Abstract
Background
Cardiometabolic multimorbidity (CMM) is a significant concern in patients with non-alcoholic fatty liver disease (NAFLD). However, the relationship between residual cholesterol (RC) levels and CMM risk in adult NAFLD patients remains poorly understood. This study aimed to investigate the association between RC levels and the risk of CMM in adult NAFLD patients using data from a large-scale, cross-sectional cohort.
Methods
Data were derived from the National Health and Nutrition Examination Survey (NHANES). Three multivariable logistic regression models and restricted cubic spline (RCS) analysis were employed to explore the relationship between RC and CMM risk in NAFLD patients, adjusting for potential confounders. Subgroup analyses and interaction tests were also conducted to assess the robustness of the findings.
Results
A significant positive association between RC levels and CMM risk was observed in NAFLD patients across all models (model1: OR = 1.094, 95% CI: 1.013,1.182, P = 0.023; model2: OR = 1.153, 95% CI: 1.061,1.253, P < 0.001; model3: OR = 1.158, 95% CI: 1.066,1.259, P < 0.001). Subgroup analysis showed that a stronger association between RC and CMM in patients with high fibrosis risk (P = 0.048). RCS analysis confirmed a nonlinear relationship, with CMM risk plateauing at higher RC concentrations.
Conclusions
RC levels are significantly associated with CMM risk in adult NAFLD patients. These findings underscore the importance of monitoring RC levels as a potential marker for identifying individuals at higher risk of CMM, offering insights into the need for early intervention strategies. Further research is necessary to validate these findings and explore the association's underlying mechanisms.
Keywords: Remnant Cholesterol (RC), Cardiometabolic Multimorbidity (CMM), Non-alcoholic fatty liver disease (NAFLD), National Health and Nutrition Examination Survey (NHANES), Cardiometabolic diseases (CMDs)
Introduction
Multimorbidity, defined as the coexistence of two or more chronic diseases within an individual, is emerging as a critical challenge in international healthcare and a focal point of global health research [1]. Cardiometabolic diseases (CMDs), including diabetes, hypertension, coronary heart disease, and stroke, are prevalent chronic conditions with shared etiologies and represent major contributors to the global health burden and mortality [2]. The coexistence of two or more cardiometabolic diseases, referred to as cardiometabolic multimorbidity (CMM), is among the most prevalent and severe forms of multimorbidity. Compared with individuals without cardiometabolic diseases, patients with CMM face a 3.7- to 6.9-fold higher risk of all-cause mortality and experience a 12- to 15-year reduction in life expectancy at age 60 [3]. Recent studies have highlighted a significant association between cardiometabolic diseases and over-nutrition–induced dyslipidemia, potentially mediated by chronic inflammation resulting from lipid abnormalities [1]. Although low-density lipoprotein cholesterol (LDL-C) is a well-established risk factor for CMDs, interventions to lower LDL-C safely reduce the incidence of myocardial infarction, revascularization, and ischemic stroke [4]. Nonetheless, a substantial proportion of cardiovascular risk remains, particularly for first or recurrent events, which is largely attributed to remnant cholesterol (RC) [4].
Non-alcoholic fatty liver disease (NAFLD) is a prevalent and rapidly increasing chronic condition, representing a hepatic manifestation of metabolic syndrome [5]. NAFLD is associated with insulin resistance (IR), hyperglycemia, dyslipidemia, inflammation, coagulation abnormalities, and blood pressure dysregulation [6]. Moreover, it is recognized as a major risk factor for CMD. Increasing evidence indicates that NAFLD is linked to higher mortality from cardiovascular events [7, 8]. Therefore, identifying the risk of developing CMM in NAFLD patients is critical for reducing both the incidence and mortality associated with CMM.
RC refers to the cholesterol content in triglyceride-rich lipoproteins, including intermediate-density lipoproteins (IDL) and very-low-density lipoproteins (VLDL) in the fasting state, as well as chylomicrons in the non-fasting state. As a significant atherogenic risk factor, multiple prospective cohort studies have shown that elevated RC levels substantially increase cardiovascular disease risk [9–11]. Recent research further highlights a significant causal relationship between RC and the progressive risk of CMM, suggesting that genetically driven RC elevations increase the likelihood of various CMDs and their associated risk factors [12, 13]. Given these properties, RC was selected as a key lipid marker in this study to evaluate CMM risk in patients with NAFLD. Although prior studies have established the predictive value of RC for CMM risk and severity, its association with CMM in patients with NAFLD remains unclear. To address this gap, this study utilized data from the National Health and Nutrition Examination Survey (NHANES), which represents the U.S. population, to evaluate the relationship between RC and CMM in NAFLD patients aged 18 years and older.
