Skip to main content
Download PDF
Download ePub

Changes in non-high-density lipoprotein to high-density lipoprotein ratio (NHHR) and cardiovascular disease: insights from CHARLS

Lipids in Health and Disease volume 24, Article number: 112 (2025) Cite this article

Abstract

Background

The established association between the non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) and cardiovascular disease (CVD) risk has been well-documented. Nevertheless, the relationship between changes in NHHR and CVD events remains to be elucidated. The present study aims to clarify the correlation between NHHR change patterns and the incidence of CVD across a broad population.

Methods

The current study recruited participants from the China Health and Retirement Longitudinal Study (CHARLS). The NHHR index was calculated using the formula: NHHR = (TC-HDL-c)/HDL-c. Temporal changes in NHHR were assessed with latent profile analysis, and cumulative NHHR was also evaluated. Multivariable Cox proportional hazards regression models and multivariate-adjusted restricted cubic spline (RCS) analyses were employed to examine the association between the NHHR index and incident CVD.

Results

A total of 4,629 individuals were recruited for the study. The average age of the participants was 57.47 years, with 53.7% being female. Over the follow-up period, 879 cases of CVD were documented. Compared to participants in the lowest tertile, those in the highest tertile for both baseline NHHR and cumulative NHHR exhibited a significantly increased risk of CVD, with adjusted hazard ratios (HRs) of 1.43 (95% confidence interval [CI]: 1.21–1.70) and 1.45 (95% CI: 1.23–1.72), respectively. Participants classified in Class 2 demonstrated a 27% higher risk of CVD, while those in Class 3 showed a 41% greater risk compared to the Class 1 group. Further analysis revealed that this relationship was linear. Stratified analyses corroborated the primary findings.

Conclusion

Baseline NHHR, cumulative NHHR, and changes in NHHR are significantly associated with an increased risk of CVD among individuals aged 45 years and older, thereby confirming their potential as valuable tools for risk stratification in CVD.

Background

Cardiovascular disease (CVD) is the foremost cause of mortality among non-communicable diseases globally, accounting for approximately 19.9 million deaths annually [1]. Projections indicate that this figure will escalate to 35.6 million by 2050, imposing substantial strain and presenting significant challenges to healthcare systems, particularly in developing nations [2, 3]. Additionally, the burden of CVD is expected to continue increasing, driven primarily by the population aging [4, 5]. Consequently, it is imperative to investigate the factors influencing CVD and to implement early prevention strategies to mitigate the risk of adverse cardiovascular events, thereby alleviating the overall burden of CVD.

Abnormal lipid metabolism has been extensively linked to CVD in numerous studies [6,7,8]. The ratio of non-high-density lipoprotein cholesterol (non-HDL-c) to high-density lipoprotein cholesterol (HDL-c), referred to as the non-HDL-to-HDL-cholesterol ratio (NHHR), is a significant composite biomarker for evaluating atherosclerosis and has garnered considerable attention [9, 10]. Extensive literature has highlighted the substantial predictive value of NHHR for established cardiovascular risk factors, such as diabetes, hypertension, nonalcoholic fatty liver disease (NAFLD), and obstructive sleep apnea hypopnea syndrome [11,12,13,14]. Moreover, NHHR, which incorporates both atherogenic and anti-atherogenic lipoproteins, demonstrates superior predictive capability compared to either fraction alone concerning cardiovascular-related events [15,16,17]. Although the correlation between NHHR and the onset of CVD has been established, most existing studies have predominantly focused on individuals with prediabetes and diabetes. This focus may overemphasize the role of NHHR and limit its applicability in other clinical contexts. More importantly, these studies typically considered only a single NHHR measurement, which may not provide an exhaustive examination of the exposure. Currently, evidence regarding the association between changes in NHHR and CVD risk remains limited.

Consequently, this study aims to comprehensively explore the longitudinal association of NHHR with CVD incidence using a large-scale Chinese cohort. The findings may contribute to the early identification of high-risk individuals and support the development of tailored interventions.

Methods

Study design and population

The data used in this study were obtained from the China Health and Retirement Longitudinal Study (CHARLS), an ongoing, population-based prospective cohort study. A multistage stratified probability sampling method was employed to recruit over 17,708 participants from 150 counties across 28 provinces in China at baseline, ensuring the representativeness and reliability of the sample. The baseline survey officially commenced in 2011, followed by four subsequent follow-up surveys conducted in 2013, 2015, 2018, and 2020. Additional comprehensive information regarding the study design has been detailed elsewhere [18].

