Non-linear association between low-density lipoprotein cholesterol and risk of prediabetes: a retrospective cohort study based on Chinese adults

Abstract

Background: The pathogenesis of prediabetes remains complex, particularly regarding the interactions between lipid metabolism disorders and glucose metabolism abnormalities, which warrant in-depth exploration. Low-density lipoprotein cholesterol (LDL-C) is an important risk factor for atherosclerosis and cardiovascular disease. However, the relationship between LDL-C and prediabetes has been less extensively studied. Therefore, we conducted a retrospective cohort study to investigate this association.

Methods: This secondary retrospective cohort study utilized data from 100,608 Chinese adults. Cox proportional hazards regression models were used to examine the relationship between LDL-C and prediabetes risk. Restricted cubic spline regression and smooth curve fitting were used to explore the non-linear relationship between LDL-C and prediabetes. A two-piecewise Cox proportional hazards regression model identified inflection points. In addition, a series of subgroup and sensitivity analyses were performed to confirm the robustness of our results.

Results: After adjusting for confounding covariates, LDL-C was positively associated with prediabetes (HR: 1.49, 95% CI: 1.40-1.58, p < 0.0001). The two-piecewise Cox model identified an inflection point of 2.19 for LDL-C (p < 0.001 for log-likelihood ratio test). When LDL-C ≤ 2.19, LDL-C was positively associated with the risk of prediabetes (HR: 2.02, 95% CI: 1.71-2.36, p < 0.0001). In contrast, when LDL-C > 2.19, LDL-C was associated with a lower risk of prediabetes (HR: 1.49, 95% CI: 1.39-1.59, p < 0.0001). Sensitivity and subgroup analyses confirmed the stability and consistency of this positive association in the general population.

Conclusion: This study reveals a non-linear positive association between LDL-C levels and prediabetes risk in Chinese adults after adjusting for confounders. The dynamic monitoring of LDL-C levels may help identify individuals at high risk for prediabetes. Timely dietary and lifestyle modifications could potentially reduce the risk of prediabetes. These findings offer new insights for prediabetes prevention and treatment.

Keywords: cohort study; low-density lipoprotein cholesterol; nonlinearly; prediabetes; prevention.

Figure 1

Flowchart for screening research participants.

Figure 2

Kaplan–Meier event-free survival curve. Kaplan–Meier analysis of incident prediabetes based on LDL-C quartiles (log-rank, p < 0.0001). LDL-C, low-density lipoprotein cholesterol.

Figure 3

The non-linear relationship between LDL-C and incident prediabetes. Adjusted for gender, age, BMI, SBP, DBP, smoking status, drinking status, family history of diabetes, AST, ALT, HDL-C, TC, TG, BUN, Scr, and baseline FPG. HR, hazard ratio; CI, confidence interval; LDL-C, low-density lipoprotein cholesterol; BMI, body mass index; AST, aspartate aminotransferase; ALT, alanine aminotransferase; HDL-C, high-density lipoprotein cholesterol; TC, total cholesterol; TG, triglycerides; BUN, blood urea nitrogen; FPG, fasting plasma glucose.

Figure 4

Subgroup analysis of the associations between LDL-C and prediabetes. LDL-C, low-density lipoprotein cholesterol.

Figure 5

BMI, age, SBP, DBP, smoking status, and drinking status mediate the association of LDL-C with prediabetes. Note: In mediation analyses, adjustments were made for gender, family history of diabetes, AST, ALT, HDL-C, TC, TG, BUN, Scr, and baseline FPG. HR, hazard ratio; CI, confidence interval; BMI, body mass index; LDL-C, low-density lipoprotein cholesterol; AST, aspartate aminotransferase; ALT, alanine aminotransferase; HDL-C, high-density lipoprotein cholesterol; TC, total cholesterol; TG, triglycerides; BUN, blood urea nitrogen; FPG, fasting plasma glucose.