WashU weekly Neuroscience publications | Office of Neuroscience Research

“Common binding sites for cholesterol and neurosteroids on a pentameric ligand-gated ion channel” (2019) Biochimica et Biophysica Acta – Molecular and Cell Biology of Lipids

Common binding sites for cholesterol and neurosteroids on a pentameric ligand-gated ion channel
(2019) Biochimica et Biophysica Acta – Molecular and Cell Biology of Lipids, 1864 (2), pp. 128-136.

Budelier, M.M.^a , Cheng, W.W.L.^a , Chen, Z.-W.^a ^b , Bracamontes, J.R.^a , Sugasawa, Y.^a , Krishnan, K.^c , Mydock-McGrane, L.^c , Covey, D.F.^a ^b ^c ^d , Evers, A.S.^a ^b ^c

^a Department of Anesthesiology, Washington University in St Louis, 660 S Euclid Ave, St Louis, MO 63110, United States
^b Taylor Family Institute for Innovative Psychiatric Research, Washington University in St Louis, 660 S Euclid Ave, St Louis, MO 63110, United States
^c Department of Developmental Biology, Washington University in St Louis, 660 S Euclid Ave, St Louis, MO 63110, United States
^d Department of Psychiatry, Washington University in St Louis, 660 S Euclid Ave, St Louis, MO 63110, United States

Abstract
Cholesterol is an essential component of cell membranes, and is required for mammalian pentameric ligand-gated ion channel (pLGIC) function. Computational studies suggest direct interactions between cholesterol and pLGICs but experimental evidence identifying specific binding sites is limited. In this study, we mapped cholesterol binding to Gloeobacter ligand-gated ion channel (GLIC), a model pLGIC chosen for its high level of expression, existing crystal structure, and previous use as a prototypic pLGIC. Using two cholesterol analogue photolabeling reagents with the photoreactive moiety on opposite ends of the sterol, we identified two cholesterol binding sites: an intersubunit site between TM3 and TM1 of adjacent subunits and an intrasubunit site between TM1 and TM4. In both the inter- and intrasubunit sites, cholesterol is oriented such that the 3‑OH group points toward the center of the transmembrane domains rather than toward either the cytosolic or extracellular surfaces. We then compared this binding to that of the cholesterol metabolite, allopregnanolone, a neurosteroid that allosterically modulates pLGICs. The same binding pockets were identified for allopregnanolone and cholesterol, but the binding orientation of the two ligands was markedly different, with the 3‑OH group of allopregnanolone pointing to the intra- and extracellular termini of the transmembrane domains rather than to their centers. We also found that cholesterol increases, whereas allopregnanolone decreases the thermal stability of GLIC. These data indicate that cholesterol and neurosteroids bind to common hydrophobic pockets in the model pLGIC, GLIC, but that their effects depend on the orientation and specific molecular interactions unique to each sterol. © 2018 Elsevier B.V.

Author Keywords
Cholesterol binding; Ligand orientation; Mass spectrometry; Pentameric ligand-gated ion channel; Photoaffinity labeling; Protein-sterol interactions

Document Type: Article
Source: Scopus

“Impact of Pathways Triple P on Pediatric Health-Related Quality of Life in Maltreated Children” (2018) Journal of developmental and behavioral pediatrics : JDBP

Impact of Pathways Triple P on Pediatric Health-Related Quality of Life in Maltreated Children
(2018) Journal of developmental and behavioral pediatrics : JDBP, 39 (9), pp. 701-708.

Lanier, P.^a , Dunnigan, A.^b , Kohl, P.L.^b

^a University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
^b Washington University in St. Louis, St. Louis, MO, United States

Abstract
OBJECTIVE: Child maltreatment is an adverse childhood experience associated with reductions in child well-being. This study examines whether an evidence-based parenting intervention delivered to families served by the child welfare system (CWS) affects pediatric health-related quality of life (HRQoL). METHOD: This study is a randomized controlled trial of Pathways Triple P (PTP) delivered to families with open child welfare cases for child physical abuse or neglect (N = 119). Children were 5 to 11 years old and remained in the home after the investigation. The primary outcome measure for this study was the Pediatric Quality of Life Inventory (PedsQL) 4.0, which measures HRQoL across 4 subdomains: physical functioning, emotional functioning, social functioning, and school functioning. Child- and parent-reported PedsQL 4.0 was assessed at baseline and post-test after the 14-week intervention. RESULTS: Controlling for other factors, children in families randomly assigned to the PTP condition had a significant improvement in overall HRQoL after the intervention compared with families receiving usual services (βchild-report = 6.08, SE = 2.77, p = 0.03; βparent-report = 3.83, SE = 1.88, p = 0.04). Subdomain effect sizes differed when considering children’s self-report or parents’ proxy report. Children’s self-report yielded the largest improvement in emotional functioning, whereas social functioning had the largest gain based on parents’ proxy report. CONCLUSION: The PTP parenting intervention was associated with higher pediatric HRQoL as reported by both the child and parent. This intervention holds promise to improve child well-being when implemented in the CWS.

Document Type: Article
Source: Scopus

“Transancestral GWAS of alcohol dependence reveals common genetic underpinnings with psychiatric disorders” (2018) Nature Neuroscience

Transancestral GWAS of alcohol dependence reveals common genetic underpinnings with psychiatric disorders
(2018) Nature Neuroscience, 21 (12), pp. 1656-1669.