Materials and methods
Study design and subjects
NHANES is an extensive, nationally representative survey initiated in the 1960 s and conducted by the U.S. Centers for Disease Control and Prevention (CDC). Employing a sophisticated multistage, stratified probability sampling design, NHANES gathers comprehensive cross-sectional data on the health and nutritional status of the U.S. population, including demographic, dietary, physical activity, clinical, and laboratory information. Each participant provides informed consent, ensuring ethical standards and data confidentiality. The National Center approves the survey protocol for the Health Statistics Research Ethics Review Board. As a foundational resource in public health research, NHANES data are instrumental in tracking health trends, evaluating disease prevalence, identifying risk factors, and informing health policy, making it indispensable in fields such as epidemiology, nutrition, and environmental health. Data are publicly accessible at https://www.cdc.gov/nchs/nhanes.
Data for this study were derived from the NHANES, covering the period from 1999 to 2018, with an initial sample of 101,316 participants. Due to the absence of abdominal ultrasound data in NHANES, the diagnosis of NAFLD was based on the United States Fatty Liver Index (US FLI), with NAFLD defined as a US FLI > 30 [14, 15]. To enhance study validity, we excluded participants based on the following criteria: (1) age under 18 years (N = 42,112); (2) factors such as excessive alcohol intake (defined as > 3 drinks/day for men and > 2 drinks/day for women), positive hepatitis B or C status, missing components for calculating US FLI, or a US FLI ≤ 30 (N = 52,753) [14, 15]; and (3) those with missing remnant cholesterol data (N = 485) or missing cardiometabolic multimorbidity data (N = 303). After these exclusions, the final study cohort included 5663 participants with NAFLD. A detailed flowchart of the participant selection process is shown in Fig. 1.
Fig. 1.
Flow-chart of the study samples
Definition of RC, CMD and CMM
RC was considered the exposure variable in this study, calculated as the total cholesterol (TC) minus high-density lipoprotein cholesterol (HDL-C) and LDL-C, consistent with previous research [16]. To date, there is no universally accepted consensus on the diagnosis of CMDs. However, based on previous CMD-related studies, and considering the sample size of certain diseases in the NHANES dataset as well as clinical practice, four common CMDs were included in this study: hypertension, diabetes, coronary heart disease (CHD), and stroke [17, 18]. Diabetes was diagnosed based on any of the following criteria: (1) self-reported physician diagnosis of diabetes; (2) use of antidiabetic medications; (3) fasting plasma glucose ≥ 126 mg/dL or glycated hemoglobin ≥ 6.5% [19]. Hypertension was defined by any of the following: (1) self-reported history of hypertension; (2) use of antihypertensive medications; (3) systolic blood pressure ≥ 140 mmHg or diastolic blood pressure ≥ 90 mmHg [20]. Diagnosis of stroke was based on participants’ self-reported history. CHD was defined as a self-reported history of CHD, angina, or heart attack. CMM was defined as the presence of two or more CMDs simultaneously [17, 18].
Definition of covariates
All covariates were obtained from the NHANES database following standardized data collection protocols. Demographic characteristics and lifestyle factors were collected through structured household interviews using validated questionnaires. Anthropometric measurements were performed by trained technicians during mobile examination center (MEC) visits according to standardized procedures. Laboratory variables were measured from fasting blood samples using standardized assays with strict quality control. Disease-related variables were defined based on a combination of self-reported physician diagnosis, medication use, and objective examination or laboratory criteria, in accordance with NHANES analytic guidelines.
Several potential confounders were considered as covariates in our analysis, including age, sex (female, male), race (non-Hispanic white, non-Hispanic black, Mexican American, and others), body mass index (BMI) (< 25, 25–30, ≥ 30) [21], smoking status (non-smokers, former smokers, and current smokers), alcohol consumption (non-drinkers, light, moderate, and heavy drinkers) [22], physical activity level (low, moderate, high, or very high, based on metabolic equivalents [MET-minutes/week]) [23], use of antihypertensive medications (yes, no), use of lipid-lowering medications (yes, no), and use of antidiabetic medications (yes, no). Hyperlipidemia was diagnosed if participants met any of the following criteria: 1) triglycerides (TG) ≥ 150 mg/dL, 2) TC ≥ 200 mg/dL, 3) LDL-C ≥ 130 mg/dL, 4) HDL-C < 40 mg/dL for males or < 50 mg/dL for females, or 5) use of lipid-lowering medication [24]. The modified US FLI and the fibrosis-4 (FIB-4) score were utilized to assess NAFLD. The US FLI has been shown to predict hepatic steatosis, with a value greater than 30 indicating the presence of NAFLD [25]. The FIB-4 score is used to assess the risk of advanced liver fibrosis [26]. Patients were stratified into low (FIB-4 < 1.3), intermediate (1.3 ≤ FIB-4 ≤ 2.67), and high (FIB-4 > 2.67) fibrosis risk groups according to established thresholds [27].
