Examining time-varying dynamics of co-occurring depressed mood and anxiety
(2024)Journal of Affective Disorders,362,pp.24-35.
Piccirillo, M.L.ab, Frumkin, M.R.cd, Spink, K.M.a, Tonge, N.A.e, Foster, K.T.af
aUniversity of Washington, Department of Psychology, United States
bRutgers Robert Wood Johnson Medical School, Department of Psychiatry, United States
cWashington University in St. Louis, Department of Psychology and Brain Sciences, United States
dMassachusetts General Hospital, Department of Psychiatry, United States
eGeorge Mason University, Department of Psychology, United States
fUniversity of Washington, Department of Global Health, United States
Abstract
Background: Dimensional frameworks of psychopathology call for multivariate approaches to map co-occurring disorders to index what symptoms emerge when and for whom. Ecological momentary assessment (EMA) offers a method for assessing and differentiating the dynamics of co-occurring symptoms with greater temporal granularity and naturalistic context. The present study used multivariate mixed effects location-scale modeling to characterize the time-varying dynamics of depressed mood and anxiety for women diagnosed with social anxiety disorder (SAD) and major depression (MDD). Methods: Women completed five daily EMA surveys over 30 days (150 EMA surveys/woman, T ≈ 5250 total observations) and two clinical diagnostic and retrospective self-report measures administered approximately two months apart. Results: There was evidence of same-symptom lagged effects (bs = 0.08–0.09), but not cross-symptom lagged effects (bs < 0.01) during EMA. Symptoms co-varied such that momentary spikes from one’s typical level of anxiety were associated with increases in momentary depressed mood (b = 0.19) and greater variability of depressed mood (b = 0.06). Similarly, spikes from one’s typical levels of depressed mood were associated with increases in momentary anxiety (b = 0.19). Furthermore, the presence and magnitude of effects demonstrated person-specific heterogeneity. Limitations: Our findings are constrained to the dynamics of depressed and anxious mood among cisgender women with primary SAD and current or past MDD. Conclusions: Findings from this work help to characterize how daily experiences of co-occurring mood and anxiety fluctuate and offer insight to aid the development of momentary, person-specific interventions designed to regulate symptom fluctuations. © 2024
Author Keywords
Affective dynamics; Ecological momentary assessment; Heterogeneity; HiTOP; Internalizing
Funding details
National Science FoundationNSF
National Institutes of HealthNIHF31MH115641,K99AA029459,T32AA007455,F31MH124291
National Institutes of HealthNIH
Document Type:Article
Publication Stage:Final
Source:Scopus
Multi-scale signaling and tumor evolution in high-grade gliomas
(2024)Cancer Cell,42(7),pp.1217-1238.e19.
Liu, J.ab, Cao, S.ab, Imbach, K.J.cd, Gritsenko, M.A.e, Lih, T.-S.M.f, Kyle, J.E.e, Yaron-Barir, T.M.ghi, Binder, Z.A.j, Li, Y.ab, Strunilin, I.ab, Wang, Y.-T.e, Tsai, C.-F.e, Ma, W.k, Chen, L.f, Clark, N.M.l, Shinkle, A.ab, Naser Al Deen, N.ab, Caravan, W.ab, Houston, A.ab, Simin, F.A.ab, Wyczalkowski, M.A.ab, Wang, L.-B.ab, Storrs, E.ab, Chen, S.ab, Illindala, R.amn, Li, Y.D.amn, Jayasinghe, R.G.ab, Rykunov, D.k, Cottingham, S.L.o, Chu, R.K.p, Weitz, K.K.e, Moore, R.J.e, Sagendorf, T.e, Petyuk, V.A.e, Nestor, M.e, Bramer, L.M.e, Stratton, K.G.e, Schepmoes, A.A.e, Couvillion, S.P.e, Eder, J.e, Kim, Y.-M.e, Gao, Y.e, Fillmore, T.L.o, Zhao, R.e, Monroe, M.E.e, Southard-Smith, A.N.ab, Li, Y.E.qr, Jui-Hsien Lu, R.ab, Johnson, J.L.g, Wiznerowicz, M.st, Hostetter, G.u, Newton, C.J.u, Ketchum, K.A.v, Thangudu, R.R.v, Barnholtz-Sloan, J.S.w, Wang, P.k, Fenyö, D.xy, An, E.z, Thiagarajan, M.aa, Robles, A.I.z, Mani, D.R.l, Smith, R.D.e, Porta-Pardo, E.c, Cantley, L.C.gabac, Iavarone, A.adae, Chen, F.am, Mesri, M.z, Nasrallah, M.P.af, Zhang, H.fagah, Resnick, A.C.aiaj,Chheda, M.G.amn, Rodland, K.D.ak, Liu, T.e, Ding, L.abmq, Philadelphia Coalition for a Cureal, Clinical Proteomic Tumor Analysis Consortiumal
aDepartment of Medicine, Washington University in St. Louis, St. Louis, MO 63110, United States
bMcDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, United States
cJosep Carreras Leukaemia Research Institute, Badalona, Spain
dUniversidad Autónoma de Barcelona, Barcelona, Bellaterra, 08193, Spain
eBiological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, United States
fDepartment of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, United States
gMeyer Cancer Center, Weill Cornell Medicine, New York, NY 10021, United States
