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151. Executive Function and Spatial Cognition Mediate Psychosocial Dysfunction in Major Depressive Disorder

Author: Knight, MJ, Baune, BT, Knight, MJ, and Baune, BT
Abstract: Background: Cognitive and psychosocial dysfunction are prevalent and disabling features of Major Depressive Disorder (MDD). Emerging evidence suggests that poor cognitive functioning mediates the negative effect of MDD on psychosocial function. However, there is a lack of research examining the domain-specific nature of this relationship. The current study evaluated whether the relationship between MDD and specific psychosocial subdomains (e.g., autonomy, occupational functioning) was mediated by domain-specific cognitive deficits. Methods: Data from 155 participants was obtained from the Cognitive Function and Mood Study (CoFaMS), a cross-sectional analysis of mood, cognition, social cognition, and functioning in individuals with MDD. Cognitive functioning was assessed (Current MDD n = 45, Healthy n = 110), with the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS), the Colorado Assessment Tests (CATs), and the Psychology Experiment Building Language (PEBL). Psychosocial functioning was clinically evaluated with the Functioning Assessment Short Test (FAST). Results: The results indicated that spatial cognition and executive functioning partially mediated the negative effect of MDD on overall psychosocial functioning, autonomy, and subjective cognition. In contrast, spatial and executive domains showed divergent mediation patterns on interpersonal relationships and leisure time. Conclusions: The findings suggest that executive and spatial cognition play an important role in the pathology of overall psychosocial functioning, and specific functional issues in MDD. Treatments targeting psychosocial recovery in MDD may be improved by emphasizing executive and spatial cognitive remediation.
Published: 2018

152. Stability, reliability, and validity of the THINC-it screening tool for cognitive impairment in depression: A psychometric exploration in healthy volunteers

Author: Harrison, JE, Barry, H, Baune, BT, Best, MW, Bowie, CR, Cha, DS, Culpepper, L, Fossati, P, Greer, TL, Harmer, C, Klag, E, Lam, RW, Lee, Y, Mansur, RB, Wittchen, H-U, McIntyre, RS, Harrison, JE, Barry, H, Baune, BT, Best, MW, Bowie, CR, Cha, DS, Culpepper, L, Fossati, P, Greer, TL, Harmer, C, Klag, E, Lam, RW, Lee, Y, Mansur, RB, Wittchen, H-U, and McIntyre, RS
Abstract: OBJECTIVES: There is a need for a brief, reliable, valid, and sensitive assessment tool for screening cognitive deficits in patients with Major Depressive Disorders. This paper examines the psychometric characteristics of THINC-it, a cognitive assessment tool composed of four objective measures of cognition and a self-rated assessment, in subjects without mental disorders. METHODS: N = 100 healthy controls with no current or past history of depression were tested on four sequential assessments to examine temporal stability, reliability, and convergent validity of the THINC-it tests. We examined temporal reliability across 1 week and stability via three consecutive assessments. Consistency of assessment by the study rater (intrarater reliability) was calculated using the data from the second and third of these consecutive assessments. RESULTS: Test-retest reliability correlations varied between Pearson's r = 0.75 and 0.8. Intrarater reliability between 0.7 and 0.93. Stability for the primary measure for each test yielded within-subject standard deviation values between 5.9 and 11.23 for accuracy measures and 0.735 and 17.3 seconds for latency measures. Convergent validity for three tasks was in the acceptable range, but low for the Symbol Check task. CONCLUSIONS: Analysis shows high levels of reliability and stability. Levels of convergent validity were modest but acceptable in the case of all but one test.
Published: 2018

153. Cognitive Dysfunction in Major Depressive Disorder: Cause and Effect

Author: Knight, MJ, Aboustate, N, Baune, BT, Knight, MJ, Aboustate, N, and Baune, BT
Published: 2018

154. Anti-inflammatory treatment of depression: study protocol for a randomised controlled trial of vortioxetine augmented with celecoxib or placebo

Author: Fourrier, C, Sampson, E, Mills, NT, Baune, BT, Fourrier, C, Sampson, E, Mills, NT, and Baune, BT
Abstract: BACKGROUND: In patients with major depressive disorder (MDD), antidepressant response and remission rates are low, highlighting the need for new treatment approaches. Recently, the abundant literature linking inflammatory processes and depressive symptoms have led to the hypothesis that selecting treatment for MDD based on the patient's inflammatory status could be a promising strategy to improve outcomes in patients suffering from MDD. The aim of the randomised control trial we propose is to investigate the antidepressant efficacy of the combined treatment of MDD with antidepressant medication plus anti-inflammatory medication in individuals with raised inflammation levels. For the first time, this study will prospectively test the efficacy of an antidepressant plus anti-inflammatory augmentation based on baseline inflammatory maker levels in MDD using a randomised controlled trial design. METHODS: This study proposes to measure blood C-reactive protein (CRP) levels before the initiation of treatment in 200 participants with MDD. Study participants are then assigned into one of two study strata: either into the 'Depression with inflammation' stratum (CRP levels > 3 mg/L); or into the 'Depression without inflammation' stratum (CRP levels ≤ 3 mg/L). Within each of the two study strata, participants randomly receive either antidepressant medication alone (vortioxetine) plus anti-inflammatory medication (celecoxib) or vortioxetine plus placebo for six weeks. At the end of the treatment period, participants have the opportunity to continue vortioxetine alone for a six-month post-trial period. Clinical outcomes are measured at baseline, fortnightly during the treatment period and at the three-month and six-month post-trial visits. The primary outcome is change in MADRS score, with a primary endpoint of a score reduction by 50% from baseline to six weeks (end of augmentation treatment with celecoxib). Secondary clinical outcomes are changes in the cognitive dimensions of
Published: 2018

155. Low-frequency and rare variants may contribute to elucidate the genetics of major depressive disorder

Author: Yu, C, Arcos-Burgos, M, Baune, BT, Arolt, V, Dannlowski, U, Wong, M-L, Licinio, J, Yu, C, Arcos-Burgos, M, Baune, BT, Arolt, V, Dannlowski, U, Wong, M-L, and Licinio, J
Abstract: Major depressive disorder (MDD) is a common but serious psychiatric disorder with significant levels of morbidity and mortality. Recent genome-wide association studies (GWAS) on common variants increase our understanding of MDD; however, the underlying genetic basis remains largely unknown. Many studies have been proposed to explore the genetics of complex diseases from a viewpoint of the "missing heritability" by considering low-frequency and rare variants, copy-number variations, and other types of genetic variants. Here we developed a novel computational and statistical strategy to investigate the "missing heritability" of MDD. We applied Hamming distance on common, low-frequency, and rare single-nucleotide polymorphism (SNP) sets to measure genetic distance between two individuals, and then built the multi-dimensional scaling (MDS) pictures. Whole-exome genotyping data from a Los Angeles Mexican-American cohort (203 MDD and 196 controls) and a European-ancestry cohort (473 MDD and 497 controls) were examined using our proposed methodology. MDS plots showed very significant separations between MDD cases and healthy controls for low-frequency SNP set (P value < 2.2e-16) and rare SNP set (P value = 7.681e-12). Our results suggested that low-frequency and rare variants may play more significant roles in the genetics of MDD.
Published: 2018

156. Applying polygenic risk scoring for psychiatric disorders to a large family with bipolar disorder and major depressive disorder

Author: de Jong, S, Abdalla Diniz, MJ, Saloma, A, Gadelha, A, Santoro, ML, Ota, VK, Noto, C, Curtis, C, Newhouse, SJ, Patel, H, Hall, LS, O'Reilly, PF, Belangero, S, Bressan, RA, Breen, G, Wray, NR, Ripke, S, Mattheisen, M, Trzaskowski, M, Byrne, EM, Abdellaoui, A, Adams, MJ, Agerbo, E, Air, TM, Andlauer, TFM, Bacanu, S-A, Baekvad-Hansen, M, Beekman, ATF, Bigdeli, TB, Binder, EB, Blackwood, DHR, Bryois, J, Buttenschon, HN, Bybjerg-Grauholm, J, Cai, N, Castelao, E, Christensen, JH, Clarke, T-K, Coleman, JR, Colodro-Conde, L, Couvy-Duchesne, B, Craddock, N, Crawford, GE, Davies, G, Deary, IJ, Degenhardt, F, Derks, EM, Direk, N, Dolan, C, Dunn, EC, Eley, TC, Escott-Price, V, Kiadeh, FFH, Finucane, HK, Forstner, AJ, Frank, J, Gaspar, HA, Gill, M, Goes, FS, Gordon, SD, Grove, J, Hansen, CS, Hansen, TF, Herms, S, Hickie, IB, Hoffmann, P, Homuth, G, Horn, C, Hottenga, J-J, Hougaard, DM, Ising, M, Jansen, R, Jones, I, Jones, LA, Jorgenson, E, Knowles, JA, Kohane, IS, Kraft, J, Kretzschmar, WW, Krogh, J, Kutalik, Z, Li, Y, Lind, PA, MacIntyre, DJ, MacKinnon, DF, Maier, RM, Maier, W, Marchini, J, Mbarek, H, Mcgrath, P, Mcguffin, P, Medland, SE, Mehta, D, Middeldorp, CM, Mihailov, E, Milaneschi, Y, Milani, L, Mondimore, FM, Montgomery, GW, Mostafavi, S, Mullins, N, Nauck, M, Ng, B, Nivard, MG, Nyholt, DR, Oskarsson, H, Owen, MJ, Painter, JN, Pedersen, CB, Pedersen, MG, Peterson, RE, Pettersson, E, Peyrot, WJ, Pistis, G, Posthuma, D, Quiroz, JA, Qvist, P, Rice, JP, Riley, BP, Rivera, M, Mirza, SS, Schoevers, R, Schulte, EC, Shen, L, Shyn, S, Sigurdsson, E, Sinnamon, GCB, Smit, JH, Smith, DJ, Stefansson, H, Steinberg, S, Streit, F, Strohmaier, J, Tansey, KE, Teismann, H, Teumer, A, Thompson, W, Thomson, PA, Thorgeirsson, TE, Traylor, M, Treutlein, J, Trubetskoy, V, Uitterlinden, AG, Umbricht, D, Van der Auwera, S, van Hemert, AM, Viktorin, A, Visscher, PM, Wang, Y, Webb, BT, Weinsheimer, SM, Wellmann, J, Willemsen, G, Witt, SH, Wu, Y, Xi, HS, Yang, J, Zhang, F, Arolt, V, Baune, BT, Berger, K, Boomsma, D, Cichon, S, Dannlowski, U, de Geus, EJC, DePaulo, JR, Domenici, E, Domschke, K, Esko, T, Grabe, HJ, Hamilton, SP, Hayward, C, Heath, AC, Kendler, KS, Kloiber, S, Lewis, G, Li, QS, Lucae, S, Madden, PAF, Magnusson, PK, Martin, NG, McIntosh, AM, Metspalu, A, Mors, O, Mortensen, PB, Mueller-Myhsok, B, Nordentoft, M, Noethen, MM, O'Donovan, MC, Paciga, SA, Pedersen, NL, Penninx, BWJH, Perlis, RH, Porteous, DJ, Potash, JB, Preisig, M, Rietschel, M, Schaefer, C, Schulze, TG, Smoller, JW, Stefansson, K, Tiemeier, H, Uher, R, Voelzke, H, Weissman, MM, Werge, T, Lewis, CM, Levinson, DF, Borglum, AD, Sullivan, PF, Meier, S, Strauss, J, Xu, W, Vincent, JB, Matthews, K, Ferreira, M, O'Dushlaine, C, Purcell, S, Raychaudhuri, S, Ruderfer, DM, Sklar, P, Scott, LJ, Flickinger, M, Burmeister, M, Li, J, Guan, W, Absher, D, Thompson, RC, Meng, FG, Schatzberg, AF, Bunney, WE, Barchas, JD, Watson, SJ, Myers, RM, Akil, H, Boehnke, M, Chambert, K, Moran, J, Scolnick, E, Djurovic, S, Melle, I, Morken, G, Corvin, A, Anjorin, A, Kandaswamy, R, Lawrence, J, McLean, AW, Pickard, BS, Bergen, SE, Nimgaonkar, V, Landen, M, Schalling, M, Osby, U, Backlund, L, Frisen, L, Langstrom, N, Stahl, E, Dobbyn, A, Jamain, S, Etain, B, Bellivier, F, Leber, M, Maaser, A, Fischer, SB, Reinbold, CS, Kittel-Schneider, S, Fullerton, JM, Oruc, L, Para, JG, Mayoral, F, Rivas, F, Czerski, PM, Kammerer-Ciernioch, J, Vedder, H, Borrmann-Hassenbach, M, Pfennig, A, Brennan, P, McKay, JD, Kogevinas, M, Schwarz, M, Schofield, PR, Muehleisen, TW, Schumacher, J, Bauer, M, Wright, A, Mitchell, PB, Hautzinger, M, Kelsoe, JR, Greenwood, TA, Nievergelt, CM, Shilling, PD, Smith, EN, Bloss, CS, Edenberg, HJ, Koller, DL, Gershon, ES, Liu, C, Badner, JA, Scheftner, WA, Lawson, WB, Nwulia, EA, Hipolito, M, Coryell, W, Rice, J, Byerley, W, McMahon, FJ, Lohoff, FW, Zandi, PP, Mahon, PB, McInnis, MG, Zollner, S, Zhang, P, Szelinger, S, St Clair, D, Caesar, S, Gordon-Smith, K, Fraser, C, Green, EK, Grozeva, D, Hamshere, ML, Kirov, G, Nikolov, I, Collier, DA, Elkin, A, Williamson, R, Young, AH, Ferrier, IN, Milanova, V, Alda, M, Cervantes, P, Cruceanu, C, Rouleau, GA, Turecki, G, Paciga, S, Winslow, AR, Grigoroiu-Serbanescu, M, Ophoff, R, Adolfsson, R, Adolfsson, AN, Del-Favero, J, Pato, C, Biernacka, JM, Frye, MA, Morris, D, Schork, NJ, Reif, A, Lissowska, J, Hauser, J, Szeszenia-Dabrowska, N, McGhee, K, Quinn, E, Moskvina, V, Holmans, PA, Farmer, A, Kennedy, JL, Andreassen, OA, Mattingsdal, M, Bass, NJ, Gurling, H, McQuillin, A, Breuer, R, Hultman, C, Lichtenstein, P, Huckins, LM, Leboyer, M, Lathrop, M, Nurnberger, J, Steffens, M, Foroud, TM, Berrettini, WH, Craig, DW, Shi, J, de Jong, S, Abdalla Diniz, MJ, Saloma, A, Gadelha, A, Santoro, ML, Ota, VK, Noto, C, Curtis, C, Newhouse, SJ, Patel, H, Hall, LS, O'Reilly, PF, Belangero, S, Bressan, RA, Breen, G, Wray, NR, Ripke, S, Mattheisen, M, Trzaskowski, M, Byrne, EM, Abdellaoui, A, Adams, MJ, Agerbo, E, Air, TM, Andlauer, TFM, Bacanu, S-A, Baekvad-Hansen, M, Beekman, ATF, Bigdeli, TB, Binder, EB, Blackwood, DHR, Bryois, J, Buttenschon, HN, Bybjerg-Grauholm, J, Cai, N, Castelao, E, Christensen, JH, Clarke, T-K, Coleman, JR, Colodro-Conde, L, Couvy-Duchesne, B, Craddock, N, Crawford, GE, Davies, G, Deary, IJ, Degenhardt, F, Derks, EM, Direk, N, Dolan, C, Dunn, EC, Eley, TC, Escott-Price, V, Kiadeh, FFH, Finucane, HK, Forstner, AJ, Frank, J, Gaspar, HA, Gill, M, Goes, FS, Gordon, SD, Grove, J, Hansen, CS, Hansen, TF, Herms, S, Hickie, IB, Hoffmann, P, Homuth, G, Horn, C, Hottenga, J-J, Hougaard, DM, Ising, M, Jansen, R, Jones, I, Jones, LA, Jorgenson, E, Knowles, JA, Kohane, IS, Kraft, J, Kretzschmar, WW, Krogh, J, Kutalik, Z, Li, Y, Lind, PA, MacIntyre, DJ, MacKinnon, DF, Maier, RM, Maier, W, Marchini, J, Mbarek, H, Mcgrath, P, Mcguffin, P, Medland, SE, Mehta, D, Middeldorp, CM, Mihailov, E, Milaneschi, Y, Milani, L, Mondimore, FM, Montgomery, GW, Mostafavi, S, Mullins, N, Nauck, M, Ng, B, Nivard, MG, Nyholt, DR, Oskarsson, H, Owen, MJ, Painter, JN, Pedersen, CB, Pedersen, MG, Peterson, RE, Pettersson, E, Peyrot, WJ, Pistis, G, Posthuma, D, Quiroz, JA, Qvist, P, Rice, JP, Riley, BP, Rivera, M, Mirza, SS, Schoevers, R, Schulte, EC, Shen, L, Shyn, S, Sigurdsson, E, Sinnamon, GCB, Smit, JH, Smith, DJ, Stefansson, H, Steinberg, S, Streit, F, Strohmaier, J, Tansey, KE, Teismann, H, Teumer, A, Thompson, W, Thomson, PA, Thorgeirsson, TE, Traylor, M, Treutlein, J, Trubetskoy, V, Uitterlinden, AG, Umbricht, D, Van der Auwera, S, van Hemert, AM, Viktorin, A, Visscher, PM, Wang, Y, Webb, BT, Weinsheimer, SM, Wellmann, J, Willemsen, G, Witt, SH, Wu, Y, Xi, HS, Yang, J, Zhang, F, Arolt, V, Baune, BT, Berger, K, Boomsma, D, Cichon, S, Dannlowski, U, de Geus, EJC, DePaulo, JR, Domenici, E, Domschke, K, Esko, T, Grabe, HJ, Hamilton, SP, Hayward, C, Heath, AC, Kendler, KS, Kloiber, S, Lewis, G, Li, QS, Lucae, S, Madden, PAF, Magnusson, PK, Martin, NG, McIntosh, AM, Metspalu, A, Mors, O, Mortensen, PB, Mueller-Myhsok, B, Nordentoft, M, Noethen, MM, O'Donovan, MC, Paciga, SA, Pedersen, NL, Penninx, BWJH, Perlis, RH, Porteous, DJ, Potash, JB, Preisig, M, Rietschel, M, Schaefer, C, Schulze, TG, Smoller, JW, Stefansson, K, Tiemeier, H, Uher, R, Voelzke, H, Weissman, MM, Werge, T, Lewis, CM, Levinson, DF, Borglum, AD, Sullivan, PF, Meier, S, Strauss, J, Xu, W, Vincent, JB, Matthews, K, Ferreira, M, O'Dushlaine, C, Purcell, S, Raychaudhuri, S, Ruderfer, DM, Sklar, P, Scott, LJ, Flickinger, M, Burmeister, M, Li, J, Guan, W, Absher, D, Thompson, RC, Meng, FG, Schatzberg, AF, Bunney, WE, Barchas, JD, Watson, SJ, Myers, RM, Akil, H, Boehnke, M, Chambert, K, Moran, J, Scolnick, E, Djurovic, S, Melle, I, Morken, G, Corvin, A, Anjorin, A, Kandaswamy, R, Lawrence, J, McLean, AW, Pickard, BS, Bergen, SE, Nimgaonkar, V, Landen, M, Schalling, M, Osby, U, Backlund, L, Frisen, L, Langstrom, N, Stahl, E, Dobbyn, A, Jamain, S, Etain, B, Bellivier, F, Leber, M, Maaser, A, Fischer, SB, Reinbold, CS, Kittel-Schneider, S, Fullerton, JM, Oruc, L, Para, JG, Mayoral, F, Rivas, F, Czerski, PM, Kammerer-Ciernioch, J, Vedder, H, Borrmann-Hassenbach, M, Pfennig, A, Brennan, P, McKay, JD, Kogevinas, M, Schwarz, M, Schofield, PR, Muehleisen, TW, Schumacher, J, Bauer, M, Wright, A, Mitchell, PB, Hautzinger, M, Kelsoe, JR, Greenwood, TA, Nievergelt, CM, Shilling, PD, Smith, EN, Bloss, CS, Edenberg, HJ, Koller, DL, Gershon, ES, Liu, C, Badner, JA, Scheftner, WA, Lawson, WB, Nwulia, EA, Hipolito, M, Coryell, W, Rice, J, Byerley, W, McMahon, FJ, Lohoff, FW, Zandi, PP, Mahon, PB, McInnis, MG, Zollner, S, Zhang, P, Szelinger, S, St Clair, D, Caesar, S, Gordon-Smith, K, Fraser, C, Green, EK, Grozeva, D, Hamshere, ML, Kirov, G, Nikolov, I, Collier, DA, Elkin, A, Williamson, R, Young, AH, Ferrier, IN, Milanova, V, Alda, M, Cervantes, P, Cruceanu, C, Rouleau, GA, Turecki, G, Paciga, S, Winslow, AR, Grigoroiu-Serbanescu, M, Ophoff, R, Adolfsson, R, Adolfsson, AN, Del-Favero, J, Pato, C, Biernacka, JM, Frye, MA, Morris, D, Schork, NJ, Reif, A, Lissowska, J, Hauser, J, Szeszenia-Dabrowska, N, McGhee, K, Quinn, E, Moskvina, V, Holmans, PA, Farmer, A, Kennedy, JL, Andreassen, OA, Mattingsdal, M, Bass, NJ, Gurling, H, McQuillin, A, Breuer, R, Hultman, C, Lichtenstein, P, Huckins, LM, Leboyer, M, Lathrop, M, Nurnberger, J, Steffens, M, Foroud, TM, Berrettini, WH, Craig, DW, and Shi, J
Abstract: Psychiatric disorders are thought to have a complex genetic pathology consisting of interplay of common and rare variation. Traditionally, pedigrees are used to shed light on the latter only, while here we discuss the application of polygenic risk scores to also highlight patterns of common genetic risk. We analyze polygenic risk scores for psychiatric disorders in a large pedigree (n ~ 260) in which 30% of family members suffer from major depressive disorder or bipolar disorder. Studying patterns of assortative mating and anticipation, it appears increased polygenic risk is contributed by affected individuals who married into the family, resulting in an increasing genetic risk over generations. This may explain the observation of anticipation in mood disorders, whereby onset is earlier and the severity increases over the generations of a family. Joint analyses of rare and common variation may be a powerful way to understand the familial genetics of psychiatric disorders.
Published: 2018

157. A gene co-expression module implicating the mitochondrial electron transport chain is associated with long-term response to lithium treatment in bipolar affective disorder