The formula is as follows:
[25]
[28].
Statistical analysis
This study was conducted by NHANES guidelines, using a stratified, non-random sampling approach. All analyses accounted for the complex survey design and sampling weights of NHANES to ensure nationally representative estimates. Continuous variables were reported as weighted means ± standard error (SE) and analyzed using weighted linear regression, while categorical variables were presented as numbers of participants with weighted percentages and examined using weighted Rao-Scott chi-square tests. To assess the independent association between RC levels and the risk of CMM/CMD in NAFLD participants, multivariable logistic regression models were implemented with adjustments for clinical confounders. As part of sensitivity analysis, RC levels were categorized into quartiles, with trend tests conducted to calculate P-values. Three adjustment models were developed: model 1 without adjustments, model 2 controlling for age, sex, and race, and model 3 with additional adjustments for BMI, smoking and alcohol use, physical activity, and the use of antihypertensive, antidiabetic, and lipid-lowering medications. Further stratified analyses and interaction tests were performed based on age, sex, race, and BMI to examine effect modifications. Restricted cubic spline (RCS) models with knots at the 10th, 50th, and 90th percentiles were employed to explore potential non-linear relationships between RC levels and the odds of CMM in NAFLD patients. A significance level of P ≤ 0.05 was applied, and all statistical analyses were performed using R software (version 4.2.2).
Results
Basic characteristics of study participants
This study included a total of 5,663 participants. Based on weighted analyses, 28.15% of participants with NAFLD had concurrent CMM, while 68.36% had at least one CMD. Participants were divided into two groups according to the presence or absence of CMM, with their weighted baseline characteristics summarized in Table 1. Significant differences were observed in the weighted baseline distributions between the two groups. Compared to the non-CMM group, participants in the CMM group were older and had higher levels of fasting glucose, glycated hemoglobin (HbA1c), USFLI, FIB-4 score, serum creatinine, and uric acid. Additionally, the CMM group exhibited a higher prevalence of hyperlipidemia, lower levels of physical activity, and a greater proportion of smokers (all p-values < 0.0001). Figure 2 presents the overlap of the prevalence rates of the four major cardiometabolic conditions.
Table 1.
Weighted characteristics of the NAFLD participants with and without cardiometabolic multimorbidity
| Variables | total | non-CMM (N = 3757) | CMM (N = 1906) | P value |
|---|---|---|---|---|
| Age (year) | 53.77(0.29) | 50.24(0.31) | 62.77(0.38) | < 0.0001 |
| RC (mg/dL) | 31.49(0.32) | 31.11(0.37) | 32.47(0.52) | 0.02 |
| BMI (kg/m^2) | 34.15(0.14) | 34.05(0.16) | 34.40(0.22) | 0.19 |
| Fast glucose (mg/dl) | 119.27(0.59) | 108.42(0.41) | 146.98(1.57) | < 0.0001 |
| HbA1c (%) | 5.98(0.02) | 5.66(0.01) | 6.81(0.05) | < 0.0001 |
| ALT (U/L) | 31.40(0.60) | 32.54(0.81) | 28.49(0.51) | < 0.0001 |
| AST (U/L) | 26.73(0.25) | 26.81(0.32) | 26.52(0.36) | 0.56 |
| ALP (U/L) | 74.79(0.53) | 74.49(0.58) | 75.56(0.86) | 0.25 |
| GGT (U/L) | 38.89(0.70) | 39.08(0.87) | 38.41(1.15) | 0.64 |
| Serum Triglyceride (mg/dL) | 157.48(1.61) | 155.54(1.86) | 162.43(2.61) | 0.02 |
| Serum Cholesterol (mg/dL) | 195.49(0.86) | 200.11(1.06) | 183.70(1.27) | < 0.0001 |
| HDL (mg/dL) | 46.65(0.22) | 46.62(0.26) | 46.70(0.39) | 0.86 |
| LDL (mg/dL) | 117.36(0.74) | 122.38(0.93) | 104.54(1.10) | < 0.0001 |
| FLI | 52.34(0.34) | 49.89(0.36) | 58.59(0.65) | < 0.0001 |
| FIB4 score | 1.16(0.01) | 1.04(0.01) | 1.47(0.02) | < 0.0001 |
| Albumin (g/L) | 41.80(0.07) | 41.98(0.08) | 41.33(0.10) | < 0.0001 |
