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Beyond LDL cholesterol: remnant cholesterol is associated with cardiometabolic risk factors in children

BMC Medicine volume 23, Article number: 28 (2025) Cite this article

Abstract

Background

Recent evidence from both randomized controlled trials and cohort studies in adults suggests that plasma remnant cholesterol (RC) levels predict cardiovascular disease. In children, studies are scarce, although high levels of RC might represent a marker of early atherosclerotic damage. Thus, the aim of this study was to explore the cardiometabolic risk associated with RC, which extends beyond low-density lipoprotein cholesterol (LDL-c) in children.

Methods

Cardiometabolic risk factors (plasma insulin levels, homeostatic model assessment for insulin resistance, mean arterial blood pressure (MAP), waist circumference (WC), and cardiorespiratory fitness (CRF)) were examined in 3417 Spanish schoolchildren aged 8–11 years. The children were categorized into four subgroups (low vs. high) based on the cutoff of ≥ 110 mg/dL for LDL-c and of ≥ 15 mg/dL for RC to define higher levels, and ANCOVA models were applied to assess the role of both lipid parameters in cardiometabolic risk. Additionally, multilevel mixed-effects generalized linear regression models were used to assess the associations of RC or LDL-c with cardiometabolic risk factors and to examine whether the associations between RC and these factors varied in children with low or high LDL-c levels.

Results

Children in the high-RC subgroups, specifically those with low LDL-c/high RC and high LDL-c/high RC, presented significantly greater insulin levels and WC than did their peers in the low-RC subgroups. RC was more strongly associated with cardiometabolic risk factors than LDL-c (insulin β = 2.073/ − 0.026; HOMA-IR β = 0.451/ − 0.002; MAP β = 1.214/0.300; WC β = 2.842/1.058; and CRF β = − 0.316/ − 0.194 for RC and LDL-c, respectively). Furthermore, RC exhibited associations even in children with low LDL-c levels: insulin (β = 2.305; p < 0.001), HOMA-IR (β = 0.499; p < 0.001), MAP (β = 1.397, p < 0.001), WC (β = 2.842; p < 0.001), and CRF (β = − 0.367; p < 0.001).

Conclusions

The associations between RC and cardiometabolic risk factors were stronger than those between LDL-c and cardiometabolic risk, extending its significance even in children with low LDL-c levels. These findings may be clinically useful for cardiovascular risk stratification and for guiding future interventions in children, although they should be confirmed by longitudinal studies.

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Background

Cardiovascular diseases (CVDs) continue to be a major public health concern worldwide and contribute significantly to morbidity and mortality in all age groups [1]. Cardiometabolic alterations may appear at early ages and frequently track into adulthood. The prevalence of high cardiometabolic risk in children and adolescents has been steadily increasing over the past four decades, driven mainly by rising rates of childhood obesity and sedentary lifestyles [2, 3]. Identifying accurate and reliable markers for assessing cardiometabolic risk in this vulnerable population is essential for implementing early intervention strategies and curbing the burden of CVD in adulthood.

Traditionally, low-density lipoprotein cholesterol (LDL-c) has been considered the primary lipid fraction for assessing cardiometabolic risk [4]. Elevated plasma LDL-c levels have long been recognized as critical contributors to atherosclerosis and subsequent CVD in adults [5]. Consequently, LDL-c has been extensively studied as a target for lipid-lowering therapies in both pediatric and adult populations [6, 7]. In recent years, however, remnant cholesterol (RC), also known as remnant lipoprotein cholesterol, has emerged as a potential cardiovascular risk marker [8] because there is consistent evidence of the associations between increased plasma RC levels and high risk of CVD, stroke, cardiac death, and all-cause mortality in adulthood [9]. In fact, even after lowering LDL-c to the recommended level, a substantial burden of CVD remains unaddressed. Part of this residual risk may be due to triglyceride-rich lipoproteins [8, 10]. In children, studies are scarce, although high levels of RC might represent a marker of early atherosclerotic damage [11].

The plasma RC includes cholesterol-rich lipoproteins, such as chylomicron remnants, intermediate-density lipoproteins, and very-low-density lipoprotein remnants, which are generated during the hydrolysis of triglyceride-rich lipoproteins. These remnants are smaller, denser particles that are not efficiently cleared from the bloodstream, potentially leading to increased deposition of cholesterol in arterial walls and atherogenic processes [12]; this may explain, at least in part, the residual risk for CVD despite LDL-c being reduced to the recommended levels [8, 13].

Given the potential importance of RC as an independent CVD risk factor, comparing its associations with cardiometabolic risk with LDL-c is important for developing targeted intervention strategies and optimizing lipid management approaches. To address this issue, the aims of this study were (i) to examine the associations of plasma RC and LDL-c levels with several cardiometabolic risk factors, including insulin, blood pressure, waist circumference (WC), and cardiorespiratory fitness (CRF); (ii) to determine the associations of LDL-c and RC with cardiometabolic risk factors; and (iii) to assess the associations of RC with cardiometabolic risk factors accounting for LDL-c levels.

Methods

Study design and participants

We analyzed data from a repeated cross-sectional study consisting of baseline measurements from four cluster randomized trials conducted in 2004 (MOVI study; n = 1103) [14], 2010 (MOVI-2 study; n = 1138) [15], 2017 (MOVI-da-Fit study; n = 487) [16], and 2022 (e-MOVI study; n = 689) [17] involving 3417 schoolchildren aged 8–11 years in 4th–6th grades from public schools in Cuenca, Spain. These studies aimed to analyze physical activity, dietary behavior, lifestyle, and cardiovascular risk factors that occur during childhood. The Ethics Committee for Clinical Research of the Virgen de la Luz Hospital in Cuenca approved the study protocol. In accordance with the Declaration of Helsinki, legal guardians were asked to sign informed consent forms allowing their children to participate. Participants’ privacy and confidentiality were ensured. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines [18].

