Evidence map›Paper›PMID 26825882›Full record

SynthesisMedicine2016

Significantly Higher Peripheral Insulin-Like Growth Factor-1 Levels in Patients With Major Depressive Disorder or Bipolar Disorder Than in Healthy Controls: A Meta-Analysis and Review Under Guideline of PRISMA.

Kun-Yu Tu, Ming-Kung Wu, Yen-Wen Chen, Pao-Yen Lin, Hung-Yu Wang, Ching-Kuan Wu, Ping-Tao Tseng

Open access · goldAbstract readMeta-AnalysisReview
In one paragraph

Synthesis in Medicine, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed, 3 pooled it
4.0field-weighted citation impact, top 6% 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

27 citing papers in PubMed, 3 syntheses or guidelines pooled it, 47 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Article
  5. Premature aging in serious mental illness.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2026
    Review
  6. Review
  7. Review
  8. Article
  9. Article
  10. Article
  11. Review
  12. Article
  13. Article
  14. Review
  15. Potential Candidates for Biomarkers in Bipolar Disorder: A Proteomic Approach through Systems Biology.Clinical psychopharmacology and neuroscience : the official scientific journal of the Korean College of Neuropsychopharmacology · 2022
    Review
  16. Article
  17. Article
  18. Article
  19. Review
  20. Peripheral Markers of Depression.Journal of clinical medicine · 2020
    Review
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 1 institution in 1 country.

Kun-Yu TuFrom the Department of Psychiatry, Tsyr-Huey Mental Hospital, Kaohsiung Jen-Ai's Home, Taiwan (K-YT, H-YW, C-KW, P-TT); Department of Neurology, E-Da Hospital, Kaohsiung, Taiwan (Y-WC); Department of Psychiatry, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung, Taiwan (M-KW, P-YL); and Center for Translational Research in Biomedical Sciences, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung, Taiwan (P-YL).
Ming-Kung Wu
Yen-Wen Chen
Pao-Yen Lin
Hung-Yu Wang
Ching-Kuan Wu
Ping-Tao Tseng
Kaohsiung Chang Gung Memorial Hospital · TW

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

An increasing amount of research has focused on insulin-like growth factor-1 (IGF-1) because of multiple neurotrophic effects, including neurogenesis, remyelination, and synaptogenesis. In addition, IGF-1 can mediate an antidepressant effect in patients with major affective disorder, and its levels in the cerebrospinal fluid have been found to vary with antidepressant treatment. Furthermore, it has been proven to crossover the blood-brain barrier, with a reciprocal feedback loop being the central effect. However, recent studies have reported inconclusive findings about the role of IGF-1 in major affective disorder. The aim of the current study was to conduct a thorough meta-analysis of changes in peripheral IGF-1 levels in patients with major depressive disorder (MDD) or bipolar disorder (BD). We conducted a thorough literature search and compared peripheral IGF-1 levels in patients with MDD or BD and in healthy controls, and investigated clinical variables through meta-regression. Electronic research was conducted through platform of PubMed. We used inclusion criteria as clinical trials discussing comparisons of peripheral IGF-1 protein levels in patients with MDD or BD and those in healthy controls. We analyzed the cases from 9 studies with the random-effect model. The main finding was that peripheral IGF-1 levels in the patients were significantly higher than in the healthy controls (P < 0.001), with a significant inverse association with duration of illness (P = 0.03). In meta-analysis comparing peripheral IGF-1 levels in patients with BD or MDD before and after treatment, there was no significant change in peripheral IGF-1 levels after treatment (P = 0.092). The small numbers of studies and lack of correlation data with growth hormone in current studies are the main limitations of this meta-analysis. Our results indicated that peripheral IGF-1 levels may not be an indicator of disease severity, but may be a disease trait marker or an indicator of cognition. However, further investigations on the correlation between cognitive function and peripheral IGF-1 levels are needed to explore the role of IGF-1 in the pathophysiology of MDD and BD.

Indexed as

Bipolar DisorderCase-Control StudiesFastingHumansInsulin-Like Growth Factor IMajor Depressive DisorderTime FactorsInsulin-Like Growth Factor I

Identifiers

PMID26825882
PMCPMC5291552
OpenAlexW2334471942

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.