In the present study, a total of 17,708 participants were initially recruited for the baseline survey. Following the predefined exclusion criteria, 13,041 participants were excluded from the analysis. The primary reasons for exclusion were as follows: unavailable NHHR data at wave 1 and wave 3 (n = 10,229), receipt of antihyperlipidemic treatment (n = 422), cancer diagnosis (n = 51), age less than 45 years or missing age information (n = 213), diagnosis of CVD at or prior to wave 3 (n = 1,397), unclear CVD status or follow-up dropout (n = 729), and cumulative NHHR values falling outside the range of mean ± 3 standard deviation (SD) (n = 38). Ultimately, 4,629 participants were eligible for the final analysis (Fig. 1).

Fig. 1
figure 1

Flowchart of the study population. (A) the timeline of the study; (B) the inclusion and exclusion of participants. Abbreviations: CVD cardiovascular disease, NHHR non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio, T1–3 tertile1–3

Full size image

Data collection and definition

The data was collected by medically trained interviewers through face-to-face interviews using a meticulously designed, standardized, and structured questionnaire. The questionnaires covered an extensive range of variables, including sociodemographic characteristics (age, gender, residential areas, marital status, education level), lifestyle factors (smoking and drinking status), anthropometric measurements (body mass index [BMI]), medical history (diabetes, hypertension, lung disease, kidney disease), and laboratory test results (hemoglobin levels, white blood cell [WBC], total cholesterol [TC], triglycerides [TG], high-density lipoprotein cholesterol [HDL-c], low-density lipoprotein cholesterol [LDL-c], serum creatinine, uric acid [UA], high sensitivity C-reactive protein [hsCRP]).

Diabetes was diagnosed in participants who met at least one of the following criteria: (1) a self-reported history of diabetes; (2) current use of antihyperglycemic medications or insulin therapy; (3) fasting plasma glucose (FPG) levels ≥ 7.0 mmol/L and/or HbA1c (glycosylated hemoglobin A1c) levels ≥ 6.5% at baseline [19]. Hypertension was diagnosed in participants who met at least one of the following criteria: (1) a self-reported history of hypertension; (2) current use of antihypertensive medications; (3) mean blood pressure ≥ 140/90 mmHg [20]. Venous blood samples were collected from participants after an overnight fast of at least 8 h. Complete blood counts were performed locally, whereas other blood indicators were analyzed in Beijing within two weeks of sample transport, following standard procedures.

Ascertainment of exposures and outcomes

Respondents underwent blood tests at wave 1 and wave 3 of the study. Lipid profiles were measured using an enzymatic colorimetric method, with coefficients of variation of 0.80% for TC and 1.00% for HDL-c, respectively. The NHHR was calculated using the formula: NHHR = non-HDL-c (mg/dL)/HDL-c (mg/dL), where non-HDL-c (mg/dL) is derived by subtracting HDL-c (mg/dL) from TC (mg/dL) [21]. Exposure variables were defined as follows: (1) Baseline NHHR = non-HDL-cwave1 (mg/dL)/HDL-cwave1 (mg/dL). (2) Cumulative NHHR = (NHHRwave1 + NHHRwave3)/2 × time (2015 − 2012) [22]. (3) Changes in NHHR over time: Changes in NHHR over time were quantified as the difference in NHHR between wave 1 and wave 3. Subgroups exhibiting similar patterns of NHHR changes were identified through latent profile analysis (LPA). The optimal number of classes was determined based on fit indices, as detailed in the statistical analysis section.

The primary outcome was the incidence of CVD during the follow-up period from wave 3 to wave 5, with priority given to the earlier occurrence of stroke and/or heart disease. The occurrence of these primary events was determined based on self-reported medical diagnoses. Participants were asked a standardized question: “Have you ever been diagnosed with stroke or any form of heart disease, including myocardial infarction, coronary artery disease, angina, heart failure, or other heart diseases?”. Trained interviewers implemented rigorous quality control measures for data documentation and confirmation to ensure the reliability and accuracy of the data.

Statistical analysis

LPA was utilized to identify subgroups exhibiting similar patterns in NHHR changes. Six models were sequentially constructed, each with an incrementally increasing number of latent classes. To determine the optimal class solution, several fit indices were evaluated: the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), adjusted Bayesian Information Criterion (aBIC), Bootstrapped Likelihood Ratio Test (BLRT), Lo-Mendell-Rubin likelihood ratio test (LMR), as well as minimum sample proportions. These results are summarized in Table S1. Among the models considered, those with relatively lower AIC and aBIC values were given priority. Models 5 and 6 did not provide significant improvements over models with one fewer class. Model 4 was excluded due to its sample proportion being less than 5%, which is deemed insufficient for reliable statistical inference. Based on both statistical criteria and interpretability, Model 3 was identified as the most appropriate model for our dataset. Participants were categorized into three distinct groups (Fig. S1): Class 1 comprised individuals with consistently low NHHR values across all waves. Class 2 included participants with moderate NHHR levels that exhibited a gradual decline over time. Class 3 consisted of subjects with initially high NHHR levels, characterized by a significant decrease.