Study variables

Methods for each study have been reported elsewhere [14, 17]. In each study, trained researchers measured the variables under standardized conditions. Anthropometric variables were measured twice at 5-min intervals, and the mean was used for the analyses. Weight was measured to the nearest 100 g with a calibrated digital scale (SECA model 861; Vogel & Halke, Hamburg, Germany), with the children lightly dressed and without shoes. Height was measured to the nearest mm using a wall-mounted stadiometer (SECA 222, Vogel and Halke, Hamburg, Germany), with the children standing upright against the wall without shoes to align the spine with the stadiometer. The head was positioned with the chin parallel to the floor. Body mass index was calculated as weight in kg divided by the square of the height in meters (kg/m2).

Blood samples were drawn from the cubital vein at school between 8.15 and 9.00 am and after a 12-h fast. Total cholesterol, triglyceride (TG), high-density lipoprotein cholesterol (HDL-c), and insulin levels were determined using procedures extensively described elsewhere [14, 17]. The homeostatic model assessment for insulin resistance (HOMA-IR) score was calculated via the following formula: fasting insulin in μU/mL × fasting glucose in mg/dL)/405 [19]. The standard serum lipid profile was used to calculate LDL-c. Because the Friedewald equation tends to provide lower estimates of LDL-c when hypertriglyceridemia is present (≥ 150 mg/dL), we chose the Sampson equation, which has been shown to be a more accurate estimate in individuals with hypertriglyceridemia and/or low LDL-C levels [20, 21]. RC levels were calculated by subtracting the sum of HDL-c and LDL-c from total cholesterol (TC), as previously described [8].

Diastolic and systolic blood pressures (DBP and SBP) were determined by the mean of two measurements taken at an interval of 5 min, with the subject resting for at least 5 min before the first measurement. The participant was seated in a quiet and calm environment, with the right arm placed in a semi-flexed position at heart level. Blood pressure was measured by an automated procedure using an Omron M5-I monitor (Omron Healthcare Europe BV, Hoofddorp, the Netherlands). The mean arterial pressure (MAP) was then calculated using the following formula: DBP + [0.333 (SBP—DBP)].

Waist circumference was determined by the mean of two measurements taken with flexible tape at the waist (at the midpoint between the last rib and the iliac crest). The CRF was determined using the Course-Navette test (20-m shuttle run test) following Leger’s protocol [22]. The last half-stage completed (1 stage = 1 min) by the children was considered the CRF level. WC and CRF were not assessed in the 2004 study.

Sexual maturity was obtained by a standardized procedure in which parents identified their children’s pubertal status using figures based on Tanner stages [23]. Food consumption was estimated using the Children’s Eating Habits Questionnaire (CEHQ) [24], which is completed by parents. In this study, we included daily fruit and vegetable consumption as covariates, as they are recommended for a healthy diet. In contrast, daily intake of sweetened soft drinks and sweets was included as a potential indicator of an unhealthy diet. Physical activity was measured using GENEActive accelerometers (ActivInsights) for 7 consecutive days (including nights), with a fixed frequency of 30.0 Hz, to record the raw acceleration data measured in “g” for each movement axis (x, y, and z) in units of milligrams (1000 mg = 1 g = 9.81 m/s2). A valid measurement was considered for reports of at least 5 days, including one weekend day. The daily time (min) of moderate or vigorous physical activity was considered as a covariate.

Statistical analysis

Sex differences in the children’s baseline characteristics were tested using Student’s t-test for continuous variables and the chi-square test for categorical variables. Levene’s test was used to assess variance homogeneity. Before the analyses, we tested the normality of the distribution of the variables using both statistical (the Kolmogorov–Smirnov test) and graphical (normal probability plot) approaches. To limit the influence of outliers, we winsorized all continuous variables at the 1st and 99th percentiles. In addition, likelihood ratio tests were performed to explore sex as a potential effect modification of the study associations.

Bivariate correlation coefficients were estimated to examine the associations of RC and LDL-c with other cardiometabolic risk factors, including the CRF level. Scatterplots depicting the correlations between LDL-c or RC and cardiometabolic risk factors are presented only for the study conducted in 2010 because figures involving the entire sample made visualization challenging owing to the large sample size. However, the corresponding correlations for each study year and by sex are also reported.

To identify the minimum sufficient adjustment set (MSAS) for the associations of LDL-c, RC, and cardiovascular risk in children, we built a theoretical causal diagram based on previous associations between lipid parameters and insulin, HOMA-IR, MAP, WC, and CRF available in the scientific literature [25,26,27,28]. We used the online tool DAGitty [29] to construct a directed acyclic graph (DAG) [30]. The covariates age, sex, HDL-c, Tanner stage, physical activity, and diet were identified as the MSAS (Additional file 1: Fig. S1, panel “a”). Panel b shows the DAG after adjustment for the covariates available in the four studies included in the main analyses (i.e., age, sex, and HDL-c).

Concordance/discordance subgroups between LDL-c and RC levels were determined using a clinical cutoff of ≥ 110 mg/dL for LDL-c and of ≥ 15 mg/dL for RC to define higher levels [31, 32]. Consequently, the sample was categorized into four subgroups: low LDL-c/low RC (both lipid levels below the clinical cutoff), high LDL-c/low RC, low LDL-c/high RC, and high LDL-c/high RC (both above the clinical cutoff). These subgroups were identified in scatterplots, and the proportion of each subgroup was calculated for each year of study.

Analysis of covariance (ANCOVA) models were used to estimate marginal means (with 95% confidence intervals) for cardiometabolic risk factors across subgroups based on LDL-c and RC levels.

Multilevel mixed-effects generalized regression models were used, including cardiometabolic risk factors (i.e., insulin, MAP, WC and CRF) as the dependent variables, z-score of LDL-c or RC levels as fixed-effect factors, and study year (i.e., 2004, 2010, 2017, and 2022) as the random-effects factor. An initial unadjusted model was estimated, followed by a model controlling for age, sex, and HDL-c. Furthermore, these models were replicated for children with high LDL-c levels (≥ 110 mg/dL) and low LDL-c levels (< 110 mm/dL).

Analyses were conducted for the full sample (studies from 2004, 2010, 2017, and 2022). Additional analyses were also performed using data from the 2017 study, as this was the only year in which all variables identified as MSAS in the DAG were available.