| Globulin (g/L) | 29.72(0.11) | 29.60(0.12) | 30.03(0.16) | 0.01 |
| Creatinine (mg/dL) | 0.90(0.01) | 0.87(0.01) | 0.97(0.01) | < 0.0001 |
| Uric acid (mg/dL) | 6.08(0.03) | 6.03(0.03) | 6.20(0.06) | 0.01 |
| BMI (kg/m^2) | 34.15(0.14) | 34.05(0.16) | 34.40(0.22) | 0.19 |
| Age (year) | < 0.0001 | |||
| 18–39 | 1005(20.32) | 940(26.57) | 65(4.37) | |
| 40–59 | 1918(41.17) | 1438(44.99) | 480(31.42) | |
| ≥ 60 | 2740(38.50) | 1379(28.44) | 1361(64.21) | |
| Sex | 0.06 | |||
| Male | 2993(54.58) | 1997(55.77) | 996(51.55) | |
| Female | 2670(45.42) | 1760(44.23) | 910(48.45) | |
| Race | < 0.0001 | |||
| Non-Hispanic White | 2629(72.33) | 1728(72.38) | 901(72.19) | |
| Non-Hispanic Black | 685(6.29) | 373(5.18) | 312(9.11) | |
| Mexican American | 1918(15.67) | 1369(17.01) | 549(12.26) | |
| other | 431(5.71) | 287(5.42) | 144(6.44) | |
| BMI category | 0.53 | |||
| < 25 | 306(4.29) | 190(4.20) | 116(4.50) | |
| ≥ 25, < 30 | 1551(24.66) | 1062(25.18) | 489(23.35) | |
| ≥ 30 | 3806(71.05) | 2505(70.62) | 1301(72.15) | |
| Physical activity | < 0.0001 | |||
| Mild | 1158(21.40) | 793(21.98) | 365(19.93) | |
| Moderate | 672(12.66) | 481(13.41) | 191(10.76) | |
| High | 371(7.13) | 249(7.16) | 122(7.04) | |
| Very high | 1603(30.92) | 1138(32.79) | 465(26.17) | |
| Not recorded | 1859(27.89) | 1096(24.67) | 763(36.10) | |
| Hypertension | < 0.0001 | |||
| No | 2262(41.66) | 2176(56.32) | 86(4.24) | |
| Yes | 3401(58.34) | 1581(43.68) | 1820(95.76) | |
| Diabetes | < 0.0001 | |||
| No | 3548(68.59) | 3272(89.43) | 276(15.40) | |
| Yes | 2115(31.41) | 485(10.57) | 1630(84.60) | |
| CHD | < 0.0001 | |||
| No | 5219(92.88) | 3710(99.00) | 1509(78.53) | |
| Yes | 414(6.72) | 34(1.00) | 380(21.47) | |
| Stroke | < 0.0001 | |||
| No | 5360(95.74) | 3740(99.46) | 1620(86.23) | |
| Yes | 303(4.26) | 17(0.54) | 286(13.77) | |
| Hyperlipidemia | < 0.0001 | |||
| No | 699(11.62) | 534(13.25) | 165(7.45) | |
| Yes | 4964(88.38) | 3223(86.75) | 1741(92.55) | |
| Drinking behavior | < 0.0001 | |||
| never drunk | 903(12.82) | 578(11.96) | 325(14.99) | |
| mild drunk | 2029(40.92) | 1379(41.37) | 650(39.76) | |
| moderate drunk | 792(16.11) | 607(17.79) | 185(11.83) | |
| heavy drunk | 28(0.65) | 24(0.85) | 4(0.16) | |
| not recorded | 1911(29.50) | 1169(28.03) | 742(33.25) | |
| Smoking behavior | < 0.0001 | |||
| Current smoke | 777(14.13) | 548(14.77) | 229(12.50) | |
| Ever smoke | 1835(33.06) | 1057(29.83) | 778(41.30) | |
| Never smoke | 3045(52.73) | 2148(55.34) | 897(46.09) | |
| not recorded | 6(0.08) | 4(0.07) | 2(0.11) |
Data are expressed as the number of participants and the weighted proportions for categorical variables and as weighted means ± SE for continuous variables. Linear regression and Rao-Scott chi-square test were used to compare groups
Fig. 2.
Venn diagram showing the overlap of prevalence of four cardiometabolic diseases (diabetes, CHD, hypertension, and stroke) in NAFLD participants. Abbreviations: CHD, coronary heart disease
Association between RC and CMM in NAFLD
We investigated the association between RC levels and the risk of CMM among patients with NAFLD. Table 2 summarizes the findings from three multivariable logistic regression models, all of which consistently demonstrated a statistically significant positive association between RC levels and the risk of CMM in NAFLD patients. Notably, in the fully adjusted model, each one-standard deviation (SD) increase in RC was associated with a 1.140-fold higher risk of CMM (OR = 1.158, 95% CI: 1.066,1.259, P < 0.001). Furthermore, when RC levels were categorized into quartiles, individuals in the higher quartiles (Q3 and Q4) had a 1.362-fold and 1.405-fold increased risk of CMM, respectively, compared to those in the lowest quartile (Q1) (all p-values < 0.05). This association remained significant even after adjusting for all other covariates in Model 3 (p for trend < 0.05).
