAS, and general risk tolerance is genetically correlated (∣r̂ g∣ ~ 0.25 to 0.50) with a range of risky behaviors. Bioinformatics analyses imply that genes near SNPs associated with general risk tolerance are highly expressed in brain tissues and point to a role for glutamatergic and GABAergic neurotransmission. We found no evidence of enrichment for genes previously hypothesized to relate to risk tolerance. © 2019, The Author(s), under exclusive licence to Springer Nature America, Inc.

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

Restricted and Repetitive Behavior and Brain Functional Connectivity in Infants at Risk for Developing Autism Spectrum Disorder
(2019) Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 4 (1), pp. 50-61. Cited 1 time.

McKinnon, C.J.^{a f} , Eggebrecht, A.T.^c , Todorov, A.^a , Wolff, J.J.^g , Elison, J.T.^h , Adams, C.M.^a , Snyder, A.Z.^c , Estes, A.M.ⁱ , Zwaigenbaum, L.^k , Botteron, K.N.^{a c} , McKinstry, R.C.^c , Marrus, N.^a , Evans, A.^l , Hazlett, H.C.^m , Dager, S.R.^j , Paterson, S.J.ⁿ , Pandey, J.^{o p} , Schultz, R.T.^{o p} , Styner, M.A.^m , Gerig, G.^q , Schlaggar, B.L.^{a b c} , Petersen, S.E.^{b c d e} , Piven, J.^m , Pruett, J.R., Jr.^a , IBIS Network^r

^a Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, United States
^b Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
^c Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, United States
^d Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO, United States
^e Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, United States
^f Biological Sciences Division, University of Chicago, Chicago, IL, United States
^g Department of Educational Psychology, University of Minnesota, Minneapolis, MN, United States
^h Institute of Child Development, University of Minnesota, Minneapolis, MN, United States
ⁱ Department of Speech and Hearing Sciences, University of Washington, Seattle, WA, United States
^j Department of Radiology and Bioengineering, University of Washington, Seattle, WA, United States
^k Department of Pediatrics, University of Alberta, Edmonton, Alberta, Canada
^l McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
^m The Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Carborro, NC, United States
ⁿ Department of Psychology, Temple University, Philadelphia, PA, United States
^o Center for Autism Research, Children’s Hospital of Philadelphia, Philadelphia, PA, United States
^p Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, United States
^q Tandon School of Engineering, New York University, Brooklyn, NY, United States

Abstract
Background: Restricted and repetitive behaviors (RRBs), detectable by 12 months in many infants in whom autism spectrum disorder (ASD) is later diagnosed, may represent some of the earliest behavioral markers of ASD. However, brain function underlying the emergence of these key behaviors remains unknown. Methods: Behavioral and resting-state functional connectivity (fc) magnetic resonance imaging data were collected from 167 children at high and low familial risk for ASD at 12 and 24 months (n = 38 at both time points). Twenty infants met criteria for ASD at 24 months. We divided RRBs into four subcategories (restricted, stereotyped, ritualistic/sameness, self-injurious) and used a data-driven approach to identify functional brain networks associated with the development of each RRB subcategory. Results: Higher scores for ritualistic/sameness behavior were associated with less positive fc between visual and control networks at 12 and 24 months. Ritualistic/sameness and stereotyped behaviors were associated with less positive fc between visual and default mode networks at 12 months. At 24 months, stereotyped and restricted behaviors were associated with more positive fc between default mode and control networks. Additionally, at 24 months, stereotyped behavior was associated with more positive fc between dorsal attention and subcortical networks, whereas restricted behavior was associated with more positive fc between default mode and dorsal attention networks. No significant network-level associations were observed for self-injurious behavior. Conclusions: These observations mark the earliest known description of functional brain systems underlying RRBs, reinforce the construct validity of RRB subcategories in infants, and implicate specific neural substrates for future interventions targeting RRBs. © 2018 Society of Biological Psychiatry

Author Keywords
Autism spectrum disorder;  Brain development;  Functional connectivity;  Functional magnetic resonance imaging;  Infant;  Restricted and repetitive behavior

Document Type: Article
Publication Stage: Final
Source: Scopus

Blood–brain barrier breakdown is an early biomarker of human cognitive dysfunction
(2019) Nature Medicine, . Article in Press. 