All analyses were performed using the Statistical Package for the Social Sciences (SPSS, version 28.0; IBM Corp., Chicago). The level of significance was set at α = 0.05.

Results

The characteristics of the participants in the total sample and by sex are summarized in Table 1. Overall, boys had a greater mean WC (67.4 cm) than girls (65.9 cm) and greater CRF levels (4.3 stages) than girls (3.2 stages). The results from the likelihood ratio tests revealed sex as a modifying factor between the z-score of RC, WC, and CRF (p < 0.05) (Additional file 1: Table S1). In general, boys with high RC had significantly higher WC values and CRF levels than those boys with low RC, whereas in girls, there were significant differences only between the low LDL-c/low RC and high LDL-c/high RC groups (Additional file 1: Fig. S3).

Table 1 Characteristics of the study participants in the total sample and stratified by sex
Full size table

Baseline cardiometabolic risk factor characteristics by study year showed slightly higher insulin and HOMA-IR levels and higher levels of CRF over the years (Additional file 1: Table S2). Overall, the characteristics of participants in the 2017 study were similar to those of the pooled sample including all studies (Additional file 1: Table S3).

Figure 1 displays the bivariate correlation coefficients between cardiometabolic risk factors (insulin, HOMA-IR, MAP, WC, and CRF) and the levels of RC and LDL-c by sex. The RC levels were significantly correlated with all cardiometabolic risk factors in boys and girls; however, LDL-c levels were significantly correlated with all cardiometabolic factors in boys and with only WC and CRF in girls. In addition, all the correlation coefficients had a much greater magnitude for the RC than for LDL-c in both sexes. Similar correlation coefficient estimates were found when stratified by study year (in the 2004, 2010, 2017, and 2022 cross-sectional analyses) and in participants from the 2017 study (Additional file 1: Table S4 and Fig. S4).

Fig. 1
figure 1

Scatterplots depicting the relationships between cardiometabolic risk factors and levels of LDL-c and remnant cholesterol (RC) and including Pearson’s correlation coefficient (p value) by sex

Full size image

Figure 2 shows the lipid profile subgroups according to clinical cutoff levels (≥ 110 mg/dL for LDL-c and ≥ 15 mg/dL for RC) by year of study. Most of the children were in the low LDL-c/low RC subgroup (between 56.8.2 and 68.4%, depending on the sample), between 13.0 and 24.0% were in the low LDL-c/high RC subgroup, and, overall, one quarter of the children were in the high RC subgroups. Analysis of the entire sample by sex (Additional file 1: Fig. S2) and in participants from the 2017 study (Additional file 1: Fig. S6) revealed similar results.

Fig. 2
figure 2

Subgroups of concordance/discordance on levels of low-density lipoprotein cholesterol (LDL-c; mg/dL) and remnant cholesterol (RC; mg/dL) across year of study (observations: the lower part of the figure displays columns illustrating the frequencies of lipid profile subgroups concerning the values of LDL-c and RC)

Full size image

Figure 3 shows the marginal mean differences in cardiometabolic risk factors between participants in lipid profile subgroups according to their LDL-c and RC levels. Overall, children in high RC subgroups (low LDL-c/high RC and high LDL-c/high RC) had significantly greater insulin levels, HOMA-IR, MAP, and WC and lower levels of CRF than did their peers in low RC subgroups. The results were significant for insulin, HOMA-IR, and WC in both unadjusted means and adjusted means by age, sex, and HDL-c. Age- and HDL-c-adjusted analyses stratified by sex revealed that, in boys, compared with those with low RC, all cardiometabolic risk factors, except for MAP and CRF, were significantly lower among subgroups with high RC. In girls, these differences were similar except for MAP, WC, and CRF (p > 0.05) (Additional file 1: Fig. S3). The results were similar for participants in the 2017 study after adjustment for age, HDL-c, Tanner, physical activity, and diet (Additional file 1: Fig. S5, Fig. S7).

Fig. 3
figure 3

Analysis of covariance (ANCOVA) models estimating marginal means of cardiometabolic risk factors by subgroup (lipid profile categories according to low-density lipoprotein cholesterol [LDL-c] and remnant cholesterol [RC] levels)

Full size image

Table 2 shows the variation in cardiometabolic risk factors according to continuous z-scores of LDL-c and RC levels, unadjusted and adjusted for age, sex, and HDL-c. According to the unadjusted models, LDL-c was significantly positively associated with MAP and WC (β from 0.261 to 0.939) but negatively associated with CRF (β = − 0.213). On the other hand, we observed a greater significant effect of RC on insulin, HOMA-IR, MAP, and WC (β from 0.528 to 3.952) and on CRF (β = − 0.482). The results were similar when the models were adjusted for age, sex, and HDL-c; in the 2017 study, they were adjusted for age, sex, HDL-c, Tanner stage, physical activity, and diet (Additional file 1: Table S5) and when the analyses were performed by sex for the entire sample (Additional file 1: Table S7) and for the 2017 study (Additional file 1: Table S9).

Table 2 Beta coefficient (95% confidence interval) of cardiometabolic risk factors (dependent variables) for continuous z-scores of LDL cholesterol (LDL-c) and remnant cholesterol (RC) levels (main independent variables), obtained using multilevel mixed-effects generalized linear regression models
Full size table

Table 3 shows the associations of the z-score of RC with cardiometabolic risk factors in children with low and high LDL-c levels. Similar values of RC, for both LDL-c levels, were observed for insulin (β = 2.305 vs. β = 1.573), HOMA-IR (β = 0.499 vs. β = 0.347), MAP (β = 1.397 vs. β = 0.749), and CRF (β = − 0.367 vs. β = −0.284). The results were similar for the 2017 study after adjustment for age, sex, HDL-c, Tanner stage, physical activity, and diet (Additional file 1: Table S6) and when the analyses were performed by sex for the entire sample (Additional file 1: Table S8) and for the 2017 study (Additional file 1: Table S10).