Table 2.
Association between RC and the risk of prevalent CMM in NAFLD participants
| Model 1 OR (95% CI) | P value | Model 2 OR (95% CI) | P value | Model 3 OR (95% CI) | P value | |
|---|---|---|---|---|---|---|
| RC (per SD increase) (mg/dL) | 1.094(1.013,1.182) | 0.023 | 1.153(1.061,1.253) | < 0.001 | 1.158(1.066,1.259) | < 0.001 |
| Quartiles of RC | ||||||
| Quartile 1 | ref | ref | ref | |||
| Quartile 2 | 1.112(0.903,1.370) | 0.316 | 1.130(0.900,1.420) | 0.291 | 1.119(0.870,1.438) | 0.379 |
| Quartile 3 | 1.272(1.013,1.596) | 0.038 | 1.325(1.040,1.688) | 0.023 | 1.362(1.037,1.789) | 0.027 |
| Quartile 4 | 1.241(1.001,1.538) | 0.049 | 1.401(1.108,1.772) | 0.005 | 1.405(1.100,1.794) | 0.007 |
| P for trend | 0.031 | 0.003 | 0.003 | |||
Model 1: no covariates were adjusted. Model 2: Age, sex, and race were adjusted. Model 3: Adjusted for age, sex, race, BMI, smoking behavior, drinking behavior, physical activity, anti-hypertensive therapy, anti-diabetic therapy, and lipid-lowering therapy
Abbreviations: NAFLD Non-alcoholic Fatty Liver Disease, CMM Cardiometabolic multimorbidity, RC Remnant Cholesterol, BMI Body mass index
Next, we explored the relationship between RC levels and the prevalence of individuals with one or more CMDs. To assess this relationship, we applied three distinct multivariable logistic regression models, as presented in Table 3. The results consistently demonstrated a positive association between RC levels and the prevalence of both having at least one CMD and having three or more CMDs, after adjusting for all the covariates (all p-values < 0.05). This finding highlights the potential role of elevated RC as a contributing factor to the occurrence of multiple CMDs in NAFLD patients, further emphasizing its significance as a risk marker for cardiometabolic health.
Table 3.
Association between RC and the risk of prevalent cardiometabolic diseases in NAFLD participants
| RC (per SD increase) (mg/dL) | Model 1 OR (95% CI) | P value | Model 2 OR (95% CI) | P value | Model 3 OR (95% CI) | P value |
|---|---|---|---|---|---|---|
| One cardiometabolic disease | 1.088(0.998,1.186) | 0.055 | 1.111(1.014,1.217) | 0.024 | 1.159(1.047,1.282) | 0.005 |
| Two cardiometabolic disease | 1.094(1.013,1.182) | 0.023 | 1.153(1.061,1.253) | < 0.001 | 1.158(1.066,1.259) | < 0.001 |
| Three cardiometabolic disease | 1.094(0.975,1.227) | 0.125 | 1.172(1.027,1.337) | 0.019 | 1.168(1.010,1.350) | 0.037 |
Model 1: no covariates were adjusted. Model 2: Age, sex, and race were adjusted. Model 3: Adjusted for age, sex, race, BMI, smoking behavior, drinking behavior, physical activity, anti-hypertensive therapy, anti-diabetic therapy, and lipid-lowering therapy
Abbreviations: NAFLD Non-alcoholic Fatty Liver Disease, CMM cardiometabolic multimorbidity, RC Remnant Cholesterol, BMI Body mass index
Subgroup analysis
To further elucidate the complex relationship between RC levels and the risk of CMM in NAFLD patients, we conducted subgroup analyses and interaction tests across predefined subgroups (Fig. 3). A consistent positive association between elevated RC levels and increased CMM risk was observed across strata defined by sex, age, race, BMI, and FIB-4 score. Notably, a significant interaction was detected between RC and liver fibrosis stage as assessed by the FIB-4 score (P for interaction = 0.048). The association between RC and CMM appeared stronger in patients with high fibrosis risk.
Fig. 3.
