Evidence map›Paper›PMID 28588615›Full record

ArticleInternational journal of endocrinology2017

Relationship between Central Arterial Stiffness and Insulin Resistance in Chinese Community-Dwelling Population without Diabetes Mellitus.

Shihui Fu, Ying Lin, Leiming Luo, Ping Ye

Open access · goldAbstract read
In one paragraph

Article in International journal of endocrinology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.4field-weighted citation impact, top 36% 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

4 citing papers in PubMed, 9 citations in OpenAlex.

  1. Article
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  3. Review
  4. 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 2 institutions in 1 country.

Shihui FuDepartment of Geriatric Cardiology, Chinese People's Liberation Army General Hospital, Beijing, China.ORCID 0000-0001-6707-9049
Ying LinDepartment of Cardiology and Hainan Branch, Chinese People's Liberation Army General Hospital, Beijing, China.
Leiming LuoDepartment of Geriatric Cardiology, Chinese People's Liberation Army General Hospital, Beijing, China.ORCID 0000-0002-3123-9407
Ping YeDepartment of Geriatric Cardiology, Chinese People's Liberation Army General Hospital, Beijing, China.ORCID 0000-0002-6624-0627
Chinese People's Liberation Army · CNHainan 301 Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveInsulin resistance (IR) is a pathological condition present not only in patients with type 2 diabetes mellitus (DM), but also in community-dwelling population without DM. Both central arterial stiffness and IR are closely correlated with cardiovascular morbidity and mortality. The relationship between central arterial stiffness and IR has not been described in Chinese community-dwelling population without DM. The current analysis was designed to investigate the relationship between central arterial stiffness and IR in Chinese community-dwelling population without DM.

methodsThere were 1150 participants fully assessed for not only homeostasis model assessment of insulin resistance (HOMA-IR) but also carotid-femoral pulse wave velocity (cfPWV).

resultsMedian age was 39 (18-80) years, and 69.7% were men. Bivariate correlation analysis showed that cfPWV was significantly related to HOMA-IR (

conclusionsThis community-based analysis testified that the relationship between central arterial stiffness and IR was evident as early as during nondiabetic stage. Early interventions in Chinese community-dwelling population without DM to improve the IR are also important in the prevention of cardiovascular diseases.

Identifiers

PMID28588615
PMCPMC5446882
OpenAlexW2612316940

What Socratic 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 Socratic graph.