Table 3 Beta coefficient (95% confidence interval) of cardiometabolic risk factors for continuous z-score of remnant cholesterol levels obtained in multilevel mixed-effects generalized linear regression models according to LDL-c level (low vs. high) adjusted by age, sex, and HDL-c
Full size table

Discussion

Comparative analyses of the associations of plasma RC and LDL-c with other cardiometabolic risk factors may be useful in determining the appropriateness of including this CVD biomarker in routine lipid profile determinations. To this end, this study analyzed data from 3417 schoolchildren and examined the role of RC as a cardiometabolic risk biomarker beyond LDL-c in children. We found that (i) elevated RC levels were associated with higher insulin, MAP, WC and lower CRF levels; (ii) children in the low LDL-c/high RC subgroup had worse cardiometabolic risk profiles than did those in the high LDL-c/low RC subgroup; (iii) the value of RC for increased cardiometabolic risk factors was better than that of LDL-c; and (iv) even for children with low LDL-c levels, RC was associated with all cardiometabolic risk factors.

The role of LDL-c in the development of CVD has been consistently established [33, 34]. In the cardiovascular field, RC has been extensively studied, identifying it as the key factor contributing to the residual risk of major cardiovascular events [35, 36]. However, in childhood, the role of RC versus LDL-c in cardiometabolic risk has been underexplored. Our analysis revealed that participants with high RC/low LDL-c had greater cardiometabolic risk than did those with concordant low RC/low LDL-c. This increased risk was not observed in children with low RC/high LDL-c. These results suggest that RC may modify cardiometabolic risk beyond the atherogenic LDL-c burden and would be consistent with so-called atherogenic dyslipidemia, a lipid abnormality characterized by increased levels of circulating triglycerides and decreased levels of HDL-c, whereas LDL-c remains in the normal range. This condition is commonly associated with an increased risk of CVD and is recognized as a significant contributor to the residual risk associated with lipid levels, regardless of LDL-c levels [35, 37]. Several mechanisms have been proposed to explain this association. RC particles are considered the predominant atherogenic agent that feeds the development of arterial wall plaques [38]. In fact, it includes all cholesterol fractions that are not included in HDL-c or LDL-c, including very-low-density lipoprotein and intermediate-density lipoprotein, which are even more atherogenic than LDL-c [39]. These particles can cross the arterial wall and be endocytosed by macrophages and smooth muscle cells. The larger size of these particles compared with LDL-c may become trapped in the intima, making it difficult for them to return to the bloodstream [40]. In addition, RC has been implicated in the pathogenesis of atherosclerosis by promoting low-grade inflammation, which has not been observed for LDL-c [41]. Despite this evidence, the observations are not yet mechanistically explained and set the stage for further studies to understand the relationship between RC and cardiovascular risk in children. The greater association of RC over LDL-c with cardiometabolic risk was also supported by our regression analyses.

Serum insulin and WC were the cardiometabolic risk factors most strongly associated with RC levels. Although the mechanisms responsible for the alteration in blood glucose metabolism caused by RC are not fully understood, damage caused to β cells by excess cholesterol has been recognized [42], and individuals with low LDL-c/high RC levels may be more likely to develop diabetes mellitus [43]. Additionally, abdominal obesity is strongly associated with insulin resistance and dyslipidemia [44], especially with RC in children [45]. CRF is also considered an additional and important risk factor in any cardiometabolic cluster risk score for children [44]. Our analysis of CRF levels in both boys and girls revealed significant differences between those children with high RC and those with low RC.

The determination of plasma RC is not implemented in standard clinical practice, and international clinical practice guidelines for the assessment of blood lipids and lipid lowering strategies in the youth population are currently limited and do not consider reference ranges for RC [45]. However, our study revealed that the association between RC and major cardiometabolic risk factors in children with low LDL-c levels was significant and similar to that in children with high LDL-c levels. Thus, a nonnegligible percentage of the children included in the low LDL-c/high RC subgroup was not considered to be at cardiometabolic risk because the traditional measure of risk, LDL-c, is low. This finding reinforces the evidence from recent large primary prevention studies suggesting that consideration of RC provides important residual risk information beyond LDL-c and non-HDL-c or apoB, particularly in individuals with mild to moderate hypertriglyceridemia [35, 46].

Finally, it is important to study how reducing RC levels affects cardiometabolic risk. In adults, lifestyle interventions such as diet, increased physical activity, limiting alcohol intake, and avoiding calorie-dense foods are critical for controlling triglyceride-rich lipoproteins and RC. However, when lifestyle modifications are not sufficient to reduce it, statins have been shown to reduce cardiovascular disease and major adverse cardiovascular events with few side effects. Moreover, reducing plasma RC levels with APOC3 inhibitors, PCSK9 inhibitors, and omega-3 fatty acids may have long-term benefits [47, 48]. However, more studies are needed on the appropriate treatments for elevated levels of RC. In children, the best approach to achieve this goal has not yet been determined. Focusing on lifestyle therapy, including diet and regular physical activity, may be a viable strategy, as is recommended for lowering LDL-c [47]. Pharmacological management (fibrates and/or statins) should be reserved for specific situations in which the risk of complications in the medium term is high and conservative lifestyle interventions have been ineffective; however, to date, no studies have conclusively demonstrated their effectiveness and safety [32, 49].

