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Dynamic behavior of metabolic syndrome progression: a comprehensive systematic review on recent discoveries

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Bagheri, Pezhman, Khalili, Davood, Seif, Mozhgan, Rezaianzadeh, Abbas·2021·BMC Endocrine Disorders
DOI 10.1186/s12902-021-00716-7PMC PMC7986266
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Abstract

Background: The assessment of the natural history of metabolic syndrome (MetS) has an important role in clarifying the pathways of this disorder. Objective: This study purposed to provide a rational statistical view of MetS progression pathway. Methods: We performed a systematic review in accordance with the PRISMA Statement until September 2019 in the Medline/PubMed, Scopus, Embase, Web of Science and Google Scholar databases. From the 68 found studies, 12 studies were eligible for review finally. Results: The selected studies were divided in 2 groups with Markovian and non-Markovian approach. With the Markov approach, the most important trigger for the MetS chain was dyslipidemia with overweight/obesity in the under-50 and with hypertension in the over-50 age group, where overweight/obesity was more important in women and hypertension in men. In non-Markov approach, the most common trigger was hypertension. Transition probability (TP) from no component to MetS were higher in all Markovian studies in men than in women. In the Markovians the combination of dyslipidemia with overweight/obesity and in non-Markovians, hyperglycemia with overweight/obesity were the most common combinations. Finally, the most important components, which predict the MetS, were 2-component states and hyperglycemia in Markovian approach and overweight/obesity in non-Markovians. Conclusions: Among the components of the MetS, dyslipidemia and hypertension seems to be the main developer components in natural history of the MetS. Also, in this chain, the most likely combination over time that determines the future status of people seems to be the combination of dyslipidemia with obesity or hyperglycemia. However, more research is needed.

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  • Waist circumference indicates central obesity, a key part of metabolic syndrome.

    “Gender affects hormonal and metabolic functions, age is related to metabolic changes, height is associated with body proportions and health, and waist circumference (WC) indicates central obesity, a key part of MetS [12, 13].”

    From Machine learning with the body roundness index and associated indicators: a new approach to predicting metabolic syndrome (2025) · cites this paper as [13]

    The Background identifies central obesity as a component of metabolic syndrome. Table 6 in the Results lists waist circumference as a predictor of MetS development, but the passages do not explicitly say that waist circumference indicates central obesity. (LLM, conf=0.78)

    Experimental — accuracy not yet measured

  • Gender affects hormonal and metabolic functions.

    “Gender affects hormonal and metabolic functions, age is related to metabolic changes, height is associated with body proportions and health, and waist circumference (WC) indicates central obesity, a key part of MetS [12, 13].”

    From Machine learning with the body roundness index and associated indicators: a new approach to predicting metabolic syndrome (2025) · cites this paper as [13]

  • Age is related to metabolic changes.

    “Gender affects hormonal and metabolic functions, age is related to metabolic changes, height is associated with body proportions and health, and waist circumference (WC) indicates central obesity, a key part of MetS [12, 13].”

    From Machine learning with the body roundness index and associated indicators: a new approach to predicting metabolic syndrome (2025) · cites this paper as [13]

  • Height is associated with body proportions and health.

    “Gender affects hormonal and metabolic functions, age is related to metabolic changes, height is associated with body proportions and health, and waist circumference (WC) indicates central obesity, a key part of MetS [12, 13].”

    From Machine learning with the body roundness index and associated indicators: a new approach to predicting metabolic syndrome (2025) · cites this paper as [13]

    The Results and Discussion passages address age, sex, obesity, and waist circumference, but do not discuss height’s association with body proportions or health. Searches for “height body proportions health” and “height stature body proportions” found no passage addressing that claim. (LLM, conf=0.74)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Bagheri, P., Khalili, D., Seif, M., & Rezaianzadeh, A.
Affiliations
  • Shiraz University of Medical Sciences (IR)
  • Research Institute for Endocrine Sciences (IR)
Corresponding author
Rezaianzadeh, Abbas
ORCID
Venue
BMC Endocrine Disorders
Publisher
Springer Science and Business Media LLC
Published
2021-03-22
Volume
21
Issue
1
Pages
54-54
ISSN
1472-6823 (ISSN-L)
DOI
10.1186/s12902-021-00716-7
PMC
PMC7986266
OpenAlex
W3137246155
Cited by
30
References
6
Citations by year
2026: 3 · 2025: 7 · 2024: 4 · 2023: 6 · 2022: 7 · 2021: 3
FWCI
0.77
Type
journal article
Language
en
Open access
Licence
Locations
  • BMC Endocrine Disorders (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Shiraz University of Medical Sciences
  • Shiraz University
Topics
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes, Cardiovascular Risks, and Lipoproteins
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Obesity, Physical Activity, Diet
  • Health Sciences › Medicine › Epidemiology › Adipokines, Inflammation, and Metabolic Diseases
Keywords
metabolic syndrome, disease progression, natural history (disease), Markov model, non-Markov models, dyslipidemia, hypertension, overweight, obesity, hyperglycemia, transition probabilities, sex differences
MeSH terms
Humans, Disease Progression, Metabolic Syndrome

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