Multivariable odds ratio (or) for CMM based on RC stratified by sex, age, race, BMI. Each stratification adjusted for all the factors (age, sex, race, BMI, smoking behavior, drinking behavior, physical activity, anti-hypertensive therapy, anti-diabetic therapy, and lipid-lowering therapy) except the stratification factor itself. Abbreviations: CMM: cardiometabolic multimorbidity; RC: Remnant Cholesterol; BMI, body mass index
Dose–response relationships between RC and CMM in NAFLD
We used RCS to explore the potential nonlinear relationship between RC levels and the risk of CMM in NAFLD patients. Figure 4 visually illustrates the dose–response relationship between RC and CMM. After adjusting for covariates, a significant overall association was observed between RC and CMM risk (P for overall < 0.001), with a clear nonlinear pattern (P for nonlinear = 0.049). We found that as RC levels increased, the risk of CMM gradually elevated. However, when RC levels exceeded 40 mg/dL, the increase in CMM risk became relatively more gradual.
Fig. 4.
Dose–response relationship between RC and CMM. Values represent difference in predicted response in reference to RC of mean. Red solid lines represent restricted cubic spline models. Adjusted for age, sex, race, BMI, smoking behavior, drinking behavior, physical activity, anti-hypertensive therapy, anti-diabetic therapy, and lipid-lowering therapy. Abbreviations: CMM: cardiometabolic multimorbidity; RC: Remnant Cholesterol; BMI, body mass index
Discussion
This study investigated the potential relationship between RC levels and the risk of CMM in the general adult NAFLD population in the United States. The results revealed a significant association between increased RC levels and elevated CMM risk. This relationship remained statistically significant even after adjusting for all confounding variables (OR = 1.158, 95% CI: 1.066,1.259, P < 0.001). Additionally, RCS analysis demonstrated a progressive increase in CMM risk with rising RC levels, with the risk reaching a plateau when RC exceeded 40 mg/dL. Furthermore, subgroup analyses with interaction tests showed that the effect was more pronounced in patients at high risk of fibrosis. To our knowledge, this is the largest study to date examining the association between RC and CMM in the adult NAFLD population in the United States.
Recent studies have shown that the prevalence of cardiometabolic diseases among NAFLD patients is as follows: diabetes (26.9%), hypertension (35%), coronary heart disease (44.6%), and stroke (5.0%) [29–32]. Notably, these conditions are strongly linked to hepatic fat accumulation and the severity of metabolic syndrome. The underlying mechanisms by which NAFLD contributes to CMD are complex, including potential endothelial dysfunction [33], disruption of homocysteine metabolism leading to oxidative stress [34], the release of cytokines, hepatic factors, and adipokines that impair the cardiovascular system [35, 36], as well as alterations in lipid metabolism, such as reduced HDL-C levels and elevated triglycerides and LDL-C, which promote atherosclerosis [37, 38]. Some studies have found that NAFLD is an independent predictor of adverse CMD outcomes, regardless of traditional risk factors [39]. Moreover, increasing evidence supports a strong association between NAFLD and CMD, an association that is now widely recognized in the field of hepatology [31]. Further studies have even suggested that NAFLD is closely related to the increased risk and severity of CMD [40]. Therefore, despite the incomplete understanding of the relationship between NAFLD and CMD, NAFLD patients have a significantly higher risk of CMD compared to the general population, highlighting the importance of further investigating the risk of CMD/CMM in NAFLD patients to better predict disease progression and enable early interventions.
Previous research has shown that the presence of one CMD significantly increases the risk of developing another. Once multiple CMDs combined to form CMM, the health risks escalated exponentially, leading to significantly higher mortality rates [3, 41]. Numerous studies have investigated strategies to prevent CMD from progressing to CMM, emphasizing the critical role of lifestyle and clinical risk factors in this transition [3, 42, 43]. In this study, we found that, compared to non-CMM patients, those with CMM exhibited higher lipid levels, a higher prevalence of smoking, and lower physical activity levels, which aligned with previous findings [44–46]. Consistent with prior studies that reported an association between RC and atherosclerotic cardiovascular diseases, we found that a 1.0 mmol/L increase in RC was associated with a 2.8-fold higher risk of heart disease [47, 48]. Additionally, a meta-analysis involving 137,895 individuals showed that RC was a key target for reducing cardiovascular risk, and another two-sample Mendelian randomization analysis also confirmed the risk role of RC in cardiovascular and cerebrovascular diseases [49, 50]. In our study, we further examined the relationship between RC levels and CMM risk in NAFLD patients. As previously mentioned, NAFLD is closely linked to the onset and progression of various cardiovascular and metabolic diseases. Our findings emphasize the potential key role of RC in this process, showing that elevated RC levels are significantly associated with the risk of CMM in NAFLD patients. However, it is noteworthy that the statistical significance weakened when the number of cardiometabolic conditions increased from two to three. This attenuation is largely attributable to the small sample size of participants with three conditions, which reduces statistical power and leads to wider confidence intervals, particularly in Model 1. Furthermore, individuals with three coexisting cardiometabolic diseases tend to exhibit more complex and heterogeneous metabolic disturbances, potentially introducing residual confounding even after multivariable adjustment.
