Evidence map›Paper›PMID 41022974›Full record

ArticleScientific reports2025

Dietary patterns associated with the new onset of chronic kidney disease using clustering algorithm.

Dougho Park, Jinmi Kim, Da Woon Kim, Donghyun Lee, Taeyeon Kim, Dahyeon Koo, Youjin Lee, Won Hwa Kim, Hyo Jin Kim

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Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

9 authors.

Dougho Park *Medical Science and Engineering, Graduate School of Convergence Science and Technology, Pohang University of Science and Technology, Pohang, South Korea.
Jinmi Kim *Department of Biostatistics, Clinical Trial Center, Biomedical Research Institute, Pusan National University Hospital, Busan, South Korea.
Da Woon KimDepartment of Internal Medicine and Biomedical Research Institute, Pusan National University Hospital, Busan, South Korea.
Donghyun LeeGraduate School of Artificial Intelligence, Pohang University of Science and Technology, Pohang, South Korea.
Taeyeon KimMedical Research Institute, Pohang Stroke and Spine Hospital, Pohang, South Korea.
Dahyeon KooMedical Research Institute, Pohang Stroke and Spine Hospital, Pohang, South Korea.
Youjin LeeMedical Research Institute, Pohang Stroke and Spine Hospital, Pohang, South Korea.
Won Hwa KimGraduate School of Artificial Intelligence, Pohang University of Science and Technology, Pohang, South Korea.
Hyo Jin KimDepartment of Internal Medicine, Korea University Guro Hospital, 148, Gurodong-ro, Guro-gu, Seoul, South Korea. kimhj923@gmail.com.

Funding

The National Research Foundation of Korea RS-2023-00223764
6 · The paper itself

Abstract

Understanding how dietary patterns influence chronic kidney disease (CKD) development is crucial for effective prevention strategies. This study identified distinct dietary patterns among Korean adults and investigated their association with CKD development. This retrospective cohort study used data from the Korean Genome and Epidemiology Study health examinee study database of community-dwelling adults aged ≥ 40 years in South Korea (2004-2016). Then, dietary patterns were identified using K-means clustering analysis based on the quantity (weights) of 106 foods and intakes of energy and 22 nutrients. The dependent variable for Cox regression analyses was the development of new-onset CKD. A total of 57,213 participants were classified into three dietary clusters. Cluster C, characterized by lower overall food, energy, and nutrient intakes and higher carbohydrate intake, was independently associated with increased CKD risk (adjusted hazard ratio, 1.59; 95% confidence interval, 1.04-2.41; P = 0.031) compared to Cluster A, characterized by higher intake of vegetables and fish/shellfish. Subgroup analyses revealed that Cluster C still had a significantly high risk for CKD development in age ≥ 65 years, male sex, previous cardiovascular disease, systolic blood pressure ≥ 130 mm Hg, and body mass index ≥ 25 kg/m

Indexed as

AlgorithmsDietFeeding BehaviorRenal Insufficiency, ChronicAdultAgedCluster AnalysisClustering AlgorithmsFemaleHumansMaleMiddle AgedRepublic of KoreaRetrospective StudiesRisk FactorsChronic kidney diseaseCluster analysisCohort studiesDietNutrition survey

Identifiers

PMID41022974
PMCPMC12480947

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