The association between noodle consumption and metabolic syndrome in Korean adults.
Year: 2025
Type: article
Study design: Observational Study
Abstract: BACKGROUND AND OBJECTIVES: The proportion of noodles in the Korean diet is increasing, but the effect of noodle intake on metabolic syndrome has not been sufficiently investigated. Therefore, we investigated noodle consumption and its relation to metabolic syndrome in Korean adults. METHODS AND STUDY DESIGN: This study was conducted on 10,505 adults using the combined data of the 2012-2016 Korea National Health and Nutrition Examination Survey (KNHANES). Noodle intake was evaluated with a food frequency question-naire (FFQ) based on 112 food items. To compute odds ratios (ORs) and their 95% confidence intervals (CIs) controlled for confounders, multivariable logistic regression models were used. RESULTS: Compared to people in the lowest levels of noodle intake, the OR of the metabolic syndrome of those in the highest levels was 1.48 (95% CI, 1.16-1.90; p-trend = 0.002). This positive association was also found for hypertriglyceridemia and abdominal obesity, which were metabolic syndrome components. Specifically, the odds of having hypertriglyceridemia were 38% (OR, 1.38; 95% CI, 1.14-1.66; p-trend < 0.001) higher for people with high noodle consumption compared to those with low noodle consumption in the overall population. The tendency for people who consume a lot of noodles to have raised odds of metabolic syndrome was observed when analyzed by the type of noodles. CONCLUSIONS: This study suggested noodle intake was positively related to met-abolic syndrome and its components in Korean adults. Further clinical trials and prospective cohort studies are required to identify a causal relationship between noodle intake and metabolic syndrome in Koreans.
Source: PubMed
Authors: Da Eun Jeon, Youngyo Kim
Institution: Gyeongsang National University
Language: English
Topic: Nutritional Studies and Diet
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W4408885085