Participants were categorized into three distinct groups according to predefined criteria: tertiles (T) of baseline NHHR, tertiles of cumulative NHHR, and changes in NHHR over time. To compare the characteristics among these groups, One-way ANOVA or the Kruskal-Wallis test was utilized for continuous variables as appropriate, while the chi-square test was employed for categorical variables. Continuous variables were expressed as mean (SD) if they exhibited a normal distribution, or as median (IQR) otherwise. Categorical variables were presented as number (percentage).

Prior to formal analyses, multicollinearity diagnostics were conducted on the variables included in the study. Clinical variables exhibiting significant collinearity were identified using a threshold of GVIF^(1/2DF) ≥ 2 (GVIF: generalized variance inflation factor; DF: degrees of freedom) (Table S2). Additionally, to address missing data, multiple imputation by chained equations (MICE) was utilized, as detailed in Table S3. Specifically, five imputed datasets were generated using MICE. The associations between exposure and CVD were then estimated independently within each of the five imputed datasets. The estimated coefficients from these five models were pooled to derive the final results.

We assessed the risk of CVD based on tertiles of baseline NHHR, cumulative NHHR, and changes in NHHR. Kaplan-Meier curves were used to estimate the cumulative incidence of CVD, and the difference between groups was evaluated via the log-rank test. The incidence rates of CVD events were expressed as per 1000 person-years. Cox proportional-hazards regression analysis was utilized to examine the association between NHHR and CVD. The proportional hazards assumption was validated using Schoenfeld residuals, which showed no evidence of violations. To account for potential confounding factors, three hierarchical models were constructed: Model 1 adjusted for age and sex; Model 2 additionally adjusted for smoking status, drinking status, residential area, marital status, education level, and BMI; and Model 3 further adjusted for diabetes, hypertension, kidney disease, lung disease, hemoglobin levels, serum creatinine, UA, and hsCRP.

To explore the dose-esponse relationship between continuous variables (baseline NHHR and cumulative NHHR) and CVD, we employed restricted cubic spline (RCS) models with varying numbers of knots, caculating the corresponding AIC and BIC values (Table S4). The RCS model with three knots positioned at the 10th, 50th, and 90th percentiles was selected based on its minimal AIC and BIC values.

Stratified analyses were performed to assess the consistency of the association between baseline NHHR, cumulative NHHR, and changes in NHHR with CVD risk. Participants were stratified by sex (male vs. female), age (< 60 years vs. ≥ 60 years), BMI (< 24 kg/m2 vs. ≥ 24 kg/m2), diabetes (yes vs. no), and hypertension (yes vs. no). The significance of multiplicative interactions was examined using likelihood ratio test. Furthermore, E-values were calculated to assess the potential impact of unobserved confounding factors.

All statistical analyses were performed using R software (version 4.2.2) and Mplus software (version 8.3). A two-sided P value less than 0.05 was considered statistically significant.

Results

Baseline characteristics of participants according to NHHR change

A total of 4,629 participants (53.7% female) were included in the present study, with a mean age of 57.47 ± 8.38 years. The sample sizes for the three classes were 2,621, 1,607, and 311, respectively. Table 1 summarizes the baseline clinical characteristics of the participants stratified by NHHR progression. Compared to Class 1, participants in the other classes exhibited a lower proportion of rural residence and drinking, as well as a higher prevalence of diabetes and hypertension. Additionally, levels of hemoglobin, WBC, TC, TG, LDL-c, serum creatinine, UA, hsCRP, NHHRwave1, NHHRwave3, and cumulative NHHR were significantly elevated. Conversely, HDL-c levels were lower. Moreover, the baseline clinical characteristics of participants categorized by baseline NHHR tertiles and cumulative NHHR tertiles are detailed in Table S5-S6.

Table 1 Baseline characteristics according to the categories of change in NHHR
Full size table

Association of baseline NHHR, cumulative NHHR with incident CVD

During a median follow-up period of 108 months, a total of 879 CVD events were documented, corresponding to an incidence rate of 21.71 per 1000 person-years.