The strength of this study is that it provides evidence that over 3400 children with residual cardiometabolic risk are associated with RC, even those with low LDL-c levels, which should be confirmed in future research. However, our study has several limitations that should be acknowledged. First, its cross-sectional design prevents us from making causal inferences. Follow-up studies are needed to confirm these findings. Second, although we adjusted the analyses for some major potential confounders identified through the DAG method (i.e., age, sex, HDL-c, Tanner stage, physical activity and diet), residual confounding cannot be ruled out. Third, sexual maturation should be considered when interpreting interindividual differences in lipid profiles and blood pressure [50, 51]. However, analyses including this variable were only possible with the 2017 survey data, as parent-reported information on sexual maturity had a high nonresponse rate, mainly due to privacy limitations of physical examinations in school settings and, in some cases, cultural and religious restrictions, such as those reported by some parents of Muslim origin. The available data showed that, in general, Tanner stages were between stages I and II; therefore, a limited influence on cardiometabolic risk factors is to be expected. Fourth, because this study includes uniquely Spanish population-based samples, more studies conducted in other countries should be performed to confirm these findings. Fifth, although it would have been interesting to include interaction terms between RC and other lipid parameters in the regression models included in the analyses, the close correlation between them, with the consequent risk of multicollinearity, would have made the estimates less consistent; moreover, the correlations between the interaction terms would have been even closer, so it was decided not to test the interaction terms between RC and other lipid fractions. Finally, the lipid parameters assessed showed low reproducibility, with coefficients of variation exceeding 20% (Additional file 1: Table S11). However, these coefficients remained consistent across the four studies, suggesting that the reproducibility issues were not due to variations in methodology over the years. Variations in some of the measured variables across different studies could be attributed to potential variability in the sociodemographic profile of the participants (e.g., parents’ education level, income, immigrant status, etc.) or lifestyle factors (e.g., dietary patterns, physical activity levels, etc.).

Conclusions

Our results suggest that, beyond low-density lipoprotein cholesterol (LDL-c) levels, children who have high RC may be at high cardiometabolic risk. In addition, our data showed that RC is more strongly associated with cardiometabolic risk than LDL-c is, even in children with low LDL-c levels, suggesting that the residual risk associated with RC, together with LDL-c, is likely to be of clinical utility in terms of cardiovascular risk stratification and future interventions. Longitudinal studies are necessary to identify the mechanism that explains the association between RC and cardiometabolic risk in children and whether lowering RC levels in infancy improves clinical outcomes. Finally, because of the scarcity of studies in children and adolescents, reference values for RC have not been reported. Therefore, reference values from large population-based studies should be provided for further incorporation into the guidelines.

Data availability

Data is provided within the manuscript or supplementary information files.

Abbreviations

CRF:

Cardiorespiratory fitness

CVDs:

Cardiovascular diseases

DBP:

Diastolic blood pressure

HDL-c:

High-density lipoprotein cholesterol

HOMA-IR:

Homeostatic model assessment for insulin resistance

LDL-c:

Low-density lipoprotein cholesterol

MAP:

Mean arterial pressure

RC:

Remnant cholesterol

SBP:

Systolic blood pressure

STROBE:

Strengthening the Reporting of Observational Studies in Epidemiology

TG:

Triglycerides

TC:

Total cholesterol

WC:

Waist circumference

References

  1. Townsend N, Kazakiewicz D, Lucy Wright F, Timmis A, Huculeci R, Torbica A, et al. Epidemiology of cardiovascular disease in Europe. Nat Rev Cardiol. 2022;19:133–43.

    Article  PubMed  Google Scholar 

  2. Abrignani MG, Luca F, Favilli S, Benvenuto M, Rao CM, Di Fusco SA, et al. Lifestyles and cardiovascular prevention in childhood and adolescence. Pediatr Cardiol. 2019;40:1113–25.

    Article  PubMed  Google Scholar 

  3. Hanssen H, Moholdt T, Bahls M, Biffi A, Siegrist M, Lewandowski AJ, et al. Lifestyle interventions to change trajectories of obesity-related cardiovascular risk from childhood onset to manifestation in adulthood: a joint scientific statement of the task force for childhood health of the European Association of Preventive Cardiology and the European Childhood Obesity Group. Eur J Prev Cardiol. 2023;30(14):1462–72.

    Article  PubMed  Google Scholar 

  4. Cupido AJ, Asselbergs FW, Schmidt AF, Hovingh GK. Low-density lipoprotein cholesterol attributable cardiovascular disease risk is sex specific. J Am Heart Assoc. 2022;11:e024248.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Jin X, Yang S, Lu J, Wu M. Small, dense low-density lipoprotein-cholesterol and atherosclerosis: relationship and therapeutic strategies. Front Cardiovasc Med. 2022;8:804214.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Lan NS, Bell DA, Watts GF, Fegan PG. Lipid-lowering therapies, and cardiovascular risk-stratification strategies in adults with type 1 diabetes. Curr Opin Endocrinol Diabetes Obes. 2023;30:103–12.

    Article  CAS  PubMed  Google Scholar 

  7. Fiorentino R, Chiarelli F. Statins in children, an update. Int J Mol Sci. 2023;24:1366.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Langsted A, Madsen C, Nordestgaard B. Contribution of remnant cholesterol to cardiovascular risk. J Intern Med. 2020;288:116–27.

    Article  CAS  PubMed  Google Scholar 

  9. Yang XH, Zhang BL, Cheng Y, Fu SK, Jin HM. Association of remnant cholesterol with risk of cardiovascular disease events, stroke, and mortality: a systemic review and meta-analysis. Atherosclerosis. 2023;371:21–31.

    Article  CAS  PubMed  Google Scholar 

  10. Weaver OR, Krysa JA, Ye M, Vena JE, Eurich DT, Proctor SD. Nonfasting remnant cholesterol and cardiovascular disease risk prediction in Albertans: a prospective cohort study. Can Med Assoc J. 2023;11:E645–53.

    Google Scholar 

  11. Di Costanzo A, Perla FM, D’Erasmo L, Arca M, Chiesa C, Pacifico L. Elevated serum concentrations of remnant cholesterol associate with increased carotid intima-media thickness in children and adolescents. J Pediatr. 2021;232(133–139):e131.

    Google Scholar 

  12. Sandesara PB, Virani SS, Fazio S, Shapiro MD. The forgotten lipids: triglycerides, remnant cholesterol, and atherosclerotic cardiovascular disease risk. Endocrinol Rev. 2019;40:537–57.

    Article  Google Scholar 

  13. Doi T, Langsted A, Nordestgaard BG. Elevated remnant cholesterol reclassifies risk of ischemic heart disease and myocardial infarction. J Am Coll Cardiol. 2022;79:2383–97.

