Joint assessment of abdominal obesity and non-traditional lipid parameters for primary prevention of cardiometabolic multimorbidity: insights from the China health and retirement longitudinal study 2011-2018

Abstract

Background: Obesity and abnormal lipid metabolism increase the risk of various cardiometabolic diseases, including diabetes, heart disease, and stroke. However, the impact of abdominal obesity (AO) and non-traditional lipid parameters on the risk of cardiometabolic multimorbidity (CMM) remains unclear. This study aims to investigate the separate and combined effects of AO and non-traditional lipid parameters on the incidence risk of CMM.

Methods: This study enrolled 7,597 eligible participants from the China health and retirement longitudinal study (CHARLS). Cox proportional hazards models were used to perform adjusted regression analyses and mediation analyses, with Kaplan-Meier analysis used for cumulative hazards. Restricted cubic splines were utilized to evaluate the nonlinear relationship between non-traditional lipid parameters and the risk of CMM among participants with AO. Subgroup analyses were conducted with stratification by age, gender, BMI, smoking status, drinking status, and hypertension to investigate interaction effects across different populations. Additionally, sensitivity analyses were further performed to evaluate the impact of various subgroups on diabetes, heart disease, and stroke.

Results: During the 7-year follow-up period, a total of 699 participants (9.20%) were newly diagnosed with CMM. Kaplan-Meier curves revealed that the subgroup with both AO and high levels of non-traditional lipid parameters had the highest cumulative hazard for developing CMM. In the fully adjusted model, Cox regression analysis revealed that participants with both high levels of non-traditional lipid parameters and AO exhibited the highest risk of developing CMM. Subgroup and sensitivity analyses further confirmed the robustness of these findings, showing consistent results across different demographic groups and under various analytical conditions. Furthermore, AO was found to significantly mediated the associations between non-traditional lipid parameters and the risk of developing CMM.

Conclusion: The separate and combined effects of AO and non-traditional lipid parameters were significantly associated with the risk of developing CMM. Notably, AO may induce CMM by partially mediating the effects of serum lipids in human metabolism. The findings highlighted the importance of joint evaluation of AO and non-traditional lipid parameters for primary prevention of CMM.

Keywords: Abdominal obesity; CHARLS; Cardiometabolic Multimorbidity; Non-traditional lipid parameters.

Fig. 1

Flow chart of inclusion and exclusion criteria of participants

Fig. 2

K-M plot of CMM risk by abdominal obesity and non-traditional lipid parameters (A-F). Group 1 refers to non-abdominal obesity & non-traditional lipid parameters ≤ median; Group 2 refers to non-abdominal obesity & non-traditional lipid parameters ≥ median; Group 3 refers to abdominal obesity & non-traditional lipid parameters ≤ median; Group 4 refers to abdominal obesity & non-traditional lipid parameters ≥ median. AIP atherogenic index of plasma, AC atherogenic coefficient, CRI-I Castelli’s index-I, CRI-II Castelli’s index-II, LCI lipoprotein combine index

Fig. 3

Dose-response relationship between the non-traditional lipid parameters and the risk of CMM among participants with AO (A-F). Spline analyses were adjusted for age, gender, BMI, smoking status, drinking status, education level, marital status, residence, SBP, DBP, night sleep duration, daytime nap duration, FPG, eGFR, hs-CRP, HbA1c, chronic diseases (hypertension and cancer), and and medication use (antihypertensive, antidiabetic, antidyslipidemic, cardiovascular medications). AIP atherogenic index of plasma, AC atherogenic coefficient, CRI-I Castelli’s index-I, CRI-II Castelli’s index-II, LCI lipoprotein combine index, HR hazard ratio, CI confidence interval

Fig. 4

Forest plot of multivariable-adjusted Cox regression analysis based on the subgroup that comprises abdominal obesity and high levels of non-traditional lipid parameters (A-F). Multivariate models were adjusted for age, gender, BMI, smoking status, drinking status, education level, marital status, residence, SBP, DBP, night sleep duration, daytime nap duration, FPG, eGFR, hs-CRP, HbA1c, chronic diseases (hypertension and cancer), and medication use (antihypertensive, antidiabetic, antidyslipidemic, cardiovascular medications), with the exception of the stratification variable. AIP atherogenic index of plasma, AC atherogenic coefficient, CRI-I Castelli’s index-I, CRI-II Castelli’s index-II, LCI lipoprotein combine index, HR hazard ratio, CI confidence interval, NA not available. *P < 0.05, **P < 0.01, ***P < 0.001

Fig. 5

Mediation analysis of abdominal obesity on the association between non-traditional lipid parameters and the risk of developing CMM. Adjusted for age, gender, BMI, smoking status, drinking status, education level, marital status, residence, SBP, DBP, night sleep duration, daytime nap duration, FPG, eGFR, hs-CRP, HbA1c, chronic diseases (hypertension and cancer), and medication use (antihypertensive, antidiabetic, antidyslipidemic, cardiovascular medications). AIP atherogenic index of plasma, AC atherogenic coefficient, CRI-I Castelli’s index-I, CRI-II Castelli’s index-II, LCI lipoprotein combine index, *P < 0.05, **P < 0.01