# The value of lipid accumulation products in predicting type 2 diabetes mellitus: a cross-sectional study on elderlies over 65 in Shanghai

## TL;DR

This study shows that the lipid accumulation product (LAP) is a strong predictor of type 2 diabetes in elderly people over 65 in Shanghai.

## Contribution

The study demonstrates that LAP is a better predictor of T2DM than fasting glucose or neck circumference in elderly Chinese individuals.

## Key findings

- LAP was strongly associated with T2DM risk in elderly participants.
- LAP outperformed fasting glucose and neck circumference in predicting T2DM.
- Gender-specific cut-off values for LAP improved predictive accuracy.

## Abstract

As lifestyle changes, there is an increasing number of type 2 diabetes mellitus (T2DM) patients in China. The present study aimed to investigate the predictive value of the lipid accumulation product (LAP) for T2DM in Chinese elderlies over 65 years.

The present cross-sectional study recruited 2,092 adults from communities of Pudong New Area of Shanghai. Questionnaires were filled and anthropometric and laboratory examinations were completed by all participants. The predictive value of different risk factors for T2DM was analyzed using the receiver operating characteristics curve (ROC).

LAP was found to be closely related to T2DM (adjusted OR: 0.613, 95% CI: 0.581–0.645). Fasting plasma glucose (FPG), LAP, and urea nigrogen (UN) were associated with T2DM in females, whereas FPG, LAP, neck circumference (NC) were associated with T2DM in males. When the cut-off value was 33.8, LAP displayed the optimal predictive performance. A gender difference was observed with an LAP of 37.95 demonstrating the best predictive value in males (AUC = 0.604, 95% CI: 0.577–0.652) and 60.2 in females (AUC = 0.617, 95% CI: 0.574–0.660), respectively.

LAP is more significantly associated with the risk of T2DM in elderlies than FPG, UN or NC, and it serves as a strong predictor of T2DM. However, this is impacted by FPG and neck circumference to a certain extent. Future large-scale studies are needed to confirm its efficacy in predicting diabetes.