Walters, R.K.^a ^b , Polimanti, R.^c , Johnson, E.C.^d , McClintick, J.N.^e , Adams, M.J.^f , Adkins, A.E.^g , Aliev, F.^h , Bacanu, S.-A.ⁱ , Batzler, A.^j , Bertelsen, S.^k , Biernacka, J.M.^l , Bigdeli, T.B.^m , Chen, L.-S.^d , Clarke, T.-K.^f , Chou, Y.-L.^d , Degenhardt, F.ⁿ , Docherty, A.R.^o , Edwards, A.C.^p , Fontanillas, P.^q , Foo, J.C.^r , Fox, L.^d , Frank, J.^r , Giegling, I.^s , Gordon, S.^t , Hack, L.M.^u , Hartmann, A.M.^s , Hartz, S.M.^d , Heilmann-Heimbach, S.ⁿ , Herms, S.ⁿ ^v , Hodgkinson, C.^w , Hoffmann, P.ⁿ ^v , Jan Hottenga, J.^x , Kennedy, M.A.^y , Alanne-Kinnunen, M.^z , Konte, B.^s , Lahti, J.^aa ^ab , Lahti-Pulkkinen, M.^ab , Lai, D.^ac , Ligthart, L.^x , Loukola, A.^z , Maher, B.S.^ad , Mbarek, H.^x , McIntosh, A.M.^ae , McQueen, M.B.^af , Meyers, J.L.^ag , Milaneschi, Y.^ah , Palviainen, T.^z , Pearson, J.F.^ai , Peterson, R.E.^p , Ripatti, S.^a ^b ^z ^aj , Ryu, E.^ak , Saccone, N.L.^al , Salvatore, J.E.^h ^p , Sanchez-Roige, S.^am , Schwandt, M.^an , Sherva, R.^ao , Streit, F.^r , Strohmaier, J.^r , Thomas, N.^g , Wang, J.-C.^k , Webb, B.T.ⁱ , Wedow, R.^a ^b ^ap ^aq , Wetherill, L.^ac , Wills, A.G.^ar , Agee, M.^q , Alipanahi, B.^q , Auton, A.^q , Bell, R.K.^q , Bryc, K.^q , Elson, S.L.^q , Fontanillas, P.^q , Furlotte, N.A.^q , Hinds, D.A.^q , Huber, K.E.^q , Kleinman, A.^q , Litterman, N.K.^q , McCreight, J.C.^q , McIntyre, M.H.^q , Mountain, J.L.^q , Noblin, E.S.^q , Northover, C.A.M.^q , Pitts, S.J.^q , Sathirapongsasuti, J.F.^q , Sazonova, O.V.^q , Shelton, J.F.^q , Shringarpure, S.^q , Tian, C.^q , Tung, J.Y.^q , Vacic, V.^q , Wilson, C.H.^q , Boardman, J.D.^as , Chen, D.^b , Choi, D.-S.^at , Copeland, W.E.^au , Culverhouse, R.C.^av , Dahmen, N.^aw , Degenhardt, L.^ax , Domingue, B.W.^ay , Elson, S.L.^q , Frye, M.A.^az , Gäbel, W.^ba , Hayward, C.^bb , Ising, M.^bc , Keyes, M.^bd , Kiefer, F.^be , Kramer, J.^bf , Kuperman, S.^bf , Lucae, S.^bc , Lynskey, M.T.^bg , Maier, W.^bh , Mann, K.^be , Männistö, S.^bi , Müller-Myhsok, B.^bj , Murray, A.D.^bk , Nurnberger, J.I.^ac ^bl , Palotie, A.^a ^b ^z ^bm , Preuss, U.^s ^bn , Räikkönen, K.^ab , Reynolds, M.D.^bo , Ridinger, M.^bp , Scherbaum, N.^bq , Schuckit, M.A.^am , Soyka, M.^br ^bs , Treutlein, J.^r , Witt, S.^r , Wodarz, N.^bt , Zill, P.^bs , Adkins, D.E.^o ^bu , Boden, J.M.^y , Boomsma, D.I.^x , Bierut, L.J.^d , Brown, S.A.^am ^bv , Bucholz, K.K.^d , Cichon, S.^v , Costello, E.J.^au , de Wit, H.^bw , Diazgranados, N.^bw , Dick, D.M.^g ^bx , Eriksson, J.G.^by , Farrer, L.A.^ao ^bz , Foroud, T.M.^ac , Gillespie, N.A.^p , Goate, A.M.^k , Goldman, D.^w ^an , Grucza, R.A.^d , Hancock, D.B.^ca , Harris, K.M.^cb , Heath, A.C.^d , Hesselbrock, V.^cc , Hewitt, J.K.^cd , Hopfer, C.J.^ce , Horwood, J.^y , Iacono, W.^bd , Johnson, E.O.^cf , Kaprio, J.A.^z ^aj , Karpyak, V.M.^az , Kendler, K.S.ⁱ , Kranzler, H.R.^cg , Krauter, K.^ch , Lichtenstein, P.^ci , Lind, P.A.^t , McGue, M.^bd , MacKillop, J.^cj , Madden, P.A.F.^d , Maes, H.H.^ck , Magnusson, P.^ci , Martin, N.G.^t , Medland, S.E.^t , Montgomery, G.W.^cl , Nelson, E.C.^d , Nöthen, M.M.^cm , Palmer, A.A.^am ^cn , Pedersen, N.L.^ci , Penninx, B.W.J.H.^ah , Porjesz, B.^ag , Rice, J.P.^d , Rietschel, M.^r , Riley, B.P.ⁱ , Rose, R.^co , Rujescu, D.^s , Shen, P.-H.^w , Silberg, J.^p , Stallings, M.C.^cd , Tarter, R.E.^bo , Vanyukov, M.M.^bo , Vrieze, S.^bd , Wall, T.L.^am , Whitfield, J.B.^t , Zhao, H.^cp , Neale, B.M.^a ^b , Gelernter, J.^cq , Edenberg, H.J.^e ^ac , Agrawal, A.^d , 23andMe Research Team^cr