Author: Stacey, D, Schubert, KO, Clark, SR, Amare, AT, Milanesi, E, Maj, C, Leckband, SG, Shekhtman, T, Kelsoe, JR, Gurwitz, D, Baune, BT, Stacey, D, Schubert, KO, Clark, SR, Amare, AT, Milanesi, E, Maj, C, Leckband, SG, Shekhtman, T, Kelsoe, JR, Gurwitz, D, and Baune, BT
Abstract: Lithium is the first-line treatment for bipolar affective disorder (BPAD) but two-thirds of patients respond only partially or not at all. The reasons for this high variability in lithium response are not well understood. Transcriptome-wide profiling, which tests the interface between genes and the environment, represents a viable means of exploring the molecular mechanisms underlying lithium response variability. Thus, in the present study we performed co-expression network analyses of whole-blood-derived RNA-seq data from n = 50 lithium-treated BPAD patients. Lithium response was assessed using the well-validated ALDA scale, which we used to define both a continuous and a dichotomous measure. We identified a nominally significant correlation between a co-expression module comprising 46 genes and lithium response represented as a continuous (i.e., scale ranging 0-10) phenotype (cor = -0.299, p = 0.035). Forty-three of these 46 genes had reduced mRNA expression levels in better lithium responders relative to poorer responders, and the central regulators of this module were all mitochondrially-encoded (MT-ND1, MT-ATP6, MT-CYB). Accordingly, enrichment analyses indicated that genes involved in mitochondrial functioning were heavily over-represented in this module, specifically highlighting the electron transport chain (ETC) and oxidative phosphorylation (OXPHOS) as affected processes. Disrupted ETC and OXPHOS activity have previously been implicated in the pathophysiology of BPAD. Our data adds to previous evidence suggesting that a normalisation of these processes could be central to lithium's mode of action, and could underlie a favourable therapeutic response.
Published: 2018

158. A Network Meta-Analysis Comparing Effects of Various Antidepressant Classes on the Digit Symbol Substitution Test (DSST) as a Measure of Cognitive Dysfunction in Patients with Major Depressive Disorder

Author: Baune, BT, Brignone, M, Larsen, KG, Baune, BT, Brignone, M, and Larsen, KG
Abstract: BACKGROUND: Major depressive disorder is a common condition that often includes cognitive dysfunction. A systematic literature review of studies and a network meta-analysis were carried out to assess the relative effect of antidepressants on cognitive dysfunction in major depressive disorder. METHODS: MEDLINE, Embase, Cochrane, CDSR, and PsychINFO databases; clinical trial registries; and relevant conference abstracts were searched for randomized controlled trials assessing the effects of antidepressants/placebo on cognition. A network meta-analysis comparing antidepressants was conducted using a random effects model. RESULTS: The database search retrieved 11337 citations, of which 72 randomized controlled trials from 103 publications met the inclusion criteria. The review identified 86 cognitive tests assessing the effect of antidepressants on cognitive functioning. However, the Digit Symbol Substitution Test, which targets multiple domains of cognition and is recognized as being sensitive to change, was the only test that was used across 12 of the included randomized controlled trials and that allowed the construction of a stable network suitable for the network meta-analysis. The interventions assessed included selective serotonin reuptake inhibitors, serotonin-norepinephrine reuptake inhibitors, and other non-selective serotonin reuptake inhibitors/serotonin-norepinephrine reuptake inhibitors. The network meta-analysis using the Digit Symbol Substitution Test showed that vortioxetine was the only antidepressant that improved cognitive dysfunction on the Digit Symbol Substitution Test vs placebo {standardized mean difference: 0.325 (95% CI = 0.120; 0.529, P=.009}. Compared with other antidepressants, vortioxetine was statistically more efficacious on the Digit Symbol Substitution Test vs escitalopram, nortriptyline, and the selective serotonin reuptake inhibitor and tricyclic antidepressant classes. CONCLUSIONS: This study highlighted the large variability in mea
Published: 2018

159. Personality: Distraction or driver in the diagnosis of depression

Author: Berk, M, Boyce, P, Hamilton, A, Morris, G, Outhred, T, Das, P, Bassett, D, Baune, BT, Lyndon, B, Mulder, R, Parker, G, Singh, AB, Malhi, GS, Berk, M, Boyce, P, Hamilton, A, Morris, G, Outhred, T, Das, P, Bassett, D, Baune, BT, Lyndon, B, Mulder, R, Parker, G, Singh, AB, and Malhi, GS
Published: 2018

160. Burden of obesity in the Eastern Mediterranean Region: findings from the Global Burden of Disease 2015 study

Author: Mokdad, AH, El Bcheraoui, C, Afshin, A, Charara, R, Khalil, I, Moradi-Lakeh, M, Kassebaum, NJ, Collison, M, Daoud, F, Krohn, KJ, Chew, A, Biryukov, SH, Cornaby, L, Foreman, KJ, Kutz, M, Liu, P, Reitsma, M, Sur, P, Wang, H, Zipkin, B, Arnlov, J, Abbafati, C, Abdulle, AM, Abu-Rmeileh, NME, Ahmed, MB, Al-Aly, Z, Alam, K, Alizadeh-Navaei, R, Alkerwi, A, Altirkawi, KA, Alvis-Guzman, N, Baune, BT, Bedi, N, Bennett, DA, Beyene, AS, Bhutta, ZA, Birhanu, MM, Danawi, H, Fereshtehnejad, S-M, Fischer, F, Gebrehiwot, TT, Gill, PS, Gona, PN, Gupta, V, Habtewold, TD, Hamadeh, RR, Hamidi, S, Hareri, HA, Horino, M, Hsairi, M, Javanbakht, M, John, D, Jonas, JB, Joshua, V, Kasaeian, A, Ketema, EB, Khader, YS, Khan, EA, Khubchandani, J, Kim, D, Kim, YJ, Kinfu, Y, Kokubo, Y, Larson, HJ, Lee, PH, Lunevicius, R, Abd El Razek, HM, Abd El Razek, MM, Malekzadeh, R, Mate, KK, Memish, ZA, Mengistie, MA, Mensah, GA, Ogbo, FA, Pourmalek, F, Rafay, A, Qorbani, M, Rahimi-Movaghar, V, Rana, SM, Rawaf, S, Renzaho, AMN, Rezaei, S, Rezai, MS, Safdarian, M, Sahraian, MA, Salamati, P, Samy, AM, Sanabria, JR, Milicevic, MMS, Sartorius, B, Sepanlou, SG, Shaikh, MA, Shiri, R, Santos Silva, DA, Singh, JA, Terkawi, AS, Tiruye, TY, Ukwaja, KN, Vasankari, T, Vlassov, VV, Wakayo, T, Werdecker, A, Yonemoto, N, Younis, MZ, Zein, B, Jumaan, AO, Vos, T, Hay, SI, Naghavi, M, Murray, CJL, Mokdad, AH, El Bcheraoui, C, Afshin, A, Charara, R, Khalil, I, Moradi-Lakeh, M, Kassebaum, NJ, Collison, M, Daoud, F, Krohn, KJ, Chew, A, Biryukov, SH, Cornaby, L, Foreman, KJ, Kutz, M, Liu, P, Reitsma, M, Sur, P, Wang, H, Zipkin, B, Arnlov, J, Abbafati, C, Abdulle, AM, Abu-Rmeileh, NME, Ahmed, MB, Al-Aly, Z, Alam, K, Alizadeh-Navaei, R, Alkerwi, A, Altirkawi, KA, Alvis-Guzman, N, Baune, BT, Bedi, N, Bennett, DA, Beyene, AS, Bhutta, ZA, Birhanu, MM, Danawi, H, Fereshtehnejad, S-M, Fischer, F, Gebrehiwot, TT, Gill, PS, Gona, PN, Gupta, V, Habtewold, TD, Hamadeh, RR, Hamidi, S, Hareri, HA, Horino, M, Hsairi, M, Javanbakht, M, John, D, Jonas, JB, Joshua, V, Kasaeian, A, Ketema, EB, Khader, YS, Khan, EA, Khubchandani, J, Kim, D, Kim, YJ, Kinfu, Y, Kokubo, Y, Larson, HJ, Lee, PH, Lunevicius, R, Abd El Razek, HM, Abd El Razek, MM, Malekzadeh, R, Mate, KK, Memish, ZA, Mengistie, MA, Mensah, GA, Ogbo, FA, Pourmalek, F, Rafay, A, Qorbani, M, Rahimi-Movaghar, V, Rana, SM, Rawaf, S, Renzaho, AMN, Rezaei, S, Rezai, MS, Safdarian, M, Sahraian, MA, Salamati, P, Samy, AM, Sanabria, JR, Milicevic, MMS, Sartorius, B, Sepanlou, SG, Shaikh, MA, Shiri, R, Santos Silva, DA, Singh, JA, Terkawi, AS, Tiruye, TY, Ukwaja, KN, Vasankari, T, Vlassov, VV, Wakayo, T, Werdecker, A, Yonemoto, N, Younis, MZ, Zein, B, Jumaan, AO, Vos, T, Hay, SI, Naghavi, M, and Murray, CJL
Abstract: OBJECTIVES: We used the Global Burden of Disease (GBD) 2015 study results to explore the burden of high body mass index (BMI) in the Eastern Mediterranean Region (EMR). METHODS: We estimated the prevalence of overweight and obesity among children (2-19 years) and adults (≥20 years) in 1980 and 2015. The burden of disease related to high BMI was calculated using the GBD comparative risk assessment approach. RESULTS: The prevalence of obesity increased for adults from 15.1% (95% UI 13.4-16.9) in 1980 to 20.7% (95% UI 18.8-22.8) in 2015. It increased from 4.1% (95% UI 2.9-5.5) to 4.9% (95% UI 3.6-6.4) for the same period among children. In 2015, there were 417,115 deaths and 14,448,548 disability-adjusted life years (DALYs) attributable to high BMI in EMR, which constitute about 10 and 6.3% of total deaths and DALYs, respectively, for all ages. CONCLUSIONS: This is the first study to estimate trends in obesity burden for the EMR from 1980 to 2015. We call for EMR countries to invest more resources in prevention and health promotion efforts to reduce this burden.
Published: 2018

161. Cognitive and Functional Assessment of Psychosis Stratification Study (CoFAPSS): Rationale, Design, and Characteristics

Author: Clark, SR, Schubert, KO, Olagunju, AT, Lyrtzis, EA, Baune, BT, Clark, SR, Schubert, KO, Olagunju, AT, Lyrtzis, EA, and Baune, BT
Abstract: Prediction of treatment response and illness trajectory in psychotic disorders including schizophrenia, bipolar affective disorder, schizoaffective disorder, and psychotic depression is difficult due to heterogeneity in presentation and outcome. Consequently, patients may receive prolonged ineffective treatments leading to functional decline, illness chronicity, and iatrogenic physical illness. One approach to addressing these problems is to stratify patients based on historical, clinical, and biological signatures. Such an approach has the potential to improve categorization resulting in better understanding of underlying mechanisms and earlier evidence-based treatment with reduced side effect burden. To investigate these multimodal signatures we developed the Cognitive and Functional Assessment of Psychosis Stratification Study (CoFAPSS) employing a prospective study design and a healthy control group comparison. The main aim of this study is to investigate cognitive, and biological "genomics" markers of psychotic illnesses that can be integrated with clinical data to improve prediction of risk and define functional trajectories. We also aim to identify biological "genomic" signatures underpinning variation in treatment response and adverse medical outcomes. The study commenced in June 2016, including patients with primary diagnosis of psychotic disorders including schizophrenia, bipolar affective disorder, schizoaffective disorder, and psychotic depression according to DSM-5 criteria. The assessment covers a wide range of participant history (life stressors, trauma, and family history), cognitive dimensions (social perception, memory and learning, attention, executive function, and general cognition), measures to assess psychosocial function and quality of life, psychotic symptom severity, clinical course of illness, and parameters for adverse medical outcome. Blood is collected for comprehensive genomic discovery analyses of biological (genomic, transcriptomic
Published: 2018

162. Internet use by older adults with bipolar disorder: international survey results

Author: Bauer, R, Glenn, T, Strejilevich, S, Conell, J, Alda, M, Ardau, R, Baune, BT, Berk, M, Bersudsky, Y, Bilderbeck, A, Bocchetta, A, Castro, AMP, Cheung, EYW, Chillotti, C, Choppin, S, Cuomo, A, Del Zompo, M, Dias, R, Dodd, S, Duffy, A, Etain, B, Fagiolini, A, Fernandez Hernandez, M, Garnham, J, Geddes, J, Gildebro, J, Gitlin, MJ, Gonzalez-Pinto, A, Goodwin, GM, Grof, P, Harima, H, Hassel, S, Henry, C, Hidalgo-Mazzei, D, Lund, AH, Kapur, V, Kunigiri, G, Lafer, B, Larsen, ER, Lewitzka, U, Licht, RW, Misiak, B, Piotrowski, P, Miranda-Scippa, A, Monteith, S, Munoz, R, Nakanotani, T, Nielsen, RE, O'Donovan, C, Okamura, Y, Osher, Y, Reif, A, Ritter, P, Rybakowski, JK, Sagduyu, K, Sawchuk, B, Schwartz, E, Slaney, C, Sulaiman, AH, Suominen, K, Suwalska, A, Tam, P, Tatebayashi, Y, Tondo, L, Veeh, J, Vieta, E, Vinberg, M, Viswanath, B, Zetin, M, Whybrow, PC, Bauer, M, Bauer, R, Glenn, T, Strejilevich, S, Conell, J, Alda, M, Ardau, R, Baune, BT, Berk, M, Bersudsky, Y, Bilderbeck, A, Bocchetta, A, Castro, AMP, Cheung, EYW, Chillotti, C, Choppin, S, Cuomo, A, Del Zompo, M, Dias, R, Dodd, S, Duffy, A, Etain, B, Fagiolini, A, Fernandez Hernandez, M, Garnham, J, Geddes, J, Gildebro, J, Gitlin, MJ, Gonzalez-Pinto, A, Goodwin, GM, Grof, P, Harima, H, Hassel, S, Henry, C, Hidalgo-Mazzei, D, Lund, AH, Kapur, V, Kunigiri, G, Lafer, B, Larsen, ER, Lewitzka, U, Licht, RW, Misiak, B, Piotrowski, P, Miranda-Scippa, A, Monteith, S, Munoz, R, Nakanotani, T, Nielsen, RE, O'Donovan, C, Okamura, Y, Osher, Y, Reif, A, Ritter, P, Rybakowski, JK, Sagduyu, K, Sawchuk, B, Schwartz, E, Slaney, C, Sulaiman, AH, Suominen, K, Suwalska, A, Tam, P, Tatebayashi, Y, Tondo, L, Veeh, J, Vieta, E, Vinberg, M, Viswanath, B, Zetin, M, Whybrow, PC, and Bauer, M
Abstract: BACKGROUND: The world population is aging and the number of older adults with bipolar disorder is increasing. Digital technologies are viewed as a framework to improve care of older adults with bipolar disorder. This analysis quantifies Internet use by older adults with bipolar disorder as part of a larger survey project about information seeking. METHODS: A paper-based survey about information seeking by patients with bipolar disorder was developed and translated into 12 languages. The survey was anonymous and completed between March 2014 and January 2016 by 1222 patients in 17 countries. All patients were diagnosed by a psychiatrist. General estimating equations were used to account for correlated data. RESULTS: Overall, 47% of older adults (age 60 years or older) used the Internet versus 87% of younger adults (less than 60 years). More education and having symptoms that interfered with regular activities increased the odds of using the Internet, while being age 60 years or older decreased the odds. Data from 187 older adults and 1021 younger adults were included in the analysis excluding missing values. CONCLUSIONS: Older adults with bipolar disorder use the Internet much less frequently than younger adults. Many older adults do not use the Internet, and technology tools are suitable for some but not all older adults. As more health services are only available online, and more digital tools are developed, there is concern about growing health disparities based on age. Mental health experts should participate in determining the appropriate role for digital tools for older adults with bipolar disorder.
Published: 2018

163. The association of psychological stress and health related quality of life among patients with stroke and hypertension in Gaza Strip

Author: Baune BT and Aljeesh Y
Subjects: Psychiatry, RC435-571
Abstract: Abstract Background The study was performed to investigate the association of psychological stress and quality of life (QOL) among patients with the cardiovascular disease (CVD) of hypertension plus stroke or hypertension only. Methods The WHOQOL-BREF questionnaire was applied to 112 hypertensive patients with hypertension plus stroke and 224 hypertensive patients without stroke. Psychological stress was assessed with SCL-90. Means scale scores were compared using student-t-test and predictors of QOL were calculated with covariance analysis. Results Patients with stroke had a significant lower QOL than patients without stroke and a significantly higher level of stress (p < 0.01). In analyses of covariance psychological stress was significantly correlated to all domains of QOL among non-stroke patients. The same psychological and sociodemographic factors showed little impact on the stroke patients in these multivariable analyses. In these models psychological stress had a significant impact on the global domain of QOL among stroke patients. Income and gender were the only sociodemographic factors being significantly associated with the physical (education) and social (gender) domains of QOL in stroke patients. Conclusion Psychological stress was strongly correlated with all domains of QOL in patients without stroke and was only partly associated with QOL among patients with stroke. Future studies should investigate if psychological stress is a factor suitable for educational and psychological interventions aiming at stress reduction in CVD patients which might substantially contribute to better health related quality of life in these patients.
Published: 2006
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164. Erratum:Measuring progress and projecting attainment on the basis of past trends of the health-related Sustainable Development Goals in 188 countries: an analysis from the Global Burden of Disease Study 2016 (The Lancet (2017) 390(10100) (1423–1459) (S014067361732336X)(10.1016/S0140-6736(17)32336-X))

Author: Fullman, N, Barber, RM, Abajobir, AA, Abate, KH, Abbafati, C, Abbas, KM, Abd-Allah, F, Abdulkader, RS, Abdulle, AM, Abera, SF, Aboyans, V, Abu-Raddad, LJ, Abu-Rmeileh, NME, Adedeji, IA, Adetokunboh, O, Afshin, A, Agrawal, A, Agrawal, S, Ahmad Kiadaliri, A, Ahmadieh, H, Ahmed, MB, Aichour, MTE, Aichour, AN, Aichour, I, Aiyar, S, Akinyemi, RO, Akseer, N, Al-Aly, Z, Alam, K, Alam, N, Alasfoor, D, Alene, KA, Alizadeh-Navaei, R, Alkerwi, A, Alla, F, Allebeck, P, Allen, C, Al-Raddadi, R, Alsharif, U, Altirkawi, KA, Alvis-Guzman, N, Amare, AT, Amini, E, Ammar, W, Ansari, H, Antonio, CAT, Anwari, P, Arora, M, Artaman, A, Aryal, KK, Asayesh, H, Asgedom, SW, Assadi, R, Atey, TM, Atre, SR, Avila-Burgos, L, Avokpaho, EFGA, Awasthi, A, Azzopardi, P, Bacha, U, Badawi, A, Balakrishnan, K, Bannick, MS, Barac, A, Barker-Collo, SL, Bärnighausen, T, Barrero, LH, Basu, S, Battle, KE, Baune, BT, Beardsley, J, Bedi, N, Beghi, E, Béjot, Y, Bell, ML, Bennett, DA, Bennett, JR, Bensenor, IM, Berhane, A, Berhe, DF, Bernabé, E, Betsu, BD, Beuran, M, Beyene, AS, Bhala, N, Bhansali, A, Bhatt, S, Bhutta, ZA, Bicer, BK, Bidgoli, HH, Bikbov, B, Bilal, AI, Birungi, C, Biryukov, S, Bizuayehu, HM, Blosser, CD, Boneya, DJ, Bose, D, Bou-Orm, IR, and Brauer, M
Subjects: General & Internal Medicine, 11 Medical And Health Sciences
Abstract: GBD 2016 SDG Collaborators. Measuring progress and projecting attainment on the basis of past trends of the health-related Sustainable Development Goals in 188 countries: an analysis from the Global Burden of Disease Study 2016. Lancet 2017; 390: 1423–59—In figure 8B of this Article (published Online First on Sept 12, 2017), the number of indicator targets has been changed from 1 to 9 for Turkmenistan, from 0 to 1 for Afghanistan, and from 1 to 2 for Yemen. Ettore Beghi, Neeraj Bhala, Hélène Carabin, Raimundas Lunevicius, Donald H Silberberg, and Caitlyn Steiner have been added to the list of GBD 2016 SDG Collaborators. Their affiliations, along with the affiliation of Soumya Swaminathan, have been added to the Affiliations section. These corrections have been made to the online version as of Sept 18, 2017, and the printed Article is correct.
Published: 2017

165. Erratum: Measuring progress and projecting attainment on the basis of past trends of the health-related Sustainable Development Goals in 188 countries: an analysis from the Global Burden of Disease Study 2016 (The Lancet (2017) 390(10100) (1423–1459) (S014067361732336X) (10.1016/S0140-6736(17)32336-X))

Author: Fullman, N, Barber, RM, Abajobir, AA, Abate, KH, Abbafati, C, Abbas, KM, Abd-Allah, F, Abdulkader, RS, Abdulle, AM, Abera, SF, Aboyans, V, Abu-Raddad, LJ, Abu-Rmeileh, NME, Adedeji, IA, Adetokunboh, O, Afshin, A, Agrawal, A, Agrawal, S, Ahmad Kiadaliri, A, Ahmadieh, H, Ahmed, MB, Aichour, MTE, Aichour, AN, Aichour, I, Aiyar, S, Akinyemi, RO, Akseer, N, Al-Aly, Z, Alam, K, Alam, N, Alasfoor, D, Alene, KA, Alizadeh-Navaei, R, Alkerwi, A, Alla, F, Allebeck, P, Allen, C, Al-Raddadi, R, Alsharif, U, Altirkawi, KA, Alvis-Guzman, N, Amare, AT, Amini, E, Ammar, W, Ansari, H, Antonio, CAT, Anwari, P, Arora, M, Artaman, A, Aryal, KK, Asayesh, H, Asgedom, SW, Assadi, R, Atey, TM, Atre, SR, Avila-Burgos, L, Avokpaho, EFGA, Awasthi, A, Azzopardi, P, Bacha, U, Badawi, A, Balakrishnan, K, Bannick, MS, Barac, A, Barker-Collo, SL, Bärnighausen, T, Barrero, LH, Basu, S, Battle, KE, Baune, BT, Beardsley, J, Bedi, N, Beghi, E, Béjot, Y, Bell, ML, Bennett, DA, Bennett, JR, Bensenor, IM, Berhane, A, Berhe, DF, Bernabé, E, Betsu, BD, Beuran, M, Beyene, AS, Bhala, N, Bhansali, A, Bhatt, S, Bhutta, ZA, Bicer, BK, Bidgoli, HH, Bikbov, B, Bilal, AI, Birungi, C, Biryukov, S, Bizuayehu, HM, Blosser, CD, Boneya, DJ, Bose, D, Bou-Orm, IR, and Brauer, M
Subjects: General & Internal Medicine, 11 Medical And Health Sciences
Abstract: GBD 2016 SDG Collaborators. Measuring progress and projecting attainment on the basis of past trends of the health-related Sustainable Development Goals in 188 countries: an analysis from the Global Burden of Disease Study 2016. Lancet 2017; 390: 1423–59—The full-text version of this Article has been updated so that the list of authors is displayed in the correct order, in line with the pdf version, rather than in alphabetical order. This correction has been made to the online version as of Oct 12, 2017.
Published: 2017

