Akeren Zahide, Apaydın Emine
Nursing Department, Bayburt University Health Sciences Faculty, Bayburt, 69000, Turkey.
Vocational School of Health Services, Medical Services and Techniques Department, First and Emergency Aid Programme, Bayburt University, Bayburt, 69000, Turkey.
BMC Public Health. 2025 Jan 6;25(1):51. doi: 10.1186/s12889-025-21304-7.
Identifying the risks of metabolic syndrome (MetS) can lead to early targeted interventions and thus contribute to improved quality of life by reducing the risk of developing MetS, diabetes or heart disease in the future. We aimed to develop a valid and reliable measurement tool to measure the MetS risk of the population.
In the methodological study, an item pool was created by reviewing the literature. Pre-application was performed after the weighting of the items whose content validity was ensured by taking expert opinions. Data were collected from 43 patients with MetS from a state hospital affiliated to the Ministry of Health and 405 individuals without MetS from the community, from a total of 448 individuals using the Individual Information Form, Finnish Diabetes Risk Scale (FINDRISC) and Metabolic Syndrome Index (MSI). The data obtained were evaluated using SPSS 22.0 and MedCalc 19.1 statistical programmes. Scale discrimination was analyzed by independent samples t-test between the upper and lower 27% groups. The cut-off point of the scale score in predicting the diagnosis of MetS was tested by ROC analysis. Correlation analysis was performed with the parallel form for criterion validity.
As a result of the ROC analysis, a perfectly compatible scale with a sensitivity of 100%, a specificity of 85.43% and a cut-off score of 48 was obtained. When the correlation analyses between MSI and FINDRISC scores were examined for criterion validity, a positive moderate (r = 0.632, p < 0.001) correlation was found between FINDRISC and MSI. When the discrimination of the scale was analysed, it was found that there was a significant difference between the lower 27% and upper 27% groups (p < 0.05) and it was revealed that the MSI made sensitive measurements to discriminate.
The MSI scale is a valid and reliable tool for early detection of MetS risk.
识别代谢综合征（MetS）风险可实现早期靶向干预，从而通过降低未来患MetS、糖尿病或心脏病的风险来提高生活质量。我们旨在开发一种有效且可靠的测量工具，以测量人群的MetS风险。
在方法学研究中，通过查阅文献创建了一个条目池。在通过专家意见确保内容效度的条目加权后进行预应用。使用个人信息表、芬兰糖尿病风险量表（FINDRISC）和代谢综合征指数（MSI），从一家隶属于卫生部的国立医院的43例MetS患者以及社区的405例无MetS个体中，共448人收集数据。使用SPSS 22.0和MedCalc 19.1统计程序对获得的数据进行评估。通过独立样本t检验分析上下27%组之间的量表区分度。通过ROC分析测试量表分数在预测MetS诊断中的截断点。使用平行形式进行相关分析以验证效标效度。
ROC分析结果显示，获得了一个灵敏度为100%、特异度为85.43%且截断分数为48的完全兼容量表。在检查MSI和FINDRISC分数之间的相关分析以验证效标效度时，发现FINDRISC和MSI之间存在中度正相关（r = 0.632，p < 0.001）。在分析量表的区分度时，发现下27%组和上27%组之间存在显著差异（p < 0.05），并且表明MSI进行了敏感测量以进行区分。
MSI量表是早期检测MetS风险的有效且可靠工具。