    Article  CAS  PubMed  Google Scholar 

  14. Martínez Vizcaíno V, Salcedo Aguilar F, Franquelo Gutiérrez R, Solera Martínez M, Sánchez López M, Serrano Martínez S, et al. Assessment of an after-school physical activity program to prevent obesity among 9-to 10-year-old children: a cluster randomized trial. Int J Obes. 2008;32:12–22.

    Article  Google Scholar 

  15. Martínez-Vizcaíno V, Sánchez-López M, Salcedo-Aguilar F, Notario-Pacheco B, Solera-Martínez M, Moya-Martínez P, et al. Protocol of a randomized cluster trial to assess the effectiveness of the MOVI-2 program on overweight prevention in schoolchildren. Rev Esp Cardiol (Engl Ed). 2012;65:427–33.

    Article  PubMed  Google Scholar 

  16. Martínez-Vizcaíno V, Álvarez-Bueno C, Cavero-Redondo I, Pozuelo-Carrascosa DP, Garrido-Miguel M, Martínez-Hortelano JA, et al. MOVI-daFIT! Intervention: rationale and design of a cluster randomized controlled trial testing the effects on improving adiposity, cognition, and subclinical atherosclerosis by increasing cardiorespiratory fitness in children. Med. 2019;98(9):e14737.

    Article  Google Scholar 

  17. Sequí-Domínguez I, Martínez-Vizcaíno V, Rodríguez-Gutiérrez E, Bizzozero-Peroni B, Martinez-Madrid V, Prada de Medio E, et al. Association of daily steps on lipid and glycaemic profiles in children: the mediator role of cardiorespiratory fitness. Acta Paediatr. 2024;113:296–302.

  18. Von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Lancet. 2007;370:1453–7.

    Article  Google Scholar 

  19. Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC. Homeostasis model assessment: insulin resistance and β-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia. 1985;28(7):412–9.

    Article  CAS  PubMed  Google Scholar 

  20. Cicero AF, Fogacci F, Patrono D, Mancini R, Ramazzotti E, Borghi C, et al. Application of the Sampson equation to estimate LDL-C in children: comparison with LDL direct measurement and Friedewald equation in the BLIP study. Nutr Metab Cardiovasc Dis. 2021;31(6):1911–5.

    Article  CAS  PubMed  Google Scholar 

  21. Sampson M, Ling C, Sun Q, Harb R, Ashmaig M, Warnick R, et al. A new equation for calculation of low-density lipoprotein cholesterol in patients with normolipidemia and/or hypertriglyceridemia. JAMA Cardiol. 2020;5(5):540–8.

    Article  PubMed  Google Scholar 

  22. Leger LA, Mercier D, Gadoury C, Lambert J. The multistage 20 metre shuttle run test for aerobic fitness. J Sports Sci. 1988;6(2):93–101.

    Article  CAS  PubMed  Google Scholar 

  23. Tanner JM. Growth at adolescence. 2nd ed. Oxford: Blackwell Scientific Publications; 1962.

    Google Scholar 

  24. Lanfer A, Hebestreit A, Ahrens W, Krogh V, Sieri S, Lissner L, et al. Reproducibility of food consumption frequencies derived from the Children’s Eating Habits Questionnaire used in the IDEFICS study. Int J Obes. 2011;35(1):S61–8.

    Article  Google Scholar 

  25. Pozuelo-Carrascosa DP, Martínez-Vizcaíno V, Torres-Costoso A, Martinez MS, Rodríguez-Gutiérrez E, Garrido-Miguel M. “Fat but fit” paradox and cardiometabolic risk in children: the role of physical activity. Child Obes. 2023;19(4):282–91.

    Article  PubMed  Google Scholar 

  26. Ruiz JR, Cavero-Redondo I, Ortega FB, Welk GJ, Andersen LB, Martínez-Vizcaíno V. Cardiorespiratory fitness cut points to avoid cardiovascular disease risk in children and adolescents; what level of fitness should raise a red flag? A systematic review and meta-analysis. Br J Sports Med. 2016;50(23):1451–8.

    Article  PubMed  Google Scholar 

  27. Chen MM, Huang X, Xu C, Song XH, Liu YM, Yao D, et al. High remnant cholesterol level potentiates the development of hypertension. Front Endocrinol. 2022;13:830347.

    Article  Google Scholar 

  28. Di Costanzo A, Perla FM, D’Erasmo L, Arca M, Chiesa C, Pacifico L. Elevated serum concentrations of remnant cholesterol associate with increased carotid intima-media thickness in children and adolescents. J Pediatr. 2021;232:133–9.

    Article  PubMed  Google Scholar 

  29. Textor J, van der Zander B, Gilthorpe MS, Liśkiewicz M, Ellison GT. Robust causal inference using directed acyclic graphs: the R package ‘dagitty.’ Int J Epidemiol. 2016;45(6):1887–94.

    PubMed  Google Scholar 

  30. Tennant PW, Murray EJ, Arnold KF, Berrie L, Fox MP, Gadd SC, et al. Use of directed acyclic graphs (DAGs) to identify confounders in applied health research: review and recommendations. Int J Epidemiol. 2021;50(2):620–32.

    Article  PubMed  Google Scholar 

  31. Grundy SM, Stone NJ, Bailey AL, Beam CK, Birtcher KK, Blumenthal RS, et al. 2018 AHA/ACC/AACVPR/AAPA/ABC/ACPM/ADA/AGS/APhA/ASPC/NLA/PCNA guideline on the management of blood cholesterol: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. J Am Coll Cardiol. 2019;73(24):e285–350.

    Article  PubMed  Google Scholar 

  32. Khoury M, Bigras JL, Cummings EA, Harris KC, Hegele RA, Henderson M, et al. The detection, evaluation, and management of dyslipidemia in children and adolescents: a Canadian Cardiovascular Society/Canadian Pediatric Cardiology Association clinical practice update. Can J Cardiol. 2022;38(8):1168–79.

    Article  PubMed  Google Scholar 

  33. Visseren FL, Mach F, Smulders YM, Carballo D, Koskinas KC, Bäck M, et al. 2021 ESC Guidelines on cardiovascular disease prevention in clinical practice: developed by the Task Force for cardiovascular disease prevention in clinical practice with representatives of the European Society of Cardiology and 12 medical societies with the special contribution of the European Association of Preventive Cardiology (EAPC). Eur J Prev Cardiol. 2022;29:5–115.