Importantly, we observed a significant interaction between RC levels and liver fibrosis stage, suggesting that the association between RC and CMM may be modified by hepatic fibrosis severity. This may be explained by the fact that individuals with advanced fibrosis often exhibit more pronounced metabolic disturbances, systemic inflammation, and atherogenic dyslipidemia. These factors could amplify the adverse cardiometabolic effects of elevated RC, reflecting greater impairments in lipid metabolism and IR specifically in patients with high fibrosis risk. To further explore the nature of the relationship between RC and CMM risk, we conducted an RCS analysis, which demonstrated a nonlinear association in NAFLD patients. The risk of CMM increased progressively with rising RC levels; however, the rate of increase tended to plateau at approximately 40 mg/dL. Although the biological mechanisms underlying this apparent saturation effect are not fully elucidated, a potential explanation may be inferred from observations in atherosclerosis research. Specifically, excessive concentrations of certain amino acids have been shown to induce sustained activation of the mTORC1 pathway, resulting in maximal suppression of protective autophagy, beyond which pro-inflammatory and pro-atherogenic effects no longer increase in a linear manner [51]. By analogy, the atherogenic effects of RC, including macrophage foam cell formation and vascular inflammation, may also be subject to capacity limits, a possibility that warrants further mechanistic and longitudinal studies. Consequently, although the incremental risk beyond this level appeared attenuated, the overall risk remained elevated, and RC levels at or above this range were still indicative of a relatively high cardiometabolic burden.
The exact mechanisms underlying the relationship between RC, NAFLD, and CMM have not yet been fully elucidated. In Chen's study, a significant association was found between RC levels and the incidence of NAFLD, with each 1-SD increase in RC corresponding to a 14.3% increase in the risk of developing NAFLD [52]. Additionally, a study from an Italian hospital involving 798 unselected patients with cardiometabolic diseases found that 79.2% of them had NAFLD. Compared to patients without NAFLD, those with NAFLD had higher median fasting RC levels, and RC levels were correlated with the severity of liver disease in NAFLD patients [53]. The potential mechanisms behind this association are worth further investigation. RC represents different lipoprotein particles with varying densities, volumes, protein content, and core lipid compositions [54]. Similar to LDL-C, RC can traverse the arterial wall and be engulfed by macrophages and smooth muscle cells, leading to foam cell formation, atherosclerosis, and low-grade sterile inflammation [55, 56]. In patients with elevated RC but low LDL-C levels, increased inflammation in the arterial wall is also a hallmark, further triggering inflammatory responses [57]. These mechanisms suggest how elevated RC could contribute to the development of CMM.
Additionally, IR may play an intermediary role in the development of CMD. In NAFLD patients, the disease promotes the development of IR through mechanisms such as lipid metabolism dysfunction, oxidative stress, and chronic inflammation [58]. IR impairs the liver's ability to clear VLDL, leading to the excessive accumulation of triglyceride-rich remnant lipoproteins, which further exacerbates dyslipidemia [59]. This atherogenic dyslipidemia is characterized by elevated triglyceride and VLDL remnant lipoprotein levels, reduced HDL-C levels, an increase in small dense LDL (sd-LDL) particles at high concentrations, and normal or slightly elevated LDL-C levels [60, 61]. Furthermore, in patients with obesity, diabetes, or metabolic syndrome, the activation of hormone-sensitive lipase accelerates triglyceride hydrolysis from adipose tissue, inducing elevated plasma-free fatty acid levels and contributing to hepatic fat accumulation [62]. These metabolic disturbances and lipoprotein profile changes may collectively explain the association between RC levels and CMM risk in NAFLD patients. However, the exact role of RC in NAFLD patients with concurrent CMM and its underlying mechanisms have not been fully elucidated, warranting further investigation in future studies.