The incidence of CVD was observed to progressively increase with higher baseline and cumulative NHHR tertiles (Table 2). As depicted in Figures S2-S3, Kaplan-Meier survival curves indicated a significantly elevated cumulative incidences of CVD among individuals in higher NHHR tertiles (log-rank P < 0.05 for both). Multivariate-adjusted RCS curves (Fig. 2) demonstrated a linear relationship between NHHR and CVD risk (P for nonlinearity > 0.05 for both). After adjusting for potential confounding variables (Model 3), the corresponding hazard ratios (HRs) and 95% confidence intervals (CIs) indicated that each SD increase in baseline NHHR and cumulative NHHR was linked to a 14% (HR = 1.14, 95% CI: 1.07–1.22) and 15% (HR = 1.15, 95% CI: 1.08–1.23) increased risk of CVD, respectively. Using the baseline NHHR T1 group as the reference, the fully adjusted HRs for T2 and T3 groups were 1.26 (95% CI: 1.06–1.50) and 1.43 (95% CI: 1.21–1.70), respectively. Similar results were observed when stratifying individuals by cumulative tertiles. The E-values for the T2 and T3 groups were 1.63 (lower CI: 1.25) and 1.88 (lower CI: 1.54) for baseline NHHR, 1.56 (lower CI: 1.13) and 1.91 (lower CI: 1.58) for cumulative NHHR.

Table 2 Association between baseline, cumulative, and change in NHHR and the risk of CVD incidence
Full size table
Fig. 2
figure 2

Adjusted restricted cubic spline curves for CVD according to the baseline NHHR (A) and cumulative NHHR (B). Hazard ratios are indicated by red lines and 95% confidence intervals by ribbon areas. The knots were set at 10th, 50th and 90th centiles. The multivariate models adjusted for age, sex, smoking, drinking, rural residence, marital status, education, BMI, diabetes, hypertension, kidney disease, lung disease, hemoglobin, Serum creatinine, UA, and hsCRP. Abbreviations: NHHR non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio

Full size image

Association of change in NHHR with incident CVD

The incidence of CVD gradually increased from Class 1 to Class 3 (Fig. S4). Individuals in Classes 2 and 3 exhibited a significantly higher CVD risk compared to those in Class 1, with HRs of 1.27 (95% CI: 1.10–1.46) and 1.41 (95% CI: 1.09–1.83), respectively. The E-values for the association between changes in NHHR and CVD risk in Classes 2 and 3 were 1.64 and 1.85, respectively, with corresponding lower 95% CIs of 1.34 and 1.32.

Mediation analyses

After adjusting for confounders in Model 3, WBC exhibited no significant mediation effects in the associations between NHHR, both baseline and cumulative NHHR, and CVD risk (Fig. S5).

Subgroup analyses

We performed stratified analyses to examine the associations between NHHR and CVD across various subgroups. The association between NHHR and incident CVD was aligned with the primary results in most subgroups (Fig. 3 and Table S8-S9). Notably, no significant heterogeneity was observed in the association between different NHHR indices and the onset of CVD across all subgroups (Fig. 3 and Table. S8-S9).

Fig. 3
figure 3

Subgroup analysis of the association between NHHR change and CVD. Each stratification anlysis was adjusted for age, sex, smoking, drinking, rural residence, marital status, education, BMI, diabetes, hypertension, kidney disease, lung disease, hemoglobin, Serum creatinine, UA, and hsCRP, with the exception of the stratification variable itself. Abbreviations: CI confidence interval, CVD cardiovascular disease, HR hazard ratios, NHHR non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio

Full size image

Discussion

The findings demonstrated that exposure to a higher baseline and cumulative NHHR index, whether defined as continuous or categorical variables, was significantly associated with an elevated risk of CVD incidence after adjusting for other conventional risk factors. Three distinct subgroups were identified according to dynamic NHHR patterns. Individuals in Classes 2 and 3 exhibited a significantly higher risk of CVD compared to those in Class 1. Furthermore, the results of the subgroup analysis corroborated the overall findings.

The results demonstrated a positive association between both baseline and cumulative NHHR and incident CVD, consistent with previous studies [23, 24]. Liu et al. found that each 1-unit increase in NHHR corresponds to a 12% higher risk of adverse cardiovascular events among patients with type 2 diabetes [25]. A large Swedish cohort study involving 18,673 diabetic patients identified NHHR as a superior predictor of CVD risk, with patients in the lowest tertile exhibiting a significantly reduced risk of incident coronary heart disease by approximately 30% compared to those in the highest tertile [26]. A recent study of 12,578 individuals with diabetes or prediabetes in the USA indicated that NHHR values exceeding 2.83 were associated with an 8% increased risk of cardiovascular mortality [27]. Nevertheless, the aforementioned research predominantly focuses on individuals with dysglycemia, and there is a paucity of studies examining the relationship between NHHR and CVD events in the general population. Liu et al. found that participants with higher baseline NHHR faced a higher risk of CVD utilizing data from CHARLS [28]. Overall, our findings strongly support the notion that NHHR is a risk factor for CVD, thereby extending existing evidence from specific participants to broader and more diverse populations.

