# Associations Between Dietary Amino Acid Intake and Elevated High-Sensitivity C-Reactive Protein in Children: Insights from a Cross-Sectional Machine Learning Study

**Authors:** Lianlong Yu, Xiaodong Zheng, Jilan Li, Changqing Liu, Yiya Liu, Meina Tian, Qianrang Zhu, Zhenchuang Tang, Maoyu Wu

PMC · DOI: 10.3390/nu17132235 · Nutrients · 2025-07-05

## TL;DR

This study finds that certain amino acids in children's diets are linked to higher inflammation levels, suggesting dietary changes could help prevent inflammation-related diseases.

## Contribution

This is the first large-scale machine learning study in China linking amino acid intake to pediatric inflammation.

## Key findings

- Amino acids like Ser, Cys, Tyr, and Pro significantly increase the risk of elevated hs-CRP in children.
- The LightGBM algorithm achieved high accuracy (AUC 0.927) in predicting elevated hs-CRP risk.
- The effect of amino acids on inflammation is more pronounced in children with low protein intake and normal weight.

## Abstract

Background High-sensitivity C-reactive protein (hs-CRP) is a protein that indicates inflammation and the risk of cardiovascular diseases. The intake of dietary amino acids can influence immune and inflammatory reactions. However, studies on the relationship between dietary amino acids and hs-CRP, especially in children, remain scarce. Methods This cross-sectional study analyzed data from the Nutrition and China Children and Lactating Women Nutrition and Health Survey (2016–2019), focusing on 3514 children (724 with elevated hs-CRP ≥ 3 mg/L and 2790 with normal levels). Dietary information was gathered via a food frequency questionnaire, and hs-CRP levels were obtained from blood samples. Boruta algorithm and propensity scores were used to select and match dietary factors and sample sizes. Machine learning (ML) algorithms and logistic regression models assessed the link between amino acid intake and elevated hs-CRP risk, adjusting for age, sex, BMI, and lifestyle factors. Results The odds ratios (ORs) for elevated hs-CRP were significant for several amino acids, including Ile, Leu, Lys, Ser, Cys, Tyr, His, Pro, SAA, and AAA, with values ranging from 1.10 to 2.07. The LightGBM algorithm was the most effective in predicting elevated hs-CRP risk, achieving an AUC of 0.927. Tyrosine, methionine, cysteine, and proline were identified as important features by SHAP analysis and logistic regression. The intake of Ser, Cys, Tyr, and Pro showed a linear increase in the risk of elevated hs-CRP, especially in individuals with low protein intake and normal weight (p < 0.1). Conclusions Intake of amino acids like Ser, Cys, Tyr, and Pro significantly impacts hs-CRP levels in children, indicating that regulating these could help prevent inflammation-related diseases. This study supports future dietary and health management strategies. This is first large-scale ML study linking amino acids to pediatric inflammation in China. The main limitations are the cross-section design and the use of self-reported dietary data.

## Linked entities

- **Chemicals:** Ile (PubChem CID 6306), Leu (PubChem CID 6106), Lys (PubChem CID 5962), Ser (PubChem CID 5951), Cys (PubChem CID 5862), Tyr (PubChem CID 6057), His (PubChem CID 6274), Pro (PubChem CID 145742), AAA (PubChem CID 5478845)

## Full-text entities

- **Genes:** CRP (C-reactive protein) [NCBI Gene 1401] {aka PTX1}, SHROOM4 (shroom family member 4) [NCBI Gene 57477] {aka MRXSSDS, SHAP, shrm4}, AAA1 (aortic aneurysm, familial abdominal 1) [NCBI Gene 100329167] {aka AAA}, SAA [NCBI Gene 6287]
- **Diseases:** cardiovascular diseases (MESH:D002318), inflammation (MESH:D007249)
- **Chemicals:** Ser (MESH:D012694), proline (MESH:D011392), methionine (MESH:D008715), Tyr (MESH:D014443), Amino Acid (MESH:D000596), Cys (MESH:D003545)
- **Species:** Homo sapiens (human, species) [taxon 9606]

## Full text

_Full body text omitted from this summary view._ Fetch the complete paper as Markdown: https://tomesphere.com/paper/PMC12252160/full.md

## Figures

7 figures with captions in the complete paper: https://tomesphere.com/paper/PMC12252160/full.md

## References

39 references — full list in the complete paper: https://tomesphere.com/paper/PMC12252160/full.md

---
Source: https://tomesphere.com/paper/PMC12252160