The Consortium on Vulnerability to Externalizing Disorders and Addictions (c-VEDA): an accelerated longitudinal cohort of children and adolescents in India.
Journal
Molecular psychiatry
ISSN: 1476-5578
Titre abrégé: Mol Psychiatry
Pays: England
ID NLM: 9607835
Informations de publication
Date de publication:
08 2020
08 2020
Historique:
received:
01
07
2019
accepted:
17
01
2020
revised:
06
01
2020
pubmed:
24
3
2020
medline:
23
3
2021
entrez:
24
3
2020
Statut:
ppublish
Résumé
The global burden of disease attributable to externalizing disorders such as alcohol misuse calls urgently for effective prevention and intervention. As our current knowledge is mainly derived from high-income countries such in Europe and North-America, it is difficult to address the wider socio-cultural, psychosocial context, and genetic factors in which risk and resilience are embedded in low- and medium-income countries. c-VEDA was established as the first and largest India-based multi-site cohort investigating the vulnerabilities for the development of externalizing disorders, addictions, and other mental health problems. Using a harmonised data collection plan coordinated with multiple cohorts in China, USA, and Europe, baseline data were collected from seven study sites between November 2016 and May 2019. Nine thousand and ten participants between the ages of 6 and 23 were assessed during this time, amongst which 1278 participants underwent more intensive assessments including MRI scans. Both waves of follow-ups have started according to the accelerated cohort structure with planned missingness design. Here, we present descriptive statistics on several key domains of assessments, and the full baseline dataset will be made accessible for researchers outside the consortium in September 2019. More details can be found on our website [cveda.org].
Identifiants
pubmed: 32203154
doi: 10.1038/s41380-020-0656-1
pii: 10.1038/s41380-020-0656-1
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
1618-1630Subventions
Organisme : Medical Research Council
ID : MC_UU_12011/3
Pays : United Kingdom
Organisme : MRF
ID : MRF_MRF-058-0004-RG-DESRI
Pays : United Kingdom
Organisme : Medical Research Council
ID : G0400519
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/L01341X/1
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/N000390/1
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/L022206/1
Pays : United Kingdom
Organisme : Medical Research Council
ID : MC_UP_A620_1016
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/S019669/1
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/S020306/1
Pays : United Kingdom
Organisme : MRF
ID : MRF_MRF-058-0009-RG-DESR-C0759
Pays : United Kingdom
Organisme : Medical Research Council
ID : MC_U147585821
Pays : United Kingdom
Investigateurs
Vivek Benegal
(V)
Gunter Schumann
(G)
Pratima Murthy
(P)
Bharath Holla
(B)
Eesha Sharma
(E)
Meera Purushottam
(M)
Rose Dawn Bharath
(RD)
Sanjeev Jain
(S)
Mathew Varghese
(M)
Thennarasu Kandavel
(T)
Deepak Jayarajan
(D)
Keshav Kumar
(K)
Preeti Jacob
(P)
Amit Chakrabarti
(A)
Rajkumar Lenin Singh
(RL)
Roshan Lourembam Singh
(RL)
Debasish Basu
(D)
Subodh Bhagyalakshmi Nanjayya
(SB)
Chirag Kumar Ahuja
(CK)
Kartik Kalyanram
(K)
Kamakshi Kartik
(K)
Kumaran Kalyanram
(K)
Krishnaveni Ghattu
(K)
Murali Krishna
(M)
