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Record W4401979515 · doi:10.14740/jem945

Evaluation of Nine Forms of Metabolic Syndrome Diagnosis as Risk for Cardiovascular Disease: An Analysis of Isolated and Combined Metabolic Factors

2024· article· en· W4401979515 on OpenAlexvenueno aff
Víctor Juan Vera-Ponce, Fiorella E. Zuzunaga-Montoya, Luisa Erika Milagros Vásquez-Romero, Joan A. Loayza-Castro, Eder Jesús Orihuela-Manrique, Mario J. Valladares-Garrido, Enrique Vigil-Ventura, Rafael Tapia‐Limonchi

Bibliographic record

VenueJournal of Endocrinology and Metabolism · 2024
Typearticle
Languageen
FieldMedicine
TopicDiabetes, Cardiovascular Risks, and Lipoproteins
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMetabolic syndromeDiseaseInternal medicineIntensive care medicineCardiologyObesity

Abstract

fetched live from OpenAlex

Background: Metabolic syndrome (MetS) is a pathological condition varying according to the guidelines used, leading to ongoing debate on whether all these forms of defining MetS offer the same level of risk for developing cardiovascular diseases (CVDs). The aim of the study was: 1) to determine the prevalence of each type of MetS; 2) to assess the association of each type with CVDs over a 5-year follow-up period; and 3) to analyze whether each possible combination of MetS carries the same level of risk for developing CVD in the time above frame. Methods: This study is a secondary analysis of a Peruvian cohort database. The dependent variable was the development of CVD. In contrast, the independent variable was MetS, defined based on nine diagnostic methods: Adult Treatment Panel III (ATPIII), International Diabetes Federation (IDF), World Health Organization (WHO), Joint Interim Statement (JIS), European Group for the Study of Insulin Resistance (EGIR), American Heart Association and National Heart, Lung, and Blood Institute (AHA/NHLBI), American Association of Clinical Endocrinologists (AACE), Latin American Diabetes Association (ALAD), and International Lipid Information Bureau Latin America (ILIBLA). Results were presented as relative risk (RR). Results: The overall prevalence of MetS was 40.59%, while the 5-year incidence of CVD was 1.69%. The lowest prevalence was found with ALAD criteria (5.6%), while the highest was ILIBLA (37%). Diagnostic forms of MetS according to ILIBLA (RR = 5.06; 95% confidence interval (CI): 1.64 - 15.62), AHA/NHLBI (RR = 5.06; 95% CI: 1.64 - 15.62), JIS (RR = 3.66; 95% CI: 1.22 - 10.97), and API (RR = 2.83; 95% CI: 1.11 - 7.20) showed a risk of CVD. Additionally, hyperglycemia, hypertriglyceridemia, and elevated blood pressure were found to be individually associated with the presence of CVD. In contrast, other factors, such as altered waist circumference (WC) and low high-density lipoprotein (HDL), are only associated with an increased risk in combination with other markers. Conclusions: Significant variations in the prevalence of MetS according to the definition used were revealed, as well as significant differences in the risk of CVD associated with different types of MetS. J Endocrinol Metab. 2024;14(4):194-206 doi: https://doi.org/10.14740/jem945

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.023
GPT teacher head0.294
Teacher spread0.271 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2024
Admission routes1
Has abstractyes

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