Jahanmiri Reihaneh, Djafarian Kurosh, Janbozorgi Nasim, Dehghani-Firouzabadi Fatemeh, Shab-Bidar Sakineh
Department of Community Nutrition, School of Nutritional Sciences and Dietetics, Tehran University of Medical Sciences (TUMS), No 44, Hojjat-dost Alley, Naderi St., Keshavarz Blvd, Tehran, Iran.
Department of Clinical Nutrition, School of Nutritional Sciences and Dietetics, Tehran University of Medical Sciences, Tehran, Iran.
Diabetol Metab Syndr. 2022 Aug 26;14(1):123. doi: 10.1186/s13098-022-00894-x.
Gaussian graphical models (GGM) are an innovative method for deriving dietary networks which reflect dietary intake patterns and demonstrate how food groups are consuming in relation to each other, independently. The aim of this study was to derive dietary networks and assess their association with metabolic syndrome in a sample of the Iranian population.
In this cross-sectional study, 850 apparently healthy adults were selected from referral health care centers. 168 food items food frequency questionnaire was used to assess dietary intakes. Food networks were driven by applying GGM to 40 food groups. Metabolic syndrome was defined based on the guidelines of the National Cholesterol Education Program Adult Treatment Panel III (ATP III).
Three GGM networks were identified: healthy, unhealthy and saturated fats. Results showed that adherence to saturated fats networks with the centrality of butter, was associated with higher odds of having metabolic syndrome after adjusting for potential confounders (OR = 1.81, 95% CI 1.61-2.82; P trend = 0.009) and higher odds of having hyperglycemia (P trend = 0.04). No significant association was observed between healthy and unhealthy dietary networks with metabolic syndrome, hypertension, hypertriglyceridemia and central obesity. Furthermore, metabolic syndrome components were not related to the identified networks.
Our findings suggested that greater adherence to the saturated fats network is associated with higher odds of having metabolic syndrome in Iranians. These findings highlight the effect of dietary intake patterns with metabolic syndrome.
高斯图形模型（GGM）是一种用于推导饮食网络的创新方法，该网络反映饮食摄入模式，并展示食物组之间如何相互独立地消耗。本研究的目的是在伊朗人群样本中推导饮食网络并评估它们与代谢综合征的关联。
在这项横断面研究中，从转诊医疗保健中心选取了850名表面健康的成年人。使用168项食物频率问卷来评估饮食摄入量。通过将GGM应用于40个食物组来构建食物网络。代谢综合征根据美国国家胆固醇教育计划成人治疗小组III（ATP III）的指南进行定义。
识别出三个GGM网络：健康、不健康和饱和脂肪网络。结果显示，在调整潜在混杂因素后，遵循以黄油为中心的饱和脂肪网络与患代谢综合征的较高几率相关（OR = 1.81，95% CI 1.61 - 2.82；P趋势 = 0.009）以及患高血糖的较高几率相关（P趋势 = 0.04）。未观察到健康和不健康饮食网络与代谢综合征、高血压、高甘油三酯血症和中心性肥胖之间存在显著关联。此外，代谢综合征的组成部分与所识别的网络无关。
我们的研究结果表明，在伊朗人中，更多地遵循饱和脂肪网络与患代谢综合征的较高几率相关。这些发现突出了饮食摄入模式对代谢综合征的影响。