Rebecca Kuriyan
(R)
Sunita Simon Kurpad
(SS)
Sylvane Desrivieres
(S)
Gareth Barker
(G)
Udita Iyengar
(U)
Yuning Zhang
(Y)
Nilakshi Vaidya
(N)
Matthew Hickman
(M)
Jon Heron
(J)
Gwen Fernandes
(G)
Mireille Toledano
(M)
Dimitri Papadopoulos Orfanos
(DP)
Madhavi Rangaswamy
(M)
Gitanjali Narayanan
(G)
Urvakhsh Meherwan Mehta
(UM)
Paul Elliott
(P)
Satish Chandra Girimaji
(SC)
Madhu Khullar
(M)
Niranjan Khandelwal
(N)
Nainesh Joshi
(N)
None Amit
Debangana Bhattacharya
(D)
Bidisha Haque
(B)
Arpita Ghosh
(A)
Alisha Nagraj
(A)
Anirban Basu
(A)
Mriganka Mouli Pandit
(MM)
Subhadip Das
(S)
Anupa Yadav
(A)
Surajit Das
(S)
Sanjit Roy
(S)
Pawan Kumar Maurya
(PK)
Ningthoujam Debala Chanu
(ND)
Fujica M C
(F)
Victoria Ph
(V)
Celina Phurailatpam
(C)
Amritha Gourisankar
(A)
Geetha Rani
(G)
Sujatha B
(S)
Caroline Fall
(C)
Kiran K N
(K)
Ramya M C
(R)
Chaithra Urs
(C)
Santhosh N
(S)
Somashekhara R
(S)
Divyashree K
(D)
Arathi Rao
(A)
Poornima R
(P)
Saswathika Tripathy
(S)
Neha Parashar
(N)
Dhanalakshmi D
(D)
Nayana K B
(N)
Ashwini Kalkunte Seshadri
(AK)
Sathish Kumar
(S)
Thamodaran Arumugam
(T)
Apoorva Safai
(A)
Suneela Kuman Baligar
(SK)
Anthony Mary Cyril
(AM)
Aanchal Sharda
(A)
None Rashmitha
Ashika Anne Roy
(AA)
Shivamma D
(S)
Kiran L
(K)
Bhavana B R
(B)
Références
Bogdan R, Salmeron BJ, Carey CE, Agrawal A, Calhoun VD, Garavan H, et al. Imaging genetics and genomics in psychiatry: a critical review of progress and potential. Biol Psychiatry. 2017;82:165–75.
doi: 10.1016/j.biopsych.2016.12.030
Pine DS, Ernst M, Leibenluft E. Imaging–genetics applications in child psychiatry. J Am Acad Child Adolesc Psychiatry. 2010;49:772–82.
doi: 10.1016/j.jaac.2009.12.022
Viding E, Williamson DE, Hariri AR. Developmental imaging genetics: challenges and promises for translational research. Dev Psychopathol. 2006;18:877–92.
doi: 10.1017/S0954579406060433
Meyer-Lindenberg A. Imaging genetics of schizophrenia. Dialogues Clin Neurosci. 2010;12:449.
pubmed: 21319490
pmcid: 3181991
Scharinger C, Rabl U, Sitte HH, Pezawas L. Imaging genetics of mood disorders. Neuroimage. 2010;53:810–21.
doi: 10.1016/j.neuroimage.2010.02.019
Durston S. Imaging genetics in ADHD. Neuroimage. 2010;53:832–8.
doi: 10.1016/j.neuroimage.2010.02.071
Domschke K, Dannlowski U. Imaging genetics of anxiety disorders. Neuroimage. 2010;53:822–31.
doi: 10.1016/j.neuroimage.2009.11.042
Bigos KL, Weinberger DR. Imaging genetics—days of future past. Neuroimage. 2010;53:804–9.
doi: 10.1016/j.neuroimage.2010.01.035
Schumann G, Benegal V, Yu C, Tao S, Jernigan T, Heinz A, et al. Precision medicine and global mental health. Lancet Glob Health. 2019;7:e32.
doi: 10.1016/S2214-109X(18)30406-6
Thompson PM, Stein JL, Medland SE, Hibar DP, Vasquez AA, Renteria ME, et al. The ENIGMA Consortium: large-scale collaborative analyses of neuroimaging and genetic data. Brain Imaging Behav. 2014;8:153–82.
doi: 10.1007/s11682-013-9269-5
Griswold MG, Fullman N, Hawley C, Arian N, Zimsen SR, Tymeson HD, et al. Alcohol use and burden for 195 countries and territories, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet. 2018;392:1015–35.
doi: 10.1016/S0140-6736(18)31310-2
Manthey J, Shield KD, Rylett M, Hasan OS, Probst C, Rehm J. Global alcohol exposure between 1990 and 2017 and forecasts until 2030: a modelling study. The Lancet. 2019;393:2493–502.
