ArticleCureus2025
Development and Validation of a Multidimensional Risk Spectrum Screening Tool for the Early Detection of Metabolic Syndrome.
Article in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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.
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.
Who cites it
2 citing papers in PubMed.
- A lifestyle-derived risk score for chronic atrophic gastritis: development and validation in 21,008 Chinese adults.Frontiers in public health · 2026Article
- Serum 25-hydroxyvitamin D and metabolic syndrome: a large cross-section study with dose response analysis in a health screening population.Frontiers in nutrition · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background Metabolic syndrome (MetS) is a cluster of risk factors, including dyslipidemia, hypertension, hyperglycemia, and central obesity, underpinned by insulin resistance (IR). Individuals with MetS have a markedly increased risk of developing type 2 diabetes mellitus and cardiovascular disease. Early detection before significant metabolic deterioration is essential for prevention. Objective The objective of this study is to develop and validate a practical screening tool for identifying individuals at varying risk levels for MetS by integrating anthropometric, biochemical, lifestyle, and demographic factors. Methods A literature review informed the selection of 23 modifiable and non-modifiable risk factors, including waist circumference (WC), BMI, fasting blood glucose (FBG), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), blood pressure (BP), lifestyle habits, and female-specific factors. Each factor was assigned a weighted risk score. The tool was pilot-tested and refined before being applied to 148 adults (47 (31.8%) men and 101 (68.2%) women; mean age, 42.4 ± 11.7 years) attending a clinical setting. Data were collected through structured interviews, anthropometric assessments, and biochemical tests. The triglyceride-glucose (TyG) index was calculated as a surrogate for IR. Differences in clinical parameters across risk categories were evaluated using one-way ANOVA. Results Participants were classified as low risk (31 (21.0%)), moderate risk (93 (63.0%)), or high risk (24 (16.2%)). MetS risk scores increased significantly with systolic BP, diastolic BP, WC, FBG, TG, and the TyG index, while HDL-C showed a significant negative association (all p < 0.01). The mean TyG index increased progressively from low (8.3 ± 0.4) to moderate (8.7 ± 0.4) to high risk (9.4 ± 0.3). Conclusions The MetS spectrum assessment tool demonstrated strong construct validity (through associations with established MetS diagnostic criteria) and criterion validity (via correlation with the TyG index), allowing early risk detection even in individuals who do not meet full diagnostic thresholds. Integration into clinical practice may facilitate timely intervention to prevent progression to MetS and related complications.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.