Nation, D.A.^{a b c} , Sweeney, M.D.^a , Montagne, A.^a , Sagare, A.P.^a , D’Orazio, L.M.^{b d} , Pachicano, M.^a , Sepehrband, F.^e , Nelson, A.R.^a , Buennagel, D.P.^f , Harrington, M.G.^f , Benzinger, T.L.S.^{g h} , Fagan, A.M.^{h i j} , Ringman, J.M.^{b d} , Schneider, L.S.^{b d k} , Morris, J.C.^{h i} , Chui, H.C.^{b d} , Law, M.^{b e l} , Toga, A.W.^{b e} , Zlokovic, B.V.^{a b}

^a Department of Physiology and Neuroscience, Zilkha Neurogenetic Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
^b Alzheimer’s Disease Research Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
^c Department of Psychology, University of Southern California, Los Angeles, CA, United States
^d Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
^e Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
^f Huntington Medical Research Institutes, Pasadena, CA, United States
^g Department of Radiology, Washington University School of Medicine, St. Louis, MO, United States
^h The Hope Center for Neurodegenerative Disorders, Washington University School of Medicine, St. Louis, MO, United States
ⁱ Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
^j The Knight Alzheimer’s Disease Research Center, Washington University School of Medicine, St. Louis, MO, United States
^k Department of Psychiatry and Behavioral Sciences, University of Southern California, Los Angeles, CA, United States
^l Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States

Abstract
Vascular contributions to cognitive impairment are increasingly recognized1–5 as shown by neuropathological6,7, neuroimaging4,8–11, and cerebrospinal fluid biomarker4,12 studies. Moreover, small vessel disease of the brain has been estimated to contribute to approximately 50% of all dementias worldwide, including those caused by Alzheimer’s disease (AD)3,4,13. Vascular changes in AD have been typically attributed to the vasoactive and/or vasculotoxic effects of amyloid-β (Aβ)3,11,14, and more recently tau15. Animal studies suggest that Aβ and tau lead to blood vessel abnormalities and blood–brain barrier (BBB) breakdown14–16. Although neurovascular dysfunction3,11 and BBB breakdown develop early in AD1,4,5,8–10,12,13, how they relate to changes in the AD classical biomarkers Aβ and tau, which also develop before dementia17, remains unknown. To address this question, we studied brain capillary damage using a novel cerebrospinal fluid biomarker of BBB-associated capillary mural cell pericyte, soluble platelet-derived growth factor receptor-β8,18, and regional BBB permeability using dynamic contrast-enhanced magnetic resonance imaging8–10. Our data show that individuals with early cognitive dysfunction develop brain capillary damage and BBB breakdown in the hippocampus irrespective of Alzheimer’s Aβ and/or tau biomarker changes, suggesting that BBB breakdown is an early biomarker of human cognitive dysfunction independent of Aβ and tau. © 2019, The Author(s), under exclusive licence to Springer Nature America, Inc.

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

“I just want to be skinny.”: A content analysis of tweets expressing eating disorder symptoms
(2019) PLoS ONE, 14 (1), art. no. e0207506, . 

Cavazos-Rehg, P.A., Krauss, M.J., Costello, S.J., Kaiser, N., Cahn, E.S., Fitzsimmons-Craft, E.E., Wilfley, D.E.

Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, United States

Abstract
There is increasing concern about online communities that promote eating disorder (ED) behaviors through messages and/or images that encourage a “thin ideal” (i.e., promotion of thinness as attractive) and harmful weight loss/weight control practices. The purpose of this paper is to assess the content of body image and ED-related content on Twitter and provide a deeper understanding of EDs that may be used for future studies and online-based interventions. Tweets containing ED or body image-related keywords were collected from January 1-January 31, 2015 (N = 28,642). A random sample (n = 3000) was assessed for expressions of behaviors that align with subscales of the Eating Disorder Examination (EDE) 16.0. Demographic characteristics were inferred using a social media analytics company. The comprehensive research that we conducted indicated that 2,584 of the 3,000 tweets were ED-related; 65% expressed a preoccupation with body shape, 13% displayed issues related to food/eating/calories, and 4% expressed placing a high level of importance on body weight. Most tweets were sent by girls (90%) who were 19 years old (77%). Our findings stress a need to better understand if and how ED-related content on social media can be used for targeting prevention and intervention messages towards those who are in-need and could potentially benefit from these efforts. © 2019 Cavazos-Rehg et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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

Halogen Substitution Influences Ketamine Metabolism by Cytochrome P450 2B6: In Vitro and Computational Approaches
(2019) Molecular Pharmaceutics, . Article in Press. 

