Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2025 Jul 10:12:1616520.
doi: 10.3389/fmed.2025.1616520. eCollection 2025.

Unraveling the role of cumulative triglyceride-total cholesterol-body weight index in stroke development: evidence from the CHARLS cohort

Huang Luwen #  1 , Zhang Yunwei #  1 , Xu Lei  1 , Li Linlin #  1 , Yu Ming #  1
Affiliations

Unraveling the role of cumulative triglyceride-total cholesterol-body weight index in stroke development: evidence from the CHARLS cohort

Huang Luwen et al. Front Med (Lausanne). .

Abstract

Background: This study investigated the association between the cumulative triglyceride-total cholesterol-body weight index (TCBI) and the risk of stroke among middle-aged and older adults, focusing on hypertension as a potential mediator.

Methods: Data from 5,598 participants aged ≥ 45 years in the China Health and Retirement Longitudinal Study were analyzed over a median follow-up of 57.2 months. CumTCBI was calculated as ((TCBI(2011) + TCBI(2015))/2) × (2015-2011). The risk of stroke was the primary outcome. Cox proportional hazards models and restricted cubic splines were used to examine the association between CumTCBI and stroke risk. Mediation analysis investigated the role of hypertension as a potential mediator of the association between CumTCBI and stroke risk.

Results: During the follow-up period, 480 (8.93%) participants experienced stroke. The fully adjusted CumTCBI was significantly associated with stroke (HR per 1 SD = 1.166). A non-linearly association was observed, with stroke risk increasing when CumTCBI was below 12.639 (HR per 100 units = 1.166, P = 0.002) and remaining stable beyond this threshold (P = 0.356). Additionally, hypertension mediated 27.4% of the association.

Conclusion: CumTCBI is non-linearly associated with stroke risk, partially mediated by hypertension. Managing both metabolic status and hyperternsion may reduce stroke risk in aging populations.

Keywords: CHARLS; mediation analysis; non-linearly relationship; stroke; triglyceride-total cholesterol-body weight index.

PubMed Disclaimer

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Flowchart showing participant selection for a study on TCBI. Out of 17,708 participants at Wave 1, 8,820 were excluded due to various reasons. Remaining 8,888 formed the baseline. Exclusions reduced this to 5,598 for cumulative TCBI analysis. Follow-up visits were conducted in 2011, 2015, with an endpoint in 2020.
FIGURE 1
Flowchart of study populations.
Cone chart illustrating stroke incidence risk across four CumTCBI categories. Q1 shows 5.90%, Q2 shows 8.80%, Q3 shows 10.10%, and Q4 shows 10.90%, with risks increasing from Q1 to Q4.
FIGURE 2
Incidence rates of stroke categorized by quartiles of the cumulative. CumTCBI. Q1, Quartile 1; Q2, Quartile 2; Q3, Quartile 3; Q4, Quartile 4.
Line graph showing cumulative incidence probability over 60 months for four quartiles (Q1 to Q4). Q1 begins lower, while Q3 and Q4 show notably higher probabilities nearing the end. At-risk numbers and events for each quartile are listed below the graph, indicating significant increases in events around 60 months.
FIGURE 3
Cumulative incidence of stroke across quartiles of CumTCBI.
A graph illustrating the hazard ratio against cumulative TCBI (CumTCBI). A red line shows the trend with a shaded area indicating the confidence interval. Key findings include a p-value of less than 0.001 for overall significance, and 0.009 for non-linearity. A histogram with blue bars is present at the bottom, representing the distribution of CumTCBI.
FIGURE 4
Association between the CumTCBI (per 100 scaled) and the risk of stroke. The model was adjusted for gender, age, marital status, residence, education, smoking, drinking, hypertension, heart disease, diabetes, liver disease, kidney disease, CumFBG, CumHbA1c, CumLDL-C, CumScr, and CumUA. The RCS model was used with 3 knots to account for non-linearity in the relationship.
Forest plot showing hazard ratios (HR) with 95% confidence intervals for various subgroups related to age, sex, smoking, drinking, hypertension, diabetes, heart disease, and dyslipidemia. Subgroups include age under and over sixty, sex, smoking status, drinking status, presence of hypertension, diabetes, heart disease, and dyslipidemia. P-values for interaction suggest statistical significance across categories. Horizontal lines represent confidence intervals around the HR for each subgroup.
FIGURE 5
Subgroup analyses of the association between CumTCBI (per 100 scaled) and the risk of stroke. The model was adjusted for gender, age, marital status, residence, education, smoking, drinking, hypertension, dyslipidemia, heart disease, diabetes, liver disease, kidney disease, CumFBG, CumHbA1c, CumLDL-C, CumScr, and CumUA.
Flowchart illustrating the mediation pathway between CumTCBI and stroke through hypertension. It shows that 27.39% of the effect is mediated. The p-value for the proportion is 0.012, the indirect effort hazard ratio is 0.003, and the total effort hazard ratio is 0.01.
FIGURE 6
Hypertension as a mediator of the relationship between CumTCBI and the risk of stroke. The model was adjusted for gender, age, marital status, residence, education, smoking, drinking, hypertension, heart disease, diabetes, liver disease, kidney disease, CumFBG, CumHbA1c, CumLDL-C, CumScr, and CumUA.
See this image and copyright information in PMC

References

    1. Ahamed M, Chowdhury M, Chowdhury P, Hossain M. Evaluation of risk factors associated with stroke. Bangladesh Med J Khulna. (2016) 48:24–7. 10.3329/bmjk.v48i1-2.27094 - DOI
    1. Ji C, Ge X, Zhang J, Tong H. The stroke burden in China and its long-term trends: Insights from the global burden of disease (GBD) study 1990-2021. Nutr Metab Cardiovasc Dis. (2025) 35:103848. 10.1016/j.numecd.2025.103848 - DOI - PubMed
    1. GBD 2021 Stroke Risk Factor Collaborators. Global, regional, and national burden of stroke and its risk factors, 1990-2021: A systematic analysis for the Global Burden of Disease Study 2021. Lancet Neurol. (2024) 23:973–1003. 10.1016/S1474-442200369-7 - DOI - PMC - PubMed
    1. Kitagawa K, Hosomi N, Nagai Y, Kagimura T, Ohtsuki T, Maruyama H, et al. Cumulative effects of LDL cholesterol and CRP levels on recurrent stroke and TIA. J Atheroscler Thromb. (2019) 26:432–41. 10.5551/jat.45989 - DOI - PMC - PubMed
    1. Shridharan P, Nair R, Gorthi S, Prakashini K, Chatterjee A. Effects of serum cholesterol on severity of stroke and dosage of statins on functional outcome in acute ischemic stroke. Neurol India. (2023) 71:923–7. 10.4103/0028-3886.388115 - DOI - PubMed

LinkOut - more resources