hEnglander Institute for Precision Medicine, Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY 10021, United States
iColumbia University Vagelos College of Physicians and Surgeons, New York, NY 10032, United States
jDepartment of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States
kDepartment of Genetics and Genomic Sciences, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
lThe Broad Institute of MIT and Harvard, Cambridge, MA 02142, United States
mSiteman Cancer Center, Washington University in St. Louis, St. Louis, MO 63130, United States
nDepartment of Neurology, Washington University in St. Louis, St. Louis, MO 63130, United States
oDepartment of Pathology, Spectrum Health and Helen DeVos Children’s Hospital, Grand Rapids, MI, United States
pEnvironmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA 99354, United States
qDepartment of Genetics, Washington University School of Medicine, St. Louis, MO 63110, United States
rDepartment of Neurosurgery, Washington University School of Medicine, St. Louis, MO 63110, United States
sInternational Institute for Molecular Oncology, Poznań, Poland
tPoznan University of Medical Sciences, Poznań, Poland
uVan Andel Research Institute, Grand Rapids, MI, United States
vICF, 530 Gaither Road Suite 500, Rockville, MD 20850, United States
wCenter for Biomedical Informatics and Information Technology & Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20850, United States
xInstitute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, United States
yDepartment of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, United States
zOffice of Cancer Clinical Proteomics Research, National Cancer Institute, Rockville, MD 20850, United States
aaFrederick National Laboratory for Cancer Research, Frederick, MD 21701, United States
abDepartment of Cell Biology, Harvard Medical School, Boston, MA 02115, United States
acDana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02215, United States
adDepartment of Neurological Surgery and Department of Biochemistry, University of Miami Miller School of Medicine, Miami, FL 33136, United States
aeSylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, United States
afDepartment of Pathology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States
agDepartment of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, United States
ahDepartment of Urology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, United States
aiCenter for Data Driven Discovery in Biomedicine, The Children’s Hospital of Philadelphia, Philadelphia, PA 19104, United States
ajDivision of Neurosurgery, The Children’s Hospital of Philadelphia, Philadelphia, PA 19104, United States
akDepartment of Cell, Developmental, and Cancer Biology, Oregon Health & Science University, Portland, OR 97221, United States
Abstract
Although genomic anomalies in glioblastoma (GBM) have been well studied for over a decade, its 5-year survival rate remains lower than 5%. We seek to expand the molecular landscape of high-grade glioma, composed of IDH-wildtype GBM and IDH-mutant grade 4 astrocytoma, by integrating proteomic, metabolomic, lipidomic, and post-translational modifications (PTMs) with genomic and transcriptomic measurements to uncover multi-scale regulatory interactions governing tumor development and evolution. Applying 14 proteogenomic and metabolomic platforms to 228 tumors (212 GBM and 16 grade 4 IDH-mutant astrocytoma), including 28 at recurrence, plus 18 normal brain samples and 14 brain metastases as comparators, reveals heterogeneous upstream alterations converging on common downstream events at the proteomic and metabolomic levels and changes in protein-protein interactions and glycosylation site occupancy at recurrence. Recurrent genetic alterations and phosphorylation events on PTPN11 map to important regulatory domains in three dimensions, suggesting a central role for PTPN11 signaling across high-grade gliomas. © 2024 The Authors
Author Keywords
CPTAC; glioblastoma; glycoproteomics; lipidome; metabolome; proteomics; single nuclei ATAC-seq; single nuclei RNA-seq; tumor recurrence
Funding details
Merck
Pacific Northwest National LaboratoryPNNL
U.S. Department of EnergyUSDOE
Orbus Therapeutics
BattelleBMI
DE-AC05-76RL01830
LABAE20038PORT
P41-GM103311
National Institutes of HealthNIHRYC2019-026415-I,PID2019- 107043RA-I00
National Institutes of HealthNIH
National Human Genome Research InstituteNHGRIR01NS107833,R01NS117149
National Human Genome Research InstituteNHGRI
R01HG009711
Document Type:Article
Publication Stage:Final
Source:Scopus