World Health Organization. Global status report on alcohol and health. Geneva: World Health Organization Violence Against Women: Intimate partner and sexual; 2011.
Quinlan EB, Cattrell A, Jia T, Artiges E, Banaschewski T, Barker G, et al. Psychosocial stress and brain function in adolescent psychopathology. Am J Psychiatry. 2017;174:785–94.
doi: 10.1176/appi.ajp.2017.16040464
Barch DM, Belden AC, Tillman R, Whalen D, Luby JL. Early childhood adverse experiences, inferior frontal gyrus connectivity, and the trajectory of externalizing psychopathology. J Am Acad Child Adolesc Psychiatry. 2018;57:183–90.
doi: 10.1016/j.jaac.2017.12.011
Stacey D, Bilbao A, Maroteaux M, Jia T, Easton AC, Longueville S, et al. RASGRF2 regulates alcohol-induced reinforcement by influencing mesolimbic dopamine neuron activity and dopamine release. Proc Natl Acad Sci USA. 2012;109:21128–33.
doi: 10.1073/pnas.1211844110
Schumann G, Coin LJ, Lourdusamy A, Charoen P, Berger KH, Stacey D, et al. Genome-wide association and genetic functional studies identify autism susceptibility candidate 2 gene (AUTS2) in the regulation of alcohol consumption. Proc Natl Acad Sci USA. 2011;108:7119–24.
doi: 10.1073/pnas.1017288108
Dong L, Bilbao A, Laucht M, Henriksson R, Yakovleva T, Ridinger M, et al. Effects of the circadian rhythm gene period 1 (per1) on psychosocial stress-induced alcohol drinking. Am J Psychiatry. 2011;168:1090–8.
doi: 10.1176/appi.ajp.2011.10111579
Clarke T-K, Laucht M, Ridinger M, Wodarz N, Rietschel M, Maier W, et al. KCNJ6 is associated with adult alcohol dependence and involved in gene× early life stress interactions in adolescent alcohol drinking. Neuropsychopharmacology.2011;36:1142.
doi: 10.1038/npp.2010.247
Blomeyer D, Treutlein J, Esser G, Schmidt MH, Schumann G, Laucht M. Interaction between CRHR1 gene and stressful life events predicts adolescent heavy alcohol use. Biol Psychiatry. 2008;63:146–51.
doi: 10.1016/j.biopsych.2007.04.026
Venkata JA, Panicker AS. Prevalence of attention deficit hyperactivity disorder in primary school children. Indian J Psychiatry. 2013;55:338.
doi: 10.4103/0019-5545.120544
Pathak R, Sharma RC, Parvan UC, Gupta BP, Ojha RK, Goel N. Behavioural and emotional problems in school going adolescents. Australas Med J. 2011;4:15–21.
doi: 10.4066/AMJ.2011.464
Benegal V, Nayak M, Murthy P, Chandra P, Gururaj G, Obot I. Women and alcohol use in India. In: Alcohol, gender and drinking problems: perspectives from low and middle income countries. World Health Organisation, Geneva. 2005;89:123.
Charlson FJ, Baxter AJ, Cheng HG, Shidhaye R, Whiteford HA. The burden of mental, neurological, and substance use disorders in China and India: a systematic analysis of community representative epidemiological studies. Lancet. 2016;388:376–89.
doi: 10.1016/S0140-6736(16)30590-6
Khandelwal S, Reddy K. Eliciting a policy response for the rising epidemic of overweight‐obesity in India. Obes Rev. 2013;14:114–25.
doi: 10.1111/obr.12097
Varadharajan KS, Thomas T, Kurpad AV. Poverty and the state of nutrition in India. Asia Pac J Clin Nutr. 2013;22:326–39.
pubmed: 23945402
Jedrychowski WA, Perera FP, Majewska R, Camman D, Spengler JD, Mroz E, et al. Separate and joint effects of tranplacental and postnatal inhalatory exposure to polycyclic aromatic hydrocarbons: prospective birth cohort study on wheezing events. Pediatr Pulmonol. 2014;49:162–72.
