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Record W2992440468 · doi:10.1038/s41380-019-0614-y

Genetic risk for major depressive disorder and loneliness in sex-specific associations with coronary artery disease

2019· article· en· W2992440468 on OpenAlexfundno aff
Jessica Dennis, Julia Sealock, Rebecca T. Levinson, Eric Farber‐Eger, Jacob Franco, Sarah Fong, Péter Straub, Donald Hucks, Wen‐Liang Song, MacRae F. Linton, Pierre Fontanillas, Sarah L. Elson, Douglas M. Ruderfer, Abdel Abdellaoui, Sandra Sanchez‐Roige, Abraham A. Palmer, Dorret I. Boomsma, Nancy J. Cox, Guanhua Chen, Jonathan D. Mosley, Quinn S. Wells, Lea K. Davis

Bibliographic record

VenueMolecular Psychiatry · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsnot available
FundersCIHR Skin Research Training CentreNational Center for Advancing Translational SciencesNational Human Genome Research InstituteNational Institute on Drug AbuseNational Institute of Mental HealthNational Heart, Lung, and Blood InstituteNational Institute on AgingAmerican Heart AssociationCanadian Institutes of Health ResearchVanderbilt University Medical CenterVanderbilt UniversityNational Institute on Minority Health and Health DisparitiesNational Center for Research ResourcesTobacco-Related Disease Research ProgramU.S. Department of Health and Human ServicesNational Institutes of HealthGovernment of Canada
KeywordsLonelinessMajor depressive disorderMedicineGenome-wide association studyOdds ratioCoronary artery diseaseComorbidityGenetic associationClinical psychologyInternal medicinePsychiatryGeneticsSingle-nucleotide polymorphismBiologyGenotype

Abstract

fetched live from OpenAlex

Abstract Major depressive disorder (MDD) and loneliness are phenotypically and genetically correlated with coronary artery disease (CAD), but whether these associations are explained by pleiotropic genetic variants or shared comorbidities is unclear. To tease apart these scenarios, we first assessed the medical morbidity pattern associated with genetic risk factors for MDD and loneliness by conducting a phenome-wide association study in 18,385 European-ancestry individuals in the Vanderbilt University Medical Center biobank, BioVU. Polygenic scores for MDD and loneliness were developed for each person using previously published meta-GWAS summary statistics, and were tested for association with 882 clinical diagnoses ascertained via billing codes in electronic health records. We discovered strong associations with heart disease diagnoses, and next embarked on targeted analyses of CAD in 3893 cases and 4197 controls. We found odds ratios of 1.11 (95% CI, 1.04–1.18; P 8.43 × 10 −4 ) and 1.13 (95% CI, 1.07–1.20; P 4.51 × 10 −6 ) per 1-SD increase in the polygenic scores for MDD and loneliness, respectively. Results were similar in patients without psychiatric symptoms, and the increased risk persisted in females even after adjusting for multiple conventional risk factors and a polygenic score for CAD. In a final sensitivity analysis, we statistically adjusted for the genetic correlation between MDD and loneliness and re-computed polygenic scores. The polygenic score unique to loneliness remained associated with CAD (OR 1.09, 95% CI 1.03–1.15; P 0.002), while the polygenic score unique to MDD did not (OR 1.00, 95% CI 0.95–1.06; P 0.97). Our replication sample was the Atherosclerosis Risk in Communities (ARIC) cohort of 7197 European-ancestry participants (1598 incident CAD cases). In ARIC, polygenic scores for MDD and loneliness were associated with hazard ratios of 1.07 (95% CI, 0.99–1.14; P = 0.07) and 1.07 (1.01–1.15; P = 0.03), respectively, and we replicated findings from the BioVU sensitivity analyses. We conclude that genetic risk factors for MDD and loneliness act pleiotropically to increase CAD risk in females.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.427

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.008
GPT teacher head0.271
Teacher spread0.263 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations56
Published2019
Admission routes1
Has abstractyes

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