Nursing Career, Catholic University of Cuenca, Azogues 010107, Ecuador.
Faculty of Pharmacy, University of Granada, 18071 Granada, Spain.
Nutrients. 2024 Sep 1;16(17):2924. doi: 10.3390/nu16172924.
The association between dietary nutritional patterns, psychological factors, and metabolic health status has not been investigated in university students. There are studies that include numerous variables to test hypotheses from various theoretical bases, but due to their complexity, they have not been studied in combination. The scientific community recognizes the use of Gaussian graphical models (GGM) as a set of novel methods capable of addressing this.
To apply GGMs to derive specific networks for groups of healthy and unhealthy obese individuals that represent nutritional, psychological, and metabolic patterns in an Ecuadorian population.
This was a quantitative, non-experimental, cross-sectional, correlational study conducted on a sample of 230 obese/overweight university students, selected through a multi-stage random sampling method. To assess usual dietary intake, a Food Frequency Questionnaire (FFQ) was used; to evaluate psychological profiles (anxiety, depression, and stress), the DASS-21 scale was employed; blood pressure and anthropometric data were collected; and insulin levels, lipid profiles, and glucose levels were determined using fasting blood samples. The International Diabetes Federation (IDF) criteria were applied to identify metabolically healthy and unhealthy individuals. Statistical analysis relied on univariate methods (frequencies, measures of central tendency, and dispersion), and the relationships were analyzed through networks. The Mann-Whitney U test was used to analyze differences between groups.
In metabolically unhealthy obese individuals, GGMs identified a primary network consisting of the influence of waist circumference on blood pressure and insulin levels. In the healthy obese group, a different network was identified, incorporating stress and anxiety variables that influenced blood pressure, anthropometry, and insulin levels. Other identified networks show the dynamics of obesity and the effect of waist circumference on triglycerides, anxiety, and riboflavin intake.
GGMs are an exploratory method that can be used to construct networks that illustrate the behavior of obesity in the studied population. In the future, the identified networks could form the basis for updating obesity management protocols in Primary Care Units and supporting clinical interventions in Ecuador.
目前，有关饮食营养模式、心理因素与代谢健康状况之间的关系，尚未在大学生群体中得到研究。有研究纳入了众多变量，以测试来自不同理论基础的假设，但由于其复杂性，这些研究并未结合进行。科学界认识到，使用高斯图形模型（GGM）作为一系列新方法，可以解决这一问题。
应用 GGM 构建厄瓜多尔肥胖人群健康和不健康肥胖个体的特定网络，以代表其营养、心理和代谢模式。
这是一项在厄瓜多尔肥胖/超重大学生样本中进行的定量、非实验、横断面、相关性研究。通过多阶段随机抽样方法选取了 230 名参与者。使用食物频率问卷（FFQ）评估习惯性饮食摄入，采用 DASS-21 量表评估心理状况（焦虑、抑郁和压力），收集血压和人体测量数据，并通过空腹血样测定胰岛素水平、血脂谱和血糖水平。采用国际糖尿病联盟（IDF）标准来识别代谢健康和不健康个体。统计分析依赖于单变量方法（频率、集中趋势和离散程度的度量），并通过网络分析关系。采用 Mann-Whitney U 检验分析组间差异。
在代谢不健康肥胖个体中，GGM 确定了一个主要网络，该网络由腰围对血压和胰岛素水平的影响组成。在健康肥胖组中，确定了另一个网络，其中包含压力和焦虑变量，这些变量影响血压、人体测量和胰岛素水平。其他确定的网络则显示了肥胖的动态以及腰围对甘油三酯、焦虑和核黄素摄入的影响。
GGM 是一种探索性方法，可以用于构建说明研究人群中肥胖行为的网络。未来，所确定的网络可以为更新基层医疗单位的肥胖管理方案和支持厄瓜多尔的临床干预提供依据。