Previous studies have predominantly focused on single-point analyses or utilized cumulative NHHR to reflect fluctuation, resulting in insufficient evidence regarding the impact of dynamic NHHR changes on the risk of CVD. By employing LPA, we identified latent classes based on homogenous NHHR change patterns across multiple waves. Through rigorous comparison of fit indices and ensuring adequate sample size within each model, we identified the optimal number of classes, enhancing the reliability and accuracy of our classification. In this study, three distinct categories were identified, with 80% of participants falling into categories 2 and 3. Notably, despite a decreasing trend, an initially high NHHR level remains strongly associated with elevated cardiovascular risk. Therefore, regular monitoring of NHHR and timely targeted interventions are imperative to maintain appropriate levels, which may offer significant value for the prevention of CVD.

The impact of NHHR on CVD risk can be elucidated through multiple mechanisms. Elevated NHHR is significantly correlated with insulin resistance, leading to metabolic disorders and consequently increasing CVD risk [29, 30]. Moreover, NHHR plays a vital role in vascular atherosclerosis by promoting vascular inflammation, impairing endothelial function, accelerating vascular lesions progression, facilitating thrombus formation, and reducing plaque stability [31, 32]. The rise in NHHR is likely attributable to increased levels of non-HDL-c and decreased levels of HDL-c. Previous studies have consistently demonstrated that elevated non-HDL-c levels are positively correlated with an increased risk of CVD [33, 34], while lower HDL-c levels, which affect reverse cholesterol transport and von Willebrand factor secretion, indicate a diminished cardioprotective profile [35, 36]. Furthermore, higher NHHR correlates with impaired development of coronary collateral circulation, suggesting that it may compromise myocardial compensatory mechanisms and thereby increase susceptibility to cardiac events [37].

The present study possesses several notable strengths. Firstly, latent profile analysis was applied to stratify individuals based on similar long-term trends in NHHR. The best-fitting profile was identified using stringent statistical selection criteria. Additionally, the generalizability of our findings is enhanced by including a diverse population, which encompasses individuals without dysglycemia who are at lower risk for adverse cardiovascular events. Consequently, our conclusions regarding the detrimental impact of NHHR on cardiovascular events are more likely to accurately reflect its true effect, thereby enhancing their applicability to a broader spectrum of real-world scenarios. To the best of our knowledge, this study represents the first investigation into the longitudinal associations between NHHR and CVD risk within a large-scale, prospective cohort of middle-aged and elderly Chinese individuals. This underscores the importance of dynamic monitoring and provides valuable insights into the clinical utility of NHHR. Overall, NHHR emerges as a cost-effective and reliable predictor that is critical for assessing the development of cardiovascular events.

There are still several limitations that warrant consideration. Firstly, the CHARLS dataset primarily represents the middle-aged population in China, which may limit the generalizability of our findings to younger individuals under 45 years old or those residing outside China. Secondly, due to the absence of medical records, CVD diagnoses relied on self-reported physician assessments, inevitably introducing information and recall bias. Nevertheless, the CVD status was verified and reconfirmed in every wave to ensure better quality control. Besides, the CHARLS has been harmonized with distinguished international cohort studies such as the English Longitudinal Study of Ageing (ELSA) [18], where a high degree of concordance between self-reported coronary heart disease and medical documentation supports the reliability of the data [38]. Moreover, while we have collected repeated measurements of NHHR across two waves, incorporating NHHR data from three or more surveys would refine the assessment of changes in NHHR, potentially providing greater value. Additionally, the potential presence of mediating variables in the associations between NHHR and CVD warrants further investigation. Finally, given the observational study design, we cannot establish causality between NHHR and CVD, and unmeasured confounding factors also cannot be completely ruled out, both of which are common issues in cohort studies.

Conclusion

In conclusion, elevated baseline NHHR, cumulative NHHR, and distinct change patterns of NHHR are significantly associated with an increased risk of CVD among a nationally representative sample. Consequently, this straightforward yet effective predictor should be integrated into routine clinical monitoring protocols to facilitate the early identification of high-risk individuals and enable timely implementation of targeted interventions.

