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Published on: April 20, 2018
Profiling Dietary Patterns for Metabolic Syndrome: A Study Integrating Reduced Rank Regression and Food Network
Ningsu Chen1, Liyu Huang2, Kai Zhao1
1Department of Clinical Nutrition, Clinical Epidemiology and Evidence-based Medicine Center, West China Hospital, Sichuan University.
Background:
Metabolic syndrome (MetS) is a major public health concern, yet few studies have jointly applied complementary analytic frameworks to assess overall dietary structure. This study aimed to derive a dietary pattern associated with MetS by combining food-based network analysis and reduced rank regression (RRR).
Methods:
Participants from the China Nutrition and Health Surveillance (2010-2022) were included. Dietary intake was assessed using three-day 24-hour recalls and household condiment weighing. Food network analysis visualized and compared dietary structures of adults with and without MetS. RRR derived a dietary pattern based on six nutrient response variables (magnesium, zinc, calcium, potassium, insoluble fiber, riboflavin) selected for their biological relevance to MetS. Logistic regression evaluated associations between dietary patterns and MetS.
Results:
Among 3,884 participants (median age 55.5 years), the non-MetS group showed a denser food network than the MetS group. The first RRR-derived pattern (DP1), explaining 40.15% of the variance in nutrient responses, was characterized by higher intakes of vegetables, fungi and algae, soybean products, dairy and beef/lamb, and lower intakes of alcohol, refined grains, and edible oils. DP1 was inversely associated with MetS (OR = 0.93, 95% CI: 0.87-0.99). A significant inverse trend across DP1 quintiles was observed, with the highest quintile showing the strongest protective association (OR = 0.66, 95% CI: 0.50-0.88).
Conclusions:
A protective dietary pattern for MetS was identified through RRR and contextualized using food network analysis. This nutrient-dense pattern may help prevent MetS, and the integrated approach offers broader structural insight for nutritional epidemiology.