166. Neural correlates of apathy in late-life depression: a pilot [F-18]FDDNP positron emission tomography study

Author: Eyre, HA, Siddarth, P, van Dyk, K, St Cyr, N, Baune, BT, Barrio, JR, Small, GW, and Lavretsky, H
Subjects: late-life depression, PET, amyloid, apathy, [F-18]FDDNP, tau
Abstract: Neurotoxicity associated with amyloid and tau protein aggregation could represent a pathophysiological cascade that, along with vascular compromise, may predispose individuals to late-life depression (LLD). In LLD, apathy is common, leads to worsening of functioning, and responds poorly to antidepressant treatment. Better understanding of the pathophysiological mechanisms of apathy in LLD would facilitate development of more effective diagnostic and treatment approaches. In this cross-sectional pilot study, we performed positron emission tomography scans after injection of 2-(1-{6-[(2-[18 F]fluoroethyl)(methyl)-amino]-2-naphthyl}ethylidene) malononitrile ([18 F]FDDNP), an in vivo amyloid and tau neuroimaging study, in patients with LLD to explore neural correlates of apathy.Sixteen depressed elderly volunteers received clinical assessments and [18 F]FDDNP positron emission tomography scans. The cross-sectional relationship of [18 F]FDDNP binding levels with depression (Hamilton Depression Rating Scale) and apathy (Apathy Evaluation Scale) were studied using Spearman's correlation analyses because of the relatively small sample size. Age, sex, and years of education were partialed out. Significance levels were set at P ≤ 0.05.[18 F]FDDNP binding in the anterior cingulate cortex was negatively associated with the Apathy Evaluation Scale total (r = -0.62, P = 0.02; where low Apathy Evaluation Scale score equals greater severity of apathy). This suggests that apathy in LLD is associated with higher amyloid and/or tau levels in the anterior cingulate cortex. None of the regional [18 F]FDDNP binding levels was significantly associated with the Hamilton Depression Rating Scale total.This pilot study suggests that increased apathy in subjects with LLD may be associated with greater amyloid and/or tau burden in certain brain regions. Future studies in larger samples would elucidate the generalizability of these results, which eventually could lead to improved diagnostic and treatment methods in LLD.
Published: 2017

167. Pharmacogenetics of antidepressant response:A polygenic approach

Author: García-González, J., Tansey, K.E., Hauser, J., Henigsberg, Neven, Maier, W., Mors, O., Placentino, A., Rietschel, M., Souery, D., Žagar, T., Czerski, P.M., Jerman, B., Buttenschøn, H.N., Schulze, T.G., Zobel, A., Farmer, A., Aitchison, K.J., Craig, I., McGuffin, P., Giupponi, M., Perroud, N., Bondolfi, G., Evans, D., O'Donovan, M., Peters, T.J., Wendland, J.R., Lewis, G., Kapur, S., Perlis, R., Arolt, V., Domschke, K., Major Depressive Disorder Working Group of the Psychiatric Genomic Consortium, Breen, G, Curtis, C, Sang-Hyuk, L, Kan, C, Newhouse, S, Patel, H, Baune, BT, Uher, R, Lewis, CM, Fabbri, C, Garcia-Gonzalez J., Tansey K.E., Hauser J., Henigsberg N., Maier W., Mors O., Placentino A., Rietschel M., Souery D., Zagar T., Czerski P.M., Jerman B., Buttenschon H.N., Schulze T.G., Zobel A., Farmer A., Aitchison K.J., Craig I., McGuffin P., Giupponi M., Perroud N., Bondolfi G., Evans D., O'Donovan M., Peters T.J., Wendland J.R., Lewis G., Kapur S., Perlis R., Arolt V., Domschke K., Breen G., Curtis C., Sang-Hyuk L., Kan C., Newhouse S., Patel H., Baune B.T., Uher R., Lewis C.M., Fabbri C., García-González, Judit, Perroud, Nader Ali, and Bondolfi, Guido
Subjects: 0301 basic medicine, Oncology, Male, Multifactorial Inheritance, Antidepressant, Schizophrenia/drug therapy/genetics, Antidepressive Agents/therapeutic use, ddc:616.89, Pharmacogenomic, 0302 clinical medicine, Risk Factors, Depression (differential diagnoses), 0303 health sciences, Major depressive disorder, Pharmacogenomics, Polygenic risk scores, Schizophrenia, Antidepressive Agents, 3. Good health, Homogeneous, Antidepressive Agent, Female, Psychology, medicine.drug, Clinical psychology, Human, medicine.medical_specialty, Genetic Association Studie, Citalopram, behavioral disciplines and activities, 03 medical and health sciences, Polygenic risk score, Internal medicine, mental disorders, medicine, Journal Article, Escitalopram, Humans, Multifactorial Inheritance/genetics, Biological Psychiatry, Genetic Association Studies, 030304 developmental biology, Pharmacology, Depressive Disorder, Major, Depressive Disorder, Major/drug therapy/genetics, business.industry, Risk Factor, medicine.disease, 030104 developmental biology, Pharmacogenetics, business, 030217 neurology & neurosurgery
Abstract: BackgroundMajor depressive disorder (MDD) has a high personal and socio-economic burden and more than 60% of patients fail to achieve remission with the first antidepressant. The biological mechanisms behind antidepressant response are only partially known but genetic factors play a relevant role. A combined predictor across genetic variants may be useful to investigate this complex trait.MethodsPolygenic risk scores (PRS) were used to estimate multi-allelic contribution to: 1) antidepressant efficacy; 2) its overlap with MDD and schizophrenia. We constructed PRS and tested whether these predicted symptom improvement or remission from the GENDEP study (n=736) to the STAR*D study (n=1409) and vice-versa, including the whole sample or only patients treated with escitalopram or citalopram. Using summary statistics from Psychiatric Genomics Consortium for MDD and schizophrenia, we tested whether PRS from these disorders predicted symptom improvement in GENDEP, STAR*D, and five further studies (n=3756).ResultsNo significant prediction of antidepressant efficacy was obtained from PRS in GENDEP/STAR*D but this analysis might have been underpowered. There was no evidence of overlap in the genetics of antidepressant response with either MDD or schizophrenia, either in individual studies or a meta-analysis. Stratifying by antidepressant did not alter the results.DiscussionWe identified no significant predictive effect using PRS between pharmacogenetic studies. The genetic liability to MDD or schizophrenia did not predict response to antidepressants, suggesting differences between the genetic component of depression and treatment response. Larger or more homogeneous studies will be necessary to obtain a polygenic predictor of antidepressant response.
Published: 2017

168. Measuring progress and projecting attainment on the basis of past trends of the health-related Sustainable Development Goals in 188 countries: An analysis from the Global Burden of Disease Study 2016

Author: Fullman, N, Barber, RM, Abajobir, AA, Abate, KH, Abbafati, C, Abbas, KM, Abd-Allah, F, Abdulle, AM, Abera, SF, Aboyans, V, Abu-Raddad, LJ, Abu-Rmeileh, NME, Adedeji, IA, Adetokunboh, O, Afshin, A, Agrawal, A, Agrawal, S, Kiadaliri, AA, Ahmadieh, H, Ahmed, MB, Aichour, AN, Aichour, I, Aichour, MTE, Aiyar, S, Akinyemi, RO, Akseer, N, Al-Aly, Z, Alam, K, Alam, N, Alasfoor, D, Alene, KA, Alizadeh-Navaei, R, Alkerwi, A, Alla, F, Allebeck, P, Allen, C, Al-Raddadi, R, Alsharif, U, Altirkawi, KA, Alvis-Guzman, N, Amare, AT, Amini, E, Ammar, W, Antonio, CAT, Ansari, H, Anwari, P, Arora, M, Artaman, A, Aryal, KK, Asayesh, H, Asgedom, SW, Assadi, R, Atey, TM, Atre, SR, Avila-Burgos, L, Arthur Avokpaho, EFG, Awasthi, A, Azzopardi, P, Bacha, U, Badawi, A, Balakrishnan, K, Bannick, MS, Barac, A, Barker-Collo, SL, Bärnighausen, T, Barrero, LH, Basu, S, Battle, KE, Baune, BT, Beardsley, J, Bedi, N, Beghi, E, Béjot, Y, Bell, ML, Bennett, DA, Bennett, JR, Bensenor, IM, Berhane, A, Berhe, DF, Bernabé, E, Betsu, BD, Beuran, M, Beyene, AS, Bhala, N, Bhansali, A, Bhatt, S, Bhutta, ZA, Bikbov, B, Bilal, AI, Birungi, C, Biryukov, S, Bizuayehu, HM, Blosser, CD, Boneya, DJ, Bose, D, Bou-Orm, IR, Brauer, M, Breitborde, NJK, Brugha, TS, Bulto, LNB, Fullman, N, Barber, RM, Abajobir, AA, Abate, KH, Abbafati, C, Abbas, KM, Abd-Allah, F, Abdulle, AM, Abera, SF, Aboyans, V, Abu-Raddad, LJ, Abu-Rmeileh, NME, Adedeji, IA, Adetokunboh, O, Afshin, A, Agrawal, A, Agrawal, S, Kiadaliri, AA, Ahmadieh, H, Ahmed, MB, Aichour, AN, Aichour, I, Aichour, MTE, Aiyar, S, Akinyemi, RO, Akseer, N, Al-Aly, Z, Alam, K, Alam, N, Alasfoor, D, Alene, KA, Alizadeh-Navaei, R, Alkerwi, A, Alla, F, Allebeck, P, Allen, C, Al-Raddadi, R, Alsharif, U, Altirkawi, KA, Alvis-Guzman, N, Amare, AT, Amini, E, Ammar, W, Antonio, CAT, Ansari, H, Anwari, P, Arora, M, Artaman, A, Aryal, KK, Asayesh, H, Asgedom, SW, Assadi, R, Atey, TM, Atre, SR, Avila-Burgos, L, Arthur Avokpaho, EFG, Awasthi, A, Azzopardi, P, Bacha, U, Badawi, A, Balakrishnan, K, Bannick, MS, Barac, A, Barker-Collo, SL, Bärnighausen, T, Barrero, LH, Basu, S, Battle, KE, Baune, BT, Beardsley, J, Bedi, N, Beghi, E, Béjot, Y, Bell, ML, Bennett, DA, Bennett, JR, Bensenor, IM, Berhane, A, Berhe, DF, Bernabé, E, Betsu, BD, Beuran, M, Beyene, AS, Bhala, N, Bhansali, A, Bhatt, S, Bhutta, ZA, Bikbov, B, Bilal, AI, Birungi, C, Biryukov, S, Bizuayehu, HM, Blosser, CD, Boneya, DJ, Bose, D, Bou-Orm, IR, Brauer, M, Breitborde, NJK, Brugha, TS, and Bulto, LNB
Abstract: Background: The UN's Sustainable Development Goals (SDGs) are grounded in the global ambition of "leaving no one behind". Understanding today's gains and gaps for the health-related SDGs is essential for decision makers as they aim to improve the health of populations. As part of the Global Burden of Diseases, Injuries, and Risk Factors Study 2016 (GBD 2016), we measured 37 of the 50 health-related SDG indicators over the period 1990-2016 for 188 countries, and then on the basis of these past trends, we projected indicators to 2030. Methods: We used standardised GBD 2016 methods to measure 37 health-related indicators from 1990 to 2016, an increase of four indicators since GBD 2015. We substantially revised the universal health coverage (UHC) measure, which focuses on coverage of essential health services, to also represent personal health-care access and quality for several non-communicable diseases. We transformed each indicator on a scale of 0-100, with 0 as the 25th percentile estimated between 1990 and 2030, and 100 as the 975th percentile during that time. An index representing all 37 health-related SDG indicators was constructed by taking the geometric mean of scaled indicators by target. On the basis of past trends, we produced projections of indicator values, using a weighted average of the indicator and country-specific annualised rates of change from 1990 to 2016 with weights for each annual rate of change based on out-of-sample validity. 24 of the currently measured health-related SDG indicators have defined SDG targets, against which we assessed attainment. Findings Globally, the median health-related SDG index was 567 (IQR 319-668) in 2016 and country-level performance markedly varied, with Singapore (868, 95% uncertainty interval 846-889), Iceland (860, 841-876), and Sweden (856, 818-878) having the highest levels in 2016 and Afghanistan (109, 96-119), the Central African Republic (110, 88-138), and Somalia (113, 95-131) recording the lowest. Between
Published: 2017

169. Midlife managerial experience is linked to late life hippocampal morphology and function

Author: Suo, C, Gates, N, Fiatarone Singh, M, Saigal, N, Wilson, GC, Meiklejohn, J, Sachdev, P, Brodaty, H, Wen, W, Singh, N, Baune, BT, Baker, M, Foroughi, N, Wang, Y, Valenzuela, MJ, Suo, C, Gates, N, Fiatarone Singh, M, Saigal, N, Wilson, GC, Meiklejohn, J, Sachdev, P, Brodaty, H, Wen, W, Singh, N, Baune, BT, Baker, M, Foroughi, N, Wang, Y, and Valenzuela, MJ
Abstract: © 2016 The Author(s)An active cognitive lifestyle has been suggested to have a protective role in the long-term maintenance of cognition. Amongst healthy older adults, more managerial or supervisory experiences in midlife are linked to a slower hippocampal atrophy rate in late life. Yet whether similar links exist in individuals with Mild Cognitive Impairment (MCI) is not known, nor whether these differences have any functional implications. 68 volunteers from the Sydney SMART Trial, diagnosed with non-amnestic MCI, were divided into high and low managerial experience (HME/LME) during their working life. All participants underwent neuropsychological testing, structural and resting-state functional MRI. Group comparisons were performed on hippocampal volume, morphology, hippocampal seed-based functional connectivity, memory and executive function and self-ratings of memory proficiency. HME was linked to better memory function (p = 0.024), mediated by larger hippocampal volume (p = 0.025). More specifically, deformation analysis found HME had relatively more volume in the CA1 sub-region of the hippocampus (p < 0.05). Paradoxically, this group rated their memory proficiency worse (p = 0.004), a result correlated with diminished functional connectivity between the right hippocampus and right prefrontal cortex (p < 0.001). Finally, hierarchical regression modelling substantiated this double dissociation.
Published: 2017

170. Healthcare Access and Quality Index based on mortality from causes amenable to personal health care in 195 countries and territories, 1990–2015: a novel analysis from the Global Burden of Disease Study 2015