    Article  PubMed  Google Scholar 

  34. Mirbolouk M, Blaha MJ. ACC/AHA lipid guidelines: personalized care to prevent cardiovascular disease. Cleve Clin J Med. 2020;87:231.

    Article  PubMed  Google Scholar 

  35. Castañer O, Pintó X, Subirana I, Amor AJ, Ros E, Hernáez Á, et al. Remnant cholesterol, not LDL cholesterol, is associated with incident cardiovascular disease. J Am Coll Cardiol. 2020;76:2712–24.

    Article  PubMed  Google Scholar 

  36. Societies ENC, Mach F, Baigent C, Taskinen M-R. 2019 ESC/EAS guidelines for the management of dyslipidemias: lipid modification to reduce cardiovascular risk. Atherosclerosis. 2019;290:140–205.

    Article  Google Scholar 

  37. Halcox JP, Banegas JR, Roy C, Dallongeville J, De Backer G, Guallar E, et al. Prevalence and treatment of atherogenic dyslipidemia in the primary prevention of cardiovascular disease in Europe: EURIKA, a cross-sectional observational study. BMC Cardiovasc Disord. 2017;17:1–11.

    Article  Google Scholar 

  38. Packard CJ. Remnants, LDL, and the quantification of lipoprotein-associated risk in atherosclerotic cardiovascular disease. Curr Atheroscler Rep. 2022;24(3):133–42.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Nordestgaard BG. Triglyceride-rich lipoproteins and atherosclerotic cardiovascular disease: new insights from epidemiology, genetics, and biology. Circ Res. 2016;118:547–63.

    Article  CAS  PubMed  Google Scholar 

  40. Langsted A, Madsen CM, Nordestgaard BG. Contribution of remnant cholesterol to cardiovascular risk. J Intern Med. 2020;288(1):116–27.

    Article  CAS  PubMed  Google Scholar 

  41. Varbo A, Benn M, Tybjærg-Hansen A, Nordestgaard BG. Elevated remnant cholesterol causes both low-grade inflammation and ischemic heart disease, whereas elevated low-density lipoprotein cholesterol causes ischemic heart disease without inflammation. Circulation. 2013;128:1298–309.

    Article  CAS  PubMed  Google Scholar 

  42. Cao YX, Zhang HW, Jin JL, Liu HH, Zhang Y, Gao Y, et al. The longitudinal association of remnant cholesterol with cardiovascular outcomes in patients with diabetes and pre-diabetes. Cardiovasc Diabetol. 2020;19:1–10.

    Article  Google Scholar 

  43. Hu X, Liu Q, Guo X, Wang W, Yu B, Liang B, et al. The role of remnant cholesterol beyond low-density lipoprotein cholesterol in diabetes mellitus. Cardiovasc Diabetol. 2022;21:1–10.

    Article  Google Scholar 

  44. Arellano-Ruiz P, García-Hermoso A, García-Prieto JC, Sánchez-López M, Martínez-Vizcaíno V, Solera-Martínez M. Predictive ability of waist circumference and waist-to-height ratio for cardiometabolic risk screening among Spanish children. Nutrients. 2020;12:415.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Tong J, Li X, Liang X, Tang F, Ren Y, Hao G, et al. The relationship of remnant cholesterol and abdominal obesity in children: a cross-sectional study. Front Cardiovasc Med. 2022;9:929560.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Quispe R, Martin SS, Michos ED, Lamba I, Blumenthal RS, Saeed A, et al. Remnant cholesterol predicts cardiovascular disease beyond LDL and ApoB: a primary prevention study. Eur Heart J. 2021;42:4324–33.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Wang K, Wang R, Yang J, Liu X, Shen H, Sun Y, et al. Remnant cholesterol and atherosclerotic cardiovascular disease: Metabolism, mechanism, evidence, and treatment. Front Cardiovasc Med. 2022;17(9):913869.

    Article  Google Scholar 

  48. Visseren FL, Mach F, Smulders YM, Carballo D, Koskinas KC, Bäck M, et al. 2021 ESC Guidelines on cardiovascular disease prevention in clinical practice: developed by the Task Force for cardiovascular disease prevention in clinical practice with representatives of the European Society of Cardiology and 12 medical societies with the special contribution of the European Association of Preventive Cardiology (EAPC). Eur Heart J. 2021;42(34):3227–37.

    Article  PubMed  Google Scholar 

  49. Guirguis-Blake JM, Evans CV, Coppola EL, Redmond N, Perdue LA. Screening for lipid disorders in children and adolescents: updated evidence report and systematic review for the US Preventive Services Task Force. JAMA. 2023;330(3):261–74.

    Article  PubMed  Google Scholar 

  50. Cho SD, Mueller WH, Meininger JC, Liehr P, Chan W. Blood pressure and sexual maturity in adolescents: the Heartfelt Study. Am J Hum Biol. 2001;13:227–34.

    Article  CAS  PubMed  Google Scholar 

  51. Spinneker A, Egert S, González-Gross M, Breidenassel C, Albers U, Stoffel-Wagner B, et al. Lipid, lipoprotein and apolipoprotein profiles in European adolescents and its associations with gender, biological maturity, and body fat—the HELENA Study. Eur J Clin Nutr. 2012;66:727–35.

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

We would like to thank the schools, families, and pupils participating in the study.

Funding

ERG (2022-UNIVERS-11373) and SNA-A (2020-PREDUCLM-16704) were supported by grants from the University of Castilla-La Mancha, Spain.