doi: 10.1002/ppul.22923
McCrone P, Craig TK, Power P, Garety PA. Cost-effectiveness of an early intervention service for people with psychosis. Br J Psychiatry. 2010;196:377–82.
doi: 10.1192/bjp.bp.109.065896
Schumann G, Loth E, Banaschewski T, Barbot A, Barker G, Buchel C, et al. The IMAGEN study: reinforcement-related behaviour in normal brain function and psychopathology. Mol Psychiatry. 2010;15:1128–39.
doi: 10.1038/mp.2010.4
Toledano MB, Mutz J, Röösli M, Thomas MS, Dumontheil I, Elliott P. Cohort profile: the study of cognition, adolescents and mobile phones (SCAMP). Int J Epidemiol. 2018;48:25–6l.
doi: 10.1093/ije/dyy192
Boyd A, Golding J, Macleod J, Lawlor DA, Fraser A, Henderson J, et al. Cohort profile: the ‘children of the 90s’—the index offspring of the Avon Longitudinal Study of Parents and Children. Int J Epidemiol. 2013;42:111–27.
doi: 10.1093/ije/dys064
Duncan SC, Duncan TE, Hops H. Analysis of longitudinal data within accelerated longitudinal designs. Psycholog Methods 1996;1:236–48.
doi: 10.1037/1082-989X.1.3.236
Little TD, Rhemtulla M. Planned missing data designs for developmental researchers. Child Dev Perspect. 2013;7:199–204.
doi: 10.1111/cdep.12043
World Medical Association. World Medical Association Declaration of Helsinki. Ethical principles for medical research involving human subjects. Bulletin of the World Health Organization. 2001;79:373.
Houtepen LC, Heron J, Suderman MJ, Tilling K, Howe LD. Adverse childhood experiences in the children of the Avon Longitudinal Study of Parents and Children (ALSPAC). Wellcome Open Res. 2018;3:106–20.
Clark DB, Fisher CB, Bookheimer S, Brown SA, Evans JH, Hopfer C, Hudziak J, Montoya I, Murray M, Pfefferbaum A, Yurgelun-Todd D. Biomedical ethics and clinical oversight in multisite observational neuroimaging studies with children and adolescents: the ABCD experience. Developmental Cogn Neurosci. 2018;32:143–54.
doi: 10.1016/j.dcn.2017.06.005
Seaman SR, Bartlett JW, White IR. Multiple imputation of missing covariates with non-linear effects and interactions: an evaluation of statistical methods. BMC Med Res Methodol. 2012;12:46.
doi: 10.1186/1471-2288-12-46
Seaman SR, White IR, Copas AJ, Li L. Combining multiple imputation and inverse-probability weighting. Biometrics. 2012;68:129–37.
doi: 10.1111/j.1541-0420.2011.01666.x
Mills KL, Tamnes CK. Methods and considerations for longitudinal structural brain imaging analysis across development. Dev Cogn Neurosci. 2014;9:172–90.
doi: 10.1016/j.dcn.2014.04.004
Falk EB, Hyde LW, Mitchell C, Faul J, Gonzalez R, Heitzeg MM, et al. What is a representative brain? Neuroscience meets population science. Proc Natl Acad Sci USA. 2013;110:17615–22.
doi: 10.1073/pnas.1310134110
Matta TH, Flournoy JC, Byrne ML. Making an unknown unknown a known unknown: missing data in longitudinal neuroimaging studies. Dev Cogn Neurosci. 2018;33:83–98.
doi: 10.1016/j.dcn.2017.10.001
Reich D, Thangaraj K, Patterson N, Price AL, Singh L. Reconstructing Indian population history. Nature. 2009;461:489.
doi: 10.1038/nature08365
Thyreau B, Schwartz Y, Thirion B, Frouin V, Loth E, Vollstadt-Klein S, et al. Very large fMRI study using the IMAGEN database: sensitivity-specificity and population effect modeling in relation to the underlying anatomy. NeuroImage. 2012;61:295–303.
doi: 10.1016/j.neuroimage.2012.02.083