Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 223

The DOI matches its Crossref record.

A Review of the Evidence for a Neuroendocrine Link Between Stress, Depression and Diabetes Mellitus

Golden, Sherita·2007·Current Diabetes Reviews

Abstract

Obesity and type 2 diabetes continue to be major public health burdens with type 2 diabetes rising in epidemic proportions. Since known risk factors do not explain all of the variance in the population, it is important to identify novel risk factors that can lead to development of new preventive measures. Chronic psychological stress and depression are associated with type 2 diabetes but the mechanism remains unclear. Neuroendocrine changes induced by these stressors, specifically activation of the hypothalamic-pituitary-adrenal (HPA) axis and sympathetic nervous system (SNS), might provide a unifying explanation. The objectives of this review are (1) to summarize the metabolic impact of HPA axis and SNS dysfunction induced by depression and stress, (2) to summarize the relation of neuroendocrine parameters to risk factors for diabetes, (3) to discuss the limitations of assessing neuroendocrine function in population-based and intervention studies, and (4) to summarize the evidence of the impact of stress reduction, by cognitive behavior therapy (CBT), on neuroendocrine factors and on outcomes in diabetes and obesity.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.2174/157339907782330021

Cites

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cited by

See all 223

Metadata

Authors
Golden, S.
Venue
Current Diabetes Reviews
Published
2007-11-01
Volume
3
Issue
4
Pages
252-259
ISSN
1573-3998 (ISSN-L)
OpenAlex
W2088142711
Cited by
223
References
0
FWCI
4.59
Type
journal article
Language
en
Open access
closed

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: A Review of the Evidence for a Neuroendocrine Link Between Stress, Depression and Diabetes Mellitus. https://app.cochat.ai/papers/10.2174/157339907782330021
The paper
Golden, S. (2007). A Review of the Evidence for a Neuroendocrine Link Between Stress, Depression and Diabetes Mellitus. Current Diabetes Reviews. https://doi.org/10.2174/157339907782330021

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.