Wang, P.-F.^a , Neiner, A.^b , Lane, T.R.^c , Zorn, K.M.^c , Ekins, S.^c , Kharasch, E.D.^a

^a Department of Anesthesiology, Duke University, School of Medicine, Durham, NC 27710, United States
^b Department of Anesthesiology, Washington University in St. Louis, St. Louis, MO 63130, United States
^c Collaborations Pharmaceuticals, Inc., Lab 3510, Main Campus Drive, Raleigh, NC 27606, United States

Abstract
Ketamine is analgesic at anesthetic and subanesthetic doses, and it has been used recently to treat depression. Biotransformation mediates ketamine effects, influencing both systemic elimination and bioactivation. CYP2B6 is the major catalyst of hepatic ketamine N-demethylation and metabolism at clinically relevant concentrations. Numerous CYP2B6 substrates contain halogens. CYP2B6 readily forms halogen-protein (particularly Cl-π) bonds, which influence substrate selectivity and active site orientation. Ketamine is chlorinated, but little is known about the metabolism of halogenated analogs. This investigation evaluated halogen substitution effects on CYP2B6-catalyzed ketamine analogs N-demethylation in vitro and modeled interactions with CYP2B6 using various computational approaches. Ortho phenyl ring halogen substituent changes caused substantial (18-fold) differences in Km, on the order of Br (bromoketamine, 10 μM) &lt; Cl &lt; F &lt; H (deschloroketamine, 184 μM). In contrast, Vmax varied minimally (83-103 pmol/min/pmol CYP). Thus, apparent substrate binding affinity was the major consequence of halogen substitution and the major determinant of N-demethylation. Docking poses of ketamine and analogs were similar, sharing a π-stack with F297. Libdock scores were deschloroketamine &lt; bromoketamine &lt; ketamine &lt; fluoroketamine. A Bayesian log Km model generated with Assay Central had a ROC of 0.86. The probability of activity at 15 μM for ketamine and analogs was predicted with this model. Deschloroketamine scores corresponded to the experimental Km, but the model was unable to predict activity with fluoroketamine. The binding pocket of CYP2B6 also suggested a hydrophobic component to substrate docking, on the basis of a strong linear correlation (R2 = 0.92) between lipophilicity (AlogP) and metabolism (log Km) of ketamine and analogs. This property may be the simplest design criteria to use when considering similar compounds and CYP2B6 affinity. © 2018 American Chemical Society.

Author Keywords
CYP2B6;  cytochrome P450;  halogen;  ketamine;  substrate modeling

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

Noncontact Sensing of Facial Muscle Activity Using Laser Doppler Vibrometry: Time Domain Data Analysis
(2018) Journal of Physics: Conference Series, 1149 (1), art. no. 012027, . 

Casaccia, S.^a , Sirevaag, E.J.^b , Frank, M.G.^d , O’Sullivan, J.A.^c , Scalise, L.^a , Rohrbaugh, J.W.^b

^a Università Politecnica Delle Marche, Department of Industrial Engineering and Mathematical Sciences, v. Brecce Bianche 12, Ancona, 60131, Italy
^b Department of Psychiatry, Washington University, School of Medicine, Saint Louis, MO, United States
^c Department of Electrical and Systems Engineering, Washington University in Saint Louis, Saint Louis, MO, United States
^d Department of Communication, University of Buffalo, Buffalo, NY, United States

Abstract
Movements of the facial muscles offer a promising avenue for assessing stress, fatigue and emotion, and appear useful for a number of applied and clinical purposes. This paper describes a novel application of laser Doppler vibrometry (LDV) as a noncontact method for assessing facial myographic activity. The principle of the LDV method involves detection of the minute vibrations of contracting muscles, associated with the activation of individual motor units. Data were obtained from 11 participants who received 15-20 min of training before the measurement acquisitions. Participants produced several standardized facial expressions involving activation of the upper face (lowering and raising the eyebrows) and the lower face (raising the upper lip, stretching the lip corners, and clenching the jaw). The associated muscle vibratory activity was assessed using the LDV method, within context of the simultaneous electromyogram (EMG). A separate condition entailed study of the jaw muscle signals during repetitive chewing. The temporal, spatial and measurement sensitivity aspects were studied in separate tests; the present report focuses on the temporal aspects of the response, in comparison to the onsets and offsets of the simultaneous EMG and gross facial surface displacement. The LDV signals were obtained from a site overlying the principal involved muscle for the various movements. Results showed that LDV myographic signals (LDV-MMG) could be recorded from all facial muscles studied, although they were relatively small from the muscles of the upper face. LDV-MMG signals were especially prominent at times of contraction onset and offset, indicating that the method may be particularly useful for the study of dynamic activity as would be associated with brief changes in facial expression. The LDV-MMG signals generally were found to lead the onset of the EMG signal by about 100 ms, and to lag the offset of the EMG signal by about 200 ms. The LDV-MMG response associated with chewing was associated in time with the EMG and displacement signs of chewing, but were generally more polyphasic in form. The findings generally support the potential use of the LDV method as a non-contact and non-obtrusive method for assessing activity of the facial muscles. © 2019 Published under licence by IOP Publishing Ltd.