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

BMI:

Body mass index

CHARLS:

China health and retirement longitudinal study

CI:

Confidence interval

CVD:

Cardiovascular disease

DF:

Degree of freedom

ELSA:

English Longitudinal Study of Ageing

FPG:

Fasting plasma glucose

GVIF:

Generalized variance inflation factor

HbA1c:

Glycosylated hemoglobin A1C

HDL-c:

High-density lipoprotein cholesterol

HR:

Hazard ratios

HsCRP:

High sensitivity C-reactive protein

LDL-c:

Low-density lipoprotein cholesterol

LPA:

Latent profile analysis

MICE:

Multiple imputation by chained equations

NAFLD:

Nonalcoholic fatty liver disease

NHHR:

Non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio

non-HDL-c:

Non-high-density lipoprotein cholesterol

SD:

Standard deviation

TC:

Total cholesterol

TG:

Triglycerides

UA:

Uric acid

WBC:

White blood cell

References

  1. Martin SS, Aday AW, Almarzooq ZI, Anderson CAM, Arora P, Avery CL, et al. 2024 heart disease and stroke statistics: A report of US and global data from the American heart association. Circulation. 2024;149(8):e347–913.

    PubMed  Google Scholar 

  2. Chong B, Jayabaskaran J, Jauhari SM, Chan SP, Goh R, Kueh MTW, et al. Global burden of cardiovascular diseases: projections from 2025 to 2050. Eur J Prev Cardiol. 2024;1–15.

  3. Zhao D, Liu J, Wang M, Zhang X, Zhou M. Epidemiology of cardiovascular disease in China: current features and implications. Nat Rev Cardiol. 2019;16(4):203–12.

    PubMed  Google Scholar 

  4. Guo J, Huang X, Dou L, Yan M, Shen T, Tang W, et al. Aging and aging-related diseases: from molecular mechanisms to interventions and treatments. Signal Transduct Target Ther. 2022;7(1):391.

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Collaborators GCoD. Global, regional, and National age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980–2017: a systematic analysis for the global burden of disease study 2017. Lancet. 2018;392(10159):1736–88.

    Google Scholar 

  6. Wadström BN, Pedersen KM, Wulff AB, Nordestgaard BG. Elevated remnant cholesterol, plasma triglycerides, and cardiovascular and non-cardiovascular mortality. Eur Heart J. 2023;44(16):1432–45.

    PubMed  Google Scholar 

  7. Chen L, Chen S, Bai X, Su M, He L, Li G, et al. Low-Density lipoprotein cholesterol, cardiovascular disease risk, and mortality in China. JAMA Netw Open. 2024;7(7):e2422558.

    PubMed  PubMed Central  Google Scholar 

  8. Li S, Liu Z, Joseph P, Hu B, Yin L, Tse LA, et al. Modifiable risk factors associated with cardiovascular disease and mortality in China: a PURE substudy. Eur Heart J. 2022;43(30):2852–63.

    PubMed  Google Scholar 

  9. Zhao W, Gong W, Wu N, Li Y, Ye K, Lu B, et al. Association of lipid profiles and the ratios with arterial stiffness in middle-aged and elderly Chinese. Lipids Health Dis. 2014;13:37.

    PubMed  PubMed Central  Google Scholar 

  10. Feig JE, Hewing B, Smith JD, Hazen SL, Fisher EA. High-density lipoprotein and atherosclerosis regression: evidence from preclinical and clinical studies. Circ Res. 2014;114(1):205–13.

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Pan X, Zhang X, Wu X, Zhao Y, Li Y, Chen Z, et al. Association between non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio and obstructive sleep apnea: a cross-sectional study from NHANES. Lipids Health Dis. 2024;23(1):209.

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Tan MY, Weng L, Yang ZH, Zhu SX, Wu S, Su JH. The association between non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio with type 2 diabetes mellitus: recent findings from NHANES 2007–2018. Lipids Health Dis. 2024;23(1):151.

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Huang X, Li J, Zhang L, Zhang C, Li C. The association between the non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio and non-alcoholic fatty liver disease in US adults: a cross-sectional study. Sci Rep. 2024;14(1):24847.

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Wu J, Guo J. Non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) and hypertension in American adults: a NHANES cross-sectional study. Front Physiol. 2024;15:1398793.

    PubMed  PubMed Central  Google Scholar 

  15. Păunică I, Mihai AD, Ștefan S, Pantea-Stoian A, Serafinceanu C. Comparative evaluation of LDL-CT, non-HDL/HDL ratio, and ApoB/ApoA1 in assessing CHD risk among patients with type 2 diabetes mellitus. J Diabetes Complications. 2023;37(12):108634.