^a Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
^b Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, United States
^c Department of Psychiatry, Yale School of Medicine and Veterans Affairs Connecticut Healthcare Center, West Haven, CT, United States
^d Washington University School of Medicine, Department of Psychiatry, St. Louis, MO, United States
^e Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, United States
^f University of Edinburgh, Division of Psychiatry, Edinburgh, United Kingdom
^g Department of Psychology & College Behavioral and Emotional Health Institute, Virginia Commonwealth University, Richmond, VA, United States
^h Virginia Commonwealth University, Department of Psychology, Richmond, VA, United States
ⁱ Virginia Commonwealth University Alcohol Research Center; Virginia Institute for Psychiatric and Behavioral Genetics; Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, United States
^j Mayo Clinic, Psychiatric Genomics and Pharmacogenomics Program, Rochester, MN, United States
^k Icahn School of Medicine at Mount Sinai, Department of Neuroscience, New York, NY, United States
^l Mayo Clinic, Department of Health Sciences Research, and Department of Psychiatry and Psychology, Rochester, MN, United States
^m Department of Psychiatry and Behavioral Sciences, State University of New York Downstate Medical Center, Brooklyn, NY, United States
ⁿ Institute of Human Genetics, University of Bonn; and Department of Genomics, Life & Brain Center, University of Bonn, Bonn, Germany
^o University of Utah, Department of Psychiatry, Salt Lake City, UT, United States
^p Virginia Commonwealth University, Virginia Institute for Psychiatric and Behavioral Genetics, Department of Psychiatry, Richmond, VA, United States
^q 23andMe, Inc, Mountain View, CA, United States
^r Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
^s Martin-Luther-University Halle-Wittenberg, Department of Psychiatry, Psychotherapy and Psychosomatics, Halle, Germany
^t QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
^u Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, United States
^v Human Genomics Research Group, Department of Biomedicine, University of Basel Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, Switzerland
^w NIH/NIAAA, Laboratory of Neurogenetics, Bethesda, MD, United States
^x Department of Biological Psychology, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
^y University of Otago, Christchurch, New Zealand
^z Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
^aa Helsinki Collegium for Advanced Studies, University of Helsinki, Helsinki, Finland
^ab Department of Psychology and Logopedics, University of Helsinki, Helsinki, Finland
^ac Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, United States
^ad Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
^ae University of Edinburgh, Division of Psychiatry, Centre for Cognitive Ageing and Cognitive Epidemiology, Edinburgh, United Kingdom
^af Department of Integrative Physiology, University of Colorado Boulder, Boulder, CO, United States
^ag Henri Begleiter Neurodynamics Laboratory, Department of Psychiatry and Behavioral Sciences, SUNY Downstate Medical Center, Brooklyn, NY, United States
^ah Department of Psychiatry, Amsterdam Public Health Research Institute, VU University Medical Center/GGz inGeest, Amsterdam, Netherlands
^ai Biostatistics and Computational Biology Unit, University of Otago, Christchurch, New Zealand
^aj Department of Public Health, University of Helsinki, Helsinki, Finland
^ak Mayo Clinic, Department of Health Sciences Research, Rochester, MN, United States
^al Washington University School of Medicine, Department of Genetics, St. Louis, MO, United States
^am University of California San Diego, Department of Psychiatry, San Diego, CA, United States
^an NIH/NIAAA, Office of the Clinical Director, Bethesda, MD, United States
^ao Department of Medicine (Biomedical Genetics), Boston University School of Medicine, Boston, MA, United States
^ap Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States
^aq Department of Sociology, Harvard University, Cambridge, MA, United States
^ar University of Colorado School of Medicine, Department of Pharmacology, Aurora, CO, United States
^as Institute of Behavioral Science and Department of Sociology, University of Colorado, Boulder, CO, United States
^at Mayo Clinic, Department of Molecular Pharmacology and Experimental Therapeutics, Rochester, MN, United States
^au Duke University Medical Center, Department of Psychiatry and Behavioral Sciences, Durham, NC, United States
^av Washington University School of Medicine, Department of Medicine and Division of Biostatistics, St. Louis, MO, United States
^aw Department of Psychiatry, University of Mainz, Mainz, Germany
^ax National Drug and Alcohol Research Centre, University of New South Wales, Sydney, NSW, Australia
^ay Stanford University Graduate School of Education, Stanford, CA, United States
^az Mayo Clinic, Department of Psychiatry and Psychology, Rochester, MN, United States
^ba Department of Psychiatry and Psychotherapy, University of Düsseldorf, Düsseldorf, Germany
^bb MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, United Kingdom
^bc Max-Planck-Institute of Psychiatry, Munich, Germany
^bd University of Minnesota, Department of Psychology, Minneapolis, MN, United States
^be Department of Addictive Behavior and Addiction Medicine, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
^bf University of Iowa Roy J and Lucille A Carver College of Medicine, Department of Psychiatry, Iowa City, IA, United States
^bg Addictions Department, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, United Kingdom
^bh Department of Psychiatry, University of Bonn, Bonn, Germany
^bi Institute for Health and Welfare, Helsinki, Finland
^bj Department of Statistical Genetics, Max-Planck-Institute of Psychiatry, Munich, Germany
^bk The Institute of Medical Sciences, Aberdeen Biomedical Imaging Centre, University of Aberdeen, Aberdeen, United Kingdom
^bl Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, United States
^bm Department of Medicine, Department of Neurology and Department of Psychiatry, Massachusetts General Hospital, Boston, MA, United States
^bn Vitos Hospital Herborn, Department of Psychiatry and Psychotherapy, Herborn, Germany
^bo University of Pittsburgh, School of Pharmacy, Pittsburgh, PA, United States
^bp Department of Psychiatry and Psychotherapy, University of Regensburg Psychiatric Health Care Aargau, Regensburg, Germany
^bq LVR-Hospital Essen, Department of Psychiatry and Psychotherapy, Department of Addictive Behaviour and Addiction Medicine, Medical Faculty, University of Duisburg-Essen, Duisburg, Germany
^br Medical Park Chiemseeblick in Bernau-Felden, Chiemsee, Germany
^bs Psychiatric Hospital, Ludwig-Maximilians-University, Munich, Germany
^bt Department of Psychiatry and Psychotherapy, University of Regensburg, Regensburg, Germany
^bu University of Utah, Department of Sociology, Salt Lake City, UT, United States
^bv University of California, San Diego School of Medicine, Department of Psychology, San Diego, CA, United States
^bw NIAAA Intramural Research Program, Bethesda, MD, United States
^bx Department of Human & Molecular Genetics, Virginia Commonwealth University, Richmond, VA, United States
^by Department of General Practice and Primary Health Care, University of Helsinki, and National Institute for Health and Welfare, Helsinki, Finland
^bz Departments of Neurology, Ophthalmology, Epidemiology, and Biostatistics, Boston University Schools of Medicine and Public Health, Boston, MA, United States
^ca Center for Omics Discovery and Epidemiology, Behavioral Health Research Division, RTI International, Research Triangle Park, NC, United States
^cb Department of Sociology and Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
^cc University of Connecticut School of Medicine, Department of Psychiatry, Farmington, CT, United States
^cd University of Colorado Boulder, Institute for Behavioral Genetics, Boulder, CO, United States
^ce University of Colorado Denver, School of Medicine, Denver, CO, United States
^cf RTI International, Fellows Program, Research Triangle Park, NC, United States
^cg University of Pennsylvania Perelman School of Medicine, Center for Studies of Addiction, Department of Psychiatry and VISN 4 MIRECC, Crescenz VAMC, Philadelphia, PA, United States
^ch University of Colorado Boulder, Department of Molecular, Cellular, and Developmental Biology, Boulder, CO, United States
^ci Department of Medical Epidemiology and Biostatistics, Karolinska Instituet, Stockholm, Sweden
^cj Peter Boris Centre for Addictions Research, McMaster University/St. Joseph’s Healthcare Hamilton; Michael G. DeGroote Centre for Medicinal Cannabis Research, Hamilton, ON, Canada
^ck Virginia Commonwealth University, Virginia Institute for Psychiatric and Behavioral Genetics, Richmond, VA, United States
^cl The Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
^cm Institute of Human Genetics, University of Bonn School of Medicine & University Hospital Bonn, Bonn, Germany
^cn University of California San Diego, Institute for Genomic Medicine, San Diego, CA, United States
^co Department of Psychological & Brain Sciences, Indiana University Bloomington, Bloomington, IN, United States
^cp Department of Biostatistics, Yale School of Public Health, Yale University, New Haven, CT, United States
^cq Departments of Psychiatry, Genetics, and Neuroscience, Yale University School of Medicine, Veterans Affairs Connecticut Healthcare System, New Haven, CT, United States

Abstract
Liability to alcohol dependence (AD) is heritable, but little is known about its complex polygenic architecture or its genetic relationship with other disorders. To discover loci associated with AD and characterize the relationship between AD and other psychiatric and behavioral outcomes, we carried out the largest genome-wide association study to date of DSM-IV-diagnosed AD. Genome-wide data on 14,904 individuals with AD and 37,944 controls from 28 case–control and family-based studies were meta-analyzed, stratified by genetic ancestry (European, n = 46,568; African, n = 6,280). Independent, genome-wide significant effects of different ADH1B variants were identified in European (rs1229984; P = 9.8 × 10–13) and African ancestries (rs2066702; P = 2.2 × 10–9). Significant genetic correlations were observed with 17 phenotypes, including schizophrenia, attention deficit–hyperactivity disorder, depression, and use of cigarettes and cannabis. The genetic underpinnings of AD only partially overlap with those for alcohol consumption, underscoring the genetic distinction between pathological and nonpathological drinking behaviors. © 2018, The Author(s), under exclusive licence to Springer Nature America, Inc.

Document Type: Article
Source: Scopus

“Use of polygenic risk scores of nicotine metabolism in predicting smoking behaviors” (2018) Pharmacogenomics

Use of polygenic risk scores of nicotine metabolism in predicting smoking behaviors
(2018) Pharmacogenomics, 19 (18), pp. 1383-1394.

