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June 4, 2026Renal Failure0 citationsOpen Access

Impact of dietary component clusters identified by K-means++ on renal function decline in a Taiwanese cohort

STShang-Feng TsaiWLWei‑Ju LiuYLYi‐Cheng Lin

Key Points

  • This research aims to explore the associations between identified dietary patterns and renal function decline.
  • Utilized the K-means++ algorithm for dietary pattern identification.
  • Employed principal component analysis (PCA) for stable clustering methods.
  • Analyzed a cohort from Taiwan to assess renal outcomes in relation to dietary patterns.
  • Identified meaningful dietary patterns associated with renal outcomes.
  • Demonstrated a statistically significant association with renal function decline (p=0.042).
  • Suggests that dietary modification may help preserve renal health.

Abstract

= 0.042). The K-means++ algorithm effectively identified meaningful dietary patterns and revealed clinically relevant associations with renal outcomes. These findings suggest that dietary modification may contribute to renal health preservation and demonstrate that the principal component analysis (PCA)-based clustering approach with K-means++ initialization provides a stable and appropriate framework for identifying dietary patterns in nutritional epidemiology.

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Cite This Study

Tsai et al. (2026) studied this question.

synapsesocial.com/papers/6a2116fad499ed480b16fd1bhttps://doi.org/10.1080/0886022x.2026.2667036
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