BACKGROUND

The complex interplay of physiological conditions, chronic pathological changes, and social roles in middle-aged and elderly men presents significant challenges for clinicians in diagnosing depression within this group. Therefore, identifying simpler and more effective quantitative predictive indicators for depression risk is one of the urgent issues in the current medical system to prevent and treat depression in this population. Recent studies have found that fat accumulation has a bidirectional effect on mood, and that the Lipid Accumulation Product (LAP), a new indicator for assessing fat accumulation, may be associated with depression. However, there is no existing literature that explores the relationship between LAP and depressive symptoms in middle-aged and elderly men in China, nor any research comparing its predictive performance for depression risk against metabolic biomarkers.

METHODS

This study analyzed data from the 2015 and 2018 CHARLS surveys, with LAP divided into tertiles. Univariate logistic analysis and multivariable regression models were used to study the correlation between LAP and depressive symptoms. Subgroup analyses, interaction tests, and sensitivity analyses were conducted to validate the robustness of the model. Restricted cubic spline (RCS) regression was used to determine the potential threshold for LAP in relation to depression, revealing the non-linear relationship between LAP and depression. Finally, ROC curves were used to compare the predictive performance of LAP and metabolic biomarkers for depression risk.

RESULTS

Univariate logistic analysis and multivariable regression models explored the factors influencing depressive symptoms in middle-aged and elderly men in China, confirming the strong association and superior predictive performance of LAP for depression ( < 0.0001). RCS regression showed that, within a certain range, higher LAP levels significantly reduced depression risk in this population. Stratified subgroup analysis, interaction tests, and sensitivity analyses confirmed the stability of the results. ROC curves demonstrated that LAP had superior predictive performance for depression compared to traditional indicators and other metabolic biomarkers.

CONCLUSION

This study applied more robust statistical methods to minimize the effects of confounding factors and identified a stable, atypical U-shaped relationship between LAP and the prevalence of depression in middle-aged and elderly men in China, as well as an effective threshold. The findings strongly support the "jolly fat" hypothesis in Chinese middle-aged and elderly men and offer guidance for dietary intake in this population.