Author information

Authors and Affiliations

  1. Faculty of Physiotherapy and Nursing, Universidad de Castilla-La Mancha, Toledo, Spain

    Ana Torres-Costoso

  2. Health and Social Research Center, Universidad de Castilla-La Mancha, Cuenca, Spain

    Ana Torres-Costoso, Vicente Martínez-Vizcaíno, Mairena Sánchez-López, Eva Rodríguez-Gutiérrez, Sergio Núñez de Arenas-Arroyo, Montserrat Solera-Martínez, Blanca Notario-Pacheco & Arthur Eumann Mesas

  3. Universidad Autónoma de Chile. Facultad de Ciencias de La Salud, Talca, Chile

    Vicente Martínez-Vizcaíno

  4. EPIUnit-Instituto de Saúde Pública da Universidade Do Porto (Institute of Public Health of the University of Porto), Porto, Portugal

    Andreia Oliveira

  5. Laboratório para a Investigação Integrativa e Translacional em Saúde Populacional (ITR), [Laboratory for Integrative and Translational Research in Population Health], Porto, Portugal

    Andreia Oliveira

  6. Departamento de Ciências da Saúde Pública e Forenses e Educação Médica da Faculdade de Medicina da, Universidade Do Porto, Porto, Portugal

    Andreia Oliveira

  7. School of Education, Universidad de Castilla-La Mancha, Ciudad Real, Spain

    Mairena Sánchez-López

  8. Faculty of Nursing, Universidad de Castilla La Mancha, Cuenca, Spain

    Montserrat Solera-Martínez & Blanca Notario-Pacheco

  9. Clinical Laboratory Department, Hospital Virgen de La Luz, Cuenca, Spain

    Vanessa Martínez-Madrid

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  1. Ana Torres-Costoso
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  10. Arthur Eumann Mesas
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Contributions

ATC and VMV concepted and designed the study; MSL, BNP, MSM, VMM contributed to data collection; ATC, VMV and AEM analyzed and interpreted the data, and drafted the manuscript; ERG, SNAA and AO contributed to the interpretations of the findings and revised the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Vicente Martínez-Vizcaíno.

Ethics declarations

Ethics approval and consent to participate

The Ethics Committee for Clinical Research of the Virgen de la Luz Hospital in Cuenca approved the study protocols (MOVI study: SAN03060-00; MOVI-2 study: PI081297; MOVI-da-Fit study: PI0216; e-MOVI study: P11519). In accordance with the Declaration of Helsinki, legal guardians were asked to sign informed consent forms allowing their children to participate. Participants’ privacy and confidentiality were ensured.

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No applicable.

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The authors declare no competing interests.

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Supplementary Information

12916_2025_3859_MOESM1_ESM.docx

Additional file 1: Tables S1-S11 and Figs. S1-S7. Table S1. Potential effect modification of sex on the study associations. Table S2. Characteristics of the main lipid parameters and cardiometabolic risk factors by year of study. Table S3. Characteristics of the participants in the study carried out in 2017 in the total sample and stratified by sex. Table S4. Correlations between study variables by year of the study. Table S5. Beta coefficient (95% confidence intervals) of cardiometabolic risk factors for continuous z-scores of LDL-c and remnant cholesterol levels in participants in the study carried out in 2017, obtained using multilevel mixed-effects generalized linear regression models. Table S6. Beta coefficient (95% confidence intervals) of cardiometabolic risk factors for continuous z-score of remnant cholesterol levels in participants in the study carried out in 2017, obtained via multilevel mixed-effects generalized linear regression models according to LDL-c level (low vs. high). Table S7. Beta coefficient (95% confidence intervals) of cardiometabolic risk factors for continuous z-scores of LDL-c and remnant cholesterol (RC) levels, obtained via multilevel mixed-effects generalized linear regression models by sex. Table S8. Beta coefficient (95% confidence intervals) of cardiometabolic risk factors for continuous z-score of remnant cholesterol levels obtained via multilevel mixed-effects generalized linear regression models according to LDL-c level (low vs. high) by sex adjusted by age and HDL-c. Table S9. Beta coefficient ± standard error of cardiometabolic risk factors for continuous z-scores of LDL cholesterol (LDL-c) and remnant cholesterol (RC) levels in participants in the study carried out in 2017, obtained via multilevel mixed-effects generalized linear regression models by sex. Table S10. Beta coefficient ± standard error of cardiometabolic risk factors for continuous z-score of remnant cholesterol levels in participants in the study carried out in 2017 obtained via multilevel mixed-effects generalized linear regression models according to LDL-c level (low vs. high) by sex adjusted by age and HDL-c. Table S11. Means, standard deviations of lipid parameters and coefficients of variation by year of study. Fig. S1. Directed acyclic graphs (DAG) for the causal structure of the relationships between LDL-c and RC and the cardiometabolic risk factors studied. Fig. S2. Subgroups of concordance/discordance between levels of low-density lipoprotein cholesterol (LDL-c) and remnant cholesterol (RC) by sex. Fig. S3. Analysis of covariance (ANCOVA) models estimating marginal means (95% confidence intervals) of cardiometabolic risk factors by subgroups according to low-density lipoprotein cholesterol (LDL-c) and remnant cholesterol (RC) levels by sex. Fig. S4. Scatterplots depicting the relationships between cardiometabolic risk factors and levels of LDL-c and remnant cholesterol (RC), including Pearson’s correlation coefficient and p-value by sex, in participants in the study carried out in 2017. Fig. S5. Analysis of covariance (ANCOVA) models estimating marginal means of cardiometabolic risk parameters by subgroup (lipid profile categories according to low-density lipoprotein cholesterol [LDL-c] and remnant cholesterol [RC] levels) in participants in the study carried out in 2017. Fig. S6. Subgroups of concordance/discordance between levels of low-density lipoprotein cholesterol (LDL-c) and remnant cholesterol (RC) by sex in participants in the study carried out in 2017. Fig. S7. Analysis of covariance (ANCOVA) models estimating marginal means (95% confidence intervals) of cardiometabolic risk parameters by subgroup according to low-density lipoprotein cholesterol (LDL-c) and remnant cholesterol (RC) levels by sex in participants in the study carried out in 2017.

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Torres-Costoso, A., Martínez-Vizcaíno, V., Oliveira, A. et al. Beyond LDL cholesterol: remnant cholesterol is associated with cardiometabolic risk factors in children. BMC Med 23, 28 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12916-025-03859-9

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BMC Medicine

ISSN: 1741-7015

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