    PubMed  Google Scholar 

  16. Zhu L, Lu Z, Zhu L, Ouyang X, Yang Y, He W, et al. Lipoprotein ratios are better than conventional lipid parameters in predicting coronary heart disease in Chinese Han people. Kardiol Pol. 2015;73(10):931–8.

    PubMed  Google Scholar 

  17. Qin G, Tu J, Zhang C, Tang X, Luo L, Wu J, et al. The value of the ApoB/apoAΙ ratio and the non-HDL-C/HDL-C ratio in predicting carotid atherosclerosis among Chinese individuals with metabolic syndrome: a cross-sectional study. Lipids Health Dis. 2015;14:24.

    PubMed  PubMed Central  Google Scholar 

  18. Zhao Y, Hu Y, Smith JP, Strauss J, Yang G. Cohort profile: the China health and retirement longitudinal study (CHARLS). Int J Epidemiol. 2014;43(1):61–8.

    PubMed  Google Scholar 

  19. 2. Diagnosis and classification of diabetes: standards of care in Diabetes-2024. Diabetes Care. 2024;47(Suppl 1):S20–42.

    Google Scholar 

  20. Williams B, Mancia G, Spiering W, Agabiti Rosei E, Azizi M, Burnier M, et al. 2018 ESC/ESH guidelines for the management of arterial hypertension. Eur Heart J. 2018;39(33):3021–104.

    PubMed  Google Scholar 

  21. Qing G, Deng W, Zhou Y, Zheng L, Wang Y, Wei B. The association between non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) and suicidal ideation in adults: a population-based study in the united States. Lipids Health Dis. 2024;23(1):17.

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Liu L, Liu S, Liao Y, Zhang X, Wang M, Lin L, et al. Association of cumulative non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio with the risk of cardiometabolic disease. Front Cardiovasc Med. 2024;11:1500025.

    PubMed  PubMed Central  Google Scholar 

  23. Liu J, Oorloff MD, Nadella A, Guo P, Ye M, Wang X, et al. Association between the non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) and cardiovascular outcomes in patients undergoing percutaneous coronary intervention: a retrospective study. Lipids Health Dis. 2024;23(1):324.

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Gao P, Zhang J, Fan X. NHHR: an important independent risk factor for patients with STEMI. Rev Cardiovasc Med. 2022;23(12):398.

    PubMed  PubMed Central  Google Scholar 

  25. Liu M, Pei J, Zeng C, Xin Y, Zhang Y, Tang P, et al. Association of non-high-density lipoprotein cholesterol/high-density lipoprotein cholesterol ratio with cardiovascular outcomes in patients with type 2 diabetes mellitus: evidence from the ACCORD cohort. Diabetes Obes Metab. 2025;27(1):300–11.

  26. Eliasson B, Cederholm J, Eeg-Olofsson K, Svensson AM, Zethelius B, Gudbjörnsdottir S. Clinical usefulness of different lipid measures for prediction of coronary heart disease in type 2 diabetes: a report from the Swedish National diabetes register. Diabetes Care. 2011;34(9):2095–100.

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Yu B, Li M, Yu Z, Zheng T, Feng X, Gao A, et al. The non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) as a predictor of all-cause and cardiovascular mortality in US adults with diabetes or prediabetes: NHANES 1999–2018. BMC Med. 2024;22(1):317.

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Liu C, Zhang Z, Meng T, Li C, Wang B, Zhang X. Cross-sectional analysis of Non-HDL/HDL cholesterol ratio as a marker for cardiovascular disease risk in Middle-Aged and older adults: evidence from the CHARLS study. J Stroke Cerebrovasc Dis. 2025;34(1):108168.

  29. Lin D, Qi Y, Huang C, Wu M, Wang C, Li F, et al. Associations of lipid parameters with insulin resistance and diabetes: A population-based study. Clin Nutr. 2018;37(4):1423–9.

    CAS  PubMed  Google Scholar 

  30. Kim SW, Jee JH, Kim HJ, Jin SM, Suh S, Bae JC, et al. Non-HDL-cholesterol/HDL-cholesterol is a better predictor of metabolic syndrome and insulin resistance than Apolipoprotein B/apolipoprotein A1. Int J Cardiol. 2013;168(3):2678–83.

    PubMed  Google Scholar 

  31. Karabacak M, Uysal BA, Turkdogan AK. Alteration in serum oxidative stress balance in patients with different Circulating high-density lipoprotein cholesterol levels. Rev Port Cardiol. 2022;41(10):833–9.