This study's primary strength lies in its novel use of large-scale, cross-sectional NHANES data to investigate the relationship between RC levels and the risk of CMM in NAFLD patients. However, several limitations should be acknowledged. First, the cross-sectional design of the study precludes the establishment of a causal relationship between RC levels and CMM risk. Because the temporal sequence between RC elevation and the development of CMM cannot be determined, the possibility of reverse causality should be considered. Patients with established CMM and NAFLD often exhibit metabolic disturbances, such as chronic inflammation, IR, and hepatic dysfunction, which could themselves lead to elevated RC levels rather than RC contributing to disease onset. In addition, despite adjustment for multiple confounders, unmeasured or imprecisely measured metabolic factors may still influence the observed associations. Therefore, the findings should be interpreted cautiously, and future longitudinal cohort studies or randomized controlled trials are required to clarify the causal direction. Second, the diagnosis of NAFLD was based on the US FLI rather than liver biopsy, the clinical gold standard. A US FLI ≥ 30 has been reported to have a sensitivity of approximately 62% and a specificity of 88% for detecting hepatic steatosis in the U.S. population [25]. Importantly, the proportion of individuals with a US FLI ≥ 30 (approximately 21%) closely matches the prevalence of ultrasound-detected hepatic steatosis (around 20%) in the U.S. population [25], supporting its suitability for population-based epidemiological studies. These characteristics indicate that the US FLI is particularly useful for ruling in fatty liver in large-scale surveys such as NHANES. Nevertheless, as an indirect and non-invasive measure, the US FLI cannot fully substitute for liver biopsy, and some degree of misclassification is inevitable, which may have attenuated the observed associations. Third, laboratory results, including RC levels, were based on a single measurement, which does not account for long-term variations in these indicators. Fluctuations in lipid levels over time could influence the observed associations, and repeated measurements would provide a more robust assessment. Fourth, while a substantial number of confounding variables were controlled for, residual confounding cannot be entirely ruled out. Certain unmeasured factors, such as dietary habits, medication use, may still have influenced the findings. Fifth, the diagnosis of CMM was partly derived from baseline questionnaires and self-reports, introducing the potential for information bias or underreporting. Sixth, we acknowledge the recent shift in terminology from NAFLD to metabolic dysfunction-associated steatotic liver disease (MASLD). Our study used NAFLD terminology due to the absence of imaging- or biopsy-confirmed steatosis, which precludes strict classification under current MASLD criteria. Nevertheless, population-based studies have demonstrated an extremely high overlap, often exceeding 99 percent, between individuals meeting MASLD criteria and those previously classified as having NAFLD [63, 64], which supports the continued use of NAFLD terminology in this context. This limitation should be considered when interpreting the results. Lastly, the study cohort consisted of adult NAFLD patients from the United States, which may limit the generalizability of the results to other populations. Future multi-center, prospective studies with more comprehensive diagnostic approaches and repeated biomarker measurements are warranted to further elucidate these relationships.
Conclusions
Our cross-sectional study found a significant positive correlation between RC levels and CMM risk in adult NAFLD patients. These findings highlight the potential role of RC in assessing CMM risk in this population, offering valuable insights for developing more effective diagnostic and intervention strategies.
Acknowledgements
The CDC sponsored the data collection for the NHANES.
Abbreviations
- CMM
Cardiometabolic multimorbidity
- NAFLD
Non-alcoholic fatty liver disease
- RC
Residual cholesterol
- NHANES
National Health and Nutrition Examination Survey
- RCS
Restricted cubic spline
- CMD
Cardiometabolic disease
- IR
Insulin resistance
- LDL-C
Low-density lipoprotein cholesterol
- IDL
Intermediate-density lipoproteins
- CDC
Centers for Disease Control and Prevention
- USFLI
Ultrasound fatty liver index
- TC
Total cholesterol
- HDL-C
High-density lipoprotein cholesterol
- CHD
Coronary heart disease
- BMI
Body mass index
- MET
Metabolic equivalents
- TG
Triglycerides
- FIB-4
Fibrosis-4
- SE
Standard error
- HbA1c
Glycated hemoglobin
- sd-LDL
Small dense low-density lipoprotein
Authors’ contributions
YLX contributed to the study design, data analysis, and manuscript drafting. HL was responsible for data collection, statistical analysis, and manuscript revision. YXX and DL assisted with data interpretation and provided technical support. JGC, JYY, and KH conceived and designed the study, supervised the research, and provided critical revisions. All authors have read and approved the final manuscript.
Funding
This study was supported by the Zhejiang Medical and Health Science and Technology Program (2025KY246).
Data availability
Publicly available datasets were analyzed in this study. This data can be found in the NHANES ([https://www.cdc.gov/nchs/nhanes/index.htm](https:/www.cdc.gov/nchs/nhanes/index.htm)).
Declarations
Ethics approval and consent to participate
The National Center for Health Statistics (NCHS) Research Ethics Review Board approved the investigation protocol for NHANES and all study participants signed an informed consent term. The informed consent procedures for all participants are publicly available through the CDC.gov website.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Yilian Xie and Hui Li has contributed equally to this work.
Contributor Information
Jinguo Chu, Email: chujg@126.com.
Jiayuan Ye, Email: javin2010@163.com.
Kai Huang, Email: huangkaihra@163.com.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
Publicly available datasets were analyzed in this study. This data can be found in the NHANES ([https://www.cdc.gov/nchs/nhanes/index.htm](https:/www.cdc.gov/nchs/nhanes/index.htm)).