Chen, L.-S.^a ^b , Hartz, S.M.^a , Baker, T.B.^c , Ma, Y.^a , L Saccone, N.^d , Bierut, L.J.^a ^b

^a Department of Psychiatry, Washington University School of Medicine, St Louis, MO 63110, United States
^b Siteman Cancer Center, Washington University in St Louis, St Louis, MO 63110, United States
^c Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, WI 53711, United States
^d Department of Genetics, Washington University School of Medicine, St Louis, MO 63110, United States

Abstract
Aim: This study tests whether polygenic risk scores (PRSs) for nicotine metabolism predict smoking behaviors in independent data. Materials & methods: Linear regression, logistic regression and survival analyses were used to analyze nicotine metabolism PRSs and nicotine metabolism, smoking quantity and smoking cessation. Results: Nicotine metabolism PRSs based on two genome wide association studies (GWAS) meta-analyses significantly predicted nicotine metabolism biomarkers (R2 range: 9.2-16%; minimum p = 7.6 × 10-8). The GWAS top hit variant rs56113850 significantly predicted nicotine metabolism biomarkers (R2 range: 14-17%; minimum p = 4.4 × 10-8). There was insufficient evidence for these PRSs predicting smoking quantity and smoking cessation. Conclusion: Results suggest that nicotine metabolism PRSs based on GWAS meta-analyses predict an individual’s nicotine metabolism, so does use of the top hit variant. We anticipate that PRSs will enter clinical medicine, but additional research is needed to develop a more comprehensive genetic score to predict smoking behaviors. © 2018 Future Medicine Ltd.

Author Keywords
CYP2A6; nicotine metabolism; polygenic risk scores; smoking cessation

Document Type: Article
Source: Scopus

“The relevance of cerebrospinal fluid α-synuclein levels to sporadic and familial Alzheimer’s disease” (2018) Acta neuropathologica communications

The relevance of cerebrospinal fluid α-synuclein levels to sporadic and familial Alzheimer’s disease
(2018) Acta neuropathologica communications, 6 (1), p. 130.

Twohig, D.^a , Rodriguez-Vieitez, E.^b , Sando, S.B.^c ^d , Berge, G.^d , Lauridsen, C.^d , Møller, I.^c , Grøntvedt, G.R.^c ^d , Bråthen, G.^c ^d , Patra, K.^a , Bu, G.^e , Benzinger, T.L.S.^f , Karch, C.M.^g , Fagan, A.^h , Morris, J.C.^h , Bateman, R.J.^h , Nordberg, A.^b ⁱ , White, L.R.^c ^d , Nielsen, H.M.^a , Dominantly Inherited Alzheimer Network (DIAN)^j

^a Department of Biochemistry and Biophysics, Stockholm University, Svante Arrhenius väg 16B, Stockholm, 106 91, Sweden
^b Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
^c Department of Neurology, University Hospital of Trondheim, Trondheim, Norway
^d Department of Neuroscience, Norwegian University of Science and Technology, Trondheim, Norway
^e Department of Neuroscience, Mayo Clinic College of Medicine, Jacksonville, FL, United States
^f Department of Radiology, Washington University School of Medicine, St Louis, MO, United States
^g Department of Psychiatry, Washington University School of Medicine, St Louis, MO, United States
^h Department of Neurology, Washington University School of Medicine, St Louis, MO, United States
ⁱ Aging Research Center, Karolinska University Hospital, Stockholm, Sweden

Abstract
Accumulating evidence demonstrating higher cerebrospinal fluid (CSF) α-synuclein (αSyn) levels and αSyn pathology in the brains of Alzheimer’s disease (AD) patients suggests that αSyn is involved in the pathophysiology of AD. To investigate whether αSyn could be related to specific aspects of the pathophysiology present in both sporadic and familial disease, we quantified CSF levels of αSyn and assessed links to various disease parameters in a longitudinally followed cohort (n = 136) including patients with sporadic mild cognitive impairment (MCI) and AD, and in a cross-sectional sample from the Dominantly Inherited Alzheimer’s Network (n = 142) including participants carrying autosomal dominant AD (ADAD) gene mutations and their non-mutation carrying family members.Our results show that sporadic MCI patients that developed AD over a period of two years exhibited higher baseline αSyn levels (p = 0.03), which inversely correlated to their Mini-Mental State Examination scores, compared to cognitively normal controls (p = 0.02). In the same patients, there was a dose-dependent positive association between CSF αSyn and the APOEε4 allele. Further, CSF αSyn levels were higher in symptomatic ADAD mutation carriers versus non-mutation carriers (p = 0.03), and positively correlated to the estimated years from symptom onset (p = 0.05) across all mutation carriers. In asymptomatic (Clinical Dementia Rating < 0.5) PET amyloid-positive ADAD mutation carriers CSF αSyn was positively correlated to 11C-Pittsburgh Compound-B (PiB) retention in several brain regions including the posterior cingulate, superior temporal and frontal cortical areas. Importantly, APOEε4-positive ADAD mutation carriers exhibited an association between CSF αSyn levels and mean cortical PiB retention (p = 0.032). In both the sporadic AD and ADAD cohorts we found several associations predominantly between CSF levels of αSyn, tau and amyloid-β1-40.Our results suggest that higher CSF αSyn levels are linked to AD pathophysiology at the early stages of disease development and to the onset of cognitive symptoms in both sporadic and autosomal dominant AD. We conclude that APOEε4 may promote the processes driven by αSyn, which in turn may reflect on molecular mechanisms linked to the asymptomatic build-up of amyloid plaque burden in brain regions involved in the early stages of AD development.

Author Keywords
alpha-synuclein; Alzheimer’s disease; APOEε4; Biomarkers; Mild cognitive impairment

Document Type: Article
Source: Scopus
Access Type: Open Access

“Characterising Arm Recovery in People with Severe Stroke (CARPSS): protocol for a 12-month observational study of clinical, neuroimaging and neurophysiological biomarkers” (2018) BMJ open

Characterising Arm Recovery in People with Severe Stroke (CARPSS): protocol for a 12-month observational study of clinical, neuroimaging and neurophysiological biomarkers
(2018) BMJ open, 8 (11), p. e026435.

Hayward, K.S.^a ^b ^c , Lohse, K.R.^d , Bernhardt, J.^b ^c , Lang, C.E.^e , Boyd, L.A.^a

^a Brain Behaviour Laboratory, Physical Therapy, University of British Columbia, Vancouver, BC, Canada
^b AVERT Early Rehabilitation Research Group, Stroke Theme, Florey Institute of Neuroscience and Mental Health, Melbourne, Victoria, Australia
^c NHMRC Centre of Research Excellence in Stroke Rehabilitation and Brain Recovery, Melbourne, Victoria, Australia
^d Department of Health, Kinesiology, Recreation; Department of Physical Therapy and Athletic Training, University of Utah, Utah, Salt Lake City, USA
^e Physical Therapy, Occupational Therapy, Neurology, Washington University School of Medicine, St Louis, MO, United States