Author: Barber, RM, Fullman, N, Sorensen, RJD, Bollyky, T, McKee, M, Nolte, E, Abajobir, AA, Abate, KH, Abbafati, C, Abbas, KM, Abd-Allah, F, Abdulle, AM, Abdurahman, AA, Abera, SF, Abraham, B, Abreha, GF, Adane, K, Adelekan, AL, Adetifa, IMO, Afshin, A, Agarwal, A, Agarwal, SK, Agarwal, S, Agrawal, A, Kiadaliri, AA, Ahmadi, A, Ahmed, KY, Ahmed, MB, Akinyemi, RO, Akinyemiju, TF, Akseer, N, Al-Aly, Z, Alam, K, Alam, N, Alam, SS, Alemu, ZA, Alene, KA, Alexander, L, Ali, R, Ali, SD, Alizadeh-Navaei, R, Alkerwi, A, Alla, F, Allebeck, P, Allen, C, Al-Raddadi, R, Alsharif, U, Altirkawi, KA, Martin, EA, Alvis-Guzman, N, Amare, AT, Amini, E, Ammar, W, Amo-Adjei, J, Amoako, YA, Anderson, BO, Androudi, S, Ansari, H, Ansha, MG, Antonio, CAT, Ärnlöv, J, Artaman, A, Asayesh, H, Assadi, R, Astatkie, A, Atey, TM, Atique, S, Atnafu, NT, Atre, SR, Avila-Burgos, L, Avokpaho, EFGA, Quintanilla, BPA, Awasthi, A, Ayele, NN, Azzopardi, P, Saleem, HOB, Bärnighausen, T, Bacha, U, Badawi, A, Banerjee, A, Barac, A, Barboza, MA, Barker-Collo, SL, Barrero, LH, Basu, S, Baune, BT, Baye, K, Bayou, YT, Bazargan-Hejazi, S, Bedi, N, Beghi, E, Béjot, Y, Bello, AK, Bennett, DA, Bensenor, IM, Berhane, A, Bernabé, E, Bernal, OA, Beyene, AS, Beyene, TJ, Bhutta, ZA, Biadgilign, S, Bikbov, B, Birlik, SM, Birungi, C, Biryukov, S, Bisanzio, D, Bizuayehu, HM, Bose, D, Brainin, M, Brauer, M, Brazinova, A, Breitborde, NJK, Brenner, H, Butt, ZA, Cárdenas, R, Cahuana-Hurtado, L, Campos-Nonato, IR, Car, J, Carrero, JJ, Casey, D, Caso, V, Castañeda-Orjuela, CA, Rivas, JC, Catalá-López, F, Cecilio, P, Cercy, K, Charlson, FJ, Chen, AZ, Chew, A, Chibalabala, M, Chibueze, CE, Chisumpa, VH, Chitheer, AA, Chowdhury, R, Christensen, H, Christopher, DJ, Ciobanu, LG, Cirillo, M, Coggeshall, MS, Cooper, LT, Cortinovis, M, Crump, JA, Dalal, K, Danawi, H, Dandona, L, Dandona, R, Dargan, PI, das Neves, J, Davey, G, Davitoiu, DV, Davletov, K, De Leo, D, Del Gobbo, LC, del Pozo-Cruz, B, Dellavalle, RP, Deribe, K, Deribew, A, Des Jarlais, DC, Dey, S, Dharmaratne, SD, Dicker, D, Ding, EL, Dokova, K, Dorsey, ER, Doyle, KE, Dubey, M, Ehrenkranz, R, Ellingsen, CL, Elyazar, I, Enayati, A, Ermakov, SP, Eshrati, B, Esteghamati, A, Estep, K, Fürst, T, Faghmous, IDA, Fanuel, FBB, Faraon, EJA, Farid, TA, Farinha, CSES, Faro, A, Farvid, MS, Farzadfar, F, Feigin, VL, Feigl, AB, Fereshtehnejad, S-M, Fernandes, JG, Fernandes, JC, Feyissa, TR, Fischer, F, Fitzmaurice, C, Fleming, TD, Foigt, N, Foreman, KJ, Forouzanfar, MH, Franklin, RC, Frostad, J, G/hiwot, TT, Gakidou, E, Gambashidze, K, Gamkrelidze, A, Gao, W, Garcia-Basteiro, AL, Gebre, T, Gebremedhin, AT, Gebremichael, MW, Gebru, AA, Gelaye, AA, Geleijnse, JM, Genova-Maleras, R, Gibney, KB, Giref, AZ, Gishu, MD, Giussani, G, Godwin, WW, Gold, A, Goldberg, EM, Gona, PN, Goodridge, A, Gopalani, SV, Goto, A, Graetz, N, Greaves, F, Griswold, M, Guban, PI, Gugnani, HC, Gupta, PC, Gupta, R, Gupta, T, Gupta, V, Habtewold, TD, Hafezi-Nejad, N, Haile, D, Hailu, AD, Hailu, GB, Hakuzimana, A, Hamadeh, RR, Hambisa, MT, Hamidi, S, Hammami, M, Hankey, GJ, Hao, Y, Harb, HL, Hareri, HA, Haro, JM, Hassanvand, MS, Havmoeller, R, Hay, RJ, Hay, SI, Hendrie, D, Heredia-Pi, IB, Hoek, HW, Horino, M, Horita, N, Hosgood, HD, Htet, AS, Hu, G, Huang, H, Huang, JJ, Huntley, BM, Huynh, C, Iburg, KM, Ileanu, BV, Innos, K, Irenso, AA, Jahanmehr, N, Jakovljevic, MB, James, P, James, SL, Javanbakht, M, Jayaraman, SP, Jayatilleke, AU, Jeemon, P, Jha, V, John, D, Johnson, C, Johnson, SC, Jonas, JB, Juel, K, Kabir, Z, Kalkonde, Y, Kamal, R, Kan, H, Karch, A, Karema, CK, Karimi, SM, Kasaeian, A, Kassebaum, NJ, Kastor, A, Katikireddi, SV, Kazanjan, K, Keiyoro, PN, Kemmer, L, Kemp, AH, Kengne, AP, Kerbo, AA, Kereselidze, M, Kesavachandran, CN, Khader, YS, Khalil, I, Khan, AR, Khan, EA, Khan, G, Khang, Y-H, Khoja, ATA, Khonelidze, I, Khubchandani, J, Kibret, GD, Kim, D, Kim, P, Kim, YJ, Kimokoti, RW, Kinfu, Y, Kissoon, N, Kivipelto, M, Kokubo, Y, Kolk, A, Kolte, D, Kopec, JA, Kosen, S, Koul, PA, Koyanagi, A, Kravchenko, M, Krishnaswami, S, Krohn, KJ, Defo, BK, Bicer, BK, Kuipers, EJ, Kulkarni, VS, Kumar, GA, Kumsa, FA, Kutz, M, Kyu, HH, Lager, ACJ, Lal, A, Lal, DK, Lalloo, R, Lallukka, T, Lan, Q, Langan, SM, Lansingh, VC, Larson, HJ, Larsson, A, Laryea, DO, Latif, AA, Lawrynowicz, AEB, Leasher, JL, Leigh, J, Leinsalu, M, Leshargie, CT, Leung, J, Leung, R, Levi, M, Liang, X, Lim, SS, Lind, M, Linn, S, Lipshultz, SE, Liu, P, Liu, Y, Lo, L-T, Logroscino, G, Lopez, AD, Lorch, SA, Lotufo, PA, Lozano, R, Lunevicius, R, Lyons, RA, Macarayan, ERK, Mackay, MT, El Razek, HMA, El Razek, MMA, Mahdavi, M, Majeed, A, Malekzadeh, R, Malta, DC, Mantovani, LG, Manyazewal, T, Mapoma, CC, Marcenes, W, Marks, GB, Marquez, N, Martinez-Raga, J, Marzan, MB, Massano, J, Mathur, MR, Maulik, PK, Mazidi, M, McAlinden, C, McGrath, JJ, McNellan, C, Meaney, PA, Mehari, A, Mehndiratta, MM, Meier, T, Mekonnen, AB, Meles, KG, Memish, ZA, Mengesha, MM, Mengiste, DT, Mengistie, MA, Menota, BG, Mensah, GA, Mereta, ST, Meretoja, A, Meretoja, TJ, Mezgebe, HB, Micha, R, Millear, A, Mills, EJ, Minnig, S, Mirarefin, M, Mirrakhimov, EM, Mock, CN, Mohammad, KA, Mohammed, S, Mohanty, SK, Mokdad, AH, Mola, GLD, Molokhia, M, Monasta, L, Montico, M, Moradi-Lakeh, M, Moraga, P, Morawska, L, Mori, R, Moses, M, Mueller, UO, Murthy, S, Musa, KI, Nachega, JB, Nagata, C, Nagel, G, Naghavi, M, Naheed, A, Naldi, L, Nangia, V, Nascimento, BR, Negoi, I, Neupane, SP, Newton, CR, Ng, M, Ngalesoni, FN, Ngunjiri, JW, Nguyen, G, Ningrum, DNA, Nolte, S, Nomura, M, Norheim, OF, Norrving, B, Noubiap, JJN, Obermeyer, CM, Ogbo, FA, Oh, I-H, Okoro, A, Oladimeji, O, Olagunju, AT, Olivares, PR, Olsen, HE, Olusanya, BO, Olusanya, JO, Opio, JN, Oren, E, Ortiz, A, Osborne, RH, Osman, M, Owolabi, MO, PA, M, Pain, AW, Pakhale, S, Castillo, EP, Pana, A, Papachristou, C, Parsaeian, M, Patel, T, Patton, GC, Paudel, D, Paul, VK, Pearce, N, Pereira, DM, Perez-Padilla, R, Perez-Ruiz, F, Perico, N, Pesudovs, K, Petzold, M, Phillips, MR, Pigott, DM, Pillay, JD, Pinho, C, Polinder, S, Pond, CD, Prakash, V, Purwar, M, Qorbani, M, Quistberg, DA, Radfar, A, Rafay, A, Rahimi, K, Rahimi-Movaghar, V, Rahman, M, Rahman, MHU, Rai, RK, Ram, U, Rana, SM, Rankin, Z, Rao, PV, Rao, PC, Rawaf, S, Rego, MAS, Reitsma, M, Remuzzi, G, Renzaho, AMNN, Resnikoff, S, Rezaei, S, Rezai, MS, Ribeiro, AL, Roba, HS, Rokni, MB, Ronfani, L, Roshandel, G, Roth, GA, Rothenbacher, D, Roy, NK, Sachdev, PS, Sackey, BB, Saeedi, MY, Safiri, S, Sagar, R, Sahraian, MA, Saleh, MM, Salomon, JA, Samy, AM, Sanabria, JR, Sanchez-Niño, MD, Sandar, L, Santos, IS, Santos, JV, Milicevic, MMS, Sarmiento-Suarez, R, Sartorius, B, Satpathy, M, Savic, M, Sawhney, M, Saylan, MI, Schöttker, B, Schutte, AE, Schwebel, DC, Seedat, S, Seid, AM, Seifu, CN, Sepanlou, SG, Serdar, B, Servan-Mori, EE, Setegn, T, Shackelford, KA, Shaheen, A, Shahraz, S, Shaikh, MA, Shakh-Nazarova, M, Shamsipour, M, Islam, SMS, Sharma, J, Sharma, R, She, J, Sheikhbahaei, S, Shen, J, Shi, P, Shigematsu, M, Shin, M-J, Shiri, R, Shoman, H, Shrime, MG, Sibamo, ELS, Sigfusdottir, ID, Silva, DAS, Silveira, DGA, Sindi, S, Singh, A, Singh, JA, Singh, OP, Singh, PK, Singh, V, Sinke, AH, Sinshaw, AE, Skirbekk, V, Sliwa, K, Smith, A, Sobngwi, E, Soneji, S, Soriano, JB, Sousa, TCM, Sposato, LA, Sreeramareddy, CT, Stathopoulou, V, Steel, N, Steiner, C, Steinke, S, Stokes, MA, Stranges, S, Strong, M, Stroumpoulis, K, Sturua, L, Sufiyan, MB, Suliankatchi, RA, Sun, J, Sur, P, Swaminathan, S, Sykes, BL, Tabarés-Seisdedos, R, Tabb, KM, Taffere, GR, Talongwa, RT, Tarajia, M, Tavakkoli, M, Taveira, N, Teeple, S, Tegegne, TK, Tehrani-Banihashemi, A, Tekelab, T, Tekle, DY, Shifa, GT, Terkawi, AS, Tesema, AG, Thakur, JS, Thomson, AJ, Tillmann, T, Tiruye, TY, Tobe-Gai, R, Tonelli, M, Topor-Madry, R, Tortajada, M, Troeger, C, Truelsen, T, Tura, AK, Uchendu, US, Ukwaja, KN, Undurraga, EA, Uneke, CJ, Uthman, OA, van Boven, JFM, Van Dingenen, R, Varughese, S, Vasankari, T, Venketasubramanian, N, Violante, FS, Vladimirov, SK, Vlassov, VV, Vollset, SE, Vos, T, Wagner, JA, Wakayo, T, Waller, SG, Walson, JL, Wang, H, Wang, Y-P, Watkins, DA, Weiderpass, E, Weintraub, RG, Wen, C-P, Werdecker, A, Wesana, J, Westerman, R, Whiteford, HA, Wilkinson, JD, Wiysonge, CS, Woldeyes, BG, Wolfe, CDA, Won, S, Workicho, A, Workie, SB, Wubshet, M, Xavier, D, Xu, G, Yadav, AK, Yaghoubi, M, Yakob, B, Yan, LL, Yano, Y, Yaseri, M, Yimam, HH, Yip, P, Yonemoto, N, Yoon, S-J, Younis, MZ, Yu, C, Zaidi, Z, El Sayed Zaki, M, Zambrana-Torrelio, C, Zapata, T, Zenebe, ZM, Zodpey, S, Zoeckler, L, Zuhlke, LJ, Murray, CJL, Barber, RM, Fullman, N, Sorensen, RJD, Bollyky, T, McKee, M, Nolte, E, Abajobir, AA, Abate, KH, Abbafati, C, Abbas, KM, Abd-Allah, F, Abdulle, AM, Abdurahman, AA, Abera, SF, Abraham, B, Abreha, GF, Adane, K, Adelekan, AL, Adetifa, IMO, Afshin, A, Agarwal, A, Agarwal, SK, Agarwal, S, Agrawal, A, Kiadaliri, AA, Ahmadi, A, Ahmed, KY, Ahmed, MB, Akinyemi, RO, Akinyemiju, TF, Akseer, N, Al-Aly, Z, Alam, K, Alam, N, Alam, SS, Alemu, ZA, Alene, KA, Alexander, L, Ali, R, Ali, SD, Alizadeh-Navaei, R, Alkerwi, A, Alla, F, Allebeck, P, Allen, C, Al-Raddadi, R, Alsharif, U, Altirkawi, KA, Martin, EA, Alvis-Guzman, N, Amare, AT, Amini, E, Ammar, W, Amo-Adjei, J, Amoako, YA, Anderson, BO, Androudi, S, Ansari, H, Ansha, MG, Antonio, CAT, Ärnlöv, J, Artaman, A, Asayesh, H, Assadi, R, Astatkie, A, Atey, TM, Atique, S, Atnafu, NT, Atre, SR, Avila-Burgos, L, Avokpaho, EFGA, Quintanilla, BPA, Awasthi, A, Ayele, NN, Azzopardi, P, Saleem, HOB, Bärnighausen, T, Bacha, U, Badawi, A, Banerjee, A, Barac, A, Barboza, MA, Barker-Collo, SL, Barrero, LH, Basu, S, Baune, BT, Baye, K, Bayou, YT, Bazargan-Hejazi, S, Bedi, N, Beghi, E, Béjot, Y, Bello, AK, Bennett, DA, Bensenor, IM, Berhane, A, Bernabé, E, Bernal, OA, Beyene, AS, Beyene, TJ, Bhutta, ZA, Biadgilign, S, Bikbov, B, Birlik, SM, Birungi, C, Biryukov, S, Bisanzio, D, Bizuayehu, HM, Bose, D, Brainin, M, Brauer, M, Brazinova, A, Breitborde, NJK, Brenner, H, Butt, ZA, Cárdenas, R, Cahuana-Hurtado, L, Campos-Nonato, IR, Car, J, Carrero, JJ, Casey, D, Caso, V, Castañeda-Orjuela, CA, Rivas, JC, Catalá-López, F, Cecilio, P, Cercy, K, Charlson, FJ, Chen, AZ, Chew, A, Chibalabala, M, Chibueze, CE, Chisumpa, VH, Chitheer, AA, Chowdhury, R, Christensen, H, Christopher, DJ, Ciobanu, LG, Cirillo, M, Coggeshall, MS, Cooper, LT, Cortinovis, M, Crump, JA, Dalal, K, Danawi, H, Dandona, L, Dandona, R, Dargan, PI, das Neves, J, Davey, G, Davitoiu, DV, Davletov, K, De Leo, D, Del Gobbo, LC, del Pozo-Cruz, B, Dellavalle, RP, Deribe, K, Deribew, A, Des Jarlais, DC, Dey, S, Dharmaratne, SD, Dicker, D, Ding, EL, Dokova, K, Dorsey, ER, Doyle, KE, Dubey, M, Ehrenkranz, R, Ellingsen, CL, Elyazar, I, Enayati, A, Ermakov, SP, Eshrati, B, Esteghamati, A, Estep, K, Fürst, T, Faghmous, IDA, Fanuel, FBB, Faraon, EJA, Farid, TA, Farinha, CSES, Faro, A, Farvid, MS, Farzadfar, F, Feigin, VL, Feigl, AB, Fereshtehnejad, S-M, Fernandes, JG, Fernandes, JC, Feyissa, TR, Fischer, F, Fitzmaurice, C, Fleming, TD, Foigt, N, Foreman, KJ, Forouzanfar, MH, Franklin, RC, Frostad, J, G/hiwot, TT, Gakidou, E, Gambashidze, K, Gamkrelidze, A, Gao, W, Garcia-Basteiro, AL, Gebre, T, Gebremedhin, AT, Gebremichael, MW, Gebru, AA, Gelaye, AA, Geleijnse, JM, Genova-Maleras, R, Gibney, KB, Giref, AZ, Gishu, MD, Giussani, G, Godwin, WW, Gold, A, Goldberg, EM, Gona, PN, Goodridge, A, Gopalani, SV, Goto, A, Graetz, N, Greaves, F, Griswold, M, Guban, PI, Gugnani, HC, Gupta, PC, Gupta, R, Gupta, T, Gupta, V, Habtewold, TD, Hafezi-Nejad, N, Haile, D, Hailu, AD, Hailu, GB, Hakuzimana, A, Hamadeh, RR, Hambisa, MT, Hamidi, S, Hammami, M, Hankey, GJ, Hao, Y, Harb, HL, Hareri, HA, Haro, JM, Hassanvand, MS, Havmoeller, R, Hay, RJ, Hay, SI, Hendrie, D, Heredia-Pi, IB, Hoek, HW, Horino, M, Horita, N, Hosgood, HD, Htet, AS, Hu, G, Huang, H, Huang, JJ, Huntley, BM, Huynh, C, Iburg, KM, Ileanu, BV, Innos, K, Irenso, AA, 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Wubshet, M, Xavier, D, Xu, G, Yadav, AK, Yaghoubi, M, Yakob, B, Yan, LL, Yano, Y, Yaseri, M, Yimam, HH, Yip, P, Yonemoto, N, Yoon, S-J, Younis, MZ, Yu, C, Zaidi, Z, El Sayed Zaki, M, Zambrana-Torrelio, C, Zapata, T, Zenebe, ZM, Zodpey, S, Zoeckler, L, Zuhlke, LJ, and Murray, CJL
Published: 2017

171. Global, regional, and national under-5 mortality, adult mortality, age-specific mortality, and life expectancy, 1970-2016: a systematic analysis for the Global Burden of Disease Study 2016