    PubMed  Google Scholar 

  32. Johannesen CDL, Mortensen MB, Langsted A, Nordestgaard BG. Apolipoprotein B and Non-HDL cholesterol better reflect residual risk than LDL cholesterol in Statin-Treated patients. J Am Coll Cardiol. 2021;77(11):1439–50.

    CAS  PubMed  Google Scholar 

  33. Brunner FJ, Waldeyer C, Ojeda F, Salomaa V, Kee F, Sans S, et al. Application of non-HDL cholesterol for population-based cardiovascular risk stratification: results from the multinational cardiovascular risk consortium. Lancet. 2019;394(10215):2173–83.

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Wu F, Juonala M, Jacobs DR Jr., Daniels SR, Kähönen M, Woo JG, et al. Childhood Non-HDL cholesterol and LDL cholesterol and adult atherosclerotic cardiovascular events. Circulation. 2024;149(3):217–26.

    CAS  PubMed  Google Scholar 

  35. Soria-Florido MT, Schröder H, Grau M, Fitó M, Lassale C. High density lipoprotein functionality and cardiovascular events and mortality: A systematic review and meta-analysis. Atherosclerosis. 2020;302:36–42.

    CAS  PubMed  Google Scholar 

  36. Gragnano F, Golia E, Natale F, Bianchi R, Pariggiano I, Crisci M, et al. Von Willebrand factor and cardiovascular disease: from a biochemical marker to an attractive therapeutic target. Curr Vasc Pharmacol. 2017;15(5):404–15.

    CAS  PubMed  Google Scholar 

  37. Li Y, Chen X, Li S, Ma Y, Li J, Lin M, et al. Non-high-density lipoprotein cholesterol/high-density lipoprotein cholesterol ratio serve as a predictor for coronary collateral circulation in chronic total occlusive patients. BMC Cardiovasc Disord. 2021;21(1):311.

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Xie W, Zheng F, Yan L, Zhong B. Cognitive decline before and after incident coronary events. J Am Coll Cardiol. 2019;73(24):3041–50.

    PubMed  Google Scholar 

Download references

Acknowledgements

The data utilized in the present study was extracted from the CHARLS. We would like to express our gratitude to all individuals involved in this work.

Funding

This study was financially supported by Science and Technology Bureau of Sichuan Province (2024YFFK0290), and the Health Commission of Sichuan Province (23LCYJ042), and 1.3.5 project for Disciplines of Excellence, West China Hospital, Sichuan University (grant number: ZYGD 24005).

Author information

Author notes

  1. Bingxue Wang and LiYing Li contributed equally to this work.

Authors and Affiliations

  1. Department of Endocrinology and Metabolism, West China Hospital of Sichuan University, Chengdu, China

    Bingxue Wang, Ying Tang, Ting Lin, Jing Wu & Xingwu Ran

  2. Innovation Research Center for Diabetic Foot, Diabetic Foot Care Center, West China Hospital of Sichuan University, Chengdu, China

    Bingxue Wang, Ying Tang, Ting Lin, Jing Wu & Xingwu Ran

  3. Department of Cardiology, Fuwai Yunnan Cardiovascular Hospital, Kunming, China

    LiYing Li

  4. West China Hospital, General Practice Medical Center, Sichuan University, Chengdu, China

    Guoqi Wang

Authors
  1. Bingxue Wang
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. LiYing Li
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Ying Tang
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Ting Lin
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Jing Wu
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Guoqi Wang
    View author publications

    You can also search for this author inPubMed Google Scholar

  7. Xingwu Ran
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

BXW and XWR conceived and designed the study. BXW collected the data, performed the data analysis, and drafted the initial manuscript. LYL contributed to the data analysis and wrote specific sections of the manuscript. YT and XWR were involved in revising the manuscript. TL, JW, and GXW proofread the data. All authors reviewed and approved the final version of manuscript.

Corresponding author

Correspondence to Xingwu Ran.

Ethics declarations

Ethics approval and consent to participate

The CHARLS study was granted ethical approval by the Institutional Review Board of Peking University (approval number: IRB00001052-11015 for household survey and IRB00001052-11014 for blood sample), and all participants provided informed written consent.

Consent for publication

The publication of this manuscript has been authorized by all authors.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, B., Li, L., Tang, Y. et al. Changes in non-high-density lipoprotein to high-density lipoprotein ratio (NHHR) and cardiovascular disease: insights from CHARLS. Lipids Health Dis 24, 112 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12944-025-02536-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12944-025-02536-3

Keywords

Lipids in Health and Disease

ISSN: 1476-511X