Evidence map›Paper›PMID 31022775›Full record

ArticlePsycho-oncology2019

Depression and inflammation among epidermal growth factor receptor (EGFR) mutant nonsmall cell lung cancer patients.

Daniel C McFarland, Devika R Jutagir, Barry Rosenfeld, William Pirl, Andrew H Miller, William Breitbart, Christian Nelson

Abstract read
In one paragraph

Article in Psycho-oncology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
0.9field-weighted citation impact, top 23% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed, 17 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Investigating the Underlying Mechanisms ofMolecules (Basel, Switzerland) · 2023
    Article
  6. Article
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  9. Exploring the Mechanism of Action of Herbal Medicine (Evidence-based complementary and alternative medicine : eCAM · 2021
    Article
  10. Tumor Mutation Burden and Depression in Lung Cancer: Association With Inflammation.Journal of the National Comprehensive Cancer Network : JNCCN · 2020
    Article
  11. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 4 institutions in 1 country.

Daniel C McFarlandDepartment of Psychiatry and Behavioral Sciences, Memorial Sloan Kettering Cancer Center, New York, New York.ORCID 0000-0002-5500-1591
Devika R JutagirDepartment of Psychiatry and Behavioral Sciences, Memorial Sloan Kettering Cancer Center, New York, New York.
Barry RosenfeldDepartment of Psychology, Fordham University, Bronx, New York.ORCID 0000-0003-1399-2777
William PirlDepartment of Psychosocial Oncology and Palliative Care, Dana-Farber Cancer Institute, Boston, Massachusetts.
Andrew H MillerDepartment of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, Georgia.
William BreitbartDepartment of Psychiatry and Behavioral Sciences, Memorial Sloan Kettering Cancer Center, New York, New York.
Christian NelsonDepartment of Psychiatry and Behavioral Sciences, Memorial Sloan Kettering Cancer Center, New York, New York.
Memorial Sloan Kettering Cancer Center · USDana-Farber Cancer Institute · USEmory University · USFordham University · US

Funding

X-RAY CRYSTALLOGRAPHYP30CA008748 · NCI · SLOAN-KETTERING INSTITUTE FOR CANCER RES · PI Nicholas D Socci · 1985 to 2026
$347.4M
Psychosocial Palliative & Community Research in CancerT32CA009461 · NCI · SLOAN-KETTERING INSTITUTE FOR CANCER RES · PI JENNIFER L HAY, Jamie S. Ostroff · 1985 to 2026
$10.1M
NCI NIH HHS P30 CA008748NCI NIH HHS T32 CA009461
6 · The paper itself

Abstract

objectiveDepression is highly prevalent in nonsmall cell lung cancer (NSCLC) and is associated with elevated inflammation. However, certain subtypes of driver mutation-associated NSCLC such as epidermal growth factor receptor (EGFR)-mutated NSCLC may be associated with less depression given the differences in their underlying biology and disease trajectories. Biological variables such as inflammation, measured by C-reactive protein (CRP), may provide insight into depression variability in EGFR mutant NSCLC.

methodsPatients with EGFR mutant and wild-type metastatic NSCLC were evaluated for depression using the Hospital Anxiety and Depression Scale (HADS) on a continuous scale and meeting depression screening criteria (HADS ≥ 8). Inflammation was measured using CRP. A mediation model was created to understand how inflammation mediates EGFR wild-type associated depression.

resultsOne hundred out of 120 patients with NSCLC were recruited (83.3% response rate). The 20 participants with EGFR mutant NSCLC had less depression (HADS-D 3.0 versus 5.4) (P < .001), met depression screening criteria less often (P = .047), and exhibited less inflammation (CRP = 0.23 mg/mL versus 2.71 mg/mL) (P < .001) in comparison with EGFR wild-type NSCLC. Multivariate linear regression model revealed that only CRP predicted depression (P = .015) while controlling for age and sex. Mediation analysis found that lower CRP partially mediated less depression in EGFR mutant NSCLC.

conclusionsEGFR mutant NSCLC is associated with less depression but the relationship is partially mediated by lower CRP-related inflammation, which is a stronger predictor of depression than EGFR status. Depression in lung cancer varies by subtype and is significantly related to inflammation.

Indexed as

AdultCarcinoma, Non-Small-Cell LungC-Reactive ProteinDepressionErbB ReceptorsFemaleHumansInflammationLung NeoplasmsMaleMiddle AgedC-Reactive ProteinErbB ReceptorsC-reactive proteindepressionEGFR mutant nonsmall cell lung cancerinflammationnonsmall cell lung cancertumor mutation burden

Identifiers

PMID31022775
PMCPMC6610756
OpenAlexW2941204057

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.