Document Type: Conference Paper
Publication Stage: Final
Source: Scopus
Access Type: Open Access

Brain structure, cognition, and brain age in schizophrenia, bipolar disorder, and healthy controls
(2018) Neuropsychopharmacology, . Article in Press. 

Shahab, S.^{a b c} , Mulsant, B.H.^{b d e f} , Levesque, M.L.^{a b} , Calarco, N.^{a b e} , Nazeri, A.^g , Wheeler, A.L.^{h i} , Foussias, G.^{b d e j} , Rajji, T.K.^{b d e f} , Voineskos, A.N.^{a b d e f j}

^a Kimel Family Translational Imaging-Genetics Laboratory, Research Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
^b Campbell Family Mental Health Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
^c Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
^d Department of Psychiatry, University of Toronto, Toronto, ON, Canada
^e Institute of Medical Science, University of Toronto, Toronto, ON, Canada
^f Adult Neurodevelopmental and Geriatric Psychiatry Division, Centre for Addiction and Mental Health, Toronto, ON, Canada
^g Mallinckrodt Institute of Radiology, Washington University School of Medicine, Saint Louis, MO, United States
^h Neuroscience and Mental Health Program, Hospital for Sick Children, Toronto, ON, Canada
ⁱ Department of Physiology, University of Toronto, Toronto, ON, Canada
^j Schizophrenia Division, Centre for Addiction and Mental Health, Toronto, ON, Canada

Abstract
Schizophrenia and bipolar disorder (BD) may be disorders of accelerated aging. Direct comparison of healthy aging populations with schizophrenia and BD patients across the adult lifespan may help inform this theory. In total, 225 individuals (91 healthy controls, 81 schizophrenia, 53 euthymic BD) underwent 3T T1-weighted magnetic resonance imaging, diffusion tensor imaging, and cognitive testing. We analyzed associations among age, diagnosis, and cognition with cortical thickness and fractional anisotropy (FA) using general linear models. We then assessed “brain age” using a random forest algorithm, which was also assessed in an independent sample (n = 147). Participants with schizophrenia had lower cortical thickness and FA compared with the other two groups, most prominently in fronto-temporal circuitry. These brain changes were more evident in younger participants than in older ones, yet were associated with cognitive performance independent of diagnosis. Predicted age was 8 years greater than chronological age in individuals with schizophrenia in the first sample and 6 years greater in the second sample. Predicted and chronological age were not different in BD. Differences in brain circuitry are present from illness onset most prominently in schizophrenia and to a lesser extent in BD. These results support a non-progressive “early hit” hypothesis/etiology of illness in the major psychoses. Brain age differences support the hypothesized early aging mechanism in schizophrenia but not in BD. © 2019, American College of Neuropsychopharmacology.

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

Cholinesterase Inhibitors May Not Benefit Mild Cognitive Impairment and Mild Alzheimer Disease Dementia
(2018) Alzheimer Disease and Associated Disorders, . Article in Press. 

Han, J.-Y.^{a b} , Besser, L.M.^c , Xiong, C.^a , Kukull, W.A.^d , Morris, J.C.^a

^a Knight Alzheimer Disease Research Center, United States
^b Washington University in St. Louis, School of Medicine, St. Louis, MO, United States
^c School of Urban and Regional Planning, Institute for Healthy Aging and Lifespan Studies, Florida Atlantic University, Boca Raton, FL, United States
^d National Alzheimer’s Coordinating Center, University of Washington, Seattle, WA, United States

Abstract
Supplemental Digital Content is available in the text. © 2019 Wolters Kluwer Health, Inc. Unauthorized reproduction of this article is prohibited.All right reserved.

Author Keywords
Alzheimer disease;  Alzheimer disease dementia;  cholinesterase inhibitor;  cognitive outcome;  mild cognitive impairment

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