ArticleJournal of diabetes and metabolic disorders2021
Body impedance analyzer and anthropometric indicators; predictors of metabolic syndrome.
Article in Journal of diabetes and metabolic disorders, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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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.
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Who cites it
5 citing papers in PubMed, 19 citations in OpenAlex.
- Predictors of Metabolic Syndrome in Polish Women-The Role of Body Composition and Sociodemographic Factors.Journal of clinical medicine · 2025Article
- Supplementation with essential amino acids in the early stage of carbohydrate reintroduction after a very-low energy ketogenic therapy (VLEKT) improves body cell mass, muscle strength and inflammation.Journal of translational medicine · 2025Article
- Association of Short Sleep Duration and Obstructive Sleep Apnea with Central Obesity: A Retrospective Study Utilizing Anthropometric Measures.Nature and science of sleep · 2024Article
- Disparities in the prevalence of metabolic syndrome between Iranian industrial workers and university staff.Journal of diabetes and metabolic disorders · 2023Article
- Metabolic syndrome; Definition, Pathogenesis, Elements, and the Effects of medicinal plants on it's elements.Journal of diabetes and metabolic disorders · 2022Review
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Aim: Metabolic syndrome is one of the outcomes of a sedentary lifestyle in the modern world. In this study, we want to introduce the predictors of metabolic syndrome using anthropometric indices and Bio-Electrical Impedance Analysis (BIA) test values. Method: This cross-sectional study was performed on 2284 employees of Tehran University of Medical Sciences in different job categories. Metabolic syndrome was determined according to IDF criteria. Anthropometric dimensions, para-clinical tests, basic information were collected from the participants. Also, the body analysis of the participants was performed using a BIA method. Result: The prevalence of metabolic syndrome in this study was 23.2% based on IDF criteria, which was 21% and 26.6% in men and women, respectively. The most important factor among the components of IDF criteria was HDL deficiency. In this study, neck circumference, fat mass, visceral fat, muscle mass percentage and waist to height ratio were observed as predictors of metabolic syndrome. Conclusion: This study realized that there is association between fat mass, fat-free mass, visceral fat and muscle mass which all are some elements of body composition analysis and metabolic syndrome as a major health issue.
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