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Background <p>The issue of obesity is becoming more and more prominent. Understanding the metabolic profile of obese young adults and finding possible risk markers for early prediction and intervention is of great importance.</p> Methods <p>A total of 13,082 college students with an average age of 20 years were enrolled in this cross-sectional study. The lipid composition was measured and novel lipid profiles such as AIP, AI, LCI, Non-HDL-C, TC/HDL-C, LDL-C/HDL-C and TyG were calculated. Participants were then assessed as normal weight, overweight or obese based on their BMI. Pearson correlation analysis, multivariate logistic analysis, and predictive analysis were used to assess the association and discriminative power between lipid profile and obesity.</p> Results <p>The prevalence of obesity with dyslipidemia was 61.0% in males and 38.7% in females. Most obese patients were associated with only one dyslipidemia component, with the highest proportion having low HDL-C. We found a positive correlation between all lipid profiles except HDL-C and BMI. Multivariate logistics regression shows, AIP were strongly associated with obesity, which shows the largest OR = 12.86, 95%CI (9.46,17.48).</p> Conclusions <p>In the youth population, higher AIP levels were positively and strongly associated with obesity. AIP may be a novel and better risk biomarker for predicting obesity.</p>

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Strong association between atherogenic index of plasma and obesity in college students

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月26日

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  • Zhi-Long Wang,
  • Jiming Li,
  • Chang-Hao Sun,
  • Xin Yin,
  • Xiao-Yu Zhi,
  • Yi-Tian Liu,
  • Ying-Ying Zheng,
  • Ting-Ting Wu,
  • Xiang Xie

摘要

Background

The issue of obesity is becoming more and more prominent. Understanding the metabolic profile of obese young adults and finding possible risk markers for early prediction and intervention is of great importance.

Methods

A total of 13,082 college students with an average age of 20 years were enrolled in this cross-sectional study. The lipid composition was measured and novel lipid profiles such as AIP, AI, LCI, Non-HDL-C, TC/HDL-C, LDL-C/HDL-C and TyG were calculated. Participants were then assessed as normal weight, overweight or obese based on their BMI. Pearson correlation analysis, multivariate logistic analysis, and predictive analysis were used to assess the association and discriminative power between lipid profile and obesity.

Results

The prevalence of obesity with dyslipidemia was 61.0% in males and 38.7% in females. Most obese patients were associated with only one dyslipidemia component, with the highest proportion having low HDL-C. We found a positive correlation between all lipid profiles except HDL-C and BMI. Multivariate logistics regression shows, AIP were strongly associated with obesity, which shows the largest OR = 12.86, 95%CI (9.46,17.48).

Conclusions

In the youth population, higher AIP levels were positively and strongly associated with obesity. AIP may be a novel and better risk biomarker for predicting obesity.