Author: Wang, H, Abajobir, AA, Abate, KH, Abbafati, C, Abbas, KM, Abd-Allah, F, Abera, SF, Abraha, HN, Abu-Raddad, LJ, Abu-Rmeileh, NME, Adedeji, IA, Adedoyin, RA, Adetifa, IMO, Adetokunboh, O, Afshin, A, Aggarwal, R, Agrawal, A, Agrawal, S, Kiadaliri, AA, Ahmed, MB, Aichour, AN, Aichour, I, Aichour, MTE, Aiyar, S, Akanda, S, Akinyemiju, TF, Akseer, N, Al-Eyadhy, A, Al Lami, FH, Alabed, S, Alahdab, F, Al-Aly, Z, Alam, K, Alam, N, Alasfoor, D, Aldridge, RW, Alene, KA, Alhabib, S, Ali, R, Alizadeh-Navaei, R, Aljunid, SM, Alkaabi, JM, Alkerwi, A, Alla, F, Allam, SD, Allebeck, P, Al-Raddadi, R, Alsharif, U, Altirkawi, KA, Martin, EA, Alvis-Guzman, N, Amare, AT, Ameh, EA, Amini, E, Ammar, W, Amoako, YA, Anber, N, Andrei, CL, Androudi, S, Ansari, H, Ansha, MG, Antonio, CAT, Anwari, P, Arnlov, J, Arora, M, Al, A, Aryal, KK, Asayesh, H, Asgedom, SW, Asghar, RJ, Assadi, R, Atey, TM, Atre, SR, Avila-Burgos, L, Avokpaho, EFGA, Awasthi, A, Quintanilla, BPA, Babalola, TK, Bacha, U, Badawi, A, Balakrishnan, K, Balalla, S, Barac, A, Barber, RM, Barboza, MA, Barker-Collo, SL, Barnighausen, T, Barquera, S, Barregard, L, Barrero, LH, Baune, BT, Bazargan-Hejazi, S, Bedi, N, Beghi, E, Bejot, Y, Bekele, BB, Bell, ML, Bello, AK, Bennett, DA, Bennett, JR, Bensenor, IM, Benson, J, Berhane, A, Berhe, DF, Bernabe, E, Beuran, M, Beyene, AS, Bhala, N, Bhansali, A, Bhaumik, S, Bhutta, ZA, Bikbov, B, Birungi, C, Biryukov, S, Bisanzio, D, Bizuayehu, HM, Bjerregaard, P, Blosser, CD, Boneya, DJ, Boufous, S, Bourne, RRA, Brazinova, A, Breitborde, NJK, Brenner, H, Brugha, TS, Bukhman, G, Negesa, L, Bulto, B, Bumgarner, BR, Burch, M, Butt, ZA, Cahill, LE, Cahuana-Hurtado, L, Campos-Nonato, IR, Car, J, Car, M, Crdenas, R, Carpenter, DO, Carrero, JJ, Carter, A, Castaneda-Orjuela, CA, Rivas, JC, Castro, FF, Castro, RE, Catala-Lopez, F, Chen, H, Chiang, PP-C, Chibalabala, M, Chisumpa, VH, Chitheer, AA, Choi, J-YJ, Christensen, H, Christopher, DJ, Ciobanu, LG, Cirillo, M, Cohen, AJ, Colquhoun, SM, Coresh, J, Criqui, MH, Cromwell, EA, Crump, JA, Dandona, L, Dandona, R, Dargan, PI, das Neves, J, Davey, G, Davitoiu, DV, Davletov, K, de Courten, B, De Leo, D, Degenhardt, L, Deiparine, S, Dellavalle, RP, Deribe, K, Deribew, A, Des Jarlais, DC, Dey, S, Dharmaratne, SD, Dherani, MK, Diaz-Torne, C, Ding, EL, Dixit, P, Djalalinia, S, Huyen, PD, Doku, DT, Donnelly, CA, Priscila, K, dos Santos, B, Douwes-Schultz, D, Driscoll, TR, Duan, L, Dubey, M, Duncan, BB, Dwivedi, LK, Ebrahimi, H, El Bcheraoui, C, Ellingsen, CL, Enayati, A, Endries, AY, Ermakov, SP, Eshetie, S, Eshrati, B, Eskandarieh, S, Esteghamati, A, Estep, K, Fanuel, BBF, Faro, A, Farvid, MS, Farzadfar, F, Feigin, VL, Fereshtehnejad, S-M, Fernandes, JG, Fernandes, JC, Feyissa, TR, Filip, I, Fischer, F, Foigt, N, Foreman, KJ, Frank, T, Franklin, RC, Fraser, M, Friedman, J, Frostad, JJ, Fullman, N, Furst, T, Furtado, JM, Futran, ND, Gakidou, E, Gambashidze, K, Gamkrelidze, A, Gankpe, FG, Garcia-Basteiro, AL, Gebregergs, GB, Gebrehiwot, TT, Gebrekidan, KG, Gebremichael, MW, Gelaye, AA, Geleijnse, JM, Gemechu, BL, Gemechu, KS, Genova-Maleras, R, Gesesew, HA, Gething, PW, Gibney, KB, Gill, PS, Gillum, RF, Giref, AZ, Girma, BW, Giussani, G, Goenka, S, Gomez, B, Gona, PN, Gopalani, SV, Goulart, AC, Graetz, N, Gugnani, HC, Gupta, PC, Gupta, R, Gupta, T, Gupta, V, Haagsma, JA, Hafezi-Nejad, N, Bidgoli, HH, Hakuzimana, A, Halasa, YA, Hamadeh, RR, Hambisa, MT, Hamidi, S, Hammami, M, Hancock, J, Handal, AJ, Hankey, GJ, Hao, Y, Harb, HL, Hareri, HA, Harikrishnan, S, Haro, JM, Hassanvand, MS, Havmoeller, R, Hay, RJ, Hay, SI, He, F, Heredia-Pi, IB, Herteliu, C, Hilawe, EH, Hoek, HW, Horita, N, Hosgood, HD, Hostiuc, S, Hotez, PJ, Hoy, DG, Hsairi, M, Htet, AS, Hu, G, Huang, H, Huang, JJ, Iburg, KM, Igumbor, EU, Ileanu, BV, Inoue, M, Irenso, AA, Irvine, CMS, Islam, N, Jacobsen, KH, Jaenisch, T, Jahanmehr, N, Jakovljevic, MB, Javanbakht, M, Jayatilleke, AU, Jeemon, P, Jensen, PN, Jha, V, Jin, Y, John, D, John, O, Johnson, SC, Jonas, JB, Jurisson, M, Kabir, Z, Kadel, R, Kahsay, A, Kalkonde, Y, Kamal, R, Kan, H, Karch, A, Karema, CK, Karimi, SM, Karthikeyan, G, Kasaeian, A, Kassaw, NA, Kassebaum, NJ, Kastor, A, Katikireddi, SV, Kaul, A, Kawakami, N, Kazanjan, K, Keiyoro, PN, Kelbore, SG, Kemp, AH, Kengne, AP, Keren, A, Kereselidze, M, Kesavachandran, CN, Ketema, EB, Khader, YS, Khalil, IA, Khan, EA, Khan, G, Khang, Y-H, Khera, S, Khoja, ATA, Khosravi, MH, Kibret, GD, Kieling, C, Kim, C-I, Kim, D, Kim, P, Kim, S, Kim, YJ, Kimokoti, RW, Kinfu, Y, Kishawi, S, Kissimova-Skarbek, KA, Kissoon, N, Kivimaki, M, Knudsen, AK, Kokubo, Y, Kopec, JA, Kosen, S, Koul, PA, Koyanagi, A, Kravchenko, M, Krohn, KJ, Defo, BK, Bicer, BK, Kuipers, EJ, Kulikoff, XR, Kulkarni, VS, Kumar, GA, Kumar, P, Kumsa, FA, Kutz, M, Lachat, C, Lagat, AK, Lager, ACJ, Lal, DK, Lalloo, R, Lambert, N, Lan, Q, Lansingh, VC, Larson, HJ, Larsson, A, Laryea, DO, Lavados, PM, Laxmaiah, A, Lee, PH, Leigh, J, Leung, J, Leung, R, Levi, M, Li, Y, Liao, Y, Liben, ML, Lim, SS, Linn, S, Lipshultz, SE, Liu, S, Lodha, R, Logroscino, G, Lorch, SA, Lorkowski, S, Lotufo, PA, Lozano, R, Lunevicius, R, Lyons, RA, Ma, S, Macarayan, ERK, Machado, IE, Mackay, MT, Abd el Razek, MM, Magis-Rodriguez, C, Mahdavi, M, Majdan, M, Majdzadeh, R, Majeed, A, Malekzadeh, R, Malhotra, R, Malta, DC, Mantovani, LG, Manyazewal, T, Mapoma, CC, Marczak, LB, Marks, GB, Martinez-Raga, J, Martins-Melo, FR, Massano, J, Maulik, PK, Mayosi, BM, Mazidi, M, McAlinden, C, McGarvey, ST, McGrath, JJ, Mckee, M, Mehata, S, Mehndiratta, MM, Mehta, KM, Meier, T, Mekonnen, TC, Meles, KG, Memiah, P, Memish, ZA, Mendoza, W, Mengesha, MM, Mengistie, MA, Tadese, D, Menon, MGR, Menota, BG, Mensah, GA, Meretoja, A, Meretoja, TJ, Mezgebe, HB, Micha, R, Mikesell, J, Miller, TR, Mills, EJ, Minnig, S, Mirarefin, M, Mirrakhimov, EM, Misganaw, A, Mishra, SR, Mohammad, KA, Mohammadi, A, Mohammed, KESM, Mohan, MBV, Mohanty, SK, Mokdad, AH, Assaye, AM, Mollenkopf, SK, Molokhia, M, Monasta, L, Hernandez, JCM, Montico, M, Mooney, MD, Moore, AR, Moradi-Lakeh, M, Moraga, P, Morawska, L, Velasquez, IM, Mori, R, Morrison, SD, Mruts, KB, Mueller, UO, Mullany, E, Muller, K, Venkata, G, Murthy, S, Musa, KI, Nachega, JB, Nagata, C, Nagel, G, Naghavi, M, Naidoo, KS, Nanda, L, Nangia, V, Nascimento, BR, Natarajan, G, Negoi, I, Cuong, TN, Ningrum, DNA, Nisar, MI, Nomura, M, Vuong, MN, Norheim, OF, Norrving, B, Noubiap, JJN, Nyakarahuka, L, Obermeyer, CM, O'Donnell, MJ, Ogbo, FA, Oh, I-H, Okoro, A, Oladimeji, O, Olagunju, AT, Olusanya, BO, Olusanya, JO, Oren, E, Ortiz, A, Osgood-Zimmerman, A, Ota, E, Owolabi, MO, Oyekale, AS, Pa, M, Pacella, RE, Pakhale, S, Pana, A, Panda, BK, Panda-Jonas, S, Park, E-K, Parsaeian, M, Patel, T, Patten, SB, Patton, GC, Paudel, D, Pereira, DM, Perez-Padilla, R, Perez-Ruiz, F, Perico, N, Pervaiz, A, Pesudovs, K, Peterson, CB, Petri, WA, Petzold, M, Phillips, MR, Piel, FB, Pigott, DM, Pishgar, F, Plass, D, Polinder, S, Popova, S, Postma, MJ, Poulton, RG, 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Sharma, R, She, J, Shen, J, Shetty, BP, Shi, P, Shibuya, K, Shigematsu, M, Shiri, R, Shiue, I, Shrime, MG, Sigfusdottir, ID, Silberberg, DH, Silpakit, N, Silva, DAS, Silva, JP, Silveira, DGA, Sindi, S, Singh, A, Singh, JA, Singh, PK, Singh, V, Sinha, DN, Skiadaresi, E, Sligar, A, Smith, DL, Sobaih, BHA, Sobngwi, E, Soneji, S, Soriano, JB, Sreeramareddy, CT, Srinivasan, V, Stathopoulou, V, Steel, N, Stein, DJ, Steiner, C, Stockl, H, Stokes, MA, Strong, M, Sufiyan, MB, Suliankatchi, RA, Sunguya, BF, Sur, PJ, Swaminathan, S, Sykes, BL, Szoeke, CEI, Tabares-Seisdedos, R, Tadakamadla, SK, Tadese, F, Tandon, N, Tanne, D, Tarajia, M, Tavakkoli, M, Taveira, N, Tehrani-Banihashemi, A, Tekelab, T, Tekle, DY, Shifa, GT, Temsah, M-H, Terkawi, AS, Tesema, CL, Tesssema, B, Theis, A, Thomas, N, Thompson, AH, Thomson, AJ, Thrift, AG, Tiruye, TY, Tobe-Gai, R, Tonelli, M, Topor-Madry, R, Topouzis, F, Tortajada, M, Tran, BX, Trujillo, TTU, Tsilimparis, N, Tuem, KB, Tuzcu, EM, Tyrovolas, S, Ukwaja, KN, Undurraga, EA, Uthman, OA, Uzochukwu, BSC, van Boven, JFM, Varakin, YY, Varughese, S, Vasankari, T, Vasconcelos, AMN, Venketasubramanian, N, Vidavalur, R, Violante, FS, Vishnu, A, Vladimirov, SK, Vlassov, VV, Vollset, SE, Vos, T, Waid, JL, Wakayo, T, Wang, Y-P, Weichenthal, S, Weiderpass, E, Weintraub, RG, Werdecker, A, Wesana, J, Wijeratne, T, Wilkinson, JD, Wiysonge, CS, Woldeyes, BG, Wolfe, CDA, Workicho, A, Workie, SB, Xavier, D, Xu, G, Yaghoubi, M, Yakob, B, Yalew, AZ, Yan, LL, Yano, Y, Yaseri, M, Ye, P, Yimam, HH, Yip, P, Yirsaw, BD, Yonemoto, N, Yoon, S-J, Yotebieng, M, Younis, MZ, Zaidi, Z, Zaki, MES, Zeeb, H, Zenebe, ZM, Zerfu, TA, Zhang, AL, Zhang, X, Zodpey, S, Zuhlke, LJ, Lopez, AD, Murray, CJL, Wang, H, Abajobir, AA, Abate, KH, Abbafati, C, Abbas, KM, Abd-Allah, F, Abera, SF, Abraha, HN, Abu-Raddad, LJ, Abu-Rmeileh, NME, Adedeji, IA, Adedoyin, RA, Adetifa, IMO, Adetokunboh, O, Afshin, A, Aggarwal, R, Agrawal, A, Agrawal, S, Kiadaliri, AA, Ahmed, MB, Aichour, AN, Aichour, I, Aichour, MTE, Aiyar, S, Akanda, S, Akinyemiju, TF, Akseer, N, Al-Eyadhy, A, Al Lami, FH, Alabed, S, Alahdab, F, Al-Aly, Z, Alam, K, Alam, N, Alasfoor, D, Aldridge, RW, Alene, KA, Alhabib, S, Ali, R, Alizadeh-Navaei, R, Aljunid, SM, Alkaabi, JM, Alkerwi, A, Alla, F, Allam, SD, Allebeck, P, Al-Raddadi, R, Alsharif, U, Altirkawi, KA, Martin, EA, Alvis-Guzman, N, Amare, AT, Ameh, EA, Amini, E, Ammar, W, Amoako, YA, Anber, N, Andrei, CL, Androudi, S, Ansari, H, Ansha, MG, Antonio, CAT, Anwari, P, Arnlov, J, Arora, M, Al, A, Aryal, KK, Asayesh, H, Asgedom, SW, Asghar, RJ, Assadi, R, Atey, TM, Atre, SR, Avila-Burgos, L, Avokpaho, EFGA, Awasthi, A, Quintanilla, BPA, Babalola, TK, Bacha, U, Badawi, A, Balakrishnan, K, Balalla, S, Barac, A, Barber, RM, Barboza, MA, Barker-Collo, SL, Barnighausen, T, Barquera, S, Barregard, L, Barrero, LH, Baune, BT, Bazargan-Hejazi, S, Bedi, N, Beghi, E, Bejot, Y, Bekele, BB, Bell, ML, Bello, AK, Bennett, DA, Bennett, JR, Bensenor, IM, Benson, J, Berhane, A, Berhe, DF, Bernabe, E, Beuran, M, Beyene, AS, Bhala, N, Bhansali, A, Bhaumik, S, Bhutta, ZA, Bikbov, B, Birungi, C, Biryukov, S, Bisanzio, D, Bizuayehu, HM, Bjerregaard, P, Blosser, CD, Boneya, DJ, Boufous, S, Bourne, RRA, Brazinova, A, Breitborde, NJK, Brenner, H, Brugha, TS, Bukhman, G, Negesa, L, Bulto, B, Bumgarner, BR, Burch, M, Butt, ZA, Cahill, LE, Cahuana-Hurtado, L, Campos-Nonato, IR, Car, J, Car, M, Crdenas, R, Carpenter, DO, Carrero, JJ, Carter, A, Castaneda-Orjuela, CA, Rivas, JC, Castro, FF, Castro, RE, Catala-Lopez, F, Chen, H, Chiang, PP-C, Chibalabala, M, Chisumpa, VH, Chitheer, AA, Choi, J-YJ, Christensen, H, Christopher, DJ, Ciobanu, LG, Cirillo, M, Cohen, AJ, Colquhoun, SM, Coresh, J, Criqui, MH, Cromwell, EA, Crump, JA, Dandona, L, Dandona, R, Dargan, PI, das Neves, J, Davey, G, Davitoiu, DV, Davletov, K, de Courten, B, De Leo, D, Degenhardt, L, Deiparine, S, Dellavalle, RP, Deribe, K, Deribew, A, Des Jarlais, DC, Dey, S, Dharmaratne, SD, Dherani, MK, Diaz-Torne, C, Ding, EL, Dixit, P, Djalalinia, S, Huyen, PD, Doku, DT, Donnelly, CA, Priscila, K, dos Santos, B, Douwes-Schultz, D, Driscoll, TR, Duan, L, Dubey, M, Duncan, BB, Dwivedi, LK, Ebrahimi, H, El Bcheraoui, C, Ellingsen, CL, Enayati, A, Endries, AY, Ermakov, SP, Eshetie, S, Eshrati, B, Eskandarieh, S, Esteghamati, A, Estep, K, Fanuel, BBF, Faro, A, Farvid, MS, Farzadfar, F, Feigin, VL, Fereshtehnejad, S-M, Fernandes, JG, Fernandes, JC, Feyissa, TR, Filip, I, Fischer, F, Foigt, N, Foreman, KJ, Frank, T, Franklin, RC, Fraser, M, Friedman, J, Frostad, JJ, Fullman, N, Furst, T, Furtado, JM, Futran, ND, Gakidou, E, Gambashidze, K, Gamkrelidze, A, Gankpe, FG, Garcia-Basteiro, AL, Gebregergs, GB, Gebrehiwot, TT, Gebrekidan, KG, Gebremichael, MW, Gelaye, AA, Geleijnse, JM, Gemechu, BL, Gemechu, KS, Genova-Maleras, R, Gesesew, HA, Gething, PW, Gibney, KB, Gill, PS, Gillum, RF, Giref, AZ, Girma, BW, Giussani, G, Goenka, S, Gomez, B, Gona, PN, Gopalani, SV, Goulart, AC, Graetz, N, Gugnani, HC, Gupta, PC, Gupta, R, Gupta, T, Gupta, V, Haagsma, JA, Hafezi-Nejad, N, Bidgoli, HH, Hakuzimana, A, Halasa, YA, Hamadeh, RR, Hambisa, MT, Hamidi, S, Hammami, M, Hancock, J, Handal, AJ, Hankey, GJ, Hao, Y, Harb, HL, Hareri, HA, Harikrishnan, S, Haro, JM, Hassanvand, MS, Havmoeller, R, Hay, RJ, Hay, SI, He, F, Heredia-Pi, IB, Herteliu, C, Hilawe, EH, Hoek, HW, Horita, N, Hosgood, HD, Hostiuc, S, Hotez, PJ, Hoy, DG, Hsairi, M, Htet, AS, Hu, G, Huang, H, Huang, JJ, Iburg, KM, Igumbor, EU, Ileanu, BV, Inoue, M, Irenso, AA, Irvine, CMS, Islam, N, Jacobsen, KH, Jaenisch, T, Jahanmehr, N, Jakovljevic, MB, Javanbakht, M, Jayatilleke, AU, Jeemon, P, Jensen, PN, Jha, V, Jin, Y, John, D, John, O, Johnson, SC, Jonas, JB, Jurisson, M, Kabir, Z, Kadel, R, Kahsay, A, Kalkonde, Y, Kamal, R, Kan, H, Karch, A, Karema, CK, Karimi, SM, Karthikeyan, G, Kasaeian, A, Kassaw, NA, Kassebaum, NJ, Kastor, A, Katikireddi, SV, Kaul, A, Kawakami, N, Kazanjan, K, Keiyoro, PN, Kelbore, SG, Kemp, AH, Kengne, AP, Keren, A, Kereselidze, M, Kesavachandran, CN, Ketema, EB, Khader, YS, Khalil, IA, Khan, EA, Khan, G, Khang, Y-H, Khera, S, Khoja, ATA, Khosravi, MH, Kibret, GD, Kieling, C, Kim, C-I, Kim, D, Kim, P, Kim, S, Kim, YJ, Kimokoti, RW, Kinfu, Y, Kishawi, S, Kissimova-Skarbek, KA, Kissoon, N, Kivimaki, M, Knudsen, AK, Kokubo, Y, Kopec, JA, Kosen, S, Koul, PA, Koyanagi, A, Kravchenko, M, Krohn, KJ, Defo, BK, Bicer, BK, Kuipers, EJ, Kulikoff, XR, Kulkarni, VS, Kumar, GA, Kumar, P, Kumsa, FA, Kutz, M, Lachat, C, Lagat, AK, Lager, ACJ, Lal, DK, Lalloo, R, Lambert, N, Lan, Q, Lansingh, VC, Larson, HJ, Larsson, A, Laryea, DO, Lavados, PM, Laxmaiah, A, Lee, PH, Leigh, J, Leung, J, Leung, R, Levi, M, Li, Y, Liao, Y, Liben, ML, Lim, SS, Linn, S, Lipshultz, SE, Liu, S, Lodha, R, Logroscino, G, Lorch, SA, Lorkowski, S, Lotufo, PA, Lozano, R, Lunevicius, R, Lyons, RA, Ma, S, Macarayan, ERK, Machado, IE, Mackay, MT, Abd el Razek, MM, Magis-Rodriguez, C, Mahdavi, M, Majdan, M, Majdzadeh, R, Majeed, A, Malekzadeh, R, Malhotra, R, Malta, DC, Mantovani, LG, Manyazewal, T, Mapoma, CC, Marczak, LB, Marks, GB, Martinez-Raga, J, Martins-Melo, FR, Massano, J, Maulik, PK, Mayosi, BM, Mazidi, M, McAlinden, C, McGarvey, ST, McGrath, JJ, Mckee, M, Mehata, S, Mehndiratta, MM, Mehta, KM, Meier, T, Mekonnen, TC, Meles, KG, Memiah, P, Memish, ZA, Mendoza, W, Mengesha, MM, Mengistie, MA, Tadese, D, Menon, MGR, Menota, BG, Mensah, GA, Meretoja, A, Meretoja, TJ, Mezgebe, HB, Micha, R, Mikesell, J, Miller, TR, Mills, EJ, Minnig, S, Mirarefin, M, Mirrakhimov, EM, Misganaw, A, Mishra, SR, Mohammad, KA, Mohammadi, A, Mohammed, KESM, Mohan, MBV, Mohanty, SK, Mokdad, AH, Assaye, AM, Mollenkopf, SK, Molokhia, M, Monasta, L, Hernandez, JCM, Montico, M, Mooney, MD, Moore, AR, Moradi-Lakeh, M, Moraga, P, Morawska, L, Velasquez, IM, Mori, R, Morrison, SD, Mruts, KB, Mueller, UO, Mullany, E, Muller, K, Venkata, G, Murthy, S, Musa, KI, Nachega, JB, Nagata, C, Nagel, G, Naghavi, M, Naidoo, KS, Nanda, L, Nangia, V, Nascimento, BR, Natarajan, G, Negoi, I, Cuong, TN, Ningrum, DNA, Nisar, MI, Nomura, M, Vuong, MN, Norheim, OF, Norrving, B, Noubiap, JJN, Nyakarahuka, L, Obermeyer, CM, O'Donnell, MJ, Ogbo, FA, Oh, I-H, Okoro, A, Oladimeji, O, Olagunju, AT, Olusanya, BO, Olusanya, JO, Oren, E, Ortiz, A, Osgood-Zimmerman, A, Ota, E, Owolabi, MO, Oyekale, AS, Pa, M, Pacella, RE, Pakhale, S, Pana, A, Panda, BK, Panda-Jonas, S, Park, E-K, Parsaeian, M, Patel, T, Patten, SB, Patton, GC, Paudel, D, Pereira, DM, Perez-Padilla, R, Perez-Ruiz, F, Perico, N, Pervaiz, A, Pesudovs, K, Peterson, CB, Petri, WA, Petzold, M, Phillips, MR, Piel, FB, Pigott, DM, Pishgar, F, Plass, D, Polinder, S, Popova, S, Postma, MJ, Poulton, RG, Pourmalek, F, Prasad, N, Purwar, M, Qorbani, M, Rabiee, RHS, Radfar, A, Rafay, A, Rahimi-Movaghar, A, Rahimi-Movaghar, V, Rahman, M, Rahman, MHU, Rahman, SU, Rai, RK, Rajsic, S, Ram, U, Rana, SM, Ranabhat, CL, Rao, PV, Rawaf, S, Ray, SE, Rego, MAS, Rehm, J, Reiner, RC, Remuzzi, G, Renzaho, AMNN, Resnikoff, S, Rezaei, S, Rezai, MS, Ribeiro, AL, Rokni, MB, Ronfani, L, Roshandel, G, Roth, GA, Rothenbacher, D, Roy, A, Rubagotti, E, Ruhago, GM, Saadat, S, Sabde, YD, Sachdev, PS, Sadat, N, Safdarian, M, Safiri, SSS, Sagar, R, Sahathevan, R, Sahebkar, A, Sahraian, MA, Salama, J, Salamati, P, Salomon, JA, Salvi, SS, Samy, AM, Sanabria, JR, Sanchez-Nino, MD, Santos, IS, Milicevic, MMS, Sarmiento-Suarez, R, Sartorius, B, Satpathy, M, Sawhney, M, Saxena, S, Saylan, MI, Schmidt, MI, Schneider, IJC, Schutte, AE, Schwebel, DC, Schwendicke, F, Seedat, S, Seid, AM, Sepanlou, SG, Servan-Mori, EE, Shackelford, KA, Shaheen, A, Shahraz, S, Shaikh, MA, Shamsipour, M, Shamsizadeh, M, Islam, SMS, Sharma, J, Sharma, R, She, J, Shen, J, Shetty, BP, Shi, P, Shibuya, K, Shigematsu, M, Shiri, R, Shiue, I, Shrime, MG, Sigfusdottir, ID, Silberberg, DH, Silpakit, N, Silva, DAS, Silva, JP, Silveira, DGA, Sindi, S, Singh, A, Singh, JA, Singh, PK, Singh, V, Sinha, DN, Skiadaresi, E, Sligar, A, Smith, DL, Sobaih, BHA, Sobngwi, E, Soneji, S, Soriano, JB, Sreeramareddy, CT, Srinivasan, V, Stathopoulou, V, Steel, N, Stein, DJ, Steiner, C, Stockl, H, Stokes, MA, Strong, M, Sufiyan, MB, Suliankatchi, RA, Sunguya, BF, Sur, PJ, Swaminathan, S, Sykes, BL, Szoeke, CEI, Tabares-Seisdedos, R, Tadakamadla, SK, Tadese, F, Tandon, N, Tanne, D, Tarajia, M, Tavakkoli, M, Taveira, N, Tehrani-Banihashemi, A, Tekelab, T, Tekle, DY, Shifa, GT, Temsah, M-H, Terkawi, AS, Tesema, CL, Tesssema, B, Theis, A, Thomas, N, Thompson, AH, Thomson, AJ, Thrift, AG, Tiruye, TY, Tobe-Gai, R, Tonelli, M, Topor-Madry, R, Topouzis, F, Tortajada, M, Tran, BX, Trujillo, TTU, Tsilimparis, N, Tuem, KB, Tuzcu, EM, Tyrovolas, S, Ukwaja, KN, Undurraga, EA, Uthman, OA, Uzochukwu, BSC, van Boven, JFM, Varakin, YY, Varughese, S, Vasankari, T, Vasconcelos, AMN, Venketasubramanian, N, Vidavalur, R, Violante, FS, Vishnu, A, Vladimirov, SK, Vlassov, VV, Vollset, SE, Vos, T, Waid, JL, Wakayo, T, Wang, Y-P, Weichenthal, S, Weiderpass, E, Weintraub, RG, Werdecker, A, Wesana, J, Wijeratne, T, Wilkinson, JD, Wiysonge, CS, Woldeyes, BG, Wolfe, CDA, Workicho, A, Workie, SB, Xavier, D, Xu, G, Yaghoubi, M, Yakob, B, Yalew, AZ, Yan, LL, Yano, Y, Yaseri, M, Ye, P, Yimam, HH, Yip, P, Yirsaw, BD, Yonemoto, N, Yoon, S-J, Yotebieng, M, Younis, MZ, Zaidi, Z, Zaki, MES, Zeeb, H, Zenebe, ZM, Zerfu, TA, Zhang, AL, Zhang, X, Zodpey, S, Zuhlke, LJ, Lopez, AD, and Murray, CJL
Published: 2017

172. PET imaging of putative microglial activation in individuals at ultra-high risk for psychosis, recently diagnosed and chronically ill with schizophrenia

Author: Di Biase, MA, Zalesky, A, O'keefe, G, Laskaris, L, Baune, BT, Weickert, CS, Olver, J, McGorry, PD, Amminger, GP, Nelson, B, Scott, AM, Hickie, I, Banati, R, Turkheimer, F, Yaqub, M, Everall, IP, Pantelis, C, Cropley, V, Di Biase, MA, Zalesky, A, O'keefe, G, Laskaris, L, Baune, BT, Weickert, CS, Olver, J, McGorry, PD, Amminger, GP, Nelson, B, Scott, AM, Hickie, I, Banati, R, Turkheimer, F, Yaqub, M, Everall, IP, Pantelis, C, and Cropley, V
Abstract: We examined putative microglial activation as a function of illness course in schizophrenia. Microglial activity was quantified using [11C](R)-(1-[2-chrorophynyl]-N-methyl-N-[1-methylpropyl]-3 isoquinoline carboxamide (11C-(R)-PK11195) positron emission tomography (PET) in: (i) 10 individuals at ultra-high risk (UHR) of psychosis; (ii) 18 patients recently diagnosed with schizophrenia; (iii) 15 patients chronically ill with schizophrenia; and, (iv) 27 age-matched healthy controls. Regional-binding potential (BPND ) was calculated using the simplified reference-tissue model with four alternative reference inputs. The UHR, recent-onset and chronic patient groups were compared to age-matched healthy control groups to examine between-group BPND differences in 6 regions: dorsal frontal, orbital frontal, anterior cingulate, medial temporal, thalamus and insula. Correlation analysis tested for BPND associations with gray matter volume, peripheral cytokines and clinical variables. The null hypothesis of equality in BPND between patients (UHR, recent-onset and chronic) and respective healthy control groups (younger and older) was not rejected for any group comparison or region. Across all subjects, BPND was positively correlated to age in the thalamus (r = 0.43, P = 0.008, false discovery rate). No correlations with regional gray matter, peripheral cytokine levels or clinical symptoms were detected. We therefore found no evidence of microglial activation in groups of individuals at high risk, recently diagnosed or chronically ill with schizophrenia. While the possibility of11C-(R)-PK11195-binding differences in certain patient subgroups remains, the patient cohorts in our study, who also displayed normal peripheral cytokine profiles, do not substantiate the assumption of microglial activation in schizophrenia as a regular and defining feature, as measured by11C-(R)-PK11195 BPND.
Published: 2017

