Evidence map›Paper›PMID 26347444›Full record

ArticleDiabetes, metabolic syndrome and obesity : targets and therapy2015

Metabolic abnormalities in pituitary adenoma patients: a novel therapeutic target and prognostic factor.

Xin Zheng, Song Li, Wei-Hua Zhang, Hui Yang

Open access · goldAbstract read
In one paragraph

Article in Diabetes, metabolic syndrome and obesity : targets and therapy, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact, top 96% 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

7 citing papers in PubMed, 13 citations in OpenAlex.

  1. Article
  2. Observational
  3. Article
  4. Article
  5. Article
  6. Review
  7. 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

4 authors at 1 institution in 1 country.

Xin ZhengDepartment of Neurosurgery, Xinqiao Hospital, Third Military Medical University, Shapingba District, Chongqing, People's Republic of China.
Song LiDepartment of Neurosurgery, Xinqiao Hospital, Third Military Medical University, Shapingba District, Chongqing, People's Republic of China.
Wei-Hua ZhangDepartment of Biology and Biochemistry, University of Houston, Houston, TX, USA.
Hui YangDepartment of Neurosurgery, Xinqiao Hospital, Third Military Medical University, Shapingba District, Chongqing, People's Republic of China.
University of Houston · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Metabolic abnormalities are common in cancers, and targeting metabolism is emerging as a novel therapeutic approach to cancer management. Pituitary adenoma (PA) is a type of benign tumor. Impairment of tumor cells' metabolism in PA seems not to be as apparent as that of other malignant tumor cells; however, aberrant hormone secretion is conspicuous in most PAs. Hormones have direct impacts on systemic metabolism, which in turn, may affect the progression of PA. Nowadays, conventional therapeutic strategies for PA do not include modalities of adjusting whole-body metabolism, which is most likely due to the current consideration of the aberrant whole-body metabolism of PA patients as a passive associated symptom and not involved in PA progression. Because systemic metabolic abnormalities are presented by 22.3%-52.5% PA patients and are closely correlated with disease progression and prognosis, we propose that assessment of metabolic status should be emphasized during the treatment of PA and that control of metabolic abnormalities should be added into the current therapies for PA.

Indexed as

metabolic abnormalitiespituitary adenomaprognostic factortherapeutic target

Identifiers

PMID26347444
PMCPMC4531036
OpenAlexW1653399092

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.