Abstract
INTRODUCTION: In individuals with early (indexed ≤7 days poststroke) and severe upper limb paresis (shoulder abduction and finger extension score of <5 out of 10), our objectives are to: (1) determine if biomarkers of brain structure and function collected at <1 month poststroke explain who will experience clinically important recovery over the first 12 months poststroke; (2) compare stroke survivors’ perceptions of personally meaningful recovery to clinically important recovery; and (3) characterise the trajectory of change in measures of motor function, brain structure and function. METHODS AND ANALYSIS: Prospective observational study with an inception cohort of 78 first-time stroke survivors. Participants will be recruited from a single, large tertiary stroke referral centre. Clinical and biomarker assessments will be completed at four follow-up time points: 2 to 4 weeks and 3, 6 and 12 months poststroke. Our primary outcome is achievement of clinically important improvement on two out of three measures that span impairment (Fugl-Meyer Upper Limb, change ≥10 points), activity (Motor Assessment Scale item 6, change ≥1 point) and participation (Rating of Everyday Arm-use in the Community and Home, change ≥1 point). Brain biomarkers of structure and function will be indexed using transcranial magnetic stimulation and MRI. Multilevel modelling will be performed to examine the relationship between clinically important recovery achieved (yes/no) and a priori defined brain biomarkers related to the corticospinal tract and corpus callosum. Secondary analyses will compare stroke survivor’s perception of recovery, as well as real-world arm use via accelerometry, to the proposed metric of clinically meaningful recovery; and model trajectory of recovery across clinical, a priori defined biomarkers and exploratory variables related to functional connectivity. ETHICS AND DISSEMINATION: Approved by the hospital and university ethics review boards. Results will be disseminated through peer-reviewed publications and conference presentations. TRIAL REGISTRATION NUMBER: NCT02464085. © Author(s) (or their employer(s)) 2018. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

Author Keywords
magnetic resonance imaging; rehabilitation medicine; stroke; stroke medicine

Document Type: Article
Source: Scopus
Access Type: Open Access

“Reliability and accuracy of delirium assessments among investigators at multiple international centres” (2018) BMJ open

Reliability and accuracy of delirium assessments among investigators at multiple international centres
(2018) BMJ open, 8 (11), p. e023137.

Maybrier, H.R.^a , Mickle, A.M.^a , Escallier, K.E.^a , Lin, N.^b ^c , Schmitt, E.M.^d , Upadhyayula, R.T.^a , Wildes, T.S.^a , Mashour, G.A.^e , Palihnich, K.^d , Inouye, S.K.^d ^f , Avidan, M.S.^a , PODCAST Research Group^g

^a Department of Anesthesiology, Washington University in Saint Louis School of Medicine, Saint Louis, MO, United States
^b Department of Mathematics, Washington University in Saint Louis, St. Louis, MO, United States
^c Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, United States
^d Aging Brain Center, Institute for Aging Research, Hebrew SeniorLife, Boston, MA, United States
^e Department of Anesthesiology, University of Michigan, Ann Arbor, MI, United States
^f Department of Medicine, Beth Israel Deaconess Medical Center, Hebrew Senior Life, Harvard Medical School, Boston, MA, United States

Abstract
INTRODUCTION: Delirium is a common, serious postoperative complication. For clinical studies to generate valid findings, delirium assessments must be standardised and administered accurately by independent researchers. The Confusion Assessment Method (CAM) is a widely used delirium assessment tool. The objective was to determine whether implementing a standardised CAM training protocol for researchers at multiple international sites yields reliable inter-rater assessment and accurate delirium diagnosis. METHODS: Patients consented to video recordings of CAM delirium assessments for research purposes. Raters underwent structured training in CAM administration. Training entailed didactic education, role-playing with intensive feedback, apprenticeship with experienced researchers and group discussions of complex cases. Raters independently viewed and scored nine video-recorded CAM interviews. Inter-rater reliability was determined using Fleiss kappa. Accuracy was judged by comparing raters’ scores with those of an expert delirium researcher. RESULTS: Twenty-seven raters from eight international research centres completed the study and achieved almost perfect agreement for overall delirium diagnosis, kappa=0.88 (95% CI 0.85 to 0.92). Agreement of the four core CAM features ranged from fair to substantial. The sensitivity and specificity for identifying delirium were 72% (95% CI 60% to 81%) and 99% (95% CI 96% to 100%), considering an expert rater’s scores as the reference standard (delirious, n=3; non-delirious, n=6). Delirium severity ratings were tightly clustered, with most scores within 5% of the median. CONCLUSION: Our results demonstrate that, with appropriate training and ongoing scoring discussions, researchers at multiple sites can reliably detect delirium in postsurgical patients. These results support the premise that methodologically rigorous multi-centre studies can yield standardised and accurate determinations of delirium. © Author(s) (or their employer(s)) 2018. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

Author Keywords
adult anaesthesia; anesthesia; confusion; delirium; surgery

Document Type: Article
Source: Scopus
Access Type: Open Access

“Legislators’ Sources of Behavioral Health Research and Preferences for Dissemination: Variations by Political Party” (2018) Psychiatric services (Washington, D.C.)

Legislators’ Sources of Behavioral Health Research and Preferences for Dissemination: Variations by Political Party
(2018) Psychiatric services (Washington, D.C.), 69 (10), pp. 1105-1108.

Purtle, J., Dodson, E.A., Nelson, K., Meisel, Z.F., Brownson, R.C.

Dr. Purtle and Ms. Nelson are with the Department of Health Management and Policy, Dornsife School of Public Health, Drexel University, Philadelphia. Dr. Dodson and Dr. Brownson are with the Prevention Research Center in St. Louis, Brown School of Social Work, Washington University School of Medicine, University of Pennsylvania Perelman School of Medicine, Washington University in St. Louis. Dr. Brownson is also with the Division of Public Health SciencesWashington University in St. Louis. Dr. Meisel is with the Department of Emergency Medicine, Philadelphia, United States

Abstract
OBJECTIVES:: This study sought to characterize primary sources of behavioral health research and dissemination preferences of state legislators and assess differences by political party. METHODS:: A 2017 cross-sectional survey of state legislators (N=475) assessed where legislators seek, and the most important features of, behavioral health research. Bivariate analyses and multivariate logistic regression were conducted. RESULTS:: Advocacy organizations (53%), legislative staff (51%), and state agencies (48%) were identified most frequently as sources of behavioral health research. Universities were identified by significantly more Democrats than Republicans (34% versus 19%; adjusted odds ratio=1.79). Data about budget impact and cost-effectiveness were most frequently rated as very important, but by significantly fewer Democrats than Republicans (77% versus 87% and 76% versus 89%, respectively). CONCLUSIONS:: To reach legislators and satisfy their information preferences, behavioral health researchers should target diverse audiences, partner with intermediary organizations, and craft messages that include economic evaluation data.

Author Keywords
Dissemination; Legislators; Politics; Public policy issues; Research use

Document Type: Article
Source: Scopus

“Understanding disease progression and improving Alzheimer’s disease clinical trials: Recent highlights from the Alzheimer’s Disease Neuroimaging Initiative” (2018) Alzheimer’s and Dementia

Understanding disease progression and improving Alzheimer’s disease clinical trials: Recent highlights from the Alzheimer’s Disease Neuroimaging Initiative
(2018) Alzheimer’s and Dementia, . Article in Press.