173. Human subcortical brain asymmetries in 15,847 people worldwide reveal effects of age and sex

Author: Guadalupe, T, Mathias, SR, vanErp, TGM, Whelan, CD, Zwiers, MP, Abe, Y, Abramovic, L, Agartz, I, Andreassen, OA, Arias-Vásquez, A, Aribisala, BS, Armstrong, NJ, Arolt, V, Artiges, E, Ayesa-Arriola, R, Baboyan, VG, Banaschewski, T, Barker, G, Bastin, ME, Baune, BT, Blangero, J, Bokde, ALW, Boedhoe, PSW, Bose, A, Brem, S, Brodaty, H, Bromberg, U, Brooks, S, Büchel, C, Buitelaar, J, Calhoun, VD, Cannon, DM, Cattrell, A, Cheng, Y, Conrod, PJ, Conzelmann, A, Corvin, A, Crespo-Facorro, B, Crivello, F, Dannlowski, U, de Zubicaray, GI, de Zwarte, SMC, Deary, IJ, Desrivières, S, Doan, NT, Donohoe, G, Dørum, ES, Ehrlich, S, Espeseth, T, Fernández, G, Flor, H, Fouche, J-P, Frouin, V, Fukunaga, M, Gallinat, J, Garavan, H, Gill, M, Suarez, AG, Gowland, P, Grabe, HJ, Grotegerd, D, Gruber, O, Hagenaars, S, Hashimoto, R, Hauser, TU, Heinz, A, Hibar, DP, Hoekstra, PJ, Hoogman, M, Howells, FM, Hu, H, Hulshoff Pol, HE, Huyser, C, Ittermann, B, Jahanshad, N, Jönsson, EG, Jurk, S, Kahn, RS, Kelly, S, Kraemer, B, Kugel, H, Kwon, JS, Lemaitre, H, Lesch, K-P, Lochner, C, Luciano, M, Marquand, AF, Martin, NG, Martínez-Zalacaín, I, Martinot, J-L, Mataix-Cols, D, Mather, K, McDonald, C, McMahon, KL, Medland, SE, Menchón, JM, Morris, DW, Mothersill, O, Maniega, SM, Mwangi, B, Nakamae, T, Nakao, T, Narayanaswaamy, JC, Nees, F, Nordvik, JE, Onnink, AMH, Opel, N, Ophoff, R, Paillère Martinot, M-L, Papadopoulos Orfanos, D, Pauli, P, Paus, T, Poustka, L, Reddy, JY, Renteria, ME, Roiz-Santiáñez, R, Roos, A, Royle, NA, Sachdev, P, Sánchez-Juan, P, Schmaal, L, Schumann, G, Shumskaya, E, Smolka, MN, Soares, JC, Soriano-Mas, C, Stein, DJ, Strike, LT, Toro, R, Turner, JA, Tzourio-Mazoyer, N, Uhlmann, A, Hernández, MV, van den Heuvel, OA, van der Meer, D, van Haren, NEM, Veltman, DJ, Venkatasubramanian, G, Vetter, NC, Vuletic, D, Walitza, S, Walter, H, Walton, E, Wang, Z, Wardlaw, J, Wen, W, Westlye, LT, Whelan, R, Wittfeld, K, Wolfers, T, Wright, MJ, Xu, J, Xu, X, Yun, J-Y, Zhao, J, Franke, B, Thompson, PM, Glahn, DC, Mazoyer, B, Fisher, SE, Francks, C, Guadalupe, T, Mathias, SR, vanErp, TGM, Whelan, CD, Zwiers, MP, Abe, Y, Abramovic, L, Agartz, I, Andreassen, OA, Arias-Vásquez, A, Aribisala, BS, Armstrong, NJ, Arolt, V, Artiges, E, Ayesa-Arriola, R, Baboyan, VG, Banaschewski, T, Barker, G, Bastin, ME, Baune, BT, Blangero, J, Bokde, ALW, Boedhoe, PSW, Bose, A, Brem, S, Brodaty, H, Bromberg, U, Brooks, S, Büchel, C, Buitelaar, J, Calhoun, VD, Cannon, DM, Cattrell, A, Cheng, Y, Conrod, PJ, Conzelmann, A, Corvin, A, Crespo-Facorro, B, Crivello, F, Dannlowski, U, de Zubicaray, GI, de Zwarte, SMC, Deary, IJ, Desrivières, S, Doan, NT, Donohoe, G, Dørum, ES, Ehrlich, S, Espeseth, T, Fernández, G, Flor, H, Fouche, J-P, Frouin, V, Fukunaga, M, Gallinat, J, Garavan, H, Gill, M, Suarez, AG, Gowland, P, Grabe, HJ, Grotegerd, D, Gruber, O, Hagenaars, S, Hashimoto, R, Hauser, TU, Heinz, A, Hibar, DP, Hoekstra, PJ, Hoogman, M, Howells, FM, Hu, H, Hulshoff Pol, HE, Huyser, C, Ittermann, B, Jahanshad, N, Jönsson, EG, Jurk, S, Kahn, RS, Kelly, S, Kraemer, B, Kugel, H, Kwon, JS, Lemaitre, H, Lesch, K-P, Lochner, C, Luciano, M, Marquand, AF, Martin, NG, Martínez-Zalacaín, I, Martinot, J-L, Mataix-Cols, D, Mather, K, McDonald, C, McMahon, KL, Medland, SE, Menchón, JM, Morris, DW, Mothersill, O, Maniega, SM, Mwangi, B, Nakamae, T, Nakao, T, Narayanaswaamy, JC, Nees, F, Nordvik, JE, Onnink, AMH, Opel, N, Ophoff, R, Paillère Martinot, M-L, Papadopoulos Orfanos, D, Pauli, P, Paus, T, Poustka, L, Reddy, JY, Renteria, ME, Roiz-Santiáñez, R, Roos, A, Royle, NA, Sachdev, P, Sánchez-Juan, P, Schmaal, L, Schumann, G, Shumskaya, E, Smolka, MN, Soares, JC, Soriano-Mas, C, Stein, DJ, Strike, LT, Toro, R, Turner, JA, Tzourio-Mazoyer, N, Uhlmann, A, Hernández, MV, van den Heuvel, OA, van der Meer, D, van Haren, NEM, Veltman, DJ, Venkatasubramanian, G, Vetter, NC, Vuletic, D, Walitza, S, Walter, H, Walton, E, Wang, Z, Wardlaw, J, Wen, W, Westlye, LT, Whelan, R, Wittfeld, K, Wolfers, T, Wright, MJ, Xu, J, Xu, X, Yun, J-Y, Zhao, J, Franke, B, Thompson, PM, Glahn, DC, Mazoyer, B, Fisher, SE, and Francks, C
Abstract: The two hemispheres of the human brain differ functionally and structurally. Despite over a century of research, the extent to which brain asymmetry is influenced by sex, handedness, age, and genetic factors is still controversial. Here we present the largest ever analysis of subcortical brain asymmetries, in a harmonized multi-site study using meta-analysis methods. Volumetric asymmetry of seven subcortical structures was assessed in 15,847 MRI scans from 52 datasets worldwide. There were sex differences in the asymmetry of the globus pallidus and putamen. Heritability estimates, derived from 1170 subjects belonging to 71 extended pedigrees, revealed that additive genetic factors influenced the asymmetry of these two structures and that of the hippocampus and thalamus. Handedness had no detectable effect on subcortical asymmetries, even in this unprecedented sample size, but the asymmetry of the putamen varied with age. Genetic drivers of asymmetry in the hippocampus, thalamus and basal ganglia may affect variability in human cognition, including susceptibility to psychiatric disorders.
Published: 2017

174. Erratum: Measuring progress and projecting attainment on the basis of past trends of the health-related Sustainable Development Goals in 188 countries: an analysis from the Global Burden of Disease Study 2016 (The Lancet (2017) 390(10100) (1423–1459) (S

Author: Fullman, N, Barber, RM, Abajobir, AA, Abate, KH, Abbafati, C, Abbas, KM, Abd-Allah, F, Abdulkader, RS, Abdulle, AM, Abera, SF, Aboyans, V, Abu-Raddad, LJ, Abu-Rmeileh, NME, Adedeji, IA, Adetokunboh, O, Afshin, A, Agrawal, A, Agrawal, S, Ahmad Kiadaliri, A, Ahmadieh, H, Ahmed, MB, Aichour, MTE, Aichour, AN, Aichour, I, Aiyar, S, Akinyemi, RO, Akseer, N, Al-Aly, Z, Alam, K, Alam, N, Alasfoor, D, Alene, KA, Alizadeh-Navaei, R, Alkerwi, A, Alla, F, Allebeck, P, Allen, C, Al-Raddadi, R, Alsharif, U, Altirkawi, KA, Alvis-Guzman, N, Amare, AT, Amini, E, Ammar, W, Ansari, H, Antonio, CAT, Anwari, P, Arora, M, Artaman, A, Aryal, KK, Asayesh, H, Asgedom, SW, Assadi, R, Atey, TM, Atre, SR, Avila-Burgos, L, Avokpaho, EFGA, Awasthi, A, Azzopardi, P, Bacha, U, Badawi, A, Balakrishnan, K, Bannick, MS, Barac, A, Barker-Collo, SL, Bärnighausen, T, Barrero, LH, Basu, S, Battle, KE, Baune, BT, Beardsley, J, Bedi, N, Beghi, E, Béjot, Y, Bell, ML, Bennett, DA, Bennett, JR, Bensenor, IM, Berhane, A, Berhe, DF, Bernabé, E, Betsu, BD, Beuran, M, Beyene, AS, Bhala, N, Bhansali, A, Bhatt, S, Bhutta, ZA, Bicer, BK, Bidgoli, HH, Bikbov, B, Bilal, AI, Birungi, C, Biryukov, S, Bizuayehu, HM, Blosser, CD, Boneya, DJ, Bose, D, Bou-Orm, IR, Brauer, M, Stokes, Mark, Fullman, N, Barber, RM, Abajobir, AA, Abate, KH, Abbafati, C, Abbas, KM, Abd-Allah, F, Abdulkader, RS, Abdulle, AM, Abera, SF, Aboyans, V, Abu-Raddad, LJ, Abu-Rmeileh, NME, Adedeji, IA, Adetokunboh, O, Afshin, A, Agrawal, A, Agrawal, S, Ahmad Kiadaliri, A, Ahmadieh, H, Ahmed, MB, Aichour, MTE, Aichour, AN, Aichour, I, Aiyar, S, Akinyemi, RO, Akseer, N, Al-Aly, Z, Alam, K, Alam, N, Alasfoor, D, Alene, KA, Alizadeh-Navaei, R, Alkerwi, A, Alla, F, Allebeck, P, Allen, C, Al-Raddadi, R, Alsharif, U, Altirkawi, KA, Alvis-Guzman, N, Amare, AT, Amini, E, Ammar, W, Ansari, H, Antonio, CAT, Anwari, P, Arora, M, Artaman, A, Aryal, KK, Asayesh, H, Asgedom, SW, Assadi, R, Atey, TM, Atre, SR, Avila-Burgos, L, Avokpaho, EFGA, Awasthi, A, Azzopardi, P, Bacha, U, Badawi, A, Balakrishnan, K, Bannick, MS, Barac, A, Barker-Collo, SL, Bärnighausen, T, Barrero, LH, Basu, S, Battle, KE, Baune, BT, Beardsley, J, Bedi, N, Beghi, E, Béjot, Y, Bell, ML, Bennett, DA, Bennett, JR, Bensenor, IM, Berhane, A, Berhe, DF, Bernabé, E, Betsu, BD, Beuran, M, Beyene, AS, Bhala, N, Bhansali, A, Bhatt, S, Bhutta, ZA, Bicer, BK, Bidgoli, HH, Bikbov, B, Bilal, AI, Birungi, C, Biryukov, S, Bizuayehu, HM, Blosser, CD, Boneya, DJ, Bose, D, Bou-Orm, IR, Brauer, M, and Stokes, Mark
Published: 2017

175. Erratum:Measuring progress and projecting attainment on the basis of past trends of the health-related Sustainable Development Goals in 188 countries: an analysis from the Global Burden of Disease Study 2016 (The Lancet (2017) 390(10100) (1423–1459) (S0

Author: Fullman, N, Barber, RM, Abajobir, AA, Abate, KH, Abbafati, C, Abbas, KM, Abd-Allah, F, Abdulkader, RS, Abdulle, AM, Abera, SF, Aboyans, V, Abu-Raddad, LJ, Abu-Rmeileh, NME, Adedeji, IA, Adetokunboh, O, Afshin, A, Agrawal, A, Agrawal, S, Ahmad Kiadaliri, A, Ahmadieh, H, Ahmed, MB, Aichour, MTE, Aichour, AN, Aichour, I, Aiyar, S, Akinyemi, RO, Akseer, N, Al-Aly, Z, Alam, K, Alam, N, Alasfoor, D, Alene, KA, Alizadeh-Navaei, R, Alkerwi, A, Alla, F, Allebeck, P, Allen, C, Al-Raddadi, R, Alsharif, U, Altirkawi, KA, Alvis-Guzman, N, Amare, AT, Amini, E, Ammar, W, Ansari, H, Antonio, CAT, Anwari, P, Arora, M, Artaman, A, Aryal, KK, Asayesh, H, Asgedom, SW, Assadi, R, Atey, TM, Atre, SR, Avila-Burgos, L, Avokpaho, EFGA, Awasthi, A, Azzopardi, P, Bacha, U, Badawi, A, Balakrishnan, K, Bannick, MS, Barac, A, Barker-Collo, SL, Bärnighausen, T, Barrero, LH, Basu, S, Battle, KE, Baune, BT, Beardsley, J, Bedi, N, Beghi, E, Béjot, Y, Bell, ML, Bennett, DA, Bennett, JR, Bensenor, IM, Berhane, A, Berhe, DF, Bernabé, E, Betsu, BD, Beuran, M, Beyene, AS, Bhala, N, Bhansali, A, Bhatt, S, Bhutta, ZA, Bicer, BK, Bidgoli, HH, Bikbov, B, Bilal, AI, Birungi, C, Biryukov, S, Bizuayehu, HM, Blosser, CD, Boneya, DJ, Bose, D, Bou-Orm, IR, Brauer, M, Stokes, Mark, Fullman, N, Barber, RM, Abajobir, AA, Abate, KH, Abbafati, C, Abbas, KM, Abd-Allah, F, Abdulkader, RS, Abdulle, AM, Abera, SF, Aboyans, V, Abu-Raddad, LJ, Abu-Rmeileh, NME, Adedeji, IA, Adetokunboh, O, Afshin, A, Agrawal, A, Agrawal, S, Ahmad Kiadaliri, A, Ahmadieh, H, Ahmed, MB, Aichour, MTE, Aichour, AN, Aichour, I, Aiyar, S, Akinyemi, RO, Akseer, N, Al-Aly, Z, Alam, K, Alam, N, Alasfoor, D, Alene, KA, Alizadeh-Navaei, R, Alkerwi, A, Alla, F, Allebeck, P, Allen, C, Al-Raddadi, R, Alsharif, U, Altirkawi, KA, Alvis-Guzman, N, Amare, AT, Amini, E, Ammar, W, Ansari, H, Antonio, CAT, Anwari, P, Arora, M, Artaman, A, Aryal, KK, Asayesh, H, Asgedom, SW, Assadi, R, Atey, TM, Atre, SR, Avila-Burgos, L, Avokpaho, EFGA, Awasthi, A, Azzopardi, P, Bacha, U, Badawi, A, Balakrishnan, K, Bannick, MS, Barac, A, Barker-Collo, SL, Bärnighausen, T, Barrero, LH, Basu, S, Battle, KE, Baune, BT, Beardsley, J, Bedi, N, Beghi, E, Béjot, Y, Bell, ML, Bennett, DA, Bennett, JR, Bensenor, IM, Berhane, A, Berhe, DF, Bernabé, E, Betsu, BD, Beuran, M, Beyene, AS, Bhala, N, Bhansali, A, Bhatt, S, Bhutta, ZA, Bicer, BK, Bidgoli, HH, Bikbov, B, Bilal, AI, Birungi, C, Biryukov, S, Bizuayehu, HM, Blosser, CD, Boneya, DJ, Bose, D, Bou-Orm, IR, Brauer, M, and Stokes, Mark
Published: 2017

176. Erratum to: online information seeking by patients with bipolar disorder: results from an international multisite survey

Author: Conell, J, Bauer, R, Glenn, T, Alda, M, Ardau, R, Baune, BT, Berk, Michael, Bersudsky, Y, Bilderbeck, A, Bocchetta, A, Bossini, L, Paredes Castro, AM, Cheung, EYW, Chillotti, C, Choppin, S, Del Zompo, M, Dias, R, Dodd, Seetal, Duffy, A, Etain, B, Fagiolini, A, Garnham, J, Geddes, J, Gildebro, J, Gonzalez-Pinto, A, Goodwin, GM, Grof, P, Harima, H, Hassel, S, Henry, C, Hidalgo-Mazzei, D, Kapur, V, Kunigiri, G, Lafer, B, Lam, C, Larsen, ER, Lewitzka, U, Licht, RW, Lund, AH, Misiak, B, Piotrowski, P, Monteith, S, Munoz, R, Nakanotani, T, Nielsen, RE, O'Donovan, C, Okamura, Y, Osher, Y, Reif, A, Ritter, P, Rybakowski, JK, Sagduyu, K, Sawchuk, B, Schwartz, E, Scippa, Â, Slaney, C, Sulaiman, AH, Suominen, K, Suwalska, A, Tam, P, Tatebayashi, Y, Tondo, L, Vieta, E, Vinberg, M, Viswanath, B, Volkert, J, Zetin, M, Zorrilla, I, Whybrow, PC, Bauer, M, Conell, J, Bauer, R, Glenn, T, Alda, M, Ardau, R, Baune, BT, Berk, Michael, Bersudsky, Y, Bilderbeck, A, Bocchetta, A, Bossini, L, Paredes Castro, AM, Cheung, EYW, Chillotti, C, Choppin, S, Del Zompo, M, Dias, R, Dodd, Seetal, Duffy, A, Etain, B, Fagiolini, A, Garnham, J, Geddes, J, Gildebro, J, Gonzalez-Pinto, A, Goodwin, GM, Grof, P, Harima, H, Hassel, S, Henry, C, Hidalgo-Mazzei, D, Kapur, V, Kunigiri, G, Lafer, B, Lam, C, Larsen, ER, Lewitzka, U, Licht, RW, Lund, AH, Misiak, B, Piotrowski, P, Monteith, S, Munoz, R, Nakanotani, T, Nielsen, RE, O'Donovan, C, Okamura, Y, Osher, Y, Reif, A, Ritter, P, Rybakowski, JK, Sagduyu, K, Sawchuk, B, Schwartz, E, Scippa, Â, Slaney, C, Sulaiman, AH, Suominen, K, Suwalska, A, Tam, P, Tatebayashi, Y, Tondo, L, Vieta, E, Vinberg, M, Viswanath, B, Volkert, J, Zetin, M, Zorrilla, I, Whybrow, PC, and Bauer, M
Published: 2017

177. Trajectories of anxiety and health related quality of life during pregnancy

Author: Mazza, M, Schubert, KO, Alr, T, Clark, SR, Grzeskowiak, LE, Miller, E, Dekker, GA, Baune, BT, Clifton, VL, Mazza, M, Schubert, KO, Alr, T, Clark, SR, Grzeskowiak, LE, Miller, E, Dekker, GA, Baune, BT, and Clifton, VL
Abstract: Anxiety and health related Quality of Life (HRQoL) have emerged as important mental health measures in obstetric care. Few studies have systematically examined the longitudinal trajectories of anxiety and HRQoL in pregnancy. Using a linear growth modeling strategy, we analyzed the course of State-Trait Anxiety Inventory (STAI)- and Short Form (36) Health Survey (SF-36) scores between the 12th and the 36th week of gestation, in a sample of 355 women. We additionally analyzed the impact of depressive symptoms and a chronic medical condition (asthma), on STAI and SF-36 trajectory curves. STAI scores remained stable throughout pregnancy. A previous history of anxiety increased the overall STAI scores. Asthma and depressive symptoms scores had no impact on the STAI trajectory. Physical SF-36 scores decreased over the course of pregnancy, whereas mental SF-36 trended towards improvement. Asthma reduced physical SF-36 overall. While high depressive symptoms decreased the overall mental SF-36, they were also significantly associated with mental SF-36 improvements over time. Anxiety symptoms are stable during pregnancy and are not modulated by depressive symptoms or asthma. Physical HRQoL declines in pregnancy. In contrast, mental HRQoL appears to improve, particularly in women with high initial levels of depressive symptoms.
Published: 2017

178. Solar insolation in springtime influences age of onset of bipolar I disorder

Author: Bauer, M, Glenn, T, Alda, M, Aleksandrovich, MA, Andreassen, OA, Angelopoulos, E, Ardau, R, Ayhan, Y, Baethge, C, Bharathram, SR, Bauer, R, Baune, BT, Becerra-Palars, C, Bellivier, F, Belmaker, RH, Berk, M, Bersudsky, Y, Bicakci, S, Birabwa-Oketcho, H, Bjella, TD, Bossini, L, Cabrera, J, Cheung, EYW, Del Zompo, M, Dodd, S, Donix, M, Etain, B, Fagiolini, A, Fountoulakis, KN, Frye, MA, Gonzalez-Pinto, A, Gottlieb, JF, Grof, P, Harima, H, Henry, C, Isometsae, ET, Janno, S, Kapczinski, F, Kardell, M, Khaldi, S, Kliwicki, S, Koenig, B, Kot, TL, Krogh, R, Kunz, M, Lafer, B, Landen, M, Larsen, ER, Lewitzka, U, Licht, RW, Lopez-Jaramillo, C, MacQueen, G, Manchia, M, Marsh, W, Martinez-Cengotitabengoa, M, Melle, I, Meza-Urzua, F, Yee Ming, M, Monteith, S, Morken, G, Mosca, E, Munoz, R, Mythri, SV, Nacef, F, Nadella, RK, Nery, FG, Nielsen, RE, O'Donovan, C, Omrani, A, Osher, Y, Ostermark Sorensen, H, Ouali, U, Pica Ruiz, Y, Pilhatsch, M, Pinna, M, da Ponte, FDR, Quiroz, D, Ramesar, R, Rasgon, N, Reddy, MS, Reif, A, Ritter, P, Rybakowski, JK, Sagduyu, K, Scippa, AM, Severus, E, Simhandl, C, Stein, DJ, Strejilevich, S, Subramaniam, M, Sulaiman, AH, Suominen, K, Tagata, H, Tatebayashi, Y, Tondo, L, Torrent, C, Vaaler, AE, Veeh, J, Vieta, E, Viswanath, B, Yoldi-Negrete, M, Zetin, M, Zgueb, Y, Whybrow, PC, Bauer, M, Glenn, T, Alda, M, Aleksandrovich, MA, Andreassen, OA, Angelopoulos, E, Ardau, R, Ayhan, Y, Baethge, C, Bharathram, SR, Bauer, R, Baune, BT, Becerra-Palars, C, Bellivier, F, Belmaker, RH, Berk, M, Bersudsky, Y, Bicakci, S, Birabwa-Oketcho, H, Bjella, TD, Bossini, L, Cabrera, J, Cheung, EYW, Del Zompo, M, Dodd, S, Donix, M, Etain, B, Fagiolini, A, Fountoulakis, KN, Frye, MA, Gonzalez-Pinto, A, Gottlieb, JF, Grof, P, Harima, H, Henry, C, Isometsae, ET, Janno, S, Kapczinski, F, Kardell, M, Khaldi, S, Kliwicki, S, Koenig, B, Kot, TL, Krogh, R, Kunz, M, Lafer, B, Landen, M, Larsen, ER, Lewitzka, U, Licht, RW, Lopez-Jaramillo, C, MacQueen, G, Manchia, M, Marsh, W, Martinez-Cengotitabengoa, M, Melle, I, Meza-Urzua, F, Yee Ming, M, Monteith, S, Morken, G, Mosca, E, Munoz, R, Mythri, SV, Nacef, F, Nadella, RK, Nery, FG, Nielsen, RE, O'Donovan, C, Omrani, A, Osher, Y, Ostermark Sorensen, H, Ouali, U, Pica Ruiz, Y, Pilhatsch, M, Pinna, M, da Ponte, FDR, Quiroz, D, Ramesar, R, Rasgon, N, Reddy, MS, Reif, A, Ritter, P, Rybakowski, JK, Sagduyu, K, Scippa, AM, Severus, E, Simhandl, C, Stein, DJ, Strejilevich, S, Subramaniam, M, Sulaiman, AH, Suominen, K, Tagata, H, Tatebayashi, Y, Tondo, L, Torrent, C, Vaaler, AE, Veeh, J, Vieta, E, Viswanath, B, Yoldi-Negrete, M, Zetin, M, Zgueb, Y, and Whybrow, PC
Abstract: OBJECTIVE: To confirm prior findings that the larger the maximum monthly increase in solar insolation in springtime, the younger the age of onset of bipolar disorder. METHOD: Data were collected from 5536 patients at 50 sites in 32 countries on six continents. Onset occurred at 456 locations in 57 countries. Variables included solar insolation, birth-cohort, family history, polarity of first episode and country physician density. RESULTS: There was a significant, inverse association between the maximum monthly increase in solar insolation at the onset location, and the age of onset. This effect was reduced in those without a family history of mood disorders and with a first episode of mania rather than depression. The maximum monthly increase occurred in springtime. The youngest birth-cohort had the youngest age of onset. All prior relationships were confirmed using both the entire sample, and only the youngest birth-cohort (all estimated coefficients P < 0.001). CONCLUSION: A large increase in springtime solar insolation may impact the onset of bipolar disorder, especially with a family history of mood disorders. Recent societal changes that affect light exposure (LED lighting, mobile devices backlit with LEDs) may influence adaptability to a springtime circadian challenge.
Published: 2017

