Evidence map›Paper›PMID 38961254›Full record

ArticleScientific reports2024

The predictive capability of several anthropometric indices for identifying the risk of metabolic syndrome and its components among industrial workers.

Ekaterina D Konstantinova, Tatiana A Maslakova, Svetlana Yu Ogorodnikova

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Observational
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. 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

3 authors.

Ekaterina D KonstantinovaInstitute of Industrial Ecology of Ural Branch of the Russian Academy of Sciences, S. Kovalevskaya Str., 20, Ekaterinburg, 620990, Russian Federation. konstantinovaekateri@gmail.com.ORCID 0000-0002-2260-744X
Tatiana A MaslakovaInstitute of Industrial Ecology of Ural Branch of the Russian Academy of Sciences, S. Kovalevskaya Str., 20, Ekaterinburg, 620990, Russian Federation.ORCID 0000-0001-6642-9027
Svetlana Yu OgorodnikovaInstitute of Industrial Ecology of Ural Branch of the Russian Academy of Sciences, S. Kovalevskaya Str., 20, Ekaterinburg, 620990, Russian Federation.ORCID 0000-0001-7193-3607

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Metabolic syndrome (MetS) is closely associated with adverse cardiometabolic outcomes. The objective of this study was to identify practical methods that could enable the effective identification of MetS based on anthropometric indices. The basis of our study involved retrospective database obtained from routine medical prophylactic examinations. This was a cross-sectional study on the health status of male workers employed in hazardous working conditions at industrial enterprises in the Ural region conducted in 2019. A total of 347 male workers employed under hazardous working conditions were investigated. The presence of MetS was established by a healthcare professional in accordance with the guidelines of the International Diabetes Federation (IDF). Simple linear regression was used to evaluate the associations between anthropometric indices and MetS incidence. Logistic regression was used to determine the odds ratios of MetS in relation to increases in anthropometric indices. ROC curves were calculated to compare the ability of each anthropometric index to predict MetS and to determine the diagnostic thresholds of the indicators considered. According to the IDF criteria, 36.3% of the workers had MetS. A direct relationship was found between the individual components of MetS and the anthropometric indices studied. The highest OR was shown by the Body Roundness Index (BRI) of 2.235 (95% CI 1.796-2.781). For different age quartiles, the optimal cut-off values for predicting MetS were as follows: BRI, 4.1-4.4 r.u.; body shape index (ABSI), 0.080-0.083 m

Indexed as

AnthropometryMetabolic SyndromeAdultCross-Sectional StudiesHumansIndustryMaleMiddle AgedRetrospective StudiesRisk FactorsROC CurveAnthropometric indexLogistic regressionMetabolic syndromeROC analyses

Identifiers

PMID38961254
PMCPMC11222529

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.