Veitch, D.P.^a ^b , Weiner, M.W.^a ^c ^d ^e ^f , Aisen, P.S.^g , Beckett, L.A.^h , Cairns, N.J.ⁱ ^j , Green, R.C.^k , Harvey, D.^h , Jack, C.R., Jr.^l , Jagust, W.^m , Morris, J.C.ⁱ , Petersen, R.C.ⁿ , Saykin, A.J.^o ^p , Shaw, L.M.^q , Toga, A.W.^r , Trojanowski, J.Q.^q ^s ^t ^u , Alzheimer’s Disease Neuroimaging Initiative^v

^a Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, United States
^b Northern California Institute for Research and Education (NCIRE), Department of Veterans Affairs Medical Center, San Francisco, CA, United States
^c Department of Radiology, University of California, San Francisco, CA, United States
^d Department of Medicine, University of California, San Francisco, CA, United States
^e Department of Psychiatry, University of California, San Francisco, CA, United States
^f Department of Neurology, University of California, San Francisco, CA, United States
^g Alzheimer’s Therapeutic Research Institute, University of Southern California, San Diego, CA, United States
^h Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, CA, United States
ⁱ Knight Alzheimer’s Disease Research Center, Washington University School of Medicine, Saint Louis, MO, United States
^j Department of Neurology, Washington University School of Medicine, Saint Louis, MO, United States
^k Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, United States
^l Department of Radiology, Mayo Clinic, Rochester, MN, United States
^m Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA, United States
ⁿ Department of Neurology, Mayo Clinic, Rochester, MN, United States
^o Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, United States
^p Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, United States
^q Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
^r Laboratory of Neuroimaging, Institute of Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
^s Institute on Aging, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
^t Alzheimer’s Disease Core Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
^u Udall Parkinson’s Research Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States

Abstract
Introduction: The overall goal of the Alzheimer’s Disease Neuroimaging Initiative (ADNI) is to validate biomarkers for Alzheimer’s disease (AD) clinical trials. ADNI is a multisite, longitudinal, observational study that has collected many biomarkers since 2004. Recent publications highlight the multifactorial nature of late-onset AD. We discuss selected topics that provide insights into AD progression and outline how this knowledge may improve clinical trials. Methods: We used standard methods to identify nearly 600 publications using ADNI data from 2016 and 2017 (listed in Supplementary Material and searchable at http://adni.loni.usc.edu/news-publications/publications/). Results: (1) Data-driven AD progression models supported multifactorial interactions rather than a linear cascade of events. (2) β-Amyloid (Aβ) deposition occurred concurrently with functional connectivity changes within the default mode network in preclinical subjects and was followed by specific and progressive disconnection of functional and anatomical networks. (3) Changes in functional connectivity, volumetric measures, regional hypometabolism, and cognition were detectable at subthreshold levels of Aβ deposition. 4. Tau positron emission tomography imaging studies detailed a specific temporal and spatial pattern of tau pathology dependent on prior Aβ deposition, and related to subsequent cognitive decline. 5. Clustering studies using a wide range of modalities consistently identified a “typical AD” subgroup and a second subgroup characterized by executive impairment and widespread cortical atrophy in preclinical and prodromal subjects. 6. Vascular pathology burden may act through both Aβ dependent and independent mechanisms to exacerbate AD progression. 7. The APOE ε4 allele interacted with cerebrovascular disease to impede Aβ clearance mechanisms. 8. Genetic approaches identified novel genetic risk factors involving a wide range of processes, and demonstrated shared genetic risk for AD and vascular disorders, as well as the temporal and regional pathological associations of established AD risk alleles. 9. Knowledge of early pathological changes guided the development of novel prognostic biomarkers for preclinical subjects. 10. Placebo populations of randomized controlled clinical trials had highly variable trajectories of cognitive change, underscoring the importance of subject selection and monitoring. 11. Selection criteria based on Aβ positivity, hippocampal volume, baseline cognitive/functional measures, and APOE ε4 status in combination with improved cognitive outcome measures were projected to decrease clinical trial duration and cost. 12. Multiple concurrent therapies targeting vascular health and other AD pathology in addition to Aβ may be more effective than single therapies. Discussion: ADNI publications from 2016 and 2017 supported the idea of AD as a multifactorial disease and provided insights into the complexities of AD disease progression. These findings guided the development of novel biomarkers and suggested that subject selection on the basis of multiple factors may lower AD clinical trial costs and duration. The use of multiple concurrent therapies in these trials may prove more effective in reversing AD disease progression. © 2018 the Alzheimer’s Association

Author Keywords
Alzheimer’s disease; Amyloid; Biomarker; Disease progression; Mild cognitive impairment; Tau

Document Type: Article in Press
Source: Scopus

“Pain management in sport: therapeutic injections” (2018) Handbook of Clinical Neurology

Pain management in sport: therapeutic injections
(2018) Handbook of Clinical Neurology, 158, pp. 431-442.

Olafsen, N.P.^a , Herring, S.A.^b

^a Division of Physical Medicine and Rehabilitation, Department of Orthopaedic Surgery, Washington University School of Medicine, St. Louis, MO, United States
^b Departments of Rehabilitation Medicine, Orthopaedics and Sports Medicine, and Neurological Surgery, University of Washington, Seattle, WA, United States

Abstract
Corticosteroid, hyaluronic acid, and platelet-rich plasma injections are commonly utilized when managing pain and injury in the athlete. Although there is ample scientific literature on these injection types, there is a paucity of evidence guiding the use of any of these modalities in a younger, athletic population. Injection strategies should be used as just one aspect of a detailed and athlete-specific return to sport and rehabilitation plan. More high-quality research is needed to determine the most appropriate and optimum injection use in the management of painful musculoskeletal conditions, including patient factors and injection formulations. © 2018 Elsevier B.V.

Author Keywords
comprehensive care; corticosteroid; evidence-based; hyaluronic acid; platelet-rich plasma

Document Type: Book Chapter
Source: Scopus

“CSF progranulin increases in the course of Alzheimer’s disease and is associated with sTREM2, neurodegeneration and cognitive decline” (2018) EMBO Molecular Medicine

CSF progranulin increases in the course of Alzheimer’s disease and is associated with sTREM2, neurodegeneration and cognitive decline
(2018) EMBO Molecular Medicine, art. no. e9712, . Article in Press.

Suárez-Calvet, M.^a ^b ^v , Capell, A.^a , Araque Caballero, M.Á.^c , Morenas-Rodríguez, E.^a ^d , Fellerer, K.^a , Franzmeier, N.^c , Kleinberger, G.^a ^e , Eren, E.^a ^f ^g , Deming, Y.^h , Piccio, L.ⁱ ^j , Karch, C.M.^h ^j ^k , Cruchaga, C.^h ^j ^k , Paumier, K.ⁱ ^j ^k , Bateman, R.J.ⁱ ^j ^k , Fagan, A.M.ⁱ ^j ^k , Morris, J.C.ⁱ ^j ^k , Levin, J.^b ^l , Danek, A.^l , Jucker, M.^m ⁿ , Masters, C.L.^o , Rossor, M.N.^p , Ringman, J.M.^q , Shaw, L.M.^r ^s , Trojanowski, J.Q.^r ^s , Weiner, M.^t , Ewers, M.^c , Haass, C.^a ^b ^e , for the Dominantly Inherited Alzheimer Network^u , for the Alzheimer’s Disease Neuroimaging Initiative^u

^a Chair 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 Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-Universität München, Munich, Germany
^d Department of Neurology, Institut d’Investigacions Biomèdiques, Hospital de la Santa Creu i Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Catalonia, Spain
^e Munich Cluster for Systems Neurology (SyNergy), 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 Psychiatry, 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 Hope Center for Neurological Disorders, Washington University in St. Louis, St. Louis, MO, United States
^k Knight Alzheimer’s Disease Research Center, Washington University in St. Louis, St. Louis, MO, United States
^l Department of Neurology, Ludwig-Maximilians-Universität München, Munich, Germany
^m German Center for Neurodegenerative Diseases (DZNE) Tübingen, Tübingen, Germany
ⁿ Department of Cellular Neurology, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
^o The Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, VIC, Australia
^p Dementia Research Centre, UCL Institute of Neurology, London, United Kingdom
^q Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
^r Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
^s Center for Neurodegenerative Disease Research, Institute on Aging, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
^t University of California at San Francisco, San Francisco, CA, United States
^v Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Catalonia, Spain

Abstract
Progranulin (PGRN) is predominantly expressed by microglia in the brain, and genetic and experimental evidence suggests a critical role in Alzheimer’s disease (AD). We asked whether PGRN expression is changed in a disease severity-specific manner in AD. We measured PGRN in cerebrospinal fluid (CSF) in two of the best-characterized AD patient cohorts, namely the Dominant Inherited Alzheimer’s Disease Network (DIAN) and the Alzheimer’s Disease Neuroimaging Initiative (ADNI). In carriers of AD causing dominant mutations, cross-sectionally assessed CSF PGRN increased over the course of the disease and significantly differed from non-carriers 10 years before the expected symptom onset. In late-onset AD, higher CSF PGRN was associated with more advanced disease stages and cognitive impairment. Higher CSF PGRN was associated with higher CSF soluble TREM2 (triggering receptor expressed on myeloid cells 2) only when there was underlying pathology, but not in controls. In conclusion, we demonstrate that, although CSF PGRN is not a diagnostic biomarker for AD, it may together with sTREM2 reflect microglial activation during the disease. © 2018 The Authors. Published under the terms of the CC BY 4.0 license

Author Keywords
Alzheimer’s disease; biomarker; microglia; progranulin; TREM2

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

“Environment patterns and mental health of older adults in long-term care facilities: the role of activity profiles” (2018) Aging and Mental Health

Environment patterns and mental health of older adults in long-term care facilities: the role of activity profiles
(2018) Aging and Mental Health, . Article in Press.

Chao, S.-F.^a , Chen, Y.-C.^b

^a Department of Social Work, National Taiwan University, Taipei, Taiwan
^b Brown School of Social Work, Washington University in St. Louis, St Louis, MO, United States

Abstract
Objectives: This study adopts the International Classification of Functioning, Disability and Health (ICF) model to determine extent to which the clustered patterns of long-term care (LTC) environment and activity participation are associated with older residents’ mental health. Method: This study enrolled a stratified equal probability sample of 634 older residents in 155 LTC institutions in Taiwan. Latent profile analysis and latent class analysis were conducted to explore the profiles for environment and activity participation. Multilevel modeling was performed to elucidate the hypothesized relationships. Results: Three environment profiles (Low-, Moderate-, and High-Support Environment) based on physical, social, and attitudinal environment domains and two activity profiles (Low- and High-Activity Participation) across seven activity domains were identified. Compared to the Low-Support class, older adults in the Moderate- and High-Support Environment classes had better mental health. Older residents in those two classes were more likely to be in the “High Activity Participation” class, which in turn, exhibited better mental health. Conclusion: Environment and activity participation directly relate to older residents’ mental health. Activity participation also mediates the link between environment and mental health. A combination of enhanced physical, social, and attitudinal environments, and continual engagement in various activities may optimize older LTC residents’ mental health. © 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group.

Author Keywords
depressive symptoms; Functional disability; latent class analysis; latent profile analysis; morale

Document Type: Article in Press
Source: Scopus

“Depression and Alzheimer’s Disease Biomarkers Predict Driving Decline” (2018) Journal of Alzheimer’s Disease

Depression and Alzheimer’s Disease Biomarkers Predict Driving Decline
(2018) Journal of Alzheimer’s Disease, 66 (3), pp. 1213-1221.

Babulal, G.M.^a ^b , Chen, S.^k , Williams, M.M.^l , Trani, J.-F.ⁱ , Bakhshi, P.^g ⁱ , Chao, G.L.^j , Stout, S.H.^a ^b , Fagan, A.M.^a ^b ^c , Benzinger, T.L.S.^a ^d ^h , Holtzman, D.M.^b ^c , Morris, J.C.^a ^b ^c ^d ^e ^f ^g , Roe, C.M.^a ^b

^a Charles F. and Joanne Knight Alzheimer’s Disease Research Center, Washington University, School of Medicine, 660 S. Euclid Ave., St. Louis, MO, United States
^b Department of Neurology, Washington University, School of Medicine, St. Louis, MO, United States
^c Hope Center for Neurological Disorders, 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 Pathology and Immunology, Washington University, School of Medicine, St. Louis, MO, United States
^f Department of Physical Therapy, Washington University, School of Medicine, St. Louis, MO, United States
^g Department of Occupational Therapy, Washington University, School of Medicine, St. Louis, MO, United States
^h Department of Neurosurgery, Washington University, School of Medicine, St. Louis, MO, United States
ⁱ Brown School, Washington University, St. Louis, MO, United States
^j Chicago State University, Chicago, IL, United States
^k St. Louis College of Pharmacy, St. Louis, MO, United States
^l BJC Medical Group, St. Louis, MO, United States

Abstract
Background: Symptomatic Alzheimer’s disease (AD) and depression independently increase crash risk. Additionally, depression is both a risk factor for and a consequence of AD. Objective: To examine whether a depression diagnosis, antidepressant use, and preclinical AD are associated with driving decline among cognitively normal older adults. Methods: Cognitively normal participants, age ≥65, were enrolled. Cox proportional hazards models evaluated whether a depression diagnosis, depressive symptoms (Geriatric Depression Scale), antidepressant use, cerebrospinal fluid (amyloid-β 42 [Aβ 42 ], tau, phosphorylated tau 181 [ptau 181 ]), and amyloid imaging biomarkers (Pittsburgh Compound B and Florbetapir) were associated with time to receiving a rating of marginal/fail on a road test. Age was adjusted for in all models. Results: Data were available from 131 participants with age ranging from 65.4 to 88.2 years and mean follow up of 2.4 years (SD = 1.0). A depression diagnosis was associated with a faster time to receiving a marginal/fail rating on a road test and antidepressant use (p = 0.024, HR = 2.62). Depression diagnosis and CSF and amyloid PET imaging biomarkers were associated with driving performance on the road test (p≤0.05, HR = 2.51-3.15). In the CSF ptau 181 model, depression diagnosis (p = 0.031, HR = 2.51) and antidepressant use (p = 0.037, HR = 2.50) were statistically significant predictors. There were no interaction effects between depression diagnosis, antidepressant use, and biomarker groups. Depressive symptomology was not a statistically significant predictor of driving performance. Conclusions: While, as previously shown, preclinical AD alone predicts a faster time to receiving a marginal/fail rating, these results suggest that also having a diagnosis of depression accelerates the onset of driving problems in cognitively normal older adults. © 2018-IOS Press and the authors. All rights reserved.

Author Keywords
Alzheimer’s disease; antidepressants; biomarkers; depression; driving; older adults

Document Type: Article
Source: Scopus

“Efficient region-based test strategy uncovers genetic risk factors for functional outcome in bipolar disorder” (2018) European Neuropsychopharmacology

Efficient region-based test strategy uncovers genetic risk factors for functional outcome in bipolar disorder
(2018) European Neuropsychopharmacology, . Article in Press.

Budde, M.^a , Friedrichs, S.^b , Alliey-Rodriguez, N.^c , Ament, S.^d , Badner, J.A.^e , Berrettini, W.H.^f , Bloss, C.S.^g , Byerley, W.^h , Cichon, S.ⁱ ^j ^k , Comes, A.L.^a ^l , Coryell, W.^m , Craig, D.W.ⁿ , Degenhardt, F.^o ^p , Edenberg, H.J.^q ^r , Foroud, T.^r , Forstner, A.J.ⁱ ^o ^p ^s , Frank, J.^t , Gershon, E.S.^c , Goes, F.S.^u , Greenwood, T.A.^v , Guo, Y.^w ^x , Hipolito, M.^y , Hood, L.^d , Keating, B.J.^z ^aa , Koller, D.L.^r , Lawson, W.B.^ab , Liu, C.^ac , Mahon, P.B.^u , McInnis, M.G.^ad , McMahon, F.J.^ae , Meier, S.M.^t ^af , Mühleisen, T.W.ⁱ ^k , Murray, S.S.^ag ^ah , Nievergelt, C.M.^v , Nurnberger, J.I., Jr.^ai , Nwulia, E.A.^y , Potash, J.B.^aj , Quarless, D.^g ^ak , Rice, J.^al , Roach, J.C.^d , Scheftner, W.A.^am , Schork, N.J.^g ⁿ ^ak , Shekhtman, T.^v , Shilling, P.D.^v , Smith, E.N.^ag ^an , Streit, F.^t , Strohmaier, J.^t , Szelinger, S.ⁿ , Treutlein, J.^t , Witt, S.H.^t , Zandi, P.P.^ao , Zhang, P.^ap , Zöllner, S.^ad ^ap , Bickeböller, H.^b , Falkai, P.G.^aq , Kelsoe, J.R.^v , Nöthen, M.M.^o ^p , Rietschel, M.^t , Schulze, T.G.^a ^t ^u ^ae , Malzahn, D.^b

^a Institute of Psychiatric Phenomics and Genomics, University Hospital, LMU Munich, Nussbaumstr. 7, Munich, 80336, Germany
^b Department of Genetic Epidemiology, University Medical Center Göttingen, Georg-August-University, Göttingen, 37099, Germany
^c Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL 60637, United States
^d Institute for Systems Biology, Seattle, WA 98109, United States
^e Department of Psychiatry, Rush University Medical Center, Chicago, IL 60612, United States
^f Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, United States
^g University of California San Diego, La Jolla, CA 92093, United States
^h Department of Psychiatry, University of California at San Francisco, San Francisco, CA 94103, United States
ⁱ Human Genomics Research Group, Department of Biomedicine, University of Basel, Basel, 4031, Switzerland
^j Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, 4031, Switzerland
^k Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, 52425, Germany
^l International Max Planck Research School for Translational Psychiatry, Max Planck Institute of Psychiatry, Munich, 80804, Germany
^m University of Iowa Hospitals and Clinics, Iowa City, IA 52242, United States
ⁿ The Translational Genomics Research Institute, Phoenix, AZ 85004, United States
^o Institute of Human Genetics, School of Medicine & University Hospital Bonn, University of Bonn, Bonn, 53127, Germany
^p Department of Genomics, Life & Brain Center, University of Bonn, Bonn, 53127, Germany
^q Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN 46202, United States
^r Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN 46202, United States
^s Department of Psychiatry (UPK), University of Basel, Basel, 4012, Switzerland
^t Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, 68159, Germany
^u Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD 21287, United States
^v Department of Psychiatry, University of California San Diego, San Diego, CA 92093, United States
^w Center for Applied Genomics, Children’s Hospital of Philadelphia, Abramson Research Center, Philadelphia, PA 19104, United States
^x Beijing Genomics Institute at Shenzhen, Shenzhen, 518083, China
^y Department of Psychiatry and Behavioral Sciences, Howard University Hospital, Washington, DC 20060, United States
^z Cardiovascular Institute, University of Pennsylvania School of Medicine, Philadelphia, PA 19104-5159, United States
^aa Institute for Translational Medicine and Therapeutics, School of Medicine, University of Pennsylvania, Philadelphia, PA 19104-5158, United States
^ab Dell Medical School, University of Texas at Austin, Austin, TX 78723, United States
^ac SUNY Upstate Medical University, Syracuse, NY 13210, United States
^ad Department of Psychiatry, University of Michigan, Ann Arbor, MI 48105, United States
^ae U.S. Department of Health & Human Services, Intramural Research Program, National Institute of Mental Health, National Institutes of Health, Bethesda, MD 20894, United States
^af National Centre for Register-Based Research, Aarhus University, Aarhus V, 8210, Denmark
^ag Scripps Genomic Medicine & The Scripps Translational Sciences Institute (STSI), La Jolla, CA 92037, United States
^ah Department of Pathology, University of California San Diego, La Jolla, CA 92093, United States
^ai Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN 46202, United States
^aj Department of Psychiatry, Carver College of Medicine, University of Iowa School of Medicine, Iowa City, IA 52242, United States
^ak J. Craig Venter Institute, La Jolla, CA 92037, United States
^al Department of Psychiatry, Washington University School of Medicine in St. Louis, St. Louis, MO 63110, United States
^am Rush University Medical Center, Chicago, IL 60612, United States
^an Department of Pediatrics and Rady’s Children’s Hospital, School of Medicine, University of California San Diego, La Jolla, CA 92037, United States
^ao Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, United States
^ap Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, United States
^aq Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich 80336, Germany

Abstract
Genome-wide association studies of case-control status have advanced the understanding of the genetic basis of psychiatric disorders. Further progress may be gained by increasing sample size but also by new analysis strategies that advance the exploitation of existing data, especially for clinically important quantitative phenotypes. The functionally-informed efficient region-based test strategy (FIERS) introduced herein uses prior knowledge on biological function and dependence of genotypes within a powerful statistical framework with improved sensitivity and specificity for detecting consistent genetic effects across studies. As proof of concept, FIERS was used for the first genome-wide single nucleotide polymorphism (SNP)-based investigation on bipolar disorder (BD) that focuses on an important aspect of disease course, the functional outcome. FIERS identified a significantly associated locus on chromosome 15 (hg38: chr15:48965004 – 49464789 bp) with consistent effect strength between two independent studies (GAIN/TGen: European Americans, BOMA: Germans; n = 1592 BD patients in total). Protective and risk haplotypes were found on the most strongly associated SNPs. They contain a CTCF binding site (rs586758); CTCF sites are known to regulate sets of genes within a chromatin domain. The rs586758 – rs2086256 – rs1904317 haplotype is located in the promoter flanking region of the COPS2 gene, close to microRNA4716, and the EID1, SHC4, DTWD1 genes as plausible biological candidates. While implication with BD is novel, COPS2, EID1, and SHC4 are known to be relevant for neuronal differentiation and function and DTWD1 for psychopharmacological side effects. The test strategy FIERS that enabled this discovery is equally applicable for tag SNPs and sequence data. © 2018 The Author(s)

Author Keywords
Functional annotation; Global Assessment of Functioning; Hypothesis-driven GWAS; Kernel score test; Linkage disequilibrium; Psychiatric disorder

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