179. A novel strategy for clustering major depression individuals using whole-genome sequencing variant data

Author: Yu, C, Baune, BT, Licinio, J, Wong, M-L, Yu, C, Baune, BT, Licinio, J, and Wong, M-L
Abstract: Major depressive disorder (MDD) is highly prevalent, resulting in an exceedingly high disease burden. The identification of generic risk factors could lead to advance prevention and therapeutics. Current approaches examine genotyping data to identify specific variations between cases and controls. Compared to genotyping, whole-genome sequencing (WGS) allows for the detection of private mutations. In this proof-of-concept study, we establish a conceptually novel computational approach that clusters subjects based on the entirety of their WGS. Those clusters predicted MDD diagnosis. This strategy yielded encouraging results, showing that depressed Mexican-American participants were grouped closer; in contrast ethnically-matched controls grouped away from MDD patients. This implies that within the same ancestry, the WGS data of an individual can be used to check whether this individual is within or closer to MDD subjects or to controls. We propose a novel strategy to apply WGS data to clinical medicine by facilitating diagnosis through genetic clustering. Further studies utilising our method should examine larger WGS datasets on other ethnical groups.
Published: 2017

180. Defining disorders with permeable borders: you say bipolar, I say borderline!

Author: Bassett, D, Mulder, R, Outhred, T, Hamilton, A, Morris, G, Das, P, Berk, M, Baune, BT, Boyce, P, Lyndon, B, Parker, G, Singh, AB, Malhi, GS, Bassett, D, Mulder, R, Outhred, T, Hamilton, A, Morris, G, Das, P, Berk, M, Baune, BT, Boyce, P, Lyndon, B, Parker, G, Singh, AB, and Malhi, GS
Published: 2017

181. Department of Error.

Author: Fullman, N, Barber, RM, Abajobir, AA, Abate, KH, Abbafati, C, Abbas, KM, Abd-Allah, F, Abdulkader, RS, Abdulle, AM, Abera, SF, Aboyans, V, Abu-Raddad, LJ, Abu-Rmeileh, NME, Adedeji, IA, Adetokunboh, O, Afshin, A, Agrawal, A, Agrawal, S, Ahmad Kiadaliri, A, Ahmadieh, H, Ahmed, MB, Aichour, MTE, Aichour, AN, Aichour, I, Aiyar, S, Akinyemi, RO, Akseer, N, Al-Aly, Z, Alam, K, Alam, N, Alasfoor, D, Alene, KA, Alizadeh-Navaei, R, Alkerwi, A, Alla, F, Allebeck, P, Allen, C, Al-Raddadi, R, Alsharif, U, Altirkawi, KA, Alvis-Guzman, N, Amare, AT, Amini, E, Ammar, W, Ansari, H, Antonio, CAT, Anwari, P, Arora, M, Artaman, A, Aryal, KK, Asayesh, H, Asgedom, SW, Assadi, R, Atey, TM, Atre, SR, Avila-Burgos, L, Avokpaho, EFGA, Awasthi, A, Azzopardi, P, Bacha, U, Badawi, A, Balakrishnan, K, Bannick, MS, Barac, A, Barker-Collo, SL, Bärnighausen, T, Barrero, LH, Basu, S, Battle, KE, Baune, BT, Beardsley, J, Bedi, N, Beghi, E, Béjot, Y, Bell, ML, Bennett, DA, Bennett, JR, Bensenor, IM, Berhane, A, Berhe, DF, Bernabé, E, Betsu, BD, Beuran, M, Beyene, AS, Bhala, N, Bhansali, A, Bhatt, S, Bhutta, ZA, Bicer, BK, Bidgoli, HH, Bikbov, B, Bilal, AI, Birungi, C, Biryukov, S, Bizuayehu, HM, Blosser, CD, Boneya, DJ, Bose, D, Bou-Orm, IR, Brauer, M, Fullman, N, Barber, RM, Abajobir, AA, Abate, KH, Abbafati, C, Abbas, KM, Abd-Allah, F, Abdulkader, RS, Abdulle, AM, Abera, SF, Aboyans, V, Abu-Raddad, LJ, Abu-Rmeileh, NME, Adedeji, IA, Adetokunboh, O, Afshin, A, Agrawal, A, Agrawal, S, Ahmad Kiadaliri, A, Ahmadieh, H, Ahmed, MB, Aichour, MTE, Aichour, AN, Aichour, I, Aiyar, S, Akinyemi, RO, Akseer, N, Al-Aly, Z, Alam, K, Alam, N, Alasfoor, D, Alene, KA, Alizadeh-Navaei, R, Alkerwi, A, Alla, F, Allebeck, P, Allen, C, Al-Raddadi, R, Alsharif, U, Altirkawi, KA, Alvis-Guzman, N, Amare, AT, Amini, E, Ammar, W, Ansari, H, Antonio, CAT, Anwari, P, Arora, M, Artaman, A, Aryal, KK, Asayesh, H, Asgedom, SW, Assadi, R, Atey, TM, Atre, SR, Avila-Burgos, L, Avokpaho, EFGA, Awasthi, A, Azzopardi, P, Bacha, U, Badawi, A, Balakrishnan, K, Bannick, MS, Barac, A, Barker-Collo, SL, Bärnighausen, T, Barrero, LH, Basu, S, Battle, KE, Baune, BT, Beardsley, J, Bedi, N, Beghi, E, Béjot, Y, Bell, ML, Bennett, DA, Bennett, JR, Bensenor, IM, Berhane, A, Berhe, DF, Bernabé, E, Betsu, BD, Beuran, M, Beyene, AS, Bhala, N, Bhansali, A, Bhatt, S, Bhutta, ZA, Bicer, BK, Bidgoli, HH, Bikbov, B, Bilal, AI, Birungi, C, Biryukov, S, Bizuayehu, HM, Blosser, CD, Boneya, DJ, Bose, D, Bou-Orm, IR, and Brauer, M
Published: 2017

182. Psychosocial Dysfunction in Major Depressive Disorder-Rationale, Design, and Characteristics of the Cognitive and Emotional Recovery Training Program for Depression (CERT-D)

Author: Knight, MJ, Baune, BT, Knight, MJ, and Baune, BT
Abstract: INTRODUCTION: Psychosocial dysfunction is associated with poor longitudinal course of depression and is not sufficiently addressed by existing pharmaceutical or psychological treatments. The aim of the current study is to evaluate the efficacy of a novel intervention designed to improve psychosocial function in depressed individuals. Impaired cognition, emotion processing, and social cognition appear to underlie (i.e., cause) psychosocial dysfunction in depression. The current treatment will target functioning in these domains (i.e., cognition, emotion, social cognition) with repeated training tasks, following the rationale that therapeutic benefits will arise in psychosocial functioning. It is expected that personalizing treatment by participants' baseline functioning will enhance clinical efficacy, by comparison with standard treatment in which baseline functioning is not considered. METHODS: The study is a randomized, controlled treatment (RCT), in which the efficacy of a personalized and standard intervention will be compared. Sixteen treatment sessions will be administered over an 8-week period. These treatments are designed to improve cognition, emotion processing and social cognition. Assessments of psychosocial functioning, as well as a number of secondary outcomes, will occur at baseline, 4 weeks (mid-RCT), 8 weeks (end of RCT), and in the observational period at baseline (week 9) and 3 and 6 months post-RCT. Recruitment will commence in July 2017, including subjects diagnosed with major depressive disorder according to DSM-IV-TR criteria. DISCUSSION: This research will provide new insight into the roles of cognition, emotion processing, and social cognition in psychosocial dysfunction in depression. In addition, the relative clinical efficacy of personalized versus standard treatment approaches will be assessed. ETHICS AND DISSEMINATION: This study has been approved by the human research ethics committees of the Royal Adelaide Hospital and the University o
Published: 2017

183. Defining melancholia: A core mood disorder

Author: Parker, G, Bassett, D, Outhred, T, Morris, G, Hamilton, A, Das, P, Baune, BT, Berk, M, Boyce, P, Lyndon, B, Mulder, R, Singh, AB, Malhi, GS, Parker, G, Bassett, D, Outhred, T, Morris, G, Hamilton, A, Das, P, Baune, BT, Berk, M, Boyce, P, Lyndon, B, Mulder, R, Singh, AB, and Malhi, GS
Published: 2017

184. The genetic overlap between mood disorders and cardiometabolic diseases: a systematic review of genome wide and candidate gene studies

Author: Amare, AT, Schubert, KO, Klingler-Hoffmann, M, Cohen-Woods, S, Baune, BT, Amare, AT, Schubert, KO, Klingler-Hoffmann, M, Cohen-Woods, S, and Baune, BT
Abstract: Meta-analyses of genome-wide association studies (meta-GWASs) and candidate gene studies have identified genetic variants associated with cardiovascular diseases, metabolic diseases and mood disorders. Although previous efforts were successful for individual disease conditions (single disease), limited information exists on shared genetic risk between these disorders. This article presents a detailed review and analysis of cardiometabolic diseases risk (CMD-R) genes that are also associated with mood disorders. First, we reviewed meta-GWASs published until January 2016, for the diseases 'type 2 diabetes, coronary artery disease, hypertension' and/or for the risk factors 'blood pressure, obesity, plasma lipid levels, insulin and glucose related traits'. We then searched the literature for published associations of these CMD-R genes with mood disorders. We considered studies that reported a significant association of at least one of the CMD-R genes and 'depression' or 'depressive disorder' or 'depressive symptoms' or 'bipolar disorder' or 'lithium treatment response in bipolar disorder', or 'serotonin reuptake inhibitors treatment response in major depression'. Our review revealed 24 potential pleiotropic genes that are likely to be shared between mood disorders and CMD-Rs. These genes include MTHFR, CACNA1D, CACNB2, GNAS, ADRB1, NCAN, REST, FTO, POMC, BDNF, CREB, ITIH4, LEP, GSK3B, SLC18A1, TLR4, PPP1R1B, APOE, CRY2, HTR1A, ADRA2A, TCF7L2, MTNR1B and IGF1. A pathway analysis of these genes revealed significant pathways: corticotrophin-releasing hormone signaling, AMPK signaling, cAMP-mediated or G-protein coupled receptor signaling, axonal guidance signaling, serotonin or dopamine receptors signaling, dopamine-DARPP32 feedback in cAMP signaling, circadian rhythm signaling and leptin signaling. Our review provides insights into the shared biological mechanisms of mood disorders and cardiometabolic diseases.
Published: 2017

185. Genome-wide Regional Heritability Mapping Identifies a Locus Within the TOX2 Gene Associated With Major Depressive Disorder

Author: Zeng, Y, Navarro, P, Shirali, M, Howard, DM, Adams, MJ, Hall, LS, Clarke, T-K, Thomson, PA, Smith, BH, Murray, A, Padmanabhan, S, Hayward, C, Boutin, T, MacIntyre, DJ, Lewis, CM, Wray, NR, Mehta, D, Penninx, BWJH, Milaneschi, Y, Baune, BT, Air, T, Hottenga, J-J, Mbarek, H, Castelao, E, Pistis, G, Schulze, TG, Streit, F, Forstner, AJ, Byrne, EM, Martin, NG, Breen, G, Mueller-Myhsok, B, Lucae, S, Kloiber, S, Domenici, E, Deary, IJ, Porteous, DJ, Haley, CS, McIntosh, AM, Zeng, Y, Navarro, P, Shirali, M, Howard, DM, Adams, MJ, Hall, LS, Clarke, T-K, Thomson, PA, Smith, BH, Murray, A, Padmanabhan, S, Hayward, C, Boutin, T, MacIntyre, DJ, Lewis, CM, Wray, NR, Mehta, D, Penninx, BWJH, Milaneschi, Y, Baune, BT, Air, T, Hottenga, J-J, Mbarek, H, Castelao, E, Pistis, G, Schulze, TG, Streit, F, Forstner, AJ, Byrne, EM, Martin, NG, Breen, G, Mueller-Myhsok, B, Lucae, S, Kloiber, S, Domenici, E, Deary, IJ, Porteous, DJ, Haley, CS, and McIntosh, AM
Abstract: BACKGROUND: Major depressive disorder (MDD) is the second largest cause of global disease burden. It has an estimated heritability of 37%, but published genome-wide association studies have so far identified few risk loci. Haplotype-block-based regional heritability mapping (HRHM) estimates the localized genetic variance explained by common variants within haplotype blocks, integrating the effects of multiple variants, and may be more powerful for identifying MDD-associated genomic regions. METHODS: We applied HRHM to Generation Scotland: The Scottish Family Health Study, a large family- and population-based Scottish cohort (N = 19,896). Single-single nucleotide polymorphism (SNP) and haplotype-based association tests were used to localize the association signal within the regions identified by HRHM. Functional prediction was used to investigate the effect of MDD-associated SNPs within the regions. RESULTS: A haplotype block across a 24-kb region within the TOX2 gene reached genome-wide significance in HRHM. Single-SNP- and haplotype-based association tests demonstrated that five of nine genotyped SNPs and two haplotypes within this block were significantly associated with MDD. The expression of TOX2 and a brain-specific long noncoding RNA RP1-269M15.3 in frontal cortex and nucleus accumbens basal ganglia, respectively, were significantly regulated by MDD-associated SNPs within this region. Both the regional heritability and single-SNP associations within this block were replicated in the UK-Ireland group of the most recent release of the Psychiatric Genomics Consortium (PGC), the PGC2-MDD (Major Depression Dataset). The SNP association was also replicated in a depressive symptom sample that shares some individuals with the PGC2-MDD. CONCLUSIONS: This study highlights the value of HRHM for MDD and provides an important target within TOX2 for further functional studies.
Published: 2017

186. Childhood adversity impacts on brain subcortical structures relevant to depression

Author: Frodl, T, Janowitz, D, Schmaal, L, Tozzi, L, Dobrowolny, H, Stein, DJ, Veltman, DJ, Wittfeld, K, van Erp, TGM, Jahanshad, N, Block, A, Hegenscheid, K, Voelzke, H, Lagopoulos, J, Hatton, SN, Hickie, IB, Frey, EM, Carballedo, A, Brooks, SJ, Vuletic, D, Uhlmann, A, Veer, IM, Walter, H, Schnell, K, Grotegerd, D, Arolt, V, Kugel, H, Schramm, E, Konrad, C, Zurowski, B, Baune, BT, van der Wee, NJA, van Tol, M-J, Penninx, BWJH, Thompson, PM, Hibar, DP, Dannlowski, U, Grabe, HJ, Frodl, T, Janowitz, D, Schmaal, L, Tozzi, L, Dobrowolny, H, Stein, DJ, Veltman, DJ, Wittfeld, K, van Erp, TGM, Jahanshad, N, Block, A, Hegenscheid, K, Voelzke, H, Lagopoulos, J, Hatton, SN, Hickie, IB, Frey, EM, Carballedo, A, Brooks, SJ, Vuletic, D, Uhlmann, A, Veer, IM, Walter, H, Schnell, K, Grotegerd, D, Arolt, V, Kugel, H, Schramm, E, Konrad, C, Zurowski, B, Baune, BT, van der Wee, NJA, van Tol, M-J, Penninx, BWJH, Thompson, PM, Hibar, DP, Dannlowski, U, and Grabe, HJ
Abstract: Childhood adversity plays an important role for development of major depressive disorder (MDD). There are differences in subcortical brain structures between patients with MDD and healthy controls, but the specific impact of childhood adversity on such structures in MDD remains unclear. Thus, aim of the present study was to investigate whether childhood adversity is associated with subcortical volumes and how it interacts with a diagnosis of MDD and sex. Within the ENIGMA-MDD network, nine university partner sites, which assessed childhood adversity and magnetic resonance imaging in patients with MDD and controls, took part in the current joint mega-analysis. In this largest effort world-wide to identify subcortical brain structure differences related to childhood adversity, 3036 participants were analyzed for subcortical brain volumes using FreeSurfer. A significant interaction was evident between childhood adversity, MDD diagnosis, sex, and region. Increased exposure to childhood adversity was associated with smaller caudate volumes in females independent of MDD. All subcategories of childhood adversity were negatively associated with caudate volumes in females - in particular emotional neglect and physical neglect (independently from age, ICV, imaging site and MDD diagnosis). There was no interaction effect between childhood adversity and MDD diagnosis on subcortical brain volumes. Childhood adversity is one of the contributors to brain structural abnormalities. It is associated with subcortical brain abnormalities that are relevant to psychiatric disorders such as depression.
Published: 2017

187. Online information seeking by patients with bipolar disorder: results from an international multisite survey (vol 4, pg 1, 2016)

Author: Conell, J, Bauer, R, Glenn, T, Alda, M, Ardau, R, Baune, BT, Berk, M, Bersudsky, Y, Bilderbeck, A, Bocchetta, A, Bossini, L, Castro, AMP, Cheung, EYW, Chillotti, C, Choppin, S, Del Zompo, M, Dias, R, Dodd, S, Duffy, A, Etain, B, Fagiolini, A, Garnham, J, Geddes, J, Gildebro, J, Gonzalez-Pinto, A, Goodwin, GM, Grof, P, Harima, H, Hassel, S, Henry, C, Hidalgo-Mazzei, D, Kapur, V, Kunigiri, G, Lafer, B, Lam, C, Larsen, ER, Lewitzka, U, Licht, R, Lund, AH, Misiak, B, Piotrowski, P, Monteith, S, Munoz, R, Nakanotani, T, Nielsen, RE, O'Donovan, C, Okamura, Y, Osher, Y, Reif, A, Ritter, P, Rybakowski, JK, Sagduyu, K, Sawchuk, B, Schwartz, E, Scippa, AM, Slaney, C, Sulaiman, AH, Suominen, K, Suwalska, A, Tam, P, Tatebayashi, Y, Tondo, L, Vieta, E, Vinberg, M, Viswanath, B, Volkert, J, Zetin, M, Zorrilla, I, Whybrow, PC, Bauer, M, Conell, J, Bauer, R, Glenn, T, Alda, M, Ardau, R, Baune, BT, Berk, M, Bersudsky, Y, Bilderbeck, A, Bocchetta, A, Bossini, L, Castro, AMP, Cheung, EYW, Chillotti, C, Choppin, S, Del Zompo, M, Dias, R, Dodd, S, Duffy, A, Etain, B, Fagiolini, A, Garnham, J, Geddes, J, Gildebro, J, Gonzalez-Pinto, A, Goodwin, GM, Grof, P, Harima, H, Hassel, S, Henry, C, Hidalgo-Mazzei, D, Kapur, V, Kunigiri, G, Lafer, B, Lam, C, Larsen, ER, Lewitzka, U, Licht, R, Lund, AH, Misiak, B, Piotrowski, P, Monteith, S, Munoz, R, Nakanotani, T, Nielsen, RE, O'Donovan, C, Okamura, Y, Osher, Y, Reif, A, Ritter, P, Rybakowski, JK, Sagduyu, K, Sawchuk, B, Schwartz, E, Scippa, AM, Slaney, C, Sulaiman, AH, Suominen, K, Suwalska, A, Tam, P, Tatebayashi, Y, Tondo, L, Vieta, E, Vinberg, M, Viswanath, B, Volkert, J, Zetin, M, Zorrilla, I, Whybrow, PC, and Bauer, M
Published: 2017

188. The PHF21B gene is associated with major depression and modulates the stress response

Author: Wong, M-L, Arcos-Burgos, M, Liu, S, Velez, JI, Yu, C, Baune, BT, Jawahar, MC, Arolt, V, Dannlowski, U, Chuah, A, Huttley, GA, Fogarty, R, Lewis, MD, Bornstein, SR, Licinio, J, Wong, M-L, Arcos-Burgos, M, Liu, S, Velez, JI, Yu, C, Baune, BT, Jawahar, MC, Arolt, V, Dannlowski, U, Chuah, A, Huttley, GA, Fogarty, R, Lewis, MD, Bornstein, SR, and Licinio, J
Abstract: Major depressive disorder (MDD) affects around 350 million people worldwide; however, the underlying genetic basis remains largely unknown. In this study, we took into account that MDD is a gene-environment disorder, in which stress is a critical component, and used whole-genome screening of functional variants to investigate the 'missing heritability' in MDD. Genome-wide association studies (GWAS) using single- and multi-locus linear mixed-effect models were performed in a Los Angeles Mexican-American cohort (196 controls, 203 MDD) and in a replication European-ancestry cohort (499 controls, 473 MDD). Our analyses took into consideration the stress levels in the control populations. The Mexican-American controls, comprised primarily of recent immigrants, had high levels of stress due to acculturation issues and the European-ancestry controls with high stress levels were given higher weights in our analysis. We identified 44 common and rare functional variants associated with mild to moderate MDD in the Mexican-American cohort (genome-wide false discovery rate, FDR, <0.05), and their pathway analysis revealed that the three top overrepresented Gene Ontology (GO) processes were innate immune response, glutamate receptor signaling and detection of chemical stimulus in smell sensory perception. Rare variant analysis replicated the association of the PHF21B gene in the ethnically unrelated European-ancestry cohort. The TRPM2 gene, previously implicated in mood disorders, may also be considered replicated by our analyses. Whole-genome sequencing analyses of a subset of the cohorts revealed that European-ancestry individuals have a significantly reduced (50%) number of single nucleotide variants compared with Mexican-American individuals, and for this reason the role of rare variants may vary across populations. PHF21b variants contribute significantly to differences in the levels of expression of this gene in several brain areas, including the hippocampus. Furthermore, u
Published: 2017

189. Neural correlates of apathy in late-life depression: a pilot [18F]FDDNP positron emission tomography study

Author: Eyre, HA, Siddarth, P, van Dyk, K, St Cyr, N, Baune, BT, Barrio, JR, Small, GW, Lavretsky, H, Eyre, HA, Siddarth, P, van Dyk, K, St Cyr, N, Baune, BT, Barrio, JR, Small, GW, and Lavretsky, H
Abstract: BACKGROUND: Neurotoxicity associated with amyloid and tau protein aggregation could represent a pathophysiological cascade that, along with vascular compromise, may predispose individuals to late-life depression (LLD). In LLD, apathy is common, leads to worsening of functioning, and responds poorly to antidepressant treatment. Better understanding of the pathophysiological mechanisms of apathy in LLD would facilitate development of more effective diagnostic and treatment approaches. In this cross-sectional pilot study, we performed positron emission tomography scans after injection of 2-(1-{6-[(2-[18 F]fluoroethyl)(methyl)-amino]-2-naphthyl}ethylidene) malononitrile ([18 F]FDDNP), an in vivo amyloid and tau neuroimaging study, in patients with LLD to explore neural correlates of apathy. METHODS: Sixteen depressed elderly volunteers received clinical assessments and [18 F]FDDNP positron emission tomography scans. The cross-sectional relationship of [18 F]FDDNP binding levels with depression (Hamilton Depression Rating Scale) and apathy (Apathy Evaluation Scale) were studied using Spearman's correlation analyses because of the relatively small sample size. Age, sex, and years of education were partialed out. Significance levels were set at P ≤ 0.05. RESULTS: [18 F]FDDNP binding in the anterior cingulate cortex was negatively associated with the Apathy Evaluation Scale total (r = -0.62, P = 0.02; where low Apathy Evaluation Scale score equals greater severity of apathy). This suggests that apathy in LLD is associated with higher amyloid and/or tau levels in the anterior cingulate cortex. None of the regional [18 F]FDDNP binding levels was significantly associated with the Hamilton Depression Rating Scale total. CONCLUSION: This pilot study suggests that increased apathy in subjects with LLD may be associated with greater amyloid and/or tau burden in certain brain regions. Future studies in larger samples would elucidate the generalizability of these results, which even
Published: 2017

190. Microglia: An Interface between the Loss of Neuroplasticity and Depression

Author: Singhal, G, Baune, BT, Singhal, G, and Baune, BT
Abstract: Depression has been widely accepted as a major psychiatric disease affecting nearly 350 million people worldwide. Research focus is now shifting from studying the extrinsic and social factors of depression to the underlying molecular causes. Microglial activity is shown to be associated with pathological conditions, such as psychological stress, pathological aging, and chronic infections. These are primary immune effector cells in the CNS and regulate the extensive dialogue between the nervous and the immune systems in response to different immunological, physiological, and psychological stressors. Studies have suggested that during stress and pathologies, microglia play a significant role in the disruption of neuroplasticity and have detrimental effects on neuroprotection causing neuroinflammation and exacerbation of depression. After a systematic search of literature databases, relevant articles on the microglial regulation of bidirectional neuroimmune pathways affecting neuroplasticity and leading to depression were reviewed. Although, several hypotheses have been proposed for the microglial role in the onset of depression, it is clear that all molecular pathways to depression are linked through microglia-associated neuroinflammation and hippocampal degeneration. Molecular factors such as an excess of glucocorticoids and changes in gene expression of neurotrophic factors, as well as neuro active substances secreted by gut microbiota have also been shown to affect microglial morphology and phenotype resulting in depression. This review aims to critically analyze the various molecular pathways associated with the microglial role in depression.
Published: 2017

191. Measuring progress and projecting attainment on the basis of past trends of the health-related Sustainable Development Goals in 188 countries: an analysis from the Global Burden of Disease Study 2016 (vol 390, pg 1423, 2017)

Author: Fullman, N, Barber, RM, Abajobir, AA, Abate, KH, Abbafati, C, Abbas, KM, Abd-Allah, F, Abdulkader, RS, Abdulle, AM, Abera, SF, Aboyans, V, Abu-Raddad, LJ, Abu-Rmeileh, NME, Adedeji, IA, Adetokunboh, O, Afshin, A, Agrawal, A, Agrawal, S, Ahmad Kiadaliri, A, Ahmadieh, H, Ahmed, MB, Aichour, MTE, Aichour, AN, Aichour, I, Aiyar, S, Akinyemi, RO, Akseer, N, Al-Aly, Z, Alam, K, Alam, N, Alasfoor, D, Alene, KA, Alizadeh-Navaei, R, Alkerwi, A, Alla, F, Allebeck, P, Allen, C, Al-Raddadi, R, Alsharif, U, Altirkawi, KA, Alvis-Guzman, N, Amare, AT, Amini, E, Ammar, W, Ansari, H, Antonio, CAT, Anwari, P, Arora, M, Artaman, A, Aryal, KK, Asayesh, H, Asgedom, SW, Assadi, R, Atey, TM, Atre, SR, Avila-Burgos, L, Avokpaho, EFGA, Awasthi, A, Azzopardi, P, Bacha, U, Badawi, A, Balakrishnan, K, Bannick, MS, Barac, A, Barker-Collo, SL, Bärnighausen, T, Barrero, LH, Basu, S, Battle, KE, Baune, BT, Beardsley, J, Bedi, N, Beghi, E, Béjot, Y, Bell, ML, Bennett, DA, Bennett, JR, Bensenor, IM, Berhane, A, Berhe, DF, Bernabé, E, Betsu, BD, Beuran, M, Beyene, AS, Bhala, N, Bhansali, A, Bhatt, S, Bhutta, ZA, Bicer, BK, Bidgoli, HH, Bikbov, B, Bilal, AI, Birungi, C, Biryukov, S, Bizuayehu, HM, Blosser, CD, Boneya, DJ, Bose, D, Bou-Orm, IR, Brauer, M, Fullman, N, Barber, RM, Abajobir, AA, Abate, KH, Abbafati, C, Abbas, KM, Abd-Allah, F, Abdulkader, RS, Abdulle, AM, Abera, SF, Aboyans, V, Abu-Raddad, LJ, Abu-Rmeileh, NME, Adedeji, IA, Adetokunboh, O, Afshin, A, Agrawal, A, Agrawal, S, Ahmad Kiadaliri, A, Ahmadieh, H, Ahmed, MB, Aichour, MTE, Aichour, AN, Aichour, I, Aiyar, S, Akinyemi, RO, Akseer, N, Al-Aly, Z, Alam, K, Alam, N, Alasfoor, D, Alene, KA, Alizadeh-Navaei, R, Alkerwi, A, Alla, F, Allebeck, P, Allen, C, Al-Raddadi, R, Alsharif, U, Altirkawi, KA, Alvis-Guzman, N, Amare, AT, Amini, E, Ammar, W, Ansari, H, Antonio, CAT, Anwari, P, Arora, M, Artaman, A, Aryal, KK, Asayesh, H, Asgedom, SW, Assadi, R, Atey, TM, Atre, SR, Avila-Burgos, L, Avokpaho, EFGA, Awasthi, A, Azzopardi, P, Bacha, U, Badawi, A, Balakrishnan, K, Bannick, MS, Barac, A, Barker-Collo, SL, Bärnighausen, T, Barrero, LH, Basu, S, Battle, KE, Baune, BT, Beardsley, J, Bedi, N, Beghi, E, Béjot, Y, Bell, ML, Bennett, DA, Bennett, JR, Bensenor, IM, Berhane, A, Berhe, DF, Bernabé, E, Betsu, BD, Beuran, M, Beyene, AS, Bhala, N, Bhansali, A, Bhatt, S, Bhutta, ZA, Bicer, BK, Bidgoli, HH, Bikbov, B, Bilal, AI, Birungi, C, Biryukov, S, Bizuayehu, HM, Blosser, CD, Boneya, DJ, Bose, D, Bou-Orm, IR, and Brauer, M
Published: 2017

192. Mixed mood: The not so united states?

Author: Malhi, GS, Berk, M, Morris, G, Hamilton, A, Outhred, T, Das, P, Bassett, D, Baune, BT, Boyce, P, Lyndon, B, Mulder, R, Parker, G, Singh, AB, Malhi, GS, Berk, M, Morris, G, Hamilton, A, Outhred, T, Das, P, Bassett, D, Baune, BT, Boyce, P, Lyndon, B, Mulder, R, Parker, G, and Singh, AB
Published: 2017

193. Differential gene expression in brain and peripheral tissues in depression across the life span: A review of replicated findings

Author: Ciobanu, LG, Sachdev, PS, Trollor, JN, Reppermund, S, Thalamuthu, A, Mather, KA, Cohen-Woods, S, Baune, BT, Ciobanu, LG, Sachdev, PS, Trollor, JN, Reppermund, S, Thalamuthu, A, Mather, KA, Cohen-Woods, S, and Baune, BT
Abstract: There is a growing body of research investigating the gene expression signature of depression at the genome-wide level, with potential for the discovery of novel pathophysiological mechanisms of depression. However, heterogeneity of depression, dynamic nature of gene expression patterns and various sources of noise have resulted in inconsistent findings. We systematically review the current state of transcriptome profiling of depression in the brain and peripheral tissues with a particular focus on replicated findings at the single gene level. By examining 16 brain regions and 5 cell types from the periphery, we identified 57 replicated differentially expressed genes in the brain and 21 in peripheral tissues. Functional overlap between brain and periphery strongly implicates shared pathways in a comorbid phenotype of depression and cardiovascular disease. The findings highlight dermal fibroblasts as a promising experimental model for depression biomarker research, provide partial support for all major theories of depression and suggest a novel candidate gene, PXMP2, which plays a critical role in lipid and reactive oxygen species metabolism.
Published: 2016

194. Gene expression in the aging human brain: An overview

Author: Mohan, A, Mather, KA, Thalamuthu, A, Baune, BT, Sachdev, PS, Mohan, A, Mather, KA, Thalamuthu, A, Baune, BT, and Sachdev, PS
Abstract: Purpose of review The review aims to provide a summary of recent developments in the study of gene expression in the aging human brain. Recent findings Profiling differentially expressed genes or 'transcripts' in the human brain over the course of normal aging has provided valuable insights into the biological pathways that appear activated or suppressed in late life. Genes mediating neuroinflammation and immune system activation in particular, show significant age-related upregulation creating a state of vulnerability to neurodegenerative and neuropsychiatric disease in the aging brain. Cellular ionic dyshomeostasis and age-related decline in a host of molecular influences on synaptic efficacy may underlie neurocognitive decline in later life. Critically, these investigations have also shed light on the mobilization of protective genetic responses within the aging human brain that help determine health and disease trajectories in older age. There is growing interest in the study of pre and posttranscriptional regulators of gene expression, and the role of noncoding RNAs in particular, as mediators of the phenotypic diversity that characterizes human brain aging. Summary Gene expression studies in healthy brain aging offer an opportunity to unravel the intricately regulated cellular underpinnings of neurocognitive aging as well as disease risk and resiliency in late life. In doing so, new avenues for early intervention in age-related neurodegenerative disease could be investigated with potentially significant implications for the development of disease-modifying therapies.
Published: 2016

195. Therapeutically relevant structural and functional mechanisms triggered by physical and cognitive exercise

Author: Suo, C, Singh, MF, Gates, N, Wen, W, Sachdev, P, Brodaty, H, Saigal, N, Wilson, GC, Meiklejohn, J, Singh, N, Baune, BT, Baker, M, Foroughi, N, Wang, Y, Mavros, Y, Lampit, A, Leung, I, Valenzuela, MJ, Suo, C, Singh, MF, Gates, N, Wen, W, Sachdev, P, Brodaty, H, Saigal, N, Wilson, GC, Meiklejohn, J, Singh, N, Baune, BT, Baker, M, Foroughi, N, Wang, Y, Mavros, Y, Lampit, A, Leung, I, and Valenzuela, MJ
Abstract: © 2016 Macmillan Publishers Limited, part of Springer Nature. All rights reserved. Physical and cognitive exercise may prevent or delay dementia in later life but the neural mechanisms underlying these therapeutic benefits are largely unknown. We examined structural and functional magnetic resonance imaging (MRI) brain changes after 6 months of progressive resistance training (PRT), computerized cognitive training (CCT) or combined intervention. A total of 100 older individuals (68 females, average age=70.1, s.d.±6.7, 55-87 years) with dementia prodrome mild cognitive impairment were recruited in the SMART (Study of Mental Activity and Resistance Training) Trial. Participants were randomly assigned into four intervention groups: PRT+CCT, PRT+SHAM CCT, CCT+SHAM PRT and double SHAM. Multimodal MRI was conducted at baseline and at 6 months of follow-up (immediately after training) to measure structural and spontaneous functional changes in the brain, with a focus on the hippocampus and posterior cingulate regions. Participants' cognitive changes were also assessed before and after training. We found that PRT but not CCT significantly improved global cognition (F(90)=4.1, P<0.05) as well as expanded gray matter in the posterior cingulate (P corrected <0.05), and these changes were related to each other (r=0.25, P=0.03). PRT also reversed progression of white matter hyperintensities, a biomarker of cerebrovascular disease, in several brain areas. In contrast, CCT but not PRT attenuated decline in overall memory performance (F(90)=5.7, P<0.02), mediated by enhanced functional connectivity between the hippocampus and superior frontal cortex. Our findings indicate that physical and cognitive training depend on discrete neuronal mechanisms for their therapeutic efficacy, information that may help develop targeted lifestyle-based preventative strategies.
Published: 2016

196. Genome-wide association study of 40,000 individuals identifies two novel loci associated with bipolar disorder

Author: Hou, L, Bergen, SE, Akula, N, Song, J, Hultman, CM, Landén, M, Adli, M, Alda, M, Ardau, R, Arias, B, Aubry, JM, Backlund, L, Badner, JA, Barrett, TB, Bauer, M, Baune, BT, Bellivier, F, Benabarre, A, Bengesser, S, Berrettini, WH, Bhattacharjee, AK, Biernacka, JM, Birner, A, Bloss, CS, Brichant-Petitjean, C, Bui, ET, Byerley, W, Cervantes, P, Chillotti, C, Cichon, S, Colom, F, Coryell, W, Craig, DW, Cruceanu, C, Czerski, PM, Davis, T, Dayer, A, Degenhardt, F, Del Zompo, M, DePaulo, JR, Edenberg, HJ, Étain, B, Falkai, P, Foroud, T, Forstner, AJ, Frisén, L, Frye, MA, Fullerton, JM, Gard, S, Garnham, JS, Gershon, ES, Goes, FS, Greenwood, TA, Grigoroiu-Serbanescu, M, Hauser, J, Heilbronner, U, Heilmann-Heimbach, S, Herms, S, Hipolito, M, Hitturlingappa, S, Hoffmann, P, Hofmann, A, Jamain, S, Jiménez, E, Kahn, JP, Kassem, L, Kelsoe, JR, Kittel-Schneider, S, Kliwicki, S, Koller, DL, König, B, Lackner, N, Laje, G, Lang, M, Lavebratt, C, Lawson, WB, Leboyer, M, Leckband, SG, Liu, C, Maaser, A, Mahon, PB, Maier, W, Maj, M, Manchia, M, Martinsson, L, McCarthy, MJ, McElroy, SL, McInnis, MG, McKinney, R, Mitchell, PB, Mitjans, M, Mondimore, FM, Monteleone, P, Mühleisen, TW, Nievergelt, CM, Nöthen, MM, Novák, T, Nurnberger, JI, Nwulia, EA, Ösby, U, Hou, L, Bergen, SE, Akula, N, Song, J, Hultman, CM, Landén, M, Adli, M, Alda, M, Ardau, R, Arias, B, Aubry, JM, Backlund, L, Badner, JA, Barrett, TB, Bauer, M, Baune, BT, Bellivier, F, Benabarre, A, Bengesser, S, Berrettini, WH, Bhattacharjee, AK, Biernacka, JM, Birner, A, Bloss, CS, Brichant-Petitjean, C, Bui, ET, Byerley, W, Cervantes, P, Chillotti, C, Cichon, S, Colom, F, Coryell, W, Craig, DW, Cruceanu, C, Czerski, PM, Davis, T, Dayer, A, Degenhardt, F, Del Zompo, M, DePaulo, JR, Edenberg, HJ, Étain, B, Falkai, P, Foroud, T, Forstner, AJ, Frisén, L, Frye, MA, Fullerton, JM, Gard, S, Garnham, JS, Gershon, ES, Goes, FS, Greenwood, TA, Grigoroiu-Serbanescu, M, Hauser, J, Heilbronner, U, Heilmann-Heimbach, S, Herms, S, Hipolito, M, Hitturlingappa, S, Hoffmann, P, Hofmann, A, Jamain, S, Jiménez, E, Kahn, JP, Kassem, L, Kelsoe, JR, Kittel-Schneider, S, Kliwicki, S, Koller, DL, König, B, Lackner, N, Laje, G, Lang, M, Lavebratt, C, Lawson, WB, Leboyer, M, Leckband, SG, Liu, C, Maaser, A, Mahon, PB, Maier, W, Maj, M, Manchia, M, Martinsson, L, McCarthy, MJ, McElroy, SL, McInnis, MG, McKinney, R, Mitchell, PB, Mitjans, M, Mondimore, FM, Monteleone, P, Mühleisen, TW, Nievergelt, CM, Nöthen, MM, Novák, T, Nurnberger, JI, Nwulia, EA, and Ösby, U
Abstract: Bipolar disorder (BD) is a genetically complex mental illness characterized by severe oscillations of mood and behaviour. Genome-wide association studies (GWAS) have identified several risk loci that together account for a small portion of the heritability. To identify additional risk loci, we performed a two-stage meta-analysis of > 9 million genetic variants in 9,784 bipolar disorder patients and 30,471 controls, the largest GWAS of BD to date. In this study, to increase power we used ~2,000 lithium-treated cases with a long-term diagnosis of BD from the Consortium on Lithium Genetics, excess controls, and analytic methods optimized for markers on the X-chromosome. In addition to four known loci, results revealed genome-wide significant associations at two novel loci: an intergenic region on 9p21.3 (rs12553324, P= 5.87×10-9; odds ratio (OR)=1.12) and markers within ERBB2 (rs2517959, P= 4.53×10-9; OR=1.13). No significant X-chromosome associations were detected and Xlinked markers explained very little BD heritability. The results add to a growing list of common autosomal variants involved in BD and illustrate the power of comparing well-characterized cases to an excess of controls in GWAS.
Published: 2016

197. Therapeutically relevant structural and functional mechanisms triggered by physical and cognitive exercise (vol 21, pg 1633, 2016)

Author: Suo, C, Singh, MF, Gates, N, Wen, W, Sachdev, P, Brodaty, H, Saigal, N, Wilson, GC, Meiklejohn, J, Singh, N, Baune, BT, Baker, M, Foroughi, N, Wang, Y, Mavros, Y, Lampit, A, Leung, I, Valenzuela, MJ, Suo, C, Singh, MF, Gates, N, Wen, W, Sachdev, P, Brodaty, H, Saigal, N, Wilson, GC, Meiklejohn, J, Singh, N, Baune, BT, Baker, M, Foroughi, N, Wang, Y, Mavros, Y, Lampit, A, Leung, I, and Valenzuela, MJ
Published: 2016

198. The Enemy within: Innate Surveillance-Mediated Cell Death, the Common Mechanism of Neurodegenerative Disease

Author: Richards, RI, Robertson, SA, O'Keefe, LV, Fornarino, D, Scott, A, Lardelli, M, Baune, BT, Richards, RI, Robertson, SA, O'Keefe, LV, Fornarino, D, Scott, A, Lardelli, M, and Baune, BT
Abstract: Neurodegenerative diseases comprise an array of progressive neurological disorders all characterized by the selective death of neurons in the central nervous system. Although, rare (familial) and common (sporadic) forms can occur for the same disease, it is unclear whether this reflects several distinct pathogenic pathways or the convergence of different causes into a common form of nerve cell death. Remarkably, neurodegenerative diseases are increasingly found to be accompanied by activation of the innate immune surveillance system normally associated with pathogen recognition and response. Innate surveillance is the cell's quality control system for the purpose of detecting such danger signals and responding in an appropriate manner. Innate surveillance is an "intelligent system," in that the manner of response is relevant to the magnitude and duration of the threat. If possible, the threat is dealt with within the cell in which it is detected, by degrading the danger signal(s) and restoring homeostasis. If this is not successful then an inflammatory response is instigated that is aimed at restricting the spread of the threat by elevating degradative pathways, sensitizing neighboring cells, and recruiting specialized cell types to the site. If the danger signal persists, then the ultimate response can include not only the programmed cell death of the original cell, but the contents of this dead cell can also bring about the death of adjacent sensitized cells. These responses are clearly aimed at destroying the ability of the detected pathogen to propagate and spread. Innate surveillance comprises intracellular, extracellular, non-cell autonomous and systemic processes. Recent studies have revealed how multiple steps in these processes involve proteins that, through their mutation, have been linked to many familial forms of neurodegenerative disease. This suggests that individuals harboring these mutations may have an amplified response to innate-mediated damage in
Published: 2016

199. Cardiovascular Health in Anxiety or Mood Problems Study (CHAMPS): study protocol for a randomized controlled trial

Author: Tully, PJ, Turnbull, DA, Horowitz, JD, Beltrame, JF, Selkow, T, Baune, BT, Markwick, E, Sauer-Zavala, S, Baumeister, H, Cosh, S, Wittert, GA, Tully, PJ, Turnbull, DA, Horowitz, JD, Beltrame, JF, Selkow, T, Baune, BT, Markwick, E, Sauer-Zavala, S, Baumeister, H, Cosh, S, and Wittert, GA
Abstract: BACKGROUND: Previous psychological and pharmacological interventions have primarily focused on depression disorders in populations with cardiovascular diseases (CVDs) and the efficacy of anxiety disorder interventions is only more recently being explored. Transdiagnostic interventions address common emotional processes and the full range of anxiety and depression disorders often observed in populations with CVDs. The aim of CHAMPS is to evaluate the feasibility of a unified protocol (UP) for the transdiagnostic treatment of emotional disorders intervention in patients recently hospitalized for CVDs. The current study reports the protocol of a feasibility randomized controlled trial to inform a future trial. METHODS/DESIGN: This is a feasibility randomized, controlled trial with a single-center design. A total of 50 participants will be block-randomized to either a UP intervention or enhanced usual care. Both groups will receive standard CVD care. The UP intervention consists of 1) enhancing motivation, readiness for change, and treatment engagement; (2) psychoeducation about emotions; (3) increasing present focused emotion awareness; (4) increasing cognitive flexibility; (5) identifying and preventing patterns of emotion avoidance and maladaptive emotion-driven behaviors (EDBs, including tobacco smoking, and alcohol use); (6) increasing tolerance of emotion-related physical sensations; (7) interoceptive and situation-based emotion-focused exposure; and (8) relapse prevention strategies. Treatment duration is 12 to 18 weeks. Relevant outcomes include the standard deviation of self-rated anxiety, depression and quality of life symptoms. Other outcomes include intervention acceptability, satisfaction with care, rates of EDBs, patient adherence, physical activity, cardiac and psychiatric readmissions. Parallel to the main trial, a nonrandomized comparator cohort will be recruited comprising 150 persons scoring below the predetermined depression and anxiety severity thre
Published: 2016

200. Prediction of transition from ultra-high risk to first-episode psychosis using a probabilistic model combining history, clinical assessment and fatty-acid biomarkers

Author: Clark, SR, Baune, BT, Schubert, KO, Lavoie, S, Smesny, S, Rice, SM, Schaefer, MR, Benninger, F, Feucht, M, Klier, CM, McGorry, PD, Amminger, GP, Clark, SR, Baune, BT, Schubert, KO, Lavoie, S, Smesny, S, Rice, SM, Schaefer, MR, Benninger, F, Feucht, M, Klier, CM, McGorry, PD, and Amminger, GP
Abstract: Current criteria identifying patients with ultra-high risk of psychosis (UHR) have low specificity, and less than one-third of UHR cases experience transition to psychosis within 3 years of initial assessment. We explored whether a Bayesian probabilistic multimodal model, combining baseline historical and clinical risk factors with biomarkers (oxidative stress, cell membrane fatty acids, resting quantitative electroencephalography (qEEG)), could improve this specificity. We analyzed data of a UHR cohort (n=40) with a 1-year transition rate of 28%. Positive and negative likelihood ratios were calculated for predictor variables with statistically significant receiver operating characteristic curves (ROCs), which excluded oxidative stress markers and qEEG parameters as significant predictors of transition. We clustered significant variables into historical (history of drug use), clinical (Positive and Negative Symptoms Scale positive, negative and general scores and Global Assessment of Function) and biomarker (total omega-3, nervonic acid) groups, and calculated the post-test probability of transition for each group and for group combinations using the odds ratio form of Bayes' rule. Combination of the three variable groups vastly improved the specificity of prediction (area under ROC=0.919, sensitivity=72.73%, specificity=96.43%). In this sample, our model identified over 70% of UHR patients who transitioned within 1 year, compared with 28% identified by standard UHR criteria. The model classified 77% of cases as very high or low risk (P>0.9, <0.1) based on history and clinical assessment, suggesting that a staged approach could be most efficient, reserving fatty-acid markers for 23% of cases remaining at intermediate probability